Startseite Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma
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Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma

  • Xiao-Wen Huang , Yan Li , Li-Na Jiang , Bo-Kang Zhao , Yi-Si Liu , Chun Chen , Dan Zhao , Xue-Li Zhang , Mei-Ling Li , Yi-Yun Jiang , Shu-Hong Liu , Li Zhu und Jing-Min Zhao EMAIL logo
Veröffentlicht/Copyright: 14. Juni 2024
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Aus der Zeitschrift Open Medicine Band 19 Heft 1

Abstract

Carnosine dipeptidase 1 (CNDP1), an enzyme integral to the hydrolysis of dipeptides containing histidine, plays an indispensable role in myriad physiological processes, including hydrolysis of proteins, maturation of specific biochemical functionalities within proteins, tissue regeneration, and regulation of cell cycle. However, the implications of CNDP1 in oncogenesis and its prognostic value are not yet fully elucidated. Initially, we procured the GSE40367 dataset from the Gene Expression Omnibus and established a protein–protein interaction network. Thereafter, we conducted functional and pathway enrichment analyses utilizing GO, KEGG, and GSEA. Moreover, we undertook an association analysis concerning the expression of CNDP1 with immune infiltration, along with survival analysis across various cancers and specifically in hepatocellular carcinoma (HCC). Our study uncovered a total of 2,248 differentially expressed genes, with a down-regulation of CNDP1 in HCC and other cancers. Our explorations into the relationship between CNDP1 and immune infiltration disclosed a negative correlation between CNDP1 expression and the presence of immune cells in HCC. Survival analyses revealed that diminished expression of CNDP1 correlates with an adverse prognosis in HCC and several other types of cancer. These observations intimate that CNDP1 holds promise as a novel prognostic biomarker for both pan-cancer and HCC.

Keywords: carnosine dipeptidase 1; GEO; pan-cancer; hepatocellular carcinoma; prognosis; biomarker

1 Introduction

According to the most recent data from the World Health Organization (WHO), approximately 1,996 million new cancer cases were recorded globally in 2022, underscoring cancer’s persistent status as a formidable public health challenge. Notably, hepatocellular carcinoma (HCC) is ranked as the eighth most common cancer worldwide and emerges as the third leading cause of cancer-related mortality [1]. Given the complex mechanisms of tumorigenesis, it is imperative to undertake a thorough investigation of gene expression across diverse cancer types to discern their correlation with clinical outcomes and the underlying molecular mechanisms. Surgical resection and liver transplantation represent therapeutic strategies aimed at curing HCC [2]. Despite these interventions, a considerable proportion of individuals diagnosed with HCC encounter the recurrence of the tumor within a 5-year timeframe [3]. Consequently, there is an urgent need to develop innovative prognostic assessment tools to better predict the clinical outlook of individuals diagnosed with HCC and across various cancers. The establishment of a prognostic model to predict survival probabilities and stratify patient outcomes is of paramount importance.

Numerous biomarkers, including alpha-fetoprotein (AFP), AFP-L3, and DCP; as well as GPC3, HSP70, and SCCA, have been extensively explored as potential indicators for the diagnosis and prognosis of HCC [4–7]. Additionally, a plethora of molecular mechanisms are implicated in the onset and advancement of HCC, involving mutations in genes like TP53, CTNNB1, and AXIN1, and the disruption of signaling pathways including the Wnt/β-catenin pathway and the PI3K/AKT/mTOR pathway [8–10]. A deeper comprehension of these molecular dynamics is instrumental in forging pathways toward the creation of targeted treatments for HCC.

Carnosine dipeptidase 1 (CNDP1) is a gene responsible for encoding proteins, situated on chromosome 18q22.3. The resultant protein, CNDP1, with a molecular weight of 56.8 kDa, is predominantly expressed in cerebral tissues and constitutes a homodimeric dipeptidase, recognized as human carnosinase [11]. The gene features a trinucleotide (CTG) repeat length polymorphism within its coding sequence [12]. CNDP1 functions both as a secreted and intracellular entity, localized externally to cell membranes [11].

Recent findings indicate that CNDP1 is a molecule conspicuously downregulated in various cancer forms, including HCC. As a proteolytic enzyme, it specializes in cleaving histidine-containing dipeptides and is integral to numerous physiological functions, including protein degradation, maturation of specific proteins, tissue restoration, and cellular cycle regulation [13].

Previous studies have noted certain correlations between CNDP1 and specific cancer types [14–17]. However, these correlations have not been definitively established across all forms of cancer. The link between CNDP1 and cancer seems not to be universally applicable, but rather appears to be contingent upon a variety of factors, including the type of tumor, its developmental stage, and individual patient variations. Consequently, it is essential that we conduct an exhaustive investigation into the role and efficacy of CNDP1 in a pan-cancer context. Only through such a meticulous approach can we accurately determine whether CNDP1 presents a viable new target for cancer therapy, thus offering renewed hope to those afflicted by this disease.

In this study, we scrutinized GSE40367 dataset acquired from Gene Expression Omnibus (GEO). We identified prevalent differentially expressed genes (DEGs) within the dataset and executed protein–protein interaction (PPI), as well as functional and pathway enrichment analyses. Remarkably, CNDP1 was discerned as one of the top three genes with down-regulated expression, showing significant differential alterations, yet its association with HCC had not been documented in previous research. Consequently, CNDP1 was designated as the focal gene for this study. Our results suggest that the expression of CNDP1 is intricately connected to the immune response and holds considerable promise as a valuable prognostic biomarker for various malignancies, including HCC.

2 Materials and methods

2.1 Patients of study

Between October 2021 and June 2022, a cohort of 75 patients diagnosed with HCC was enrolled at the Fifth Medical Center of Chinese PLA General Hospital. The diagnosis adhered to the 2019 WHO classification guidelines for digestive system tumors [18], and were corroborated by two independent pathologists.

2.2 Expression profile dataset selection

Datasets pertinent to mRNA associated with HCC were meticulously selected through an exhaustive search of the publicly available GEO dataset portal on NCBI (https://www.ncbi.nlm.nih.gov/geo/), using the search terms “HCC” and “Homo sapiens.” Within the array of datasets, GSE40367, contributed by Roessler et al., was pinpointed. This particular dataset comprises 61 samples, encompassing colon adenocarcinoma, liver hemangioma, HCC, cholangiocarcinomas, and angiosarcoma. For our analysis, we selected five liver hemangioma samples to serve as normal liver controls alongside 32 HCC samples. The GSE40367 dataset is predicated on the GPL570 platform, employing the Affymetrix Human Genome U133 Plus 2.0 Array [19].

2.3 DEGs identification

Using the limma software package, we conducted an analysis on datasetGSE40367 to identify DEGs. The methodology incorporated adjusted P (adj.P) alongside the Benjamini and Hochberg false discovery rate, thus ensuring a meticulous balance between the identification of statistically significant genes and the mitigation of false positives. Probe sets devoid of gene symbols, or those corresponding to multiple gene symbols, were either excluded or consolidated, respectively. DEGs were ascertained using a threshold of |log2FC| >1.0 and adj.P < 0.05, signifying a minimum two-fold change in expression levels between the compared groups, a robust indicator of significant differential expression unlikely to be attributable to mere chance.

2.4 PPI network construction

To elucidate the gene connections, the DEGs were incorporated into the Search Tool for the Retrieval of Interacting Genes [STRING (version 12.0)] online platform (http://string-db.org) to construct the PPI network, adhering to stringent criteria (minimum requisite interaction score: highest confidence 0.900, k-means clustering: number of clusters 3). In this study, STRING facilitated the analysis of the PPI of DEGs among the top 100 in differential expression magnitude. Following this, leveraging the interaction data, the network was crafted and depicted using Cytoscape software (version 3.9.1). A roster of protein intermediaries was procured and subsequently, Cytoscape was employed to delineate and scrutinize PPI networks, considering an interaction score of no less than 0.4 as significant. The PPI network was delineated by filtering pivotal protein expression molecules via the Minimal Common Oncology Data Elements of Cytoscape plug-in. Additionally, the CytoNCA plug-in, adopting a centrality-focused methodology, was utilized to identify hub genes within the PPI networks. All targets were methodically arranged into circles, with a high centrality value denoting a paramount role within the network.

2.5 Functional and pathway enrichment analysis

DEGs were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments using the clusterProfiler package in R, as facilitated by the bioinformatics platform (https://www.bioinformatics.com.cn) [20]. The GO enrichment analysis is divided into three categories: biological processes (BP), cellular components (CC), and molecular functions (MF). Additionally, gene set enrichment analysis was performed utilizing the R packages “clusterProfiler” and “GSVA,” with the selected gene set annotated as (h.all.v7.2.symbols.gmt). The Normalized Enrichment Score (NES) was determined following 1,000 permutations. A gene set was deemed significantly enriched if it met the criteria of |NES| > 1, P < 0.05, and a false discovery rate <0.25. The results were elegantly visualized in a bubble plot created with the “ggplot2” R package.

2.6 Analysis of CNDP1 expression

The Tumor Immune Estimation Resource 2.0 (TIMER2.0, http://timer.cistrome.org/) leverages high-throughput sequencing data to scrutinize immune cell infiltration within tumor specimens, juxtaposing these findings with those from control normal tissues. The Gene Expression Profilling Interactive Analysis 2 (GEPIA2, http://gepia2.cancer-pku.cn/#index) integrates cutting-edge cancer genomics data to facilitate efficient data mining and dynamic examination of gene expression profiles. Both TIMER2.0 and GEPIA2 were selected to investigate the variances in CNDP1 expression across diverse cancers, including HCC.

2.7 Immunohistochemical staining

The differential protein expression levels of CNDP1 in HCC and corresponding normal tissues were obtained from the Human Protein Atlas (HPA, https://www.proteinatlas.org/), encompassing both healthy and oncological tissue samples. These extensive expression profiles are discernible through the examination of tissue specimens.

2.8 Biochemical function, intracellular distribution, and structural information of CNDP1

The UniProt repository, an amalgamation of data from several esteemed databases, furnishes exhaustive details concerning the biochemical functionality, intracellular localization, and structural attributes of proteins. Utilizing the terms “CNDP1” and “HUMAN,” we pinpointed the protein of interest (Q96KN2 CNDP1 HUMAN). The protein annotation information provided by UniProt, including function, subcellular localization, and structure, is crucial for gaining a deeper understanding of the biological function of CNDP1 and the potential mechanism of its action.

