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Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients

  • Jun Qiu , Haoyun Wang , Xin Lv , Lipeng Mao , Junyan Huang , Tao Hao , Junliang Li , Shuo Qi , Guodong Chen EMAIL logo and Haiping Jiang EMAIL logo
Published/Copyright: August 8, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

The aim of this study is to explore a novel classification and investigate the clinical significance of hepatocellular carcinoma (HCC) cells. We analyzed integrated single-cell RNA sequencing and bulk RNA-seq data obtained from HCC samples. Cell trajectory analysis divided HCC cells into three subgroups with different differentiation states: state 1 was closely related to phosphoric ester hydrolase activity, state 2 was involved in eukaryotic initiation factor 4E binding, translation regulator activity and ribosome, and state 3 was associated with oxidoreductase activity and metabolism. Three molecular classes based on HCC differentiation-related genes (HDRGs) from HCC samples were identified, which revealed immune checkpoint gene expression and overall survival (OS) of HCC patients. Moreover, a prognostic risk scoring (RS) model was generated based on eight HDRGs, and the results showed that the OS of the high-risk group was worse than that of the low-risk group. Further, potential therapeutic drugs were screened out based on eight prognostic RS-HDRGs. This study highlights the importance of HCC cell differentiation in immunotherapy, clinical prognosis, and potential molecular-targeted drugs for HCC patients, and proposes a direction for the development of individualized treatments for HCC.

Keywords: cell differentiation trajectory; hepatocellular carcinoma; immune checkpoint; overall survival; molecular-targeted drugs

1 Introduction

Primary liver cancer, especially hepatocellular carcinoma (HCC), is one of the most common malignant tumors of the digestive system worldwide [1]. According to the latest data released by the International Agency for Research on Cancer of the World Health Organization, the number of new liver cancer cases worldwide is projected to reach 910,000 in 2020, ranking sixth among malignant tumors. The number of deaths caused by liver cancer is expected to reach 830,000, ranking third among malignant tumors [2]. Although significant progress has been made in HCC treatment (e.g., partial hepatectomy, liver transplantation, local ablation therapy, radiotherapy, hepatic arterial infusion chemotherapy, transarterial chemoembolization, molecular-targeted therapeutics, and immunotherapy), HCC is often diagnosed in the middle or advanced stages due to its insidious onset and aggressive nature [3,4]. As a result, HCC patients often have poor prognoses, with an average 5-year survival rate of 19.6% in America, which can drop to as low as 2.5% in advanced cases [5]. HCC is a malignancy with high heterogeneity, which poses significant challenges for its treatment. Research has revealed that the intratumoral heterogeneity of HCC is mainly due to the existence of cancer stem cells (CSCs). Moreover, different CSC populations might harbor distinct oncogenic driver genes, further complicating the development of targeted therapies. Therefore, understanding the complex interplay between CSCs and HCC is essential to advance our molecular understanding of this disease and develop more effective personalized treatments [6]. Therefore, it is crucial to gain a comprehensive understanding of the intratumoral heterogeneity (ITH) of HCC and more accurately interpret the relationship between ITH and HCC at the molecular level. This knowledge is essential for improving the molecular typing and developing individualized treatments for HCC.

It is widely accepted that the formation of tumors results from the gradual accumulation of genetic variations in cells. Tumor cells containing different combinations of mutations lead to cell phenotypic heterogeneity due to genetic heterogeneity, resulting in the formation of different clonal subgroups with varying responses to treatments and metastatic abilities [7,8,9]. The traditional cytogenetic information research method is to perform high-throughput sequencing on a large number of mixed cells to obtain the average gene expression of a group of cells, disregarding the heterogeneity that exists between cells [10]. With the continuous development of sequencing technology, single-cell RNA sequencing (scRNA-Seq) has emerged as a technology that can better understand the heterogeneity of cells and is now widely used in this field [11]. This technology offers a better understanding of the heterogeneity of tumor cells in HCC, enabling the discovery of new cell subgroups and providing insights into the composition of the tumor microenvironment. A study [6] showed that scRNA-Seq was performed on Human liver cancer cell lines (HuH1 and HuH7) with a liver cancer tissue sample, revealing that liver cancer stem cell populations with different molecular markers exhibit distinct transcriptome profiles and stem cell subpopulations. The differentially expressed genes in liver cancer patients are independently correlated with the prognosis of liver cancer patients, indicating that the diversity of liver cancer stem cell transcriptomes is closely related to the heterogeneity of liver cancer cells and the occurrence and development of liver cancer. This provides new opinions for the prognosis analysis and clinical treatments of liver cancer. However, it is still unclear in which different states of differentiation HCC cells exist, whether the new classification of HCC patients based on cell differentiation trajectories is related to tumor biological behavior, and how it may impact immunotherapy, potential molecular-targeted drugs, and patient survival prediction.

