Startseite Comparative bioinformatics analysis of the Wnt pathway in breast cancer: Selection of novel biomarker panels associated with ER status
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Comparative bioinformatics analysis of the Wnt pathway in breast cancer: Selection of novel biomarker panels associated with ER status

  • Klaudia Waszczykowska EMAIL logo , Damian Kołat , Żaneta Kałuzińska-Kołat und Elżbieta Płuciennik
Veröffentlicht/Copyright: 8. Oktober 2025
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Open Life Sciences
Aus der Zeitschrift Open Life Sciences Band 20 Heft 1

Abstract

Breast cancer (BC) is a major global health concern, ranking among the most common neoplasms and representing one of the leading causes of cancer-related deaths worldwide. Early recognition and classification of BC subtypes are crucial for improving patient outcomes. Therefore, identifying novel biomarkers with diagnostic and prognostic significance is of great importance. The Wnt signaling pathway plays a significant role in BC by influencing various cell cycle regulation processes and stem cell renewal. This study aims to identify novel Wnt-associated biomarker panels for BC patients, composed of multiple molecular factors. A series of bioinformatical analyses have been employed, including weighted gene co-expression network analysis, differential expression analysis, Kaplan–Meier survival analysis, logistic regression model evaluation, and receiver operating characteristic construction. Thus, this study revealed potential diagnostic and prognostic signatures based on comprehensive analyses of BC patient data sourced from The Cancer Genome Atlas database. Consequently, four gene signatures were constructed: two differentiate ER+ from ER-BC: TTC8, SLC5A7, and PLCH1 for overall survival (OS); ZNF695, SLC7A5, and PLCH1 for disease free survival (DFS), while the other two effectively distinguish tumor from normal samples: SPC25, ANLN, KPNA2, SLC7A5 for OS; SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, and SLC7A5 for DFS.

Graphical abstract

Keywords: breast cancer; Wnt signaling; carcinogenesis; biomarker signatures; cancer biomarkers

1 Introduction

Breast cancer (BC) is one of the most prevalent neoplasms across the world, being one of the leading causes of cancer-related deaths worldwide. In 2020, approximately 2.3 million cases were diagnosed, whereas 685,000 cases were fatal. BC is estimated to affect 4.4 million individuals by the year 2070 [1,2,3]. Molecular characteristics of BC include four intrinsic subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu), namely luminal A, luminal B, HER2-enriched, and TNBC/basal-like (triple negative BC). Each subtype entails a different treatment approach, which is additionally modified by the unique molecular profile of each patient [4,5].

Currently, the major types of BC management include surgery, radiation therapy, chemotherapy, endocrine therapy, and targeted therapy. Each intervention serves a distinct purpose, and its selection depends on several factors, such as the type and stage of the malignancy, the individual’s general condition, and their preferences [6]. Apart from the selection of treatment, early recognition and characterization of BC type are key to the patient’s survival; thus, it is crucial to define novel biomarkers that could be of great diagnostic and prognostic importance [7,8,9]. Given the substantial molecular heterogeneity in the background of BC among individual patients, it is of great importance to establish personalized therapeutic interventions tailored to improve the chances of survival for each patient. An emphasized focus on advancing biomarker development holds the potential to develop innovative therapeutic strategies [10].

A signaling pathway that is often implicated in carcinogenesis, including BC, is the Wnt pathway. It influences embryonic development, being associated with various cell cycle regulation processes and stem cell renewal [11,12]. Its dysregulation in BC influences proliferation, metastasis, immune microenvironment regulation, and therapeutic resistance [13,14]. This study aimed to identify Wnt-associated molecular signatures as potential therapeutic targets, evaluated through bioinformatic analyses and literature review.

2 Materials and methods

2.1 Data acquisition and input gene selection

The data for this study were obtained from The Cancer Genome Atlas (TCGA) repository for BC patients (Breast Invasive Carcinoma (TCGA, PanCancer Atlas) obtained via the cBioPortal database (https://www.cbioportal.org/datasets), which included 1,082 BC patients and 114 matched normal samples. The genes that underwent further analysis were selected using the gene transcription regulation database (https://gtrd.biouml.org/) that consisted of 2,573 Wnt downstream effectors.

2.2 Weighted gene co-expression network analysis (WGCNA) and functional annotation

Pearson’s correlation between input genes was computed and progressed employing adjacency matrix transformation with the B-power = 5 and the scale-free topology fitting index (R 2) > 0.9, following the standard guidelines for WGCNA [15]. Based on the connection between gene pairs, a topological overlap matrix was constructed to prepare hierarchical clustering using the hclust() function with the “average” method of agglomeration. The identification of modules was performed using the cutreeDynamic() function (minimum size of the module = 40, deep split level = 2). The obtained modules were further correlated with specific clinical traits of the BC patients. Furthermore, a logarithmically transformed p-value from linear regression facilitated the computation of the correlation’s significance between gene expression and specific clinical attributes. Finally, the collective importance of modules was calculated by averaging the significance of individual genes within a cluster of genes that are interconnected within the designated module. Disparities in expression patterns among distinct modules were graphically represented by employing the gplots package through the generation of heatmaps (via the heatmap.2() function), utilizing Pearson’s distance metric for arranging rows, and adopting the “complete” method of agglomeration. The PANTHER database (https://www.pantherdb.org/) was used to functionally annotate the selected modules of genes. The blue module, encompassing 183 genes, underwent subsequent analysis specifically in the context of BC patients.

2.3 Metascape protein–protein interaction enrichment analysis

Using Metascape (https://metascape.org), analysis and interpretation of blue module gene OMIC data were performed. The core outcomes focus on enrichment analysis, where genes found in the blue module of WGCNA were compared against multiple gene sets associated with various biological processes, protein functions, pathways, and other features. The input gene list (183) was extracted as the blue module from WGCNA (Table S1).

2.4 Differential expression analysis (DEA)

The differentially expressed genes (DEGs) were identified using Bioconductor’s package edgeR in two separate comparisons: ER-positive versus ER-negative patients, as well as normal versus tumor samples. Intergroup comparisons were established using the makeContrasts() function, which was preceded by using glmFit() and glmLRT() functions. The topTags() function identified and tabulated the most pronounced DEGs between the groups. A more restrictive cutoff of log2FC ≥3.5 was applied to identify the most robust and biologically significant differentially expressed genes; the parameters of log2FC ≥3.5 and a significance threshold of p < 0.01 were employed for DEG identification. The visual representation of these genes was achieved through volcano plots, employing the ggrepel package and its geom_text_repel() function. Two volcano plots showed gene expression distributions based on the ER status (“ER+” vs “ER−”) and sample type (“tumor” vs “normal” specimens).

2.5 Survival analysis

Genes with the most significant downregulation or overexpression were analyzed for disease-free survival (DFS) and overall survival (OS) using R libraries (survminer, survival, tidyverse) for both comparisons: “normal” vs “tumor” and “ER+” vs “ER−.” Next, genes exhibiting statistically significant individual survival analyses (p-value <0.05) were combined into distinct multi-gene signatures. These signatures underwent further analysis using R libraries (survminer, survival, tidyverse). Median gene expression was selected as a cutoff value for survival analyses to provide more balanced group comparisons and avoid small subgroups. The generation of survival plots was accomplished through the application of the ggplot2 package.