2.9 Association analysis of CNDP1 expression with immune cell infiltration in HCC

Tumor-infiltrating lymphocytes have been recognized as independent prognostic indicators for both the status of sentinel lymph nodes and the overall survival rate in cancer patients. The TIMER2.0 database was employed to ascertain the relationship between immune infiltration and CNDP1 expression in HCC [21]. P < 0.05 was considered statistically significant. After adjusting for tumor purity using the Spearman correlation coefficient, a P < 0.05 and a Rho > 0 denoted a positive correlation, whereas a P < 0.05 and a Rho < 0 indicated a negative correlation.

2.10 Gene mutation, immuno-infiltration, and methylation analysis in pan-cancer

Utilizing the cBioPortal database (http://www.cbioportal.org/) and the Gene Set Cancer Analysis platform (http://bioinfo.life.hust.edu.cn/GSCA/#/), we conducted an in-depth analysis of the expression of CNDP1, emphasizing variations in gene copy number and methylation processes. The UALCAN database (http://ualcan.path.uab.edu) was instrumental in providing data concerning DNA methylation levels within the promoter of the CNDP1. For the examination of immune correlations, we employed the sophisticated EPIC [22] and CIBERSORT [23] algorithms to calculate the Spearman’s correlation coefficient, delineating the relationship between CNDP1 expression and immune cell infiltration across various tumors, depicted via a comprehensive heat map. Furthermore, we explored the association between CNDP1 expression and tumor mutational burden (TMB) within the TCGA cohort, employing the “maftools” R package for analysis. The correlation of CNDP1 expression with TMB across different cancer types was meticulously assessed using the Spearman method, with findings eloquently presented through both heat map and radar map visualizations.

2.11 Prognostic analysis

In this study, we partitioned the survival data of distinct cancers from the TCGA database into cohorts with high and low CNDP1 expression based on median gene expression levels. To ascertain the prognostic relevance of CNDP1 in cancer, we conducted a Kaplan–Meier survival analysis. We employed four clinical metrics – overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI)  – to explore the association between CNDP1 expression levels and patient prognoses. Forest plots for Cox regression analysis were generated using the “forestplot” and “survival” packages in R. We further investigated the correlation between the variation in CNDP1 expression and the prognosis of HCC patients across varying tumor microenvironments. Hazard ratios with 95% confidence intervals (CI) were calculated, along with log-rank P values. A threshold level of P < 0.05 was considered statistically significant.

2.12 Laboratory and MRI as well as histopathologic examination from our own samples

All preoperative routine examination parameters, encompassing laboratory assessments and MRI, were meticulously gathered from the electronic medical record system of the hospital. The imaging diagnostic outcomes were expertly analyzed by two distinguished senior radiologists, whereas the histopathological assessments were performed by two seasoned pathologists. Serum CNDP1 concentrations were quantified employing an enzyme-linked immunosorbent assay (ELISA). For the purpose of measuring serum CNDP1 levels, the ELISA Kit (EK1957, BOSTER, Wuhan, China) was utilized.

2.13 Statistical analysis

To evaluate statistical significance, the expression levels of CNDP1 in tumor versus normal tissues were compared employing T-tests or Wilcoxon rank sum tests. The association between variables was scrutinized using either Spearman or Pearson correlation tests. The Kaplan–Meier method, log-rank test, and Cox proportional hazards regression model facilitated the analysis of pan-cancer survival rates. Both univariate and multivariate logistic regression analyses were employed to construct a prognostic model for HCC. Statistical significance was deemed established at P < 0.05 for most analyses. The analytical procedures and the creation of the nomogram were performed using SPSS, version 21.0, and R, version 4.2.2.

  1. Ethical approval: This study received ethical approval from the Institutional Ethics Committee of the Fifth Medical Center of Chinese PLA General Hospital.

3 Results

3.1 Identification of DEGs in GSE40367 and PPI network construction of DEGs

We conducted an exhaustive analysis of GEO datasets pertaining to patients diagnosed with HCC. Consequently, GSE40367 was identified as the dataset of choice. The information of the samples contained within this dataset facilitated the identification of DEGs (healthy controls [5 samples] versus HCC patients [32 samples]). Employing the limma package, we discerned 1,121 upregulated and 1,127 downregulated DEGs in GSE40367 following Log2 transformation (Figure 1a–c). The top 20 genes exhibiting significant variations are enumerated in Table 1.

Figure 1 Transcriptome analysis of GSE40367 microarray. (a) UMAP score plot of HCC group and normal liver tissue in GSE40367. (b) Volcano plot of DEGs in GSE40367. Red dots indicate high-expressed genes, blue dots indicate low-expressed genes, and gray dots indicate genes that are undifferentially expressed according to P < 0.05 and |log2FC| > 1.0. (c) Heatmap of DEGs shows hierarchical cluster analysis of gene transcriptional changes in two groups. HCC, hepatocellular carcinoma; NLT, normal liver tissue. (d) PPI network of top 50 up-regulated targets and the top 50 down-regulated targets according to the STRING database. The network’s nodes symbolize proteins, the edges represent protein–protein associations. (e) The protein interaction network derived from the PPI analysis using the cytoNCA plugin in Cytoscape, features circular shapes representing the proteins and lines delineating the interactions between them.
Figure 1

Transcriptome analysis of GSE40367 microarray. (a) UMAP score plot of HCC group and normal liver tissue in GSE40367. (b) Volcano plot of DEGs in GSE40367. Red dots indicate high-expressed genes, blue dots indicate low-expressed genes, and gray dots indicate genes that are undifferentially expressed according to P < 0.05 and |log2FC| > 1.0. (c) Heatmap of DEGs shows hierarchical cluster analysis of gene transcriptional changes in two groups. HCC, hepatocellular carcinoma; NLT, normal liver tissue. (d) PPI network of top 50 up-regulated targets and the top 50 down-regulated targets according to the STRING database. The network’s nodes symbolize proteins, the edges represent protein–protein associations. (e) The protein interaction network derived from the PPI analysis using the cytoNCA plugin in Cytoscape, features circular shapes representing the proteins and lines delineating the interactions between them.

Table 1

Top 20 genes with different changes

ID_REF Gene symbol AveExpr P value Adj.P Val logFC
1559573_at LINC01093 5.3662 2.23 × 10−16 2.81 × 10−12 −6.4809
223699_at CNDP1 6.085 4.19 × 10−10 3.37 × 10−7 −6.3928
207608_x_at CYP1A2 7.6481 1.27 × 10−12 2.00 × 10−9 −6.3917
207201_s_at SLC22A1 6.5374 2.82 × 10−7 3.71 × 10−5 −6.0425
205476_at CCL20 8.1218 5.12 × 10−9 2.02 × 10−6 5.5766
206797_at NAT2 5.7728 7.40 × 10−10 5.19 × 10−7 −5.3452
205998_x_at CYP3A4 7.8762 7.93 × 10−8 1.45 × 10−5 −5.3105
206930_at GLYAT 6.6243 2.42 × 10−7 3.36 × 10−5 −5.2457
206354_at SLCO1B3 6.9485 1.64 × 10−5 0.00088778 −5.1534
217165_x_at MT1F 8.9417 2.92 × 10−9 1.42 × 10−6 −5.0585
206239_s_at SPINK1 9.9658 6.38 × 10−5 0.0023602 4.968
217546_at MT1M 4.9946 2.36 × 10−9 1.30 × 10−6 −4.952
201890_at RRM2 8.9564 8.67 × 10−12 9.95 × 10−9 4.8941
230577_at LINC00844 5.2931 2.01 × 10−7 2.89 × 10−5 −4.859
232494_at CYP8B1 8.793 7.30 × 10−5 0.0025834 −4.8576
220801_s_at HAO2 7.4492 0.0001387 0.0040821 −4.8079
220496_at CLEC1B 4.6874 6.04 × 10−15 3.81 × 10−11 −4.7912
207102_at AKR1D1 7.8868 3.64 × 10−5 0.0015746 −4.7662
214621_at GYS2 7.2327 2.69 × 10−6 0.00021873 −4.7534
209189_at FOS 6.2349 1.86 × 10−7 2.77 × 10−5 −4.7391

AveExpr: the average expression of the gene in all samples; logFC: takes log2 for HCC/normal NLT. HCC, hepatocellular carcinoma; NLT, normal liver tissue.

Within the constructed PPI network, which comprised 97 nodes linked by 505 edges, a significant enrichment was observed (P < 1.0 × 10−16) (Figure 1d). The network visualization was facilitated using Cytoscape software (Figure 1e). Employing the median center algorithm, we identified 16 genes of paramount importance, suggesting their potential as pivotal genetic determinants: MT1E, CYP3A4, ESR1, FOS, MT1H, FOXM1, NDC80, BCHE, CLEC4M, BHMT, SLCO1B3, CYP26A1, CDK1, FNC3, GPC3, and PCK1.

3.2 Functional and pathway enrichment analysis

To elucidate the roles of DEGs in GSE40367, we conducted a GO enrichment analysis. The findings revealed that the BP predominantly encompass the catabolism of small molecules, organelle fission, chromosome segregation, nuclear division, and the metabolic processing of fatty acids. CC functions were primarily associated with chromosomal regions, condensed chromosomes, spindles, chromosomes, centromeric regions, cytoplasmic vesicle lumens, and vesicle lumens. MF categories notably included activities including monooxygenase, oxidoreductase acting on paired donors with the incorporation or reduction of molecular oxygen, heme binding, lyase activity, and iron ion binding (Figure 2a–c).