Therefore, we conducted a study using scRNA-Seq to explore the differentiation trajectory of HCC cells and combined this with bulk RNA-Seq data to investigate the potential impact on immunotherapy and patient survival rates, and we screen for the potential molecular-targeted drugs. Our study provides a foundation for achieving clinically accurate molecular typing of HCC and offers guidance for individualized treatment plans based on molecular typing.

2 Materials and methods

2.1 Data acquisition

The scRNA-seq data and bulk RNA-seq data of human HCC samples were obtained from the following three databases in this study: Gene Expression Omnibus (GEO, GSE125449, http://www.ncbi.nlm.nih.gov/geo/) database, The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database and International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/).

2.2 Processing of scRNA-seq data of the HCC samples

First, scRNA-seq data of 5,115 HCC cells of seven HCC samples were obtained from GSE125449 dataset set1 in the GEO database. Second, Seurat software package was used to perform quality control on the scRNA-seq data of the obtained HCC cells, and the HCC cells data were excluded that meet the following two criteria: genes detected in <3 cells were excluded, and cells with <200 total detected genes were excluded. Linear regression was used to standardize the remaining scRNA-seq data of HCC cells, and the hypervariable genes were identified through analysis of variance. Finally, under the condition that the cumulative contribution of the principal component (PC) was greater than 90%, the variance contribution of the PC itself was less than 5%, and the difference between two consecutive PCs was less than 0.1%, and the Principal Component Analysis (PCA) was used to screen out the dimensions with significant separation [12]. The t-Distributed Stochastic Neighbor Embedding (tSNE) and  Uniform Manifold Approximation and Projection algorithms were used for the dimensionality reduction of the first 15 PCs and for performing cluster analysis on HCC cells [13]. Cell populations were annotated based on marker genes through the “SingleR” package. According to the two standards of |log2 fold change (FC)| >0.5 and false discovery rate (FDR) <0.05, the marker genes in each cell population were obtained. The marker genes of HCC cells were obtained from the literature. Seurat’s visualization function was used to visualize gene expression across cell populations.

2.3 Trajectory analysis and HCC differentiation-related genes (HDRGs) identification

The pseudo-time and trajectory analysis of HCC cells through the “Monocle” package [14]. Then look for genes that were differentially expressed in different differentiation trajectories, the differentially expressed genes were selected unique to different differentiation trajectories and then were identified HDRGs, a total of 1,303 HDRGs. “clusterProfiler,” “org.Hs.eg.db,” “enrichplot,” and “ggplot2” packages were applied for Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.

2.4 HDRG-based molecular classes of HCC patients from TCGA database

First, the “Sva” package and ComBat function were used to eliminate the batch effect between TCGA and ICGC; then 1,303 HDRGs are reserved for HCC molecular typing in the TCGA database. A “ConsensusClusterPlus” package that provides steady visual and quantitative evidence for measuring the number of unsupervised classes was used for HCC consensus clustering [15]. Partitioning around center was used to define the number of classes and carry out 50 iterations of max K = 9 for steady classes. Kaplan–Meier analysis was applied to estimate the survival situation, and the “ggplot2” package was applied to exhibit the proportion of clinicopathological features in each molecular class. In addition, the expression levels of HDRGs in specific molecular classes were studied in different cell differentiation trajectories, and they were represented by Heatmap.