2.6 Logistic regression model of selected genes and receiver operating characteristic (ROC) evaluation with prediction assessment and validation of the model

To assess the overall diagnostic performance of the selected gene signatures, a linear regression model was built, and its potential was evaluated with ROC curves. To construct a logistic regression model, the cohort of BC patients was partitioned into two distinct training and testing subgroups, comprising 70 and 30% of the patients, respectively. The former subgroup was utilized for model development, whereas the latter group served for control purposes. The potential predictive capability of the novel marker signatures was examined using the ROC curve generated using the pROC R package. This analysis was carried out considering the presence of ER within cancer samples, as well as the distinction between tumor and normal tissue. The model was established using the glm function in R.

3 Results

3.1 Weighted gene correlation network analysis revealed genes most closely correlated with the ER status of BC patients

A statistically significant correlation of R = 0.46 was noted between the genes included in the blue module and the status of ER (Figure 1). This particular module comprised 183 genes, which subsequently underwent further analysis to evaluate their prognostic potential and identify gene functionality, alongside their potential implications for the survival of patients diagnosed with BC. Detailed information on genes included in the blue module is provided in Table S1.

Figure 1 The weighted gene correlation network analysis heatmap showing correlation between genes grouped into modules of similar expression patterns and particular clinical traits of BC patients. Negative correlation is represented by green color, while positive correlation is marked with red. The greatest correlation was found for genes included in the blue module and ER status (0.46; p < 0.05).
Figure 1

The weighted gene correlation network analysis heatmap showing correlation between genes grouped into modules of similar expression patterns and particular clinical traits of BC patients. Negative correlation is represented by green color, while positive correlation is marked with red. The greatest correlation was found for genes included in the blue module and ER status (0.46; p < 0.05).

3.2 Enrichment analysis with Metascape revealed functional associations of blue module genes

In the subsequent phase of analysis, gene sets that were significantly enriched were analyzed to derive potential biological insights relevant to the study (Figure 2).

Figure 2 Bar graph showing the enriched processes across the input gene lists, with darker colors indicating higher statistical significance of the displayed terms. Pathway and process enrichment analysis was performed for each gene list using the following ontology sources: KEGG pathway, GO biological processes (GO), Reactome gene sets (R-HSA), canonical pathways, CORUM, Wiki pathways (WP), and PANTHER pathway. The entire genome served as the enrichment background.
Figure 2

Bar graph showing the enriched processes across the input gene lists, with darker colors indicating higher statistical significance of the displayed terms. Pathway and process enrichment analysis was performed for each gene list using the following ontology sources: KEGG pathway, GO biological processes (GO), Reactome gene sets (R-HSA), canonical pathways, CORUM, Wiki pathways (WP), and PANTHER pathway. The entire genome served as the enrichment background.

Metascape enrichment analysis revealed that genes within the blue module are significantly linked to cell cycle processes, particularly the mitotic cycle (16%; p < 0.05). Additionally, these genes showed a strong association with DNA metabolic processes (14.21%; p < 0.05). Also, 11 genes (6.01%; p < 0.05) were identified as connected to the retinoblastoma pathway in cancer. Processes that are less frequently linked to this gene module, but still hold significance, include DNA replication, RNA metabolism, chromatin remodeling, and protein–DNA complex assembly. Detailed information on these specific processes can be found in Table S2. In addition, several groups of genes were organized into clusters based on the protein–protein interactions identified (Figure 3).

Figure 3 The four major networks for the provided set of ER-associated Wnt genes.
Figure 3

The four major networks for the provided set of ER-associated Wnt genes.

Four major interaction networks were identified during this step: AEBP2, BARD1, CDK2, CDC25A, CORO1C, EMG1, EXOSC2, GINS4, KPNA2, LBR, LSM2, MPHOSPH10, PHC1, PHF19, POP1, PSMD12, RCC2, SSRP1, TIMELESS, USP39, and ZC3H18 as the red network; CCT3, KIF20A, LMNB1, LMNB2, NOP2, PHB2, RANBP1, TUBA1C, and XPO1 as the blue network; ANLN, BRIX1, CAD, DDX47, IARS1, ILF2, TUBB, and UGP2 as the green network; and COMMD2, DTL, and NAE1 as the purple network. Each network was found to be linked with distinct biological processes, which included DNA replication, cell cycle progression, and RNA metabolism (red nodes); protein localization to the nucleus, apoptosis, and cell cycle M phase (blue nodes); the assembly of non-membrane-bound organelles (green nodes); and the neddylation process (purple nodes). A detailed description of processes and p-value scores is provided in Table S3.

3.3 DEGs were identified by comparing ER-positive and negative BC patients, as well as tumor and normal breast tissue

DEA was undertaken across two distinct comparative frameworks. First, a comparison between patients with positive and negative ER status was executed, using the latter group as the control cohort. Additionally, a comparison contrasting normal tissue samples against tumor tissue samples was conducted, with the normal samples serving as the reference group. In the initial comparison, TTC8, SPRYD3, SUOX, FAM47E, TMC4, CALCOCO1, and TPCN1 genes were found to be downregulated; whereas B3GNT5, UBASH3B, CDCA2, CDC20, ZNF695, RGMA, LRP8, SLC7A5, MEX3A, PIF1, and PLCH1 displayed a significant upregulation (Figure 4a). As for the normal versus tumor comparison, a collection of genes including MRAS, UGP2, CDKN2C, FGD4, FOXN2, TK2, CALCOCO1, JRKL, RGMA, TCF7L1, and B3GNT5 exhibited downregulation, while a pattern of upregulation was observed for the following genes: SPC25, KIF2C, UHRF1, CEP55, KIF20A, DTL, SKA3, CKAP2L, ANLN, CDCA3, SPAG5, LMNB1, TTK, RAD54L, MYBL2, CDCA2, KPNA2, TUBA1C, DIAPH3, CDT1, ZNF695, HELLS, TIMELESS, ATAD2, FANCA, GINS4, SLC7A5, PIF1, ZNF367, LRP8, and CCDC150 (Figure 4b).

Figure 4 DEA comparison between ER-positive and ER-negative BC patients (a), as well as tumor and normal tissue (b).
Figure 4

DEA comparison between ER-positive and ER-negative BC patients (a), as well as tumor and normal tissue (b).

3.4 Genes statistically significant for patients’ survival were selected using Kaplan–Meier curves

Genes that displayed substantial and statistically significant impact on the survival of these patients were identified and selected for further signature construction. Particularly prominent among these genes in ER+ vs ER− comparison were TTC8, SLC7A5, PLCH1 (OS), and ZNF695, SLC7A5, PLCH1 (DFS). For normal vs tumor comparison, the most significant genes included UGP2, JRKL, SPC25, ANLN, KPNA2, SLC7A5 (OS), as well as SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, SLC7A5 (DFS) (Figures 5 and 6). The DTL gene was also included in the list for further evaluation of the gene’s potential impact, as it closely approached statistical significance (p = 0.058).