Figure 2 Enrichment pathway analysis of DEGs in GSE40367 microarray. (a)–(c) Bubble plot of GO enrichment analysis results. X-axis represents the percentage of genes associated with each functional term. Y-axis represents the annotated terms of gene enrichment. The size of each circle corresponds to the number of genes, with larger circles representing a greater gene count. The color of the circles reflects the adjusted P value. (d)–(f) Bar, chord, and bubble plot of KEGG enrichment analysis results. In the bar plot, X-axis represents the ES of protein enrichment, Y-axis represents the top 10 pathways ranked by the degree value of KEGG signaling pathways. In bubble plot, X-axis represents the percentage of genes associated with each functional term. Y-axis represents the annotated terms of gene enrichment. The size of each circle corresponds to the number of genes, with larger circles indicating a greater gene count. The color of the circles reflects the adjusted P value. In the chord plot, the line segments connect genes and enrichment pathways, with different colors representing distinct enrichment pathways. The size of the circles corresponds to the number of connected line segments; larger circles indicate a greater number of connected genes and pathways. Yellow circles represent pathways, other circles represent genes. (g)–(i) GSEA analysis of GSE40367. (g) Functional analysis of CNDP1 in pan-cancer. The heat map utilizes color-coding to visually represent the degree of gene enrichment, red signifies higher ESs and blue signifies lower ESs. Each row corresponds to a distinct gene set, and each column corresponds to a distinct type of cancer. (h) GSEA visual analysis shows the concentration of gene sets in the sorted list. (i) GSEA ridgeplot illustrates how the ES of a gene set changes with the sorting of the gene list. The X-axis represents the sequenced gene list, while the Y-axis represents the ES. The curve (or “mountain”) in the figure demonstrates how the ES of a particular gene set changes cumulatively as the list of genes is traversed. The peak ES indicates the region of enrichment for the gene set in the list. DEGs, differentially expressed genes; ES, enrichment score.
Figure 2

Enrichment pathway analysis of DEGs in GSE40367 microarray. (a)–(c) Bubble plot of GO enrichment analysis results. X-axis represents the percentage of genes associated with each functional term. Y-axis represents the annotated terms of gene enrichment. The size of each circle corresponds to the number of genes, with larger circles representing a greater gene count. The color of the circles reflects the adjusted P value. (d)–(f) Bar, chord, and bubble plot of KEGG enrichment analysis results. In the bar plot, X-axis represents the ES of protein enrichment, Y-axis represents the top 10 pathways ranked by the degree value of KEGG signaling pathways. In bubble plot, X-axis represents the percentage of genes associated with each functional term. Y-axis represents the annotated terms of gene enrichment. The size of each circle corresponds to the number of genes, with larger circles indicating a greater gene count. The color of the circles reflects the adjusted P value. In the chord plot, the line segments connect genes and enrichment pathways, with different colors representing distinct enrichment pathways. The size of the circles corresponds to the number of connected line segments; larger circles indicate a greater number of connected genes and pathways. Yellow circles represent pathways, other circles represent genes. (g)–(i) GSEA analysis of GSE40367. (g) Functional analysis of CNDP1 in pan-cancer. The heat map utilizes color-coding to visually represent the degree of gene enrichment, red signifies higher ESs and blue signifies lower ESs. Each row corresponds to a distinct gene set, and each column corresponds to a distinct type of cancer. (h) GSEA visual analysis shows the concentration of gene sets in the sorted list. (i) GSEA ridgeplot illustrates how the ES of a gene set changes with the sorting of the gene list. The X-axis represents the sequenced gene list, while the Y-axis represents the ES. The curve (or “mountain”) in the figure demonstrates how the ES of a particular gene set changes cumulatively as the list of genes is traversed. The peak ES indicates the region of enrichment for the gene set in the list. DEGs, differentially expressed genes; ES, enrichment score.

Subsequent to our analysis, the KEGG enrichment yielded the following insights: cell cycle, fatty acid degradation, drug metabolism via cytochrome P450, tryptophan metabolism, chemical carcinogenesis through DNA adducts, tyrosine metabolism, pyruvate metabolism, metabolism of xenobiotics by cytochrome P450, drug metabolism by other enzymes, and DNA replication (Figure 2d–f).

Furthermore, we investigated potential signaling pathways through which aberrant CNDP1 expression influences functional states across 33 distinct cancer types via GSEA. We noted a marked enrichment in several signaling pathways, including epithelial–mesenchymal transition, G2/M checkpoint, xenobiotic metabolism, and immune-related pathways such as IFN-α response, IFN-γ response, and TNFα signaling via NFκB. Additionally, the predominant pathways associated with GSEA enrichment encompass biological oxidations, phase I functionalization of compounds, and various cell cycle processes like checkpoints and mitotic events (Figure 2g–i). The GO–KEGG analysis results for DEGs in GSE40367 and the CNDP1 gene set enrichment are concisely presented in Table 2.

Table 2

Top five GO–KEGG enrichment analysis results

Ontology ID Description Gene ratio BgRatio P value P.adjust q value
BP GO:0044282 Small molecule catabolic process 90/1249 452/18866 1.66 × 10−21 9.38 × 10−18 8.15 × 10−18
BP GO:0016054 Organic acid catabolic process 67/1249 282/18866 1.47 × 10−20 2.76 × 10−17 2.40 × 10−17
BP GO:0046395 Carboxylic acid catabolic process 67/1249 282/18866 1.47 × 10−20 2.76 × 10−17 2.40 × 10−17
BP GO:0140014 Mitotic nuclear division 66/1249 286/18866 1.50 × 10−19 2.06 × 10−16 1.79 × 10−16
BP GO:0000070 Mitotic sister chromatid segregation 48/1249 161/18866 1.98 × 10−19 2.06 × 10−16 1.79 × 10−16
CC GO:0098687 Chromosomal region 63/1290 350/19559 2.38 × 10−13 1.50 × 10−10 1.32 × 10−10
CC GO:0000793 Condensed chromosome 46/1290 222/19559 2.74 × 10−12 8.61 × 10−10 7.57 × 10−10
CC GO:0005819 Spindle 62/1290 367/19559 6.46 × 10−12 1.35 × 10−9 1.19 × 10−9
CC GO:0000779 Condensed chromosome, centromeric region 30/1290 122/19559 2.20 × 10−10 3.46 × 10−8 3.05 × 10−8
CC GO:0000775 Chromosome, centromeric region 39/1290 196/19559 4.39 × 10−10 5.52 × 10−8 4.86 × 10−8
MF GO:0016712 Oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, reduced flavin or flavoprotein as one donor, and incorporation of one atom of oxygen 15/1271 35/18352 3.23 × 10−9 1.67 × 10−6 1.43 × 10−6
MF GO:0004497 Monooxygenase activity 26/1271 101/18352 3.33 × 10−9 1.67 × 10−6 1.43 × 10−6
MF GO:0008391 Arachidonic acid monooxygenase activity 11/1271 20/18352 1.58 × 10−8 5.28 × 10−6 4.52 × 10−6
MF GO:0070330 Aromatase activity 12/1271 25/18352 2.58 × 10−8 6.46 × 10−6 5.53 × 10−6
MF GO:0016705 Oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen 32/1271 162/18352 5.91 × 10−8 1.18 × 10−5 1.01 × 10−5
KEGG hsa04110 Cell cycle 39/689 127/8223 2.40 × 10−13 7.77 × 10−11 6.31 × 10−11
KEGG hsa00071 Fatty acid degradation 17/689 43/8223 2.15 × 10−8 3.48 × 10−6 2.83 × 10−6
KEGG hsa00982 Drug metabolism – cytochrome P450 21/689 72/8223 2.31 × 10−7 2.49 × 10−5 2.03 × 10−5
KEGG hsa00380 Tryptophan metabolism 15/689 42/8223 6.99 × 10−7 5.66 × 10−5 4.60 × 10−5
KEGG hsa05204 Chemical carcinogenesis – DNA adducts 19/689 69/8223 2.28 × 10−6 1.48 × 10−4 1.20 × 10−4

GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function.

3.3 Expression analysis of CNDP1

We explored CNDP1 expression levels across diverse cancer types (Figure 3a–d). Utilizing the Kruskal–Wallis test, we assessed the variance in CNDP1 expression among different normal tissues. Data from TIMER2.0 reveal that in cholangiocarcinoma (CHOL), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC), the CNDP1 expression levels in tumor tissues were significantly lower than those in the corresponding normal tissues. This analysis underscores a prevalent pattern of diminished CNDP1 expression in solid tumors compared to normal counterparts.

Figure 3 Differential expression of CNDP1. (a) CNDP1 gene expression in different cancer types from TIMER2.0. Red dots represent the tumor group, blue dots represent the normal control group, and for cancer species with a control group, the background color is shown as gray. The statistical significance calculated by the Wilcoxon test is displayed above the bar chart. (b) CNDP1 gene expression in different cancer types from GEPIA2. The red columns represent the tumor group and the blue columns represent the normal control group. (c) CNDP1 gene expression in different cancer types from TCGA. The red areas represent the tumor group and the yellow areas represent the normal control group. (d) CNDP1 gene expression in different cancer types from TCGA. The red dots represent the tumor group and the blue dots represent the normal control group. (e) CNDP1 gene expression in different stages of HCC progression, derived from GEPIA2. (f) CNDP1 gene expression in HCC tissues (LIHC) and normal liver tissues, derived from GEPIA2. (g) and (h) Expression of CNDP1 in HCC tissues and normal liver tissues at protein expression level, derived from the HPA database. CNDP1, carnosine dipeptidase 1; HCC, hepatocellular carcinoma (*, P < 0.05; **, P < 0.01; ***, P < 0.001).
Figure 3

Differential expression of CNDP1. (a) CNDP1 gene expression in different cancer types from TIMER2.0. Red dots represent the tumor group, blue dots represent the normal control group, and for cancer species with a control group, the background color is shown as gray. The statistical significance calculated by the Wilcoxon test is displayed above the bar chart. (b) CNDP1 gene expression in different cancer types from GEPIA2. The red columns represent the tumor group and the blue columns represent the normal control group. (c) CNDP1 gene expression in different cancer types from TCGA. The red areas represent the tumor group and the yellow areas represent the normal control group. (d) CNDP1 gene expression in different cancer types from TCGA. The red dots represent the tumor group and the blue dots represent the normal control group. (e) CNDP1 gene expression in different stages of HCC progression, derived from GEPIA2. (f) CNDP1 gene expression in HCC tissues (LIHC) and normal liver tissues, derived from GEPIA2. (g) and (h) Expression of CNDP1 in HCC tissues and normal liver tissues at protein expression level, derived from the HPA database. CNDP1, carnosine dipeptidase 1; HCC, hepatocellular carcinoma (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

We conducted an in-depth analysis concerning the expression of CNDP1 in HCC (LIHC) and different stages of HCC progression. Our findings disclosed a marked diminution in the expression levels of CNDP1 across 369 HCC specimens compared to 160 normal hepatic tissues (Figure 3f). Furthermore, it was observed that the expression of CNDP1 was preeminent during clinical stage I, diminished profoundly by clinical stage IV, and exhibited intermediate levels in clinical stages II and III (Figure 3e). The HPA database, an amalgamation of proteomic, transcriptomic, and systems biology data, corroborates these findings, indicating a reduced expression of CNDP1 protein in HCC relative to normal liver tissue (Figure 3g and h).