2.5 Immune checkpoint genes (ICG) expression across molecular classes

About 41 proved ICGs were acquired from preceding research [2,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35], and then, their expression across HCC classes was inspected by differential expression analysis through TCGA database. Kaplan–Meier analysis was applied to probe the prognostic worth of ICGs.

2.6 Generation, assessment, and confirmation of the HDRG-based prognostic risk score model

HDRGs were screened in TCGA cohort, and log2-scale conversion was used to standardize and correct the transcription profile. Then HDRGs were included in Weighted correlation network analysis (WGCNA), and key modules associated with HCC differentiation were determined through correlation analysis. Analyze genes in key modules through differential expression analysis (with |log2[FC]| > 1 and FDR < 0.05) and univariate analysis [P < 0.05]). Then, Bortuta was used to screen out 63 important genes, and these genes use the random forest to obtain 30 important genes and perform multivariate Cox regression analysis on the remaining HDRGs to generate HDRG-based prognostic RS model. The average value of HDRG-based prognostic RS model can be used to divide patients into high-risk groups and low-risk groups. RS can be calculated as the sum of the product of HDRG expression level and coefficient by the following formula:

RS = ∑ i k ( Exp i × Coe i ) ,

where ‘i’ represents the ‘i’th gene, and ‘k’ represents the total number of genes. TCGA and ICGC cohorts were used as the training cohort and the validating cohort, respectively. Kaplan–Meier analysis was used for the analysis of prognosis (http://www.oncolnc.org/); ROC curve was used to estimate accuracy.

2.7 Potential molecular-targeted drugs screening of RS-HDRGs

Based on the RNAactDrug database (http://bio-bigdata.hrbmu.edu.cn/RNAactDrug/), which includes an integrated analysis of three large pharmacogenomic databases (Genomics of Drug Sensitivity in Cancer, CellMiner, and Cancer Cell Line Encyclopedia), providing drug sensitivity and RNA molecules (including mRNAs, long non-coding RNAs, and microRNAs) at four molecular levels (expression, copy number variation, mutation, and methylation) association data.

2.8 Statistical analysis

All data in this study were statistically analyzed using R and Perl. The normality of the measurement data was first tested, and the measurement data that obeyed the normal distribution were expressed as mean ± standard deviation, and the comparison between groups was performed by T-test or analysis of variance. Enumeration data were expressed as numbers or frequencies (%), and comparisons between groups were performed using the χ 2 test. Survival analysis was performed using Kaplan–Meier analysis and log-rank test. P < 0.05 (two-sided) indicated a statistically significant difference.

3 Results

3.1 Identification and annotation of 16 cell clusters in human HCC samples based on scRNA-seq data

To begin, 5,115 cells were obtained from seven HCC samples via GSE125449. After undergoing quality control and normalization, 2,029 noncompliant cells were removed, leaving a total of 3,086 cells included in the analysis (Figure 1a). A significant correlation was observed between total intracellular gene sequences and sequencing depth (Figure 1b, R = 0.75). A total of 20,124 genes were analyzed across all cells from the HCC samples, with 18,124 genes exhibiting low intercellular variation and 20,00 genes with high intercellular variation (Figure 1c). PCA revealed that there was no significant separation among the HCC cells (Figure 1d). Utilizing the tSNE algorithm, the 3,086 HCC cells were divided into 16 clusters (Figure 1e). Among them, clusters 4, 8, and 12 have high expression of hepatocellular carcinoma-related genes [36], suggesting that these clusters were cancer cells (Figure 1f). Based on marker genes, each of the 16 clusters was annotated as follows: clusters 0, 11, and 15 were identified as B cells, cluster 1 and 9 as T cells, clusters 2, 5, 10, and 13 as endothelial cells, clusters 3, 7, and 14 as smooth muscle cells, clusters 4, 8, and 12 as cancer cells, and cluster 6 as monocytes (Figure 1g).