Figure 5 Kaplan–Meier curves for DEGs extracted from ER status comparison ((a) SLC7A5 [OS], (b) PLCH1 [OS], (c) TTC8 [OS], (d) SLC7A5 [DFS], (e) PLCH1 [DFS], and (f) ZNF695 [DFS]) that were found statistically significant.
Figure 5

Kaplan–Meier curves for DEGs extracted from ER status comparison ((a) SLC7A5 [OS], (b) PLCH1 [OS], (c) TTC8 [OS], (d) SLC7A5 [DFS], (e) PLCH1 [DFS], and (f) ZNF695 [DFS]) that were found statistically significant.

Figure 6 Kaplan–Meier curves for DEGs extracted from tumor and normal tissue comparison in the context of OS (a)–(f) and DFS (g)–(q) ((a) SPC25, (b) SLC7A5, (c) JRKL, (d) KPNA2, (e) UGP2, (f) ANLN, (g) SKA3, (h) KIF20A, (i) SPC25, (j) ANLN, (k) CDCA3, (l) DTL, (m) RAD54L, (n) MYBL2, (o) TTK, (p) SLC7A5, and (q) ZNF695) that were found statistically significant.
Figure 6

Kaplan–Meier curves for DEGs extracted from tumor and normal tissue comparison in the context of OS (a)–(f) and DFS (g)–(q) ((a) SPC25, (b) SLC7A5, (c) JRKL, (d) KPNA2, (e) UGP2, (f) ANLN, (g) SKA3, (h) KIF20A, (i) SPC25, (j) ANLN, (k) CDCA3, (l) DTL, (m) RAD54L, (n) MYBL2, (o) TTK, (p) SLC7A5, and (q) ZNF695) that were found statistically significant.

3.5 Multi-gene signature construction assessed the cumulative impact on BC patients’ survival

Gene signatures were constructed on the basis of the prognostic significance of each gene separately. Four novel signatures were analyzed, which included: TTC8, SLC7A5, PLCH1 (OS, Figure 7a), and ZNF695, SLC7A5, PLCH1 (DFS, Figure 7b) from the ER+ vs ER- comparison, as well as UGP2, JRKL, SPC25, ANLN, KPNA2, SLC7A5 (OS), alongside SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, and SLC7A5 (DFS, Figure 7d) from the comparison of tumor vs normal samples. Since the UGP2, JRKL, SPC25, ANLN, KPNA2, and SLC7A5 signatures with p = 0.18 were not statistically significant for the patients’ OS, the genes were rearranged into the most efficient pattern, resulting in the SPC25, ANLN, KPNA2, and SLC7A5 signatures with p = 0.028 (Figure 7c). Four sets of genes were further taken into account for binary regression evaluation of the results with ROC curves. The contents of each gene’s signature are summarized in Table S4.

Figure 7 Survival analysis of multi-gene signatures from ER comparisons ((a) OS – TTC8, SLC5A7, PLCH1 and (b) disease-free survival – ZNF695, SLC7A5, PLCH1), and tumor versus normal tissue comparisons ((c) OS – SPC25, ANLN, KPNA2, SLC7A5 and (d) disease-free survival – SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, SLC7A5). TTC8 expression was reversed, as its higher expression was found to be favorable in contrast to other genes.
Figure 7

Survival analysis of multi-gene signatures from ER comparisons ((a) OS – TTC8, SLC5A7, PLCH1 and (b) disease-free survival – ZNF695, SLC7A5, PLCH1), and tumor versus normal tissue comparisons ((c) OS – SPC25, ANLN, KPNA2, SLC7A5 and (d) disease-free survival – SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, SLC7A5). TTC8 expression was reversed, as its higher expression was found to be favorable in contrast to other genes.

3.6 Selected signatures demonstrated predictive capabilities as indicated by ROC curves

Predictive properties of multi-gene signatures were evaluated using the binomial logistic regression model, utilizing the collective expression of the chosen sets of genes in conjunction with the ER status (comparison of ER+ and ER− specimens) and the incidence of tumors (comparison between tumor and normal specimens). The resulting AUC values were as follows: 0.905 for OS (Figure 8a) and 0.886 for DFS (Figure 8b), within the ER+ vs ER- signatures (TTC8, SLC7A5, PLCH1 and ZNF695, SLC7A5, PLCH1). Similarly, for the normal vs tumor signatures, the corresponding AUC values were 0.992 for OS (Figure 8c) and 0.984 for DFS (Figure 8d) (SPC25, ANLN, KPNA2, SLC7A5 and SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, SLC7A5). Moreover, the predictive accuracy of the ER-associated signatures reached 88.241% for the OS-based signature and 85.519% for the DFS-based signature. In the case of ‘tumor vs normal’ signatures, the accuracy of prediction achieved notable levels: 97.906% for the OS-based signature and 96.147% for the DFS-based signature.

Figure 8 ROC curves for each signature-based binary regression model for ER status comparisons ((a) OS – TTC8, SLC5A7, PLCH1 and (b) DFS – ZNF695, SLC7A5, PLCH1), and tumor versus normal tissue comparisons ((c) OS – SPC25, ANLN, KPNA2, SLC7A5 and (d) DFS – SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, SLC7A5).
Figure 8

ROC curves for each signature-based binary regression model for ER status comparisons ((a) OS – TTC8, SLC5A7, PLCH1 and (b) DFS – ZNF695, SLC7A5, PLCH1), and tumor versus normal tissue comparisons ((c) OS – SPC25, ANLN, KPNA2, SLC7A5 and (d) DFS – SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, SLC7A5).

4 Discussion

BC, one of the most common cancers among women, has an annual global incidence of 2.3 million cases. Molecular subtypes, such as luminal A, luminal B, HER2/neu, and triple-negative, guide treatment strategies, with 70% of the cases being hormone-driven, primarily involving ER-expressing luminal subtypes. Estrogens are key drivers in these tumors [2,4,16]. The Wnt signaling pathway, crucial in cell cycle regulation and stem cell renewal, plays a significant role in carcinogenesis and hormone regulation [17,18]. Its dysregulation is linked to developmental defects and cancers. This study explores the Wnt pathway’s regulatory mechanisms to identify potential diagnostic and therapeutic targets for BC [19,20].