3.4 The subcellular location, function, and structural analysis

According to the data sourced from the HPA and GeneCards databases, CNDP1 functions as both a secreted and intracellular protein, localized externally to cell membranes. This protein is distinguished by its capacity to catalyze the hydrolysis of the Xaa-His dipeptide via peptide bonds. It exhibits pronounced enzymatic activity toward carnosine (β-propionyl-l-histidine) and homocarnosine (β-propionyl-3-methyl-histidine) [11,24]. In its role as a catalyst, CNDP1 employs Zn2+ as a cofactor, coordinating two Zn2+ ions per subunit at the binding sites His132, Asp165, Glu200, and Asp228. Activation of CNDP1 is achieved through the binding of cadmium ions at residues Asp134 and Glu199, though it is impeded by the metal chelator 1,10-o-phenantrolin, which exhibits an inhibitory concentration of 50% (IC50) at 5 µM. The kinetic properties of CNDP1 are delineated in Table 3 [11,24]. Furthermore, CNDP1 presents two antibody-binding domains, specifically spanning residues 32–133 and 256–334. The protein is composed of 507 amino acids, the initial 26 of which constitute the signal peptide. The structural configuration of CNDP1, as documented in the Protein Data Bank (PDB: 3DLJ) and AlphaFold (https://alphafold.ebi.ac.uk/entry/Q96KN2, AF-Q96KN2-F1), comprises 18 beta strands (Figure 4).

Table 3

Kinetics of CNDP1

Km Substrate Temperature (°C) Notes
1.27 μM Carnosine 30 In the absence of cadmium ions
11 μM Carnosine 30 In the presence of 200 µM cadmium ions
0.13 mM Carnosine
0.2 μM Homocarnosine 30 In the absence of cadmium ions
1 μM Homocarnosine 30 In the presence of 200 µM cadmium ions
8.7 mM Homocarnosine
V max
8.5 μmol/min/mg Toward carnosine
0.36 μmol/min/mg Toward homocarnosine

CNDP1, carnosine dipeptidase 1; Km, Michaelis-Menten constant.

Figure 4 Intracellular distribution and structure of CNDP1. (a) Subcellular locations of CNDP1 protein from the HPA database. (b) Subcellular locations of CNDP1 protein from the GeneCards database. (c) Features of CNDP1 protein from the UniProt database. (d) Three-dimensional structures of CNDP1 protein from PDB (representative) (PDB: 3DLJ). (e) Three-dimensional structures of CNDP1 protein from AlphaFold (predicted) (AF-Q96KN2-F1). CNDP1, carnosine dipeptidase 1; HPA, Human Protein Atlas; PDB, Protein Data Bank.
Figure 4

Intracellular distribution and structure of CNDP1. (a) Subcellular locations of CNDP1 protein from the HPA database. (b) Subcellular locations of CNDP1 protein from the GeneCards database. (c) Features of CNDP1 protein from the UniProt database. (d) Three-dimensional structures of CNDP1 protein from PDB (representative) (PDB: 3DLJ). (e) Three-dimensional structures of CNDP1 protein from AlphaFold (predicted) (AF-Q96KN2-F1). CNDP1, carnosine dipeptidase 1; HPA, Human Protein Atlas; PDB, Protein Data Bank.

3.5 Immune cell infiltration in HCC

The tumor microenvironment constitutes an intricate, integrated system engendered by the interaction between neoplastic cells and the adjacent tissues and immune constituents. This milieu augments the proliferative, migratory, and immune evasion capabilities of the tumor cells, thus facilitating the onset and advancement of neoplastic conditions. Tumor-infiltrating lymphocytes have been identified as independent prognosticators of sentinel lymph node status and survival rates in oncological patients. Furthermore, analyses of immune infiltration have elucidated a correlation between CNDP1 expression and the level of immune infiltration in HCC.

The expression of CNDP1 exhibited a negative correlation with various immune cells: CD4+ T cells, encompassing CD4+ T memory cells, CD4+ T memory activated cells, CD4+ T central memory cells, CD4+ Th1 cells, CD4+ Th2 cells, CD4+ T (non-regulatory) cells; CD8+ T cells; T regulatory cells; B cell, including B memory cells; myeloid derived suppressor cells (MDSC); mast cells; myeloid dendritic cells; monocyte; and common lymphoid progenitor cells. Conversely, the expression of CNDP1 was positively correlated with macrophage, including macrophage M1 cells and macrophage M2 cells; neutrophil; endothelial cells; granulocyte–monocyte progenitor; hematopoietic stem cell; and cancer-associated fibroblast (Figure 5). P < 0.05 was considered statistically significant.

Figure 5 Correlation analysis between CNDP1 expression and immune cell type in HCC. (a)–(x) Correlation analysis between CNDP1 expression and different types of immune infiltrating cells in HCC. The Spearman correlation coefficient was utilized to calculate the relationship. CNDP1, carnosine dipeptidase 1; HCC, hepatocellular carcinoma. P < 0.05 and correlation coefficient (R) > 0 indicates a positive correlation; P < 0.05 and R < 0 indicates a negative correlation.
Figure 5

Correlation analysis between CNDP1 expression and immune cell type in HCC. (a)–(x) Correlation analysis between CNDP1 expression and different types of immune infiltrating cells in HCC. The Spearman correlation coefficient was utilized to calculate the relationship. CNDP1, carnosine dipeptidase 1; HCC, hepatocellular carcinoma. P < 0.05 and correlation coefficient (R) > 0 indicates a positive correlation; P < 0.05 and R < 0 indicates a negative correlation.

3.6 Immunological landscape in pan-cancer

In this study, we employed the EPIC and CIBERSORT algorithms to explore the potential correlation between immune cell infiltration levels and CNDP1 expression across various cancer types in the TCGA dataset. The findings from the EPIC algorithms (Figure 6a) revealed a significant negative association between B cell immune infiltration and CNDP1 expression in thymoma (THYM), testicular germ cell tumors, stomach adenocarcinoma (STAD), LUSC, LUAD, and HNSC. Moreover, a positive correlation was observed between CD4 T cell infiltration and CNDP1 expression in STAD and GBM, while a negative correlation was noted between CD8 T cell infiltration and CNDP1 expression in LUSC and LUAD. The results obtained from the CIBERSORT algorithms are presented in Figure 6b.

Figure 6 Immunological landscape in pan-cancer. (a) Analysis of immune cell infiltration levels and CNDP1 expression from EPIC is presented. (b) Analysis of immune cell infiltration levels and CNDP1 expression from CIBERSORT is provided. (c) The bar chart illustrates the rate of gene CNV for CNDP1 in pan-cancer. (d) The bubble diagram demonstrates the relationship between CNDP1 expression and CNV in pan-cancer. (e) The bubble map displays the correlation between CNDP1 expression and TMB in pan-cancer. (f) The radar map showcases the association between CNDP1 expression and TMB in pan-carcinoma. (g) The bubble map shows differences in promoter methylation of CNDP1 between tumor and normal cells in pan-cancer. (h) The bubble plot illustrates the correlation between promoter methylation and expression of CNDP1 in pan-cancer. CNDP1, carnosine dipeptidase 1; CNV, copy number variation; TMB, tumor mutational burden.
Figure 6

Immunological landscape in pan-cancer. (a) Analysis of immune cell infiltration levels and CNDP1 expression from EPIC is presented. (b) Analysis of immune cell infiltration levels and CNDP1 expression from CIBERSORT is provided. (c) The bar chart illustrates the rate of gene CNV for CNDP1 in pan-cancer. (d) The bubble diagram demonstrates the relationship between CNDP1 expression and CNV in pan-cancer. (e) The bubble map displays the correlation between CNDP1 expression and TMB in pan-cancer. (f) The radar map showcases the association between CNDP1 expression and TMB in pan-carcinoma. (g) The bubble map shows differences in promoter methylation of CNDP1 between tumor and normal cells in pan-cancer. (h) The bubble plot illustrates the correlation between promoter methylation and expression of CNDP1 in pan-cancer. CNDP1, carnosine dipeptidase 1; CNV, copy number variation; TMB, tumor mutational burden.

We then extended our research to assess the copy number variations (CNV) for the CNDP1 gene in a pan-cancer context (Figure 6c). The findings disclosed a pronounced incidence of CNV in cancers such as KICH, THCA, GBM, and UCEC. Additionally, our examination of the relationship between CNV and CNDP1 expression in a pan-cancer overview (Figure 6d) revealed a distinct positive correlation between CNV levels and CNDP1 expression in multiple cancer types including bladder urothelial carcinoma (BLCA), breast invasive carcinoma, CHOL, colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), HNSC, KICH, KIRC, KIRP, LUAD, LUSC, pancreatic adenocarcinoma (PAAD), PRAD, rectum adenocarcinoma (READ), and STAD. In contrast, GBM and HCC (LIHC) exhibited an inverse correlation between CNV and CNDP1 expression levels.

To explore the potential significance of CNDP1 in predicting the effectiveness of immune checkpoint inhibitor treatment, we examined the relationship between CNDP1 expression levels and TMB, a well-established biomarker for immunotherapy prediction. Our findings revealed a positive correlation between CNDP1 expressions in BLCA, CHOL, COAD, ESCA, HNSC, KIRP, acute myeloid leukemia, and LUAD with TMB values. Conversely, we observed a negative correlation between CNDP1 expressions in KICH, LIHC, ovarian serous cystadenocarcinoma (OV), PAAD, PRAD, READ, SKCM, and UCEC with TMB (Figure 6e and f).