Figure 1 Identification and annotation of 16 cell clusters in human HCC samples based on scRNA-seq data. (a) According to quality control and normalization, 2,029 noncompliant cells were precluded, and 3,086 cells were involved in the analysis. (b) Correlation analysis between total intracellular gene sequences and sequencing depth. (c) A total of 20,124 genes were analyzed, of which 18,124 had low intercellular variation and 2,000 had high variation. (d) PCA. (e) 3,086 HCC cells were divided into 16 clusters. (f) Expression of HCC-related genes in 16 clusters. (g) 16 clusters were annotated. HCC: hepatocellular carcinoma, PCA: principal component analysis, PCs: principal components.
Figure 1

Identification and annotation of 16 cell clusters in human HCC samples based on scRNA-seq data. (a) According to quality control and normalization, 2,029 noncompliant cells were precluded, and 3,086 cells were involved in the analysis. (b) Correlation analysis between total intracellular gene sequences and sequencing depth. (c) A total of 20,124 genes were analyzed, of which 18,124 had low intercellular variation and 2,000 had high variation. (d) PCA. (e) 3,086 HCC cells were divided into 16 clusters. (f) Expression of HCC-related genes in 16 clusters. (g) 16 clusters were annotated. HCC: hepatocellular carcinoma, PCA: principal component analysis, PCs: principal components.

3.2 Identification of three states based on cell trajectory analysis and molecular functional analysis of three states based on HDRGs

Cell trajectory analysis demonstrated that all cells from the HCC samples were distributed across three states: State 1, primarily comprised of clusters 2, 3, 5, 6, 7, 10, 13, and 14; State 2, primarily comprising clusters 0, 1, 9, 11, and 15; and State 3, primarily comprising clusters 4, 8, and 12. Smooth muscle cells, monocytes, and endothelial cells were primarily distributed in State 1, while the majority of B cells and T cells were distributed in State 2, and cancer cells were primarily distributed in State 3 (Figure 2a and b). According to GO and KEGG pathway analysis, the highly differentially expressed genes (HDRGs) in State 1 were strongly associated with phosphoric ester hydrolase activity (Figure 2c); those in State 2 were involved in eukaryotic initiation factor 4E binding, translation regulator activity, and ribosome-related functions (Figure 2c and d); and those in State 3 were associated with oxidoreductase activity and metabolic processes (Figure 2c and d).

Figure 2 Identification of three states based on cell trajectory analysis and molecular functional analysis of three states based on HDRGs. (a) and (b) Pseudo-time and trajectory analysis. (c) GO analysis of State 1, State 2, and State 3 genes. (d) KEGG Pathway analysis of State 2 and State 3 genes. HDRGs: HCC differentiation-related genes, GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes.
Figure 2

Identification of three states based on cell trajectory analysis and molecular functional analysis of three states based on HDRGs. (a) and (b) Pseudo-time and trajectory analysis. (c) GO analysis of State 1, State 2, and State 3 genes. (d) KEGG Pathway analysis of State 2 and State 3 genes. HDRGs: HCC differentiation-related genes, GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes.

3.3 Identification and validation of the HDRG-based classification of HCC patients from TCGA database

HDRG-based consensus clustering analysis was achieved in the TCGA database, and three molecular classes that included all the HCC samples were identified at a clustering threshold of max K = 9 (Figure 3a–c). Kaplan–Meier analysis showed that Class 2 (C2) patients had poorer overall survival (OS) than Class 1 (C1) patients and Class 3 (C3) patients, while compared with C3 patients, C1 patients had better 5-year OS and worse 10-year OS.

Figure 3 HDRG-based consensus clustering analysis of HCC patients from the TCGA database. (a–c) Three molecular classes were identified at a clustering threshold of K = 9. (d) Kaplan–Meier analysis among the three molecular classes. (e) The HDRGs in States 1/2/3 indicated the same cluster trends as Class 1/2/3. (f–i) Proportion of clinicopathologic features among the three molecular classes.
Figure 3

HDRG-based consensus clustering analysis of HCC patients from the TCGA database. (a–c) Three molecular classes were identified at a clustering threshold of K = 9. (d) Kaplan–Meier analysis among the three molecular classes. (e) The HDRGs in States 1/2/3 indicated the same cluster trends as Class 1/2/3. (f–i) Proportion of clinicopathologic features among the three molecular classes.