The study revealed Wnt gene-regulated processes associated with the ER status in BC patients, primarily involving the cell cycle, DNA replication, and chromatin remodeling, which are also linked with β-catenin/TCF-driven cell cycle activation and estrogen-mediated proliferation [21,22,23]. A relation to the retinoblastoma pathway highlighted RB1’s role in tumor suppression, including BC [24]. Additionally, several groups of genes were organized into clusters based on the identified protein–protein interactions. The largest interaction network was identified for proteins encoded by genes such as AEBP2, BARD1, CDK2, CDC25A, CORO1C, EMG1, EXOSC2, GINS4, KPNA2, LBR, LSM2, MPHOSPH10, PHC1, PHF19, POP1, PSMD12, RCC2, SSRP1, TIMELESS, USP39, and ZC3H18. These proteins are associated with Wnt signaling, which predominantly influences DNA replication, cell cycle progression, and RNA metabolism, as indicated in red in Figure 3. For example, CDC25A is a direct target of β-catenin/TCF, as it activates the cell-cycle phosphatase CDK2, thus promoting G1/S transition and facilitating β‑catenin transactivation via PKM2, which leads to enhanced proliferation [25,26]. Additionally, in BC, CDK2 and related cyclin–CDK complexes are essential for estrogen-driven cell division, underscoring ER-mediated proliferation [27]. A second network, depicted in blue, is associated with protein localization to the nucleus, apoptosis, and cell cycle, especially its M phase. Comprising genes include CCT3, KIF20A, LMNB1, LMNB2, NOP2, PHB2, RANBP1, TUBA1C, and XPO1. This network comprises genes that facilitate processes frequently orchestrated also by Wnt signaling and ER activity in many tumors [28,29,30]. Further significant associations were observed for the genes ANLN, BRIX1, CAD, DDX47, IARS1, ILF2, TUBB, and UGP2, which are highlighted in green. These genes are implicated in the assembly of non-membrane-bound organelles. Among these genes, ANLN overexpression was previously found to be correlated with poor survival of BC patients by enhancing cell proliferation and migration, at the same time interacting with Wnt/β‑catenin signaling [31,32]. Finally, the smallest purple network comprises three nodes: COMMD2, DTL, and NAE1. These genes encode proteins involved in the neddylation process, a type of post-translational modification that adds the ubiquitin-like protein NEDD8 to substrate proteins. Neddylation can influence various biological processes, including carcinogenesis, as it has been found to be upregulated in numerous human cancers [33]. Neddylation inhibitors represent a promising direction for cancer therapy, as they may also promote cancer-related immunosuppression [34]. Notably, in BC, the inhibition of neddylation has been shown to significantly suppress the growth of HER2-positive tumors when combined with trastuzumab [34]. Moreover, the neddylation modification pathway has been activated in breast carcinoma and is associated with ER-α expression [34,35].

In addition, we have successfully extracted a range of gene signatures linked to the Wnt pathway that hold potential as prognostic indicators for individuals afflicted with BC. Existing Wnt-associated biomarkers can be broadly classified based on their function within the pathway, including regulators (e.g., AXIN2, SFRP1) [14,36], receptors (e.g., FZD, LRP receptor families) [37,38], transcription factors (e.g., LEF1, TCF4) [39], or the central mediator of canonical Wnt signaling β-catenin (CTNNB1) [40]. Although such classical Wnt targets are already proposed, their expression alone often lacks sufficient specificity or prognostic utility due to the pathway’s various crosstalks and context-dependent activation. In addition, both the Wnt pathway and ER signaling are important drivers of hormone-dependent tumor growth and progression [41,42].

Thus, instead of focusing on single-gene expression patterns among BC patients, multi-gene signatures were developed in this study. Among these, two signatures are intricately tied to the ER status (TTC8, SLC7A5, PLCH1 – OS; ZNF695, SLC7A5, PLCH1 – DFS), while another pair is closely associated with the tumor tissue itself (SPC25, ANLN, KPNA2, SLC7A5 – OS; SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, SLC7A5 – DFS). Among others, is the ER-associated gene TTC8, known to play a role in cilia formation [43]. This gene’s capacity to distinguish between luminal (ER+) and non-luminal (ER−) BC cases has been validated by another study conducted by Habashy and colleagues [44]. Moreover, research conducted by Menzl et al. has identified TTC8 as one of the genes that are commonly downregulated in BC cases, which is in line with our study, as its higher expression was associated with a better prognosis [45]. In our study, however, we have linked the TTC8 gene with SLC5A7 (LAT1) and PLCH1, standing as one of the potential signatures. It has been noted that the SLC7A5 gene is related to a variety of tumors [46–49]. Regarding BC, SLC7A5 seems to be especially linked with the aggressive and highly proliferative ER+ subtype, and is also associated with the MYC driver gene [46]. The main function of SLC7A5 involves the import of crucial amino acids into cancer cells, thus making it a viable therapeutic target for cancer management [46,50]. The last gene from this particular signature associated with both ER status and OS of BC patients is PLCH1, an enzyme linked with the breakdown of phosphatidylinositol 4,5-bisphosphate [51]. Although recognized as a potentially significant therapeutic target for BC by other studies employing multi-omics data, this gene remains insufficiently studied [52].

In summary, the ER signature, intricately associated with the survival outcomes of BC patients, encompasses TTC8, SLC7A5, and PLCH1 genes. This collective set exhibits influence on the OS of BC patients, potentially serving as a combined biomarker panel.

Concerning the ER+ signature linked to DFS, consistency is observed with two of the three notable genes that are also present in the context of OS. Specifically, SLC7A5 and PLCH1 maintain their significance, and they are accompanied by the inclusion of ZNF695. In a study evaluating KRAB-ZNF factors, ZNF695 has been found to positively correlate with advanced tumor stage in BC tissues [53]. Another research found that ZNF695 is associated with the upregulation of several cell cycle genes, mostly in basal-like BC tumors [54]. In addition to ER-associated signatures, analysis of comparisons in normal and tumor samples allowed us to select several genes influencing either OS or DFS. The signature associated with OS that was found significant as a collective set of genes included SPC25, ANLN, KPNA2, and SLC7A5. The SPC25 gene encodes a protein that participates in interactions of the kinetochore microtubule, as well as the activity of the spindle checkpoint. Recently, it has been found to positively correlate with poorer prognosis and survival of BC patients [55,56]. ANLN, crucial in cytokinesis and myosin contraction, contributes to immune evasion by cancer cells. Elevated ANLN expression is linked to poor cervical cancer survival and serves as a negative prognostic factor. In BC, in silico analysis suggests ANLN impacts Th1/Th2 balance in basal and luminal-B subtypes, correlating with poor prognosis [57–59].

Furthermore, another essential gene of this module, KPNA2, is found to be an oncogenic factor, being also implicated in nuclear transport [60]. Several studies have identified this gene as a poor prognosis factor for breast and ovarian cancer patients [60–62]. In addition, its expression has also been linked with lower concentrations of DNA repair proteins in the cell nuclei [63]. Taken together, combined SPC25, ANLN, KPNA2, and SLC7A5 expression seems to be crucial for the OS of individuals with BC. At the same time, each gene of the signature is notably amplified in these patients, in comparison to normal tissue.