Furthermore, elevated promoter methylation levels of CNDP1 were identified in GBM compared to other cancer types (Figure 6g). Additionally, the analysis of tumor and normal promoter methylation of CNDP1 across various cancers indicated a negative correlation between CNDP1 expression and CHOL, GBM, and PRAD; as well as a positive correlation with BLCA, cervical squamous cell carcinoma, endocervical adenocarcinoma, ESCA, KICH, KIRP, LIHC, PAAD, and UCEC (Figure 6h).

3.7 Prognostic analysis

The tumor data obtained from TCGA were classified into low and high expression groups based on the level of CNDP1 expression (median). We conducted an analysis to investigate the impact of CNDP1 expression on patient prognosis across various types of cancer. Our findings from forest plots revealed that a low level of CNDP1 was associated with a better OS outcome specifically in uterine carcinosarcoma (UCS), KIRP, and KIRC, while high expression of CNDP1 was linked to poorer OS in THCA (Figure 7a). Patients with low CNDP1 expression had favorable DFS in UCS and SARC. Conversely, patients with high CNDP1 expression had unfavorable DFS in PRAD and PAAD (Figure 7b). Furthermore, low expression of CNDP1 was associated with worse DSS in UCS, KIRP, and KIRC, but high expression of CNDP1 was associated with THCA (Figure 7c). Low expression of CNDP1 indicated better PFI in UCS and KIRC, while high expression showed worse PFI in THYM (Figure 7d). Additionally, low expression of CNDP1 was linked to favorable DFI in UCS and SARC, while a high level of CNDP1 was associated with a worse DFI outcome for patients (Figure 7e).

Figure 7 Correlation between CNDP1 expression and prognosis in pan-cancer and HCC. (a)–(e) Correlation between CNDP1 expression and OS, DFS, DSS, PFI, DFI in pan-cancer. (f)–(m) KM survival curves of high and low CNDP1 expression in CHOL, GBM, KICH, KIRC, KIRP, LIHC(HCC), SKCM, and THCA patients. (n)–(s) KM curves of low and high CNDP1 expression with CD4+ Th2 cells, CD8+ T cells, monocytes, macrophages, hematopoietic stem cells, and MDSC. CNDP1, carnosine dipeptidase 1; HCC, hepatocellular carcinoma; OS, overall survival; DFS, disease-free survival; DSS, disease-specific survival; PFI, progression-free interval; DFI, disease-free interval; CHOL, cholangiocarcinoma; GBM, glioblastoma multiforme; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; SKCM, skin cutaneous melanoma; THCA, thyroid carcinoma; MDSC, myeloid derived suppressor cells. P < 0.05 was considered statistically significant.
Figure 7

Correlation between CNDP1 expression and prognosis in pan-cancer and HCC. (a)–(e) Correlation between CNDP1 expression and OS, DFS, DSS, PFI, DFI in pan-cancer. (f)–(m) KM survival curves of high and low CNDP1 expression in CHOL, GBM, KICH, KIRC, KIRP, LIHC(HCC), SKCM, and THCA patients. (n)–(s) KM curves of low and high CNDP1 expression with CD4+ Th2 cells, CD8+ T cells, monocytes, macrophages, hematopoietic stem cells, and MDSC. CNDP1, carnosine dipeptidase 1; HCC, hepatocellular carcinoma; OS, overall survival; DFS, disease-free survival; DSS, disease-specific survival; PFI, progression-free interval; DFI, disease-free interval; CHOL, cholangiocarcinoma; GBM, glioblastoma multiforme; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; SKCM, skin cutaneous melanoma; THCA, thyroid carcinoma; MDSC, myeloid derived suppressor cells. P < 0.05 was considered statistically significant.

We further explored the correlation between CNDP1 expression and clinical outcomes in eight cancers, where notable differences in CNDP1 levels between cancerous tissues and their normal counterparts were observed, employing OS analysis (Figure 7f–m). The survival analysis disclosed that in KIRC, the disparity in survival between the groups with high and low CNDP1 expression was statistically significant (P = 0.012), with patients exhibiting elevated CNDP1 levels experiencing superior OS compared to their low-expression counterparts. In LIHC (HCC), the survival curves of the two groups were markedly distinct, with those in the high CNDP1 expression group achieving greater OS during an 80-month observation period (P = 0.073). These findings imply that diminished CNDP1 expression correlates strongly with adverse prognoses in HCC patients, positioning CNDP1 as a potentially valuable prognostic biomarker in HCC.

We delved further into the correlation between the variation in CNDP1 expression and the prognostic outcomes in HCC patients across varying tumor microenvironments (Figure 7n–s). The findings indicated that diminished expression of CNDP1 was linked with an adverse prognosis in HCC cases. Moreover, elevated expression levels of CD4+ Th2 cells, mast cells, hematopoietic stem cells, and MDSC were associated with unfavorable prognostic implications for patients. P < 0.05 was considered statistically significant.

3.8 Prediction model for microvascular invasion (MVI)

A nomogram was devised to forecast the likelihood of MVI, utilizing preoperative data. This model assessed tumor-specific parameters, including tumor size and count, in addition to the presence of liver cirrhosis, through preoperative diagnostic imaging. Serum samples were obtained from 75 HCC patients enrolled in this study, and the levels of serum CNDP1 were quantified by ELISA. The findings indicated a markedly lower level of serum CNDP1 in the MVI group compared to the non-MVI group (P = 0.011) (Figure 8a). Therefore, in this study, we incorporated CNDP1 into the subsequent stage of univariate logistic regression analysis to assess its prognostic significance for MVI in patients with HCC.

Figure 8 Prediction model to estimate the risk of MVI presence preoperatively in HCC. (a) Serum CNDP1 level in the MVI group exhibited a significant decrease. (b) Nomogram to estimate the risk of MVI presence preoperatively in HCC. The nomogram incorporates cirrhosis, tumor number, tumor diameter, and serum CNDP1 level. (c) ROC curve based on the prediction model and other indicators in the model (n = 75). (d) Calibration curve based on the prediction model (n = 75). (e) Decision curve based on the prediction model (n = 75). MVI, microvascular invasion; HCC, hepatocellular carcinoma; CNDP1, carnosine dipeptidase 1. P < 0.05 was considered statistically significant.
Figure 8

Prediction model to estimate the risk of MVI presence preoperatively in HCC. (a) Serum CNDP1 level in the MVI group exhibited a significant decrease. (b) Nomogram to estimate the risk of MVI presence preoperatively in HCC. The nomogram incorporates cirrhosis, tumor number, tumor diameter, and serum CNDP1 level. (c) ROC curve based on the prediction model and other indicators in the model (n = 75). (d) Calibration curve based on the prediction model (n = 75). (e) Decision curve based on the prediction model (n = 75). MVI, microvascular invasion; HCC, hepatocellular carcinoma; CNDP1, carnosine dipeptidase 1. P < 0.05 was considered statistically significant.

Univariate logistic analysis demonstrated significant associations between cirrhosis (P = 0.085), tumor number (P < 0.001), tumor size (P = 0.001), and serum CNDP1 level (P = 0.007) with MVI occurrence (Table 4). Multivariate logistic regression analysis incorporating the above four variables identified that cirrhosis (P = 0.050), multiple tumors (P = 0.002), tumor size (P = 0.007), and CNDP1 level (P = 0.021) were independent predictors of MVI (Table 5). Thus, the final logistic regression equation included these four factors: Y = 2.623 × cirrhosis + 2.278 × tumor number + 1.752 × tumor diameter − 0.010 × serum CNDP1 concentration − 2.789. A nomogram was subsequently constructed based on this model (Figure 8b), facilitating a visual prediction of the risk of MVI prior to surgical interventions in individuals diagnosed with HCC. This nomogram exhibited exemplary predictive accuracy for assessing the risk of MVI, evidenced by a concordance index of 0.867 [95% CI (0.784–0.949)] (Figure 8c). Calibration plots convincingly demonstrated a satisfactory concordance between the predicted risk by the nomogram and actual MVI estimates (Figure 8d). The decision curve associated with the nomogram is illustrated in Figure 8e.

Table 4

Univariate logistic analysis results of the patients

Variable Univariable
OR (95% CI) P value
Age (years) 0.973 (0.928–1.020) 0.257
Sex (male) 2.739 (0.701–10.709) 0.147
HBV infection 1.453 (0.435–4.855) 0.543
Blood ammonia (μmol/L) 0.999 (0.977–1.022) 0.940
PT (s) 0.896 (0.675–1.189) 0.445
WBCs (×109/L) 0.883 (0.631–1.237) 0.471
RBCs (×1012/L) 1.108 (0.522–2.353) 0.789
Platelets (×109/L) 0.998 (0.992–1.005) 0.629
AFP (ng/mL) 1.001 (0.998–1.004) 0.461
Albumin (g/L) 1.002 (0.929–1.082) 0.952
ALT (U/L) 1.002 (0.985–1.019) 0.836
Cirrhosis 7.200 (0.763–67.983) 0.085*
Tumor number, multiple vs solitary 9.772 (2.899–32.610) <0.001*
Tumor diameter, >3 vs ≤3 (cm) 5.425 (1.954–15.065) 0.001*
CNDP1 (ng/mL) 0.989 (0.981–0.997) 0.007*

OR, odds ratio; CI, confidence interval; HBV, hepatitis B virus; PT, prothrombin time; WBCs, white blood cells; RBCs, red blood cells; AFP, alpha-fetoprotein; ALT, alanine aminotransferase; CNDP1, carnosine dipeptidase 1. *Indicates P < 0.1.

Table 5

Multivariate logistic analysis results of the patients

Variable β a OR (95% CI) P value
Cirrhosis 2.623 13.783 (1.002–189.687) 0.050*
Tumor number, multiple vs solitary 2.278 9.762 (2.301–41.422) 0.002*
Tumor diameter, >3 vs ≤3 (cm) 1.752 5.768 (1.617–20.573) 0.007*
CNDP1 (ng/mL) -0.010 0.990 (0.982–0.999) 0.021*
Constant -2.789 0.061 0.050

OR, odds ratio; CI, confidence interval; CNDP1, carnosine dipeptidase 1. aUnstandardized β coefficients were calculated from the multivariate logistic regression model. *Indicates P < 0.05.