Kaplan–Meier analysis revealed that C2 patients had poorer OS compared to both C1 patients and C3 patients. Interestingly, C1 patients had better 5-year OS but worse 10-year OS compared to C3 patients (P = 0.033, Figure 3d). Furthermore, the HDRGs in State 1/2/3 showed the same cluster trend in Class 1/2/3 (Figure 3e), which suggested that Class 1/2/3 was individually composed of State 1/2/3. As C2 progressed to C3 and then C1, patient age increased (Figure 3f). There was no significant difference in the sex distribution among the three classes (Figure 3g). C2 patients tended to have early T stage and N stage (Figure 3h and i).

3.4 Expression levels of 41 ICGs across three molecular classes and prognostic analysis

About 41 proved ICGs were acquired from published research [2,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]. Differential expression analysis showed remarkably high expression of four ICGs (namely, TNFRSF4, PVR, FGL1, and B2M) in C1 (Figure 4a, all P < 0.05). Kaplan–Meier analysis revealed that high expressed TNFRSF4 predicted poor OS (overall survival) (Figure 4b, P < 0.05), while high expressed B2M predicted better OS (Figure 4c, P < 0.05). In C2, 30 ICGs (namely, YTHDF1, VTCN1, TNFSF9, TNFSF4, TNFSF18, TNFRSF9, TNFRSF18, SIRPA, PTPRC, PDCD1LG2, PDCD1, LGALS9, LDHB, LDHA, JAK1, JAK2, IL12B, IL12A, IFNG, ICOS, HAVCR2, cytotoxic T lymphocyte-associated protein-4 (CTLA4), CD8A, CD86, CD80, CD40LG, CD28, CD27, CD200R1, and BTLA) were significantly high expressed (Figure 4a, all P < 0.05), and Kaplan–Meier analysis demonstrated that high expressed YTHDF1, SIRPA, and LDHA predicted poor OS (Figure 4d–f, all P < 0.05), while high expressed CD8A predicted better OS (Figure 4g, P < 0.05). Four ICGs (namely, SIGLEC15, RAF1, LAMA3, and ICOSLG) were considerably high expressed in C3 (Figure 4a, P < 0.05), and Kaplan–Meier analysis indicated that high expressed ICOSLG predicted better OS (Figure 4h, P < 0.05). The above results showed that different ICGs have varying effects on OS in the three classes, which provides a rational molecular basis for guiding immunotherapy.

Figure 4 Expression levels of 41 ICGs across three molecular classes and prognostic analysis. (a) Differential expression analysis of 41 ICGs. (b–h) Kaplan–Meier analysis of TNFRSF4, B2M, YTHDF1, SIRPA, LDHA, CD8A, and ICOSLG.
Figure 4

Expression levels of 41 ICGs across three molecular classes and prognostic analysis. (a) Differential expression analysis of 41 ICGs. (b–h) Kaplan–Meier analysis of TNFRSF4, B2M, YTHDF1, SIRPA, LDHA, CD8A, and ICOSLG.

3.5 Generation, assessment, and confirmation of the HDRG-based prognostic risk score model

A total of 1,303 HDRGs were obtained from the TCGA database and used for WGCNA. 11 Modules were accessed with soft threshold = 10 (Figure 5a and b), two modules of which (pink module and turquoise module) were positively associated with State and Grade of HCC patients (Figure 5c). A total of 137 differentially expressed HDRGs were identified from these two modules (Figure 5c), Then, using Boruta, 63 prognostic HDRGs were further screened (Figure 5d), and 30 important HDRGs were obtained from these genes by random forest (Figure S1). Finally, eight HDRGs were selected using multivariate Cox regression analysis to construct a prognostic risk scoring (RS) model. The RS of each sample could be calculated according to the relative coefficient and gene expression. RS = (−0.15258 × expression of FMO3) + (−0.23713 × expression of ALDH5A1) + (0.26864 × expression of HSD17B6) + (0.14836 × expression of APOC4) + (−0.23968 × expression of PON1) + (