The very last signature of the tumor occurrence analysis includes genes such as SPC25, KIF20A, SKA3, DTL, CDCA3, ANLN, TTK, RAD54L, MYBL2, ZNF695, and SLC7A5, which collectively influence the DFS of BC patients. Several genes have been previously mentioned in connection with other signatures; however, KIF20A, SKA3, DTL, CDCA3, TTK, RAD54L, and MYBL2 are found to be specifically valuable for DFS, at the same time being significantly overexpressed in cancer tissue. KIF20A is a gene encoding a cytokinesis-related protein [64]. The current literature confirms that in ER+ cases of BC, the gene has been found to serve as an independent prognostic factor [65,66]. Its expression has also been linked with poorer prognosis of such patients, indicating a potential to be a therapeutic target as well [65]. Next, SKA3 encodes a protein that is a part of a larger complex, functioning during mitosis via microtubule attachment to the kinetochores [67]. The gene’s overexpression has been linked with promoting the growth of BC, and thus, is associated with a worse survival ratio in this group of patients. This is due to the regulation of PLK-1, which is also involved in eukaryotic cell division, as its expression is frequently elevated in certain tumors [68–71]. Another component of the signature, DTL, is also associated with cells [72]. It has a vital role in cancer progression by taking part in the PDCD4 protein degradation, which is known to be a tumor suppressor influencing programmed cell death [73]. DTL’s elevated expression is often associated with lower survival rates of various cancer patients, thus proving the gene’s clinical value [74–77]. CDCA3 is another gene of the signature associated with cell multiplication, being one of the pivotal regulators of cell mitosis [78]. The current literature reports that inhibiting CDCA3 expression might be of great importance in neoplastic proliferation suppression [79,80]. TTK encodes a kinase involved in mitosis and cell proliferation, playing a role in neoplastic development. Its inhibition enhances radiosensitivity in basal-like BC. Although often elevated in TNBC, TTK was identified as a positive prognostic biomarker for this type of cancer [81–83].

Next, RAD54L is a member of the DEAD-like helicase superfamily and is implicated in homologous recombination and repair of DNA [84]. The current literature confirms its association with carcinogenesis, e.g., by promoting progression or influencing repair mechanisms in various cancers [85,86]. Also, RAD45L expression is reported to be upregulated in BC and other neoplasms [87]. MYBL2, a proto-oncogene involved in de novo purine synthesis, regulates the cell cycle and is linked to poor prognosis in tumors. It promotes neoplastic proliferation and metastasis via CDCA3 activation. Frequently overexpressed in aggressive BCs like TNBC, MYBL2 is a potential therapeutic target [88–90].

Multiple genes included in our signatures have been previously reported as potential biomarkers in various cancers. However, the specific roles of several signature genes in BC remain poorly understood. Thus, the constructed signatures do not replace classical markers but rather expand the landscape of Wnt and ER-related prognostic tools in BC by incorporating less characterized effectors. In addition, such panels may better capture the molecular heterogeneity of BC [91–93].

Table 1 describes the characteristics of each identified gene to summarize the review of signature-based genes.

Table 1

Summary of genes included in at least one signature for BC patients provided with favorable expression status, short functional annotations, and experimental validation summary

Gene symbol Favorable expression status Name and summarized function of the protein [94] Experimental validation in the literature
ANLN Low Actin-binding protein that participates in cell growth, migration, and cytokinesis Often overexpressed in tumors; strongly correlated with poor prognosis [95–97]
CDCA3 Low F-box-like protein essential for mitosis initiation Found overexpressed in tumors and cell lines; linked with poor prognosis [80,98]
DTL Low Denticleless E3 ubiquitin protein ligase homolog associated with cell cycle regulation, responds to DNA damage, and translesion DNA synthesis Found overexpressed in tumors, while high expression predicts worse survival ratio [99,100]
KIF20A Low Kinesin family member 20A is engaged in the formation of microtubule bundles, midbody abscission, and the regulation of cytokinesis Found overexpressed in tumors, knockdown reduces cell proliferation, and is linked with poor prognosis [65,101]
MYBL2 Low Myb-related protein B is a transcription factor involved in cell cycle progression Overexpressed in tumors and cell lines, and high expression linked with poor prognosis; in BC cell models, inhibition of MYBL2 was found to reduce proliferation [102–104]
PLCH1 Low Phospholipase C Eta 1 is an intracellular enzyme associated with lipid metabolism, particularly lipid degradation Overexpressed in tumors; knockdown in cell lines inhibits cell proliferation [105]
RAD54L Low RAD54-like protein plays an essential role in the homologous recombination of the DNA double-strand breaks pathway High expression predicts poor prognosis; cell line studies show regulation of DNA repair dynamics; mostly supported only by expression and pan-cancer data [106,107]
SKA3 Low Spindle and kinetochore-associated protein 3; component of the outer kinetochore complex responsible for proper chromosome segregation Found overexpressed in tumors; high expression predicts poor prognosis and is associated with ER/PR status [68,108]
SLC7A5 Low Solute carrier family 7 member 5, taking part in amino-acid transport; possibly mediates the transport of thyroid hormones Overexpressed in BC tissues; expression is the highest in more aggressive BRCA subtypes; supports growth and survival of rapidly dividing cancer cells [46,109]
SPC25 Low SPC25 component of the NDC80 kinetochore complex is essential for chromosome segregation and spindle checkpoint activity Found overexpressed in tumors; high expression linked to advanced stage, higher grade, and poor prognosis [55,110]
TTC8 High Tetratricopeptide repeat domain 8 is associated with cilium biogenesis and degradation, with a function related to protein transportation Potential mechanistic links to Wnt signaling or cancer progression remain speculative and mostly characterized by bioinformatic and expression data [45]
TTK Low Monopolar spindle 1 (Mps1) kinase phosphorylates proteins on serine, threonine, and tyrosine, and is possibly related to cell proliferation Found overexpressed in breast tumors, high expression correlates with poor prognosis and enhanced tumor aggressiveness; inhibition in cell lines suppresses neoplastic growth; a promising candidate for radiosensitizing strategies for patients with basal-like BC [111,112]
ZNF695 Low Zinc finger protein 695 is a transcription factor that potentially facilitates the activity of DNA-binding transcription factors Found overexpressed in breast tumors and cell lines; associated with BRCA molecular subtypes; the highest expression found in aggressive subtypes; multiple alternatively spliced variants detected in BC cells; no direct functional studies reported [53,54,113]

5 Conclusions

The Wnt pathway plays a crucial role in BC progression, firmly linked to the ER status. Ontological analysis of its components revealed their involvement in cell cycle regulation, RNA metabolism, membraneless organelle assembly, and neddylation. The study identified four novel prognostic signatures influencing BC patients’ survival: two differentiate ER-positive from ER-negative cases, and two distinguish tumor from normal tissues with high accuracy. Studies have supported the relevance of some genes in these signatures, such as ANLN, CDCA3, KIF20A, SKA3, SLC7A5, and TTK. However, multiple genes included in our signatures, such as DTL, MYBL2, PLCH1, RAD54L, SPC25, TTC8, and ZNF695, are either poorly characterized or have not been previously described in the context of BC, making them promising candidates for further investigation. Overall, genes included in the signatures have been proven to have clinical value and could contribute to improved diagnosis and prognosis of BC patients. In the future, such panels may also serve as novel sets of therapeutic targets for further management of malignancies, possibly being more effective in particular treatments than a single factor.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results, and approved the final version of the manuscript. Klaudia Waszczykowska, Damian Kołat, Żaneta Kałuzińska-Kołat, and Elżbieta Płuciennik designed the study, contributed to data collection, and conceptualized the manuscript. Klaudia Waszczykowska and Damian Kołat performed the bioinformatics analysis, analyzed the data, and prepared the figures and tables. Klaudia Waszczykowska interpreted the results and wrote the initial draft of the manuscript. All authors reviewed and edited the manuscript and approved its final version.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2025-03-05
Revised: 2025-07-26
Accepted: 2025-07-30
Published Online: 2025-10-08