4 Discussion

Gene expression profiling using microarray technology serves as a quintessential instrument for demystifying the intrinsic mechanisms of diseases and pinpointing genes and pathways associated with various malignancies, which may have remained undiscovered. This methodology sheds light on the molecular underpinnings of cancer and delineates potential avenues for therapeutic endeavors [25]. In order to identify potential prognostic biomarkers associated with the development of pan-cancer, especially HCC, we conducted an analysis of genomic microarray data from GSE40367. Our scrutiny led to the identification of 2,248 DEGs, comprising 1,121 up-regulated and 1,127 down-regulated genes. Gene bioinformatics offers a potential molecular targeting approach for the prevention and management of HCC. We executed a comprehensive series of enrichment analyses including GO, KEGG, and GSEA. The findings suggest that the DEGs in GSE40367may be linked to pathways related to biological oxidations, cell cycle, and fatty acid degradation when compared to the control group. These results align with previous research studies [26–28].

In our literature search, we found no publications that have conducted pan-cancer analyses of CNDP1 from a comprehensive tumor perspective. Therefore, we conducted a thorough examination of the CNDP1 in pan-cancer based on data from TCGA, GEO, HPA, and UniProt databases. This examination included an analysis of molecular characteristics such as gene expression, promoter methylation, biological function, and protein structure. It was observed that CNDP1 is generally expressed at low levels in most tumors, including HCC. Additionally, it was noted that CNDP1 is both a secreted and intracellular protein located outside the cell membranes. It is recognized for its ability to catalyze the hydrolysis of Xaa-His dipeptide by peptide bonds.

Tumor-infiltrating immune cells, pivotal elements within the tumor microenvironment, are intimately linked with the genesis, progression, and dissemination of cancer [29,30]. Cancer-related fibroblasts located within the stromal region of the tumor microenvironment have been documented to play a role in influencing the activity of diverse immune cells that infiltrate the tumor [31]. In this research, we employed CIBERSORT, EPIC, and other algorithms to explore the potential interplay between varying levels of immune cell infiltration and CNDP1 expression across diverse cancer types in the TCGA. We discerned a statistically significant inverse relationship between B-cell immune infiltration, as determined by the EPIC algorithm, and CNDP1 expression in six distinct tumors, including THYM. Additionally, our scrutiny of immune cell infiltration in HCC disclosed a negative association between CNDP1 expression and the presence of B cells, CD4+ T cells, CD8+ T cells, T regulatory cells, DMSC, mast cells, myeloid dendritic cells, monocytes, and common lymphoid progenitor cells. These insights imply that CNDP1 may play a role in forecasting patient outcomes in HCC and other tumors. Specifically, elevated levels of CNDP1 expression might correlate with a more favorable prognosis for patients.

Deletion or duplication of genome fragments larger than 1 kb resulting from genome rearrangement is called CNV. Genome CNV is part of normal human genetic variation [32–34]. TMB is crucial as biomarkers in the prediction of tumor immunotherapy outcomes and functions as important immune regulatory elements [35]. DNA methylation, an epigenetic mechanism, profoundly influences gene transcription [36]. Consequently, in this study, we investigated the relationship between CNDP1 expression and CNV, TMB, and promoter methylation across various cancers. Our findings indicate that the interplay between CNV, TMB, promoter methylation, and CNDP1 expression differs across cancer types. In HCC, a negative correlation exists between CNV and TMB, while a positive correlation is observed between promoter methylation and CNDP1 expression. These observations suggest that the prognostic value of CNDP1 varies among different cancers, and elevated expression of CNDP1 in HCC may signify a more favorable prognosis.

However, survival prognostic analysis of the CNDP1 resulted in different conclusions for different tumors. Our research revealed a notable correlation between reduced levels of CNDP1 expression and unfavorable outcomes in patients with HCC. Moreover, elevated levels of CD4+ Th2 cells, mast cells, hematopoietic stem cells, and MDSC were correlated with an unfavorable prognosis in patients. These observations indicates that CNDP1 may act as a crucial prognostic biomarker in HCC.

Relevant research indicates that MVI serves as the initial stage in the progression of HCC, ultimately leading to intrahepatic tumor spreading or systemic metastases [37,38]. MVI has been acknowledged as a pivotal prognostic indicator subsequent to hepatic resection for HCC [39–42]. Following the outcomes of multivariate logistic regression analysis, we developed a logistic regression equation incorporating cirrhosis, tumor number, tumor diameter, and serum CNDP1 level. Subsequently, we constructed a nomogram for predicting MVI, which enhanced the interpretability and convenience of the prediction model for clinicians. The findings corroborate that CNDP1 serves as an independent predictor of MVI in patients with HCC.

5 Conclusion

In conclusion, our research demonstrates that CNDP1 is consistently underexpressed across various cancer types, including HCC. Moreover, diminished levels of CNDP1 correlate with adverse outcomes in these malignancies. Our analysis further disclosed that the enrichment pathway of DEGs in GSE40367, along with associated immune infiltration in pan-cancer, has been elucidated. These insights may enhance our comprehension of the molecular underpinnings and furnish clinically pertinent molecular targets for prognostication in both pan-cancer and HCC contexts. In essence, this study offers invaluable support in pinpointing critical prognostic biomarkers for both pan-cancer and HCC.

# These contributed equally to this work, and share first authorship.

Acknowledgements

The authors appreciate the support of all participants in this study and acknowledge TCGA and GEO for providing their platforms and contributors for uploading their meaningful datasets.

  1. Funding information: This study was supported by National Key Research and Development Program of China (2023YFC2308104).

  2. Author contributions: Conceptualization and methodology: Jing-Min Zhao, Xiao-Wen Huang, Yan Li, and Li-Na Jiang; validation: Bo-Kang Zhao, Yi-Si Liu, and Chun Chen; formal analysis, visualization, and writing – original draft: Xiao-Wen Huang, Yan Li, and Li-Na Jiang; investigation: Dan Zhao, Xue-Li Zhang, and Mei-Ling Li; data curation: Yi-Yun Jiang and Li Zhu; writing – review and editing: all authors; supervision: Jing-Min Zhao; project administration: Jing-Min Zhao and Shu-Hong Liu.

  3. Conflict of interest: The authors declare that they have no conflict of interest.

  4. Data availability statement: The data in this study are available from the corresponding author on reasonable request.

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Received: 2024-04-15
Revised: 2024-05-18
Accepted: 2024-05-21
Published Online: 2024-06-14

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

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https://doi.org/10.1515/med-2024-0982
Schlagwörter für diesen Artikel
carnosine dipeptidase 1; GEO; pan-cancer; hepatocellular carcinoma; prognosis; biomarker
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Artikel in diesem Heft