© 2025 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/biol-2025-1173
Schlagwörter für diesen Artikel
breast cancer; Wnt signaling; carcinogenesis; biomarker signatures; cancer biomarkers
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Biomedical Sciences
  2. Mechanism of triptolide regulating proliferation and apoptosis of hepatoma cells by inhibiting JAK/STAT pathway
  3. Maslinic acid improves mitochondrial function and inhibits oxidative stress and autophagy in human gastric smooth muscle cells
  4. Comparative analysis of inflammatory biomarkers for the diagnosis of neonatal sepsis: IL-6, IL-8, SAA, CRP, and PCT
  5. Post-pandemic insights on COVID-19 and premature ovarian insufficiency
  6. Proteome differences of dental stem cells between permanent and deciduous teeth by data-independent acquisition proteomics
  7. Optimizing a modified cetyltrimethylammonium bromide protocol for fungal DNA extraction: Insights from multilocus gene amplification
  8. Preliminary analysis of the role of small hepatitis B surface proteins mutations in the pathogenesis of occult hepatitis B infection via the endoplasmic reticulum stress-induced UPR-ERAD pathway
  9. Efficacy of alginate-coated gold nanoparticles against antibiotics-resistant Staphylococcus and Streptococcus pathogens of acne origins
  10. Battling COVID-19 leveraging nanobiotechnology: Gold and silver nanoparticle–B-escin conjugates as SARS-CoV-2 inhibitors
  11. Neurodegenerative diseases and neuroinflammation-induced apoptosis
  12. Impact of fracture fixation surgery on cognitive function and the gut microbiota in mice with a history of stroke
  13. COLEC10: A potential tumor suppressor and prognostic biomarker in hepatocellular carcinoma through modulation of EMT and PI3K-AKT pathways
  14. High-temperature requirement serine protease A2 inhibitor UCF-101 ameliorates damaged neurons in traumatic brain-injured rats by the AMPK/NF-κB pathway
  15. SIK1 inhibits IL-1β-stimulated cartilage apoptosis and inflammation in vitro through the CRTC2/CREB1 signaling
  16. Rutin–chitooligosaccharide complex: Comprehensive evaluation of its anti-inflammatory and analgesic properties in vitro and in vivo
  17. Knockdown of Aurora kinase B alleviates high glucose-triggered trophoblast cells damage and inflammation during gestational diabetes
  18. Calcium-sensing receptors promoted Homer1 expression and osteogenic differentiation in bone marrow mesenchymal stem cells
  19. ABI3BP can inhibit the proliferation, invasion, and epithelial–mesenchymal transition of non-small-cell lung cancer cells
  20. Changes in blood glucose and metabolism in hyperuricemia mice
  21. Rapid detection of the GJB2 c.235delC mutation based on CRISPR-Cas13a combined with lateral flow dipstick
  22. IL-11 promotes Ang II-induced autophagy inhibition and mitochondrial dysfunction in atrial fibroblasts
  23. Short-chain fatty acid attenuates intestinal inflammation by regulation of gut microbial composition in antibiotic-associated diarrhea
  24. Application of metagenomic next-generation sequencing in the diagnosis of pathogens in patients with diabetes complicated by community-acquired pneumonia
  25. NAT10 promotes radiotherapy resistance in non-small cell lung cancer by regulating KPNB1-mediated PD-L1 nuclear translocation
  26. Phytol-mixed micelles alleviate dexamethasone-induced osteoporosis in zebrafish: Activation of the MMP3–OPN–MAPK pathway-mediating bone remodeling
  27. Association between TGF-β1 and β-catenin expression in the vaginal wall of patients with pelvic organ prolapse
  28. Primary pleomorphic liposarcoma involving bilateral ovaries: Case report and literature review
  29. Effects of de novo donor-specific Class I and II antibodies on graft outcomes after liver transplantation: A pilot cohort study
  30. Sleep architecture in Alzheimer’s disease continuum: The deep sleep question
  31. Ephedra fragilis plant extract: A groundbreaking corrosion inhibitor for mild steel in acidic environments – electrochemical, EDX, DFT, and Monte Carlo studies
  32. Langerhans cell histiocytosis in an adult patient with upper jaw and pulmonary involvement: A case report
  33. Inhibition of mast cell activation by Jaranol-targeted Pirin ameliorates allergic responses in mouse allergic rhinitis
  34. Aeromonas veronii-induced septic arthritis of the hip in a child with acute lymphoblastic leukemia
  35. Clusterin activates the heat shock response via the PI3K/Akt pathway to protect cardiomyocytes from high-temperature-induced apoptosis
  36. Research progress on fecal microbiota transplantation in tumor prevention and treatment
  37. Low-pressure exposure influences the development of HAPE
  38. Stigmasterol alleviates endplate chondrocyte degeneration through inducing mitophagy by enhancing PINK1 mRNA acetylation via the ESR1/NAT10 axis
  39. AKAP12, mediated by transcription factor 21, inhibits cell proliferation, metastasis, and glycolysis in lung squamous cell carcinoma
  40. Association between PAX9 or MSX1 gene polymorphism and tooth agenesis risk: A meta-analysis
  41. A case of bloodstream infection caused by Neisseria gonorrhoeae
  42. Case of nasopharyngeal tuberculosis complicated with cervical lymph node and pulmonary tuberculosis
  43. p-Cymene inhibits pro-fibrotic and inflammatory mediators to prevent hepatic dysfunction
  44. GFPT2 promotes paclitaxel resistance in epithelial ovarian cancer cells via activating NF-κB signaling pathway
  45. Transfer RNA-derived fragment tRF-36 modulates varicose vein progression via human vascular smooth muscle cell Notch signaling
  46. RTA-408 attenuates the hepatic ischemia reperfusion injury in mice possibly by activating the Nrf2/HO-1 signaling pathway
  47. Decreased serum TIMP4 levels in patients with rheumatoid arthritis
  48. Sirt1 protects lupus nephritis by inhibiting the NLRP3 signaling pathway in human glomerular mesangial cells
  49. Sodium butyrate aids brain injury repair in neonatal rats
  50. Interaction of MTHFR polymorphism with PAX1 methylation in cervical cancer
  51. Convallatoxin inhibits proliferation and angiogenesis of glioma cells via regulating JAK/STAT3 pathway
  52. The effect of the PKR inhibitor, 2-aminopurine, on the replication of influenza A virus, and segment 8 mRNA splicing
  53. Effects of Ire1 gene on virulence and pathogenicity of Candida albicans
  54. Small cell lung cancer with small intestinal metastasis: Case report and literature review
  55. GRB14: A prognostic biomarker driving tumor progression in gastric cancer through the PI3K/AKT signaling pathway by interacting with COBLL1
  56. 15-Lipoxygenase-2 deficiency induces foam cell formation that can be restored by salidroside through the inhibition of arachidonic acid effects
  57. FTO alleviated the diabetic nephropathy progression by regulating the N6-methyladenosine levels of DACT1
  58. Clinical relevance of inflammatory markers in the evaluation of severity of ulcerative colitis: A retrospective study
  59. Zinc valproic acid complex promotes osteoblast differentiation and exhibits anti-osteoporotic potential
  60. Primary pulmonary synovial sarcoma in the bronchial cavity: A case report
  61. Metagenomic next-generation sequencing of alveolar lavage fluid improves the detection of pulmonary infection
  62. Uterine tumor resembling ovarian sex cord tumor with extensive rhabdoid differentiation: A case report
  63. Genomic analysis of a novel ST11(PR34365) Clostridioides difficile strain isolated from the human fecal of a CDI patient in Guizhou, China
  64. Effects of tiered cardiac rehabilitation on CRP, TNF-α, and physical endurance in older adults with coronary heart disease
  65. Changes in T-lymphocyte subpopulations in patients with colorectal cancer before and after acupoint catgut embedding acupuncture observation
  66. Modulating the tumor microenvironment: The role of traditional Chinese medicine in improving lung cancer treatment
  67. Alterations of metabolites related to microbiota–gut–brain axis in plasma of colon cancer, esophageal cancer, stomach cancer, and lung cancer patients
  68. Research on individualized drug sensitivity detection technology based on bio-3D printing technology for precision treatment of gastrointestinal stromal tumors
  69. CEBPB promotes ulcerative colitis-associated colorectal cancer by stimulating tumor growth and activating the NF-κB/STAT3 signaling pathway
  70. Oncolytic bacteria: A revolutionary approach to cancer therapy
  71. A de novo meningioma with rapid growth: A possible malignancy imposter?
  72. Diagnosis of secondary tuberculosis infection in an asymptomatic elderly with cancer using next-generation sequencing: Case report
  73. Hesperidin and its zinc(ii) complex enhance osteoblast differentiation and bone formation: In vitro and in vivo evaluations
  74. Research progress on the regulation of autophagy in cardiovascular diseases by chemokines
  75. Anti-arthritic, immunomodulatory, and inflammatory regulation by the benzimidazole derivative BMZ-AD: Insights from an FCA-induced rat model
  76. Immunoassay for pyruvate kinase M1/2 as an Alzheimer’s biomarker in CSF
  77. The role of HDAC11 in age-related hearing loss: Mechanisms and therapeutic implications
  78. Evaluation and application analysis of animal models of PIPNP based on data mining
  79. Therapeutic approaches for liver fibrosis/cirrhosis by targeting pyroptosis
  80. Fabrication of zinc oxide nanoparticles using Ruellia tuberosa leaf extract induces apoptosis through P53 and STAT3 signalling pathways in prostate cancer cells
  81. Haplo-hematopoietic stem cell transplantation and immunoradiotherapy for severe aplastic anemia complicated with nasopharyngeal carcinoma: A case report
  82. Modulation of the KEAP1-NRF2 pathway by Erianin: A novel approach to reduce psoriasiform inflammation and inflammatory signaling
  83. The expression of epidermal growth factor receptor 2 and its relationship with tumor-infiltrating lymphocytes and clinical pathological features in breast cancer patients
  84. Innovations in MALDI-TOF Mass Spectrometry: Bridging modern diagnostics and historical insights
  85. BAP1 complexes with YY1 and RBBP7 and its downstream targets in ccRCC cells
  86. Hypereosinophilic syndrome with elevated IgG4 and T-cell clonality: A report of two cases
  87. Electroacupuncture alleviates sciatic nerve injury in sciatica rats by regulating BDNF and NGF levels, myelin sheath degradation, and autophagy
  88. Polydatin prevents cholesterol gallstone formation by regulating cholesterol metabolism via PPAR-γ signaling
  89. RNF144A and RNF144B: Important molecules for health
  90. Analysis of the detection rate and related factors of thyroid nodules in the healthy population
  91. Artesunate inhibits hepatocellular carcinoma cell migration and invasion through OGA-mediated O-GlcNAcylation of ZEB1
  92. Endovascular management of post-pancreatectomy hemorrhage caused by a hepatic artery pseudoaneurysm: Case report and review of the literature
  93. Efficacy and safety of anti-PD-1/PD-L1 antibodies in patients with relapsed refractory diffuse large B-cell lymphoma: A meta-analysis
  94. SATB2 promotes humeral fracture healing in rats by activating the PI3K/AKT pathway
  95. Overexpression of the ferroptosis-related gene, NFS1, corresponds to gastric cancer growth and tumor immune infiltration
  96. Understanding risk factors and prognosis in diabetic foot ulcers
  97. Atractylenolide I alleviates the experimental allergic response in mice by suppressing TLR4/NF-kB/NLRP3 signalling
  98. FBXO31 inhibits the stemness characteristics of CD147 (+) melanoma stem cells
  99. Immune molecule diagnostics in colorectal cancer: CCL2 and CXCL11
  100. Inhibiting CXCR6 promotes senescence of activated hepatic stellate cells with limited proinflammatory SASP to attenuate hepatic fibrosis
  101. Cadmium toxicity, health risk and its remediation using low-cost biochar adsorbents
  102. Pulmonary cryptococcosis with headache as the first presentation: A case report
  103. Solitary pulmonary metastasis with cystic airspaces in colon cancer: A rare case report
  104. RUNX1 promotes denervation-induced muscle atrophy by activating the JUNB/NF-κB pathway and driving M1 macrophage polarization
  105. Morphometric analysis and immunobiological investigation of Indigofera oblongifolia on the infected lung with Plasmodium chabaudi
  106. The NuA4/TIP60 histone-modifying complex and Hr78 modulate the Lobe2 mutant eye phenotype
  107. Experimental study on salmon demineralized bone matrix loaded with recombinant human bone morphogenetic protein-2: In vitro and in vivo study
  108. A case of IgA nephropathy treated with a combination of telitacicept and half-dose glucocorticoids
  109. Analgesic and toxicological evaluation of cannabidiol-rich Moroccan Cannabis sativa L. (Khardala variety) extract: Evidence from an in vivo and in silico study
  110. Wound healing and signaling pathways
  111. Combination of immunotherapy and whole-brain radiotherapy on prognosis of patients with multiple brain metastases: A retrospective cohort study
  112. To explore the relationship between endometrial hyperemia and polycystic ovary syndrome
  113. Research progress on the impact of curcumin on immune responses in breast cancer
  114. Biogenic Cu/Ni nanotherapeutics from Descurainia sophia (L.) Webb ex Prantl seeds for the treatment of lung cancer
  115. Dapagliflozin attenuates atrial fibrosis via the HMGB1/RAGE pathway in atrial fibrillation rats
  116. Glycitein alleviates inflammation and apoptosis in keratinocytes via ROS-associated PI3K–Akt signalling pathway
  117. ADH5 inhibits proliferation but promotes EMT in non-small cell lung cancer cell through activating Smad2/Smad3