  1. Research Articles
  2. EDNRB inhibits the growth and migration of prostate cancer cells by activating the cGMP-PKG pathway
  3. STK11 (LKB1) mutation suppresses ferroptosis in lung adenocarcinoma by facilitating monounsaturated fatty acid synthesis
  4. Association of SOX6 gene polymorphisms with Kashin-Beck disease risk in the Chinese Han population
  5. The pyroptosis-related signature predicts prognosis and influences the tumor immune microenvironment in dedifferentiated liposarcoma
  6. METTL3 attenuates ferroptosis sensitivity in lung cancer via modulating TFRC
  7. Identification and validation of molecular subtypes and prognostic signature for stage I and stage II gastric cancer based on neutrophil extracellular traps
  8. Novel lumbar plexus block versus femoral nerve block for analgesia and motor recovery after total knee arthroplasty
  9. Correlation between ABCB1 and OLIG2 polymorphisms and the severity and prognosis of patients with cerebral infarction
  10. Study on the radiotherapy effect and serum neutral granulocyte lymphocyte ratio and inflammatory factor expression of nasopharyngeal carcinoma
  11. Transcriptome analysis of effects of Tecrl deficiency on cardiometabolic and calcium regulation in cardiac tissue
  12. Aflatoxin B1 induces infertility, fetal deformities, and potential therapies
  13. Serum levels of HMW adiponectin and its receptors are associated with cytokine levels and clinical characteristics in chronic obstructive pulmonary disease
  14. METTL3-mediated methylation of CYP2C19 mRNA may aggravate clopidogrel resistance in ischemic stroke patients
  15. Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis
  16. Pressure ulcers in German hospitals: Analysis of reimbursement and length of stay
  17. Metformin plus L-carnitine enhances brown/beige adipose tissue activity via Nrf2/HO-1 signaling to reduce lipid accumulation and inflammation in murine obesity
  18. Downregulation of carbonic anhydrase IX expression in mouse xenograft nasopharyngeal carcinoma model via doxorubicin nanobubble combined with ultrasound
  19. Feasibility of 3-dimensional printed models in simulated training and teaching of transcatheter aortic valve replacement
  20. miR-335-3p improves type II diabetes mellitus by IGF-1 regulating macrophage polarization
  21. The analyses of human MCPH1 DNA repair machinery and genetic variations
  22. Activation of Piezo1 increases the sensitivity of breast cancer to hyperthermia therapy
  23. Comprehensive analysis based on the disulfidptosis-related genes identifies hub genes and immune infiltration for pancreatic adenocarcinoma
  24. Changes of serum CA125 and PGE2 before and after high-intensity focused ultrasound combined with GnRH-a in treatment of patients with adenomyosis
  25. The clinical value of the hepatic venous pressure gradient in patients undergoing hepatic resection for hepatocellular carcinoma with or without liver cirrhosis
  26. Development and validation of a novel model to predict pulmonary embolism in cardiology suspected patients: A 10-year retrospective analysis
  27. Downregulation of lncRNA XLOC_032768 in diabetic patients predicts the occurrence of diabetic nephropathy
  28. Circ_0051428 targeting miR-885-3p/MMP2 axis enhances the malignancy of cervical cancer
  29. Effectiveness of ginkgo diterpene lactone meglumine on cognitive function in patients with acute ischemic stroke
  30. The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci
  31. Evaluating the impact of childhood BMI on the risk of coronavirus disease 2019: A Mendelian randomization study
  32. Lactate dehydrogenase to albumin ratio is associated with in-hospital mortality in patients with acute heart failure: Data from the MIMIC-III database
  33. CD36-mediated podocyte lipotoxicity promotes foot process effacement
  34. Efficacy of etonogestrel subcutaneous implants versus the levonorgestrel-releasing intrauterine system in the conservative treatment of adenomyosis
  35. FLRT2 mediates chondrogenesis of nasal septal cartilage and mandibular condyle cartilage
  36. Challenges in treating primary immune thrombocytopenia patients undergoing COVID-19 vaccination: A retrospective study
  37. Let-7 family regulates HaCaT cell proliferation and apoptosis via the ΔNp63/PI3K/AKT pathway
  38. Phospholipid transfer protein ameliorates sepsis-induced cardiac dysfunction through NLRP3 inflammasome inhibition
  39. Postoperative cognitive dysfunction in elderly patients with colorectal cancer: A randomized controlled study comparing goal-directed and conventional fluid therapy
  40. Long-pulsed ultrasound-mediated microbubble thrombolysis in a rat model of microvascular obstruction
  41. High SEC61A1 expression predicts poor outcome of acute myeloid leukemia
  42. Comparison of polymerase chain reaction and next-generation sequencing with conventional urine culture for the diagnosis of urinary tract infections: A meta-analysis
  43. Secreted frizzled-related protein 5 protects against renal fibrosis by inhibiting Wnt/β-catenin pathway
  44. Pan-cancer and single-cell analysis of actin cytoskeleton genes related to disulfidptosis
  45. Overexpression of miR-532-5p restrains oxidative stress response of chondrocytes in nontraumatic osteonecrosis of the femoral head by inhibiting ABL1
  46. Autologous liver transplantation for unresectable hepatobiliary malignancies in enhanced recovery after surgery model
  47. Clinical analysis of incomplete rupture of the uterus secondary to previous cesarean section
  48. Abnormal sleep duration is associated with sarcopenia in older Chinese people: A large retrospective cross-sectional study
  49. No genetic causality between obesity and benign paroxysmal vertigo: A two-sample Mendelian randomization study
  50. Identification and validation of autophagy-related genes in SSc
  51. Long non-coding RNA SRA1 suppresses radiotherapy resistance in esophageal squamous cell carcinoma by modulating glycolytic reprogramming
  52. Evaluation of quality of life in patients with schizophrenia: An inpatient social welfare institution-based cross-sectional study
  53. The possible role of oxidative stress marker glutathione in the assessment of cognitive impairment in multiple sclerosis
  54. Compilation of a self-management assessment scale for postoperative patients with aortic dissection
  55. Left atrial appendage closure in conjunction with radiofrequency ablation: Effects on left atrial functioning in patients with paroxysmal atrial fibrillation
  56. Effect of anterior femoral cortical notch grade on postoperative function and complications during TKA surgery: A multicenter, retrospective study
  57. Clinical characteristics and assessment of risk factors in patients with influenza A-induced severe pneumonia after the prevalence of SARS-CoV-2
  58. Analgesia nociception index is an indicator of laparoscopic trocar insertion-induced transient nociceptive stimuli
  59. High STAT4 expression correlates with poor prognosis in acute myeloid leukemia and facilitates disease progression by upregulating VEGFA expression
  60. Factors influencing cardiovascular system-related post-COVID-19 sequelae: A single-center cohort study
  61. HOXD10 regulates intestinal permeability and inhibits inflammation of dextran sulfate sodium-induced ulcerative colitis through the inactivation of the Rho/ROCK/MMPs axis
  62. Mesenchymal stem cell-derived exosomal miR-26a induces ferroptosis, suppresses hepatic stellate cell activation, and ameliorates liver fibrosis by modulating SLC7A11
  63. Endovascular thrombectomy versus intravenous thrombolysis for primary distal, medium vessel occlusion in acute ischemic stroke
  64. ANO6 (TMEM16F) inhibits gastrointestinal stromal tumor growth and induces ferroptosis
  65. Prognostic value of EIF5A2 in solid tumors: A meta-analysis and bioinformatics analysis
  66. The role of enhanced expression of Cx43 in patients with ulcerative colitis
  67. Choosing a COVID-19 vaccination site might be driven by anxiety and body vigilance
  68. Role of ICAM-1 in triple-negative breast cancer
  69. Cost-effectiveness of ambroxol in the treatment of Gaucher disease type 2
  70. HLA-DRB5 promotes immune thrombocytopenia via activating CD8+ T cells
  71. Efficacy and factors of myofascial release therapy combined with electrical and magnetic stimulation in the treatment of chronic pelvic pain syndrome
  72. Efficacy of tacrolimus monotherapy in primary membranous nephropathy
  73. Mechanisms of Tripterygium wilfordii Hook F on treating rheumatoid arthritis explored by network pharmacology analysis and molecular docking
  74. FBXO45 levels regulated ferroptosis renal tubular epithelial cells in a model of diabetic nephropathy by PLK1
  75. Optimizing anesthesia strategies to NSCLC patients in VATS procedures: Insights from drug requirements and patient recovery patterns
  76. Alpha-lipoic acid upregulates the PPARγ/NRF2/GPX4 signal pathway to inhibit ferroptosis in the pathogenesis of unexplained recurrent pregnancy loss
  77. Correlation between fat-soluble vitamin levels and inflammatory factors in paediatric community-acquired pneumonia: A prospective study
  78. CD1d affects the proliferation, migration, and apoptosis of human papillary thyroid carcinoma TPC-1 cells via regulating MAPK/NF-κB signaling pathway
  79. miR-let-7a inhibits sympathetic nerve remodeling after myocardial infarction by downregulating the expression of nerve growth factor
  80. Immune response analysis of solid organ transplantation recipients inoculated with inactivated COVID-19 vaccine: A retrospective analysis
  81. The H2Valdien derivatives regulate the epithelial–mesenchymal transition of hepatoma carcinoma cells through the Hedgehog signaling pathway
  82. Clinical efficacy of dexamethasone combined with isoniazid in the treatment of tuberculous meningitis and its effect on peripheral blood T cell subsets
  83. Comparison of short-segment and long-segment fixation in treatment of degenerative scoliosis and analysis of factors associated with adjacent spondylolisthesis
  84. Lycopene inhibits pyroptosis of endothelial progenitor cells induced by ox-LDL through the AMPK/mTOR/NLRP3 pathway
  85. Methylation regulation for FUNDC1 stability in childhood leukemia was up-regulated and facilitates metastasis and reduces ferroptosis of leukemia through mitochondrial damage by FBXL2
  86. Correlation of single-fiber electromyography studies and functional status in patients with amyotrophic lateral sclerosis
  87. Risk factors of postoperative airway obstruction complications in children with oral floor mass
  88. Expression levels and clinical significance of serum miR-19a/CCL20 in patients with acute cerebral infarction
  89. Physical activity and mental health trends in Korean adolescents: Analyzing the impact of the COVID-19 pandemic from 2018 to 2022
  90. Evaluating anemia in HIV-infected patients using chest CT
  91. Ponticulus posticus and skeletal malocclusion: A pilot study in a Southern Italian pre-orthodontic court
  92. Causal association of circulating immune cells and lymphoma: A Mendelian randomization study
  93. Assessment of the renal function and fibrosis indexes of conventional western medicine with Chinese medicine for dredging collaterals on treating renal fibrosis: A systematic review and meta-analysis
  94. Comprehensive landscape of integrator complex subunits and their association with prognosis and tumor microenvironment in gastric cancer
  95. New target-HMGCR inhibitors for the treatment of primary sclerosing cholangitis: A drug Mendelian randomization study
  96. Population pharmacokinetics of meropenem in critically ill patients
  97. Comparison of the ability of newly inflammatory markers to predict complicated appendicitis
  98. Comparative morphology of the cruciate ligaments: A radiological study
  99. Immune landscape of hepatocellular carcinoma: The central role of TP53-inducible glycolysis and apoptosis regulator
  100. Serum SIRT3 levels in epilepsy patients and its association with clinical outcomes and severity: A prospective observational study
  101. SHP-1 mediates cigarette smoke extract-induced epithelial–mesenchymal transformation and inflammation in 16HBE cells
  102. Acute hyper-hypoxia accelerates the development of depression in mice via the IL-6/PGC1α/MFN2 signaling pathway
  103. The GJB3 correlates with the prognosis, immune cell infiltration, and therapeutic responses in lung adenocarcinoma
  104. Physical fitness and blood parameters outcomes of breast cancer survivor in a low-intensity circuit resistance exercise program
  105. Exploring anesthetic-induced gene expression changes and immune cell dynamics in atrial tissue post-coronary artery bypass graft surgery
  106. Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism
  107. Analysis of the risk factors of the radiation-induced encephalopathy in nasopharyngeal carcinoma: A retrospective cohort study