  118. Apoptotic efficacies of AgNPs formulated by Syzygium aromaticum leaf extract on 32D-FLT3-ITD human leukemia cell line with PI3K/AKT/mTOR signaling pathway
  119. Novel cuproptosis-related genes C1QBP and PFKP identified as prognostic and therapeutic targets in lung adenocarcinoma
  120. Bee venom promotes exosome secretion and alters miRNA cargo in T cells
  121. Treatment of pure red cell aplasia in a chronic kidney disease patient with roxadustat: A case report
  122. Comparative bioinformatics analysis of the Wnt pathway in breast cancer: Selection of novel biomarker panels associated with ER status
  123. Kynurenine facilitates renal cell carcinoma progression by suppressing M2 macrophage pyroptosis through inhibition of CASP1 cleavage
  124. RFX5 promotes the growth, motility, and inhibits apoptosis of gastric adenocarcinoma cells through the SIRT1/AMPK axis
  125. ALKBH5 exacerbates early cardiac damage after radiotherapy for breast cancer via m6A demethylation of TLR4
  126. Phytochemicals of Roman chamomile: Antioxidant, anti-aging, and whitening activities of distillation residues
  127. Circadian gene Cry1 inhibits the tumorigenicity of hepatocellular carcinoma by the BAX/BCL2-mediated apoptosis pathway
  128. The TNFR-RIPK1/RIPK3 signalling pathway mediates the effect of lanthanum on necroptosis of nerve cells
  129. Ecology and Environmental Science
  130. Optimization and comparative study of Bacillus consortia for cellulolytic potential and cellulase enzyme activity
  131. The complete mitochondrial genome analysis of Haemaphysalis hystricis Supino, 1897 (Ixodida: Ixodidae) and its phylogenetic implications
  132. Epidemiological characteristics and risk factors analysis of multidrug-resistant tuberculosis among tuberculosis population in Huzhou City, Eastern China
  133. Indices of human impacts on landscapes: How do they reflect the proportions of natural habitats?
  134. Genetic analysis of the Siberian flying squirrel population in the northern Changbai Mountains, Northeast China: Insights into population status and conservation
  135. Diversity and environmental drivers of Suillus communities in Pinus sylvestris var. mongolica forests of Inner Mongolia
  136. Global assessment of the fate of nitrogen deposition in forest ecosystems: Insights from 15N tracer studies
  137. Fungal and bacterial pathogenic co-infections mainly lead to the assembly of microbial community in tobacco stems
  138. Influencing of coal industry related airborne particulate matter on ocular surface tear film injury and inflammatory factor expression in Sprague-Dawley rats
  139. Agriculture
  140. Integrated analysis of transcriptome, sRNAome, and degradome involved in the drought-response of maize Zhengdan958
  141. Variation in flower frost tolerance among seven apple cultivars and transcriptome response patterns in two contrastingly frost-tolerant selected cultivars
  142. Heritability of durable resistance to stripe rust in bread wheat (Triticum aestivum L.)
  143. Animal Science
  144. Effect of sex ratio on the life history traits of an important invasive species, Spodoptera frugiperda
  145. Plant Sciences
  146. Hairpin in a haystack: In silico identification and characterization of plant-conserved microRNA in Rafflesiaceae
  147. Widely targeted metabolomics of different tissues in Rubus corchorifolius
  148. The complete chloroplast genome of Gerbera piloselloides (L.) Cass., 1820 (Carduoideae, Asteraceae) and its phylogenetic analysis
  149. Field trial to correlate mineral solubilization activity of Pseudomonas aeruginosa and biochemical content of groundnut plants
  150. Correlation analysis between semen routine parameters and sperm DNA fragmentation index in patients with semen non-liquefaction: A retrospective study
  151. Plasticity of the anatomical traits of Rhododendron L. (Ericaceae) leaves and its implications in adaptation to the plateau environment
  152. Effects of Piriformospora indica and arbuscular mycorrhizal fungus on growth and physiology of Moringa oleifera under low-temperature stress
  153. Effects of different sources of potassium fertiliser on yield, fruit quality and nutrient absorption in “Harward” kiwifruit (Actinidia deliciosa)
  154. Comparative efficiency and residue levels of spraying programs against powdery mildew in grape varieties
  155. The DREB7 transcription factor enhances salt tolerance in soybean plants under salt stress
  156. Food Science
  157. Phytochemical analysis of Stachys iva: Discovering the optimal extract conditions and its bioactive compounds
  158. Review on role of honey in disease prevention and treatment through modulation of biological activities
  159. Computational analysis of polymorphic residues in maltose and maltotriose transporters of a wild Saccharomyces cerevisiae strain
  160. Optimization of phenolic compound extraction from Tunisian squash by-products: A sustainable approach for antioxidant and antibacterial applications
  161. Liupao tea aqueous extract alleviates dextran sulfate sodium-induced ulcerative colitis in rats by modulating the gut microbiota
  162. Toxicological qualities and detoxification trends of fruit by-products for valorization: A review
  163. Polyphenolic spectrum of cornelian cherry fruits and their health-promoting effect
  164. Optimizing the encapsulation of the refined extract of squash peels for functional food applications: A sustainable approach to reduce food waste
  165. Advancements in curcuminoid formulations: An update on bioavailability enhancement strategies curcuminoid bioavailability and formulations
  166. Impact of saline sprouting on antioxidant properties and bioactive compounds in chia seeds
  167. The dilemma of food genetics and improvement
  168. Bioengineering and Biotechnology
  169. Impact of hyaluronic acid-modified hafnium metalorganic frameworks containing rhynchophylline on Alzheimer’s disease
  170. Emerging patterns in nanoparticle-based therapeutic approaches for rheumatoid arthritis: A comprehensive bibliometric and visual analysis spanning two decades
  171. Application of CRISPR/Cas gene editing for infectious disease control in poultry
  172. Preparation of hafnium nitride-coated titanium implants by magnetron sputtering technology and evaluation of their antibacterial properties and biocompatibility
  173. Preparation and characterization of lemongrass oil nanoemulsion: Antimicrobial, antibiofilm, antioxidant, and anticancer activities
  174. Corrigendum
  175. Corrigendum to “Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells”
  176. Corrigendum to “Effects of Ire1 gene on virulence and pathogenicity of Candida albicans”
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© 2025 De Gruyter Brill
Heruntergeladen am 18.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/biol-2025-1173/html
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