  108. Reproductive outcomes in women with BRCA 1/2 germline mutations: A retrospective observational study and literature review
  109. Evaluation of upper airway ultrasonographic measurements in predicting difficult intubation: A cross-section of the Turkish population
  110. Prognostic and diagnostic value of circulating IGFBP2 in pancreatic cancer
  111. Postural stability after operative reconstruction of the AFTL in chronic ankle instability comparing three different surgical techniques
  112. Research trends related to emergence agitation in the post-anaesthesia care unit from 2001 to 2023: A bibliometric analysis
  113. Frequency and clinicopathological correlation of gastrointestinal polyps: A six-year single center experience
  114. ACSL4 mediates inflammatory bowel disease and contributes to LPS-induced intestinal epithelial cell dysfunction by activating ferroptosis and inflammation
  115. Affibody-based molecular probe 99mTc-(HE)3ZHER2:V2 for non-invasive HER2 detection in ovarian and breast cancer xenografts
  116. Effectiveness of nutritional support for clinical outcomes in gastric cancer patients: A meta-analysis of randomized controlled trials
  117. The relationship between IFN-γ, IL-10, IL-6 cytokines, and severity of the condition with serum zinc and Fe in children infected with Mycoplasma pneumoniae
  118. Paraquat disrupts the blood–brain barrier by increasing IL-6 expression and oxidative stress through the activation of PI3K/AKT signaling pathway
  119. Sleep quality associate with the increased prevalence of cognitive impairment in coronary artery disease patients: A retrospective case–control study
  120. Dioscin protects against chronic prostatitis through the TLR4/NF-κB pathway
  121. Association of polymorphisms in FBN1, MYH11, and TGF-β signaling-related genes with susceptibility of sporadic thoracic aortic aneurysm and dissection in the Zhejiang Han population
  122. Application value of multi-parameter magnetic resonance image-transrectal ultrasound cognitive fusion in prostate biopsy
  123. Laboratory variables‐based artificial neural network models for predicting fatty liver disease: A retrospective study
  124. Decreased BIRC5-206 promotes epithelial–mesenchymal transition in nasopharyngeal carcinoma through sponging miR-145-5p
  125. Sepsis induces the cardiomyocyte apoptosis and cardiac dysfunction through activation of YAP1/Serpine1/caspase-3 pathway
  126. Assessment of iron metabolism and iron deficiency in incident patients on incident continuous ambulatory peritoneal dialysis
  127. Tibial periosteum flap combined with autologous bone grafting in the treatment of Gustilo-IIIB/IIIC open tibial fractures
  128. The application of intravenous general anesthesia under nasopharyngeal airway assisted ventilation undergoing ureteroscopic holmium laser lithotripsy: A prospective, single-center, controlled trial
  129. Long intergenic noncoding RNA for IGF2BP2 stability suppresses gastric cancer cell apoptosis by inhibiting the maturation of microRNA-34a
  130. Role of FOXM1 and AURKB in regulating keratinocyte function in psoriasis
  131. Parental control attitudes over their pre-school children’s diet
  132. The role of auto-HSCT in extranodal natural killer/T cell lymphoma
  133. Significance of negative cervical cytology and positive HPV in the diagnosis of cervical lesions by colposcopy
  134. Echinacoside inhibits PASMCs calcium overload to prevent hypoxic pulmonary artery remodeling by regulating TRPC1/4/6 and calmodulin
  135. ADAR1 plays a protective role in proximal tubular cells under high glucose conditions by attenuating the PI3K/AKT/mTOR signaling pathway
  136. The risk of cancer among insulin glargine users in Lithuania: A retrospective population-based study
  137. The unusual location of primary hydatid cyst: A case series study
  138. Intraoperative changes in electrophysiological monitoring can be used to predict clinical outcomes in patients with spinal cavernous malformation
  139. Obesity and risk of placenta accreta spectrum: A meta-analysis
  140. Shikonin alleviates asthma phenotypes in mice via an airway epithelial STAT3-dependent mechanism
  141. NSUN6 and HTR7 disturbed the stability of carotid atherosclerotic plaques by regulating the immune responses of macrophages
  142. The effect of COVID-19 lockdown on admission rates in Maternity Hospital
  143. Temporal muscle thickness is not a prognostic predictor in patients with high-grade glioma, an experience at two centers in China
  144. Luteolin alleviates cerebral ischemia/reperfusion injury by regulating cell pyroptosis
  145. Therapeutic role of respiratory exercise in patients with tuberculous pleurisy
  146. Effects of CFTR-ENaC on spinal cord edema after spinal cord injury
  147. Irisin-regulated lncRNAs and their potential regulatory functions in chondrogenic differentiation of human mesenchymal stem cells
  148. DMD mutations in pediatric patients with phenotypes of Duchenne/Becker muscular dystrophy
  149. Combination of C-reactive protein and fibrinogen-to-albumin ratio as a novel predictor of all-cause mortality in heart failure patients
  150. Significant role and the underly mechanism of cullin-1 in chronic obstructive pulmonary disease
  151. Ferroptosis-related prognostic model of mantle cell lymphoma
  152. Observation of choking reaction and other related indexes in elderly painless fiberoptic bronchoscopy with transnasal high-flow humidification oxygen therapy
  153. A bibliometric analysis of Prader-Willi syndrome from 2002 to 2022
  154. The causal effects of childhood sunburn occasions on melanoma: A univariable and multivariable Mendelian randomization study
  155. Oxidative stress regulates glycogen synthase kinase-3 in lymphocytes of diabetes mellitus patients complicated with cerebral infarction
  156. Role of COX6C and NDUFB3 in septic shock and stroke
  157. Trends in disease burden of type 2 diabetes, stroke, and hypertensive heart disease attributable to high BMI in China: 1990–2019
  158. Purinergic P2X7 receptor mediates hyperoxia-induced injury in pulmonary microvascular endothelial cells via NLRP3-mediated pyroptotic pathway
  159. Investigating the role of oviductal mucosa–endometrial co-culture in modulating factors relevant to embryo implantation
  160. Analgesic effect of external oblique intercostal block in laparoscopic cholecystectomy: A retrospective study
  161. Elevated serum miR-142-5p correlates with ischemic lesions and both NSE and S100β in ischemic stroke patients
  162. Correlation between the mechanism of arteriopathy in IgA nephropathy and blood stasis syndrome: A cohort study
  163. Risk factors for progressive kyphosis after percutaneous kyphoplasty in osteoporotic vertebral compression fracture
  164. Predictive role of neuron-specific enolase and S100-β in early neurological deterioration and unfavorable prognosis in patients with ischemic stroke
  165. The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia
  166. Fluoxetine inhibited RANKL-induced osteoclastic differentiation in vitro
  167. Detection of serum FOXM1 and IGF2 in patients with ARDS and their correlation with disease and prognosis
  168. Rhein promotes skin wound healing by activating the PI3K/AKT signaling pathway
  169. Differences in mortality risk by levels of physical activity among persons with disabilities in South Korea
  170. Review Articles
  171. Cutaneous signs of selected cardiovascular disorders: A narrative review
  172. XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis
  173. A narrative review on adverse drug reactions of COVID-19 treatments on the kidney
  174. Emerging role and function of SPDL1 in human health and diseases
  175. Adverse reactions of piperacillin: A literature review of case reports
  176. Molecular mechanism and intervention measures of microvascular complications in diabetes
  177. Regulation of mesenchymal stem cell differentiation by autophagy
  178. Molecular landscape of borderline ovarian tumours: A systematic review
  179. Advances in synthetic lethality modalities for glioblastoma multiforme
  180. Investigating hormesis, aging, and neurodegeneration: From bench to clinics
  181. Frankincense: A neuronutrient to approach Parkinson’s disease treatment
  182. Sox9: A potential regulator of cancer stem cells in osteosarcoma
  183. Early detection of cardiovascular risk markers through non-invasive ultrasound methodologies in periodontitis patients
  184. Advanced neuroimaging and criminal interrogation in lie detection
  185. Maternal factors for neural tube defects in offspring: An umbrella review
  186. The chemoprotective hormetic effects of rosmarinic acid
  187. CBD’s potential impact on Parkinson’s disease: An updated overview
  188. Progress in cytokine research for ARDS: A comprehensive review
  189. Utilizing reactive oxygen species-scavenging nanoparticles for targeting oxidative stress in the treatment of ischemic stroke: A review
  190. NRXN1-related disorders, attempt to better define clinical assessment
  191. Lidocaine infusion for the treatment of complex regional pain syndrome: Case series and literature review
  192. Trends and future directions of autophagy in osteosarcoma: A bibliometric analysis
  193. Iron in ventricular remodeling and aneurysms post-myocardial infarction
  194. Case Reports
  195. Sirolimus potentiated angioedema: A case report and review of the literature
  196. Identification of mixed anaerobic infections after inguinal hernia repair based on metagenomic next-generation sequencing: A case report
  197. Successful treatment with bortezomib in combination with dexamethasone in a middle-aged male with idiopathic multicentric Castleman’s disease: A case report
  198. Complete heart block associated with hepatitis A infection in a female child with fatal outcome
  199. Elevation of D-dimer in eosinophilic gastrointestinal diseases in the absence of venous thrombosis: A case series and literature review
  200. Four years of natural progressive course: A rare case report of juvenile Xp11.2 translocations renal cell carcinoma with TFE3 gene fusion
  201. Advancing prenatal diagnosis: Echocardiographic detection of Scimitar syndrome in China – A case series
  202. Outcomes and complications of hemodialysis in patients with renal cancer following bilateral nephrectomy
  203. Anti-HMGCR myopathy mimicking facioscapulohumeral muscular dystrophy
  204. Recurrent opportunistic infections in a HIV-negative patient with combined C6 and NFKB1 mutations: A case report, pedigree analysis, and literature review
  205. Letter to the Editor
  206. Letter to the Editor: Total parenteral nutrition-induced Wernicke’s encephalopathy after oncologic gastrointestinal surgery
  207. Erratum
  208. Erratum to “Bladder-embedded ectopic intrauterine device with calculus”
  209. Retraction
  210. Retraction of “XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis”
  211. Corrigendum
  212. Corrigendum to “Investigating hormesis, aging, and neurodegeneration: From bench to clinics”
  213. Corrigendum to “Frankincense: A neuronutrient to approach Parkinson’s disease treatment”
  214. Special Issue The evolving saga of RNAs from bench to bedside - Part II
  215. Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
  216. Unlocking the future of hepatocellular carcinoma treatment: A comprehensive analysis of disulfidptosis-related lncRNAs for prognosis and drug screening
  217. Elevated mRNA level indicates FSIP1 promotes EMT and gastric cancer progression by regulating fibroblasts in tumor microenvironment
  218. Special Issue Advancements in oncology: bridging clinical and experimental research - Part I
  219. Ultrasound-guided transperineal vs transrectal prostate biopsy: A meta-analysis of diagnostic accuracy and complication rates
  220. Assessment of diagnostic value of unilateral systematic biopsy combined with targeted biopsy in detecting clinically significant prostate cancer
  221. SENP7 inhibits glioblastoma metastasis and invasion by dissociating SUMO2/3 binding to specific target proteins
  222. MARK1 suppress malignant progression of hepatocellular carcinoma and improves sorafenib resistance through negatively regulating POTEE
  223. Analysis of postoperative complications in bladder cancer patients
  224. Carboplatin combined with arsenic trioxide versus carboplatin combined with docetaxel treatment for LACC: A randomized, open-label, phase II clinical study
  225. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part I
  226. Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma
  227. Identification of signatures associated with microsatellite instability and immune characteristics to predict the prognostic risk of colon cancer
  228. Single-cell analysis identified key macrophage subpopulations associated with atherosclerosis
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Heruntergeladen am 5.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/med-2024-0982/html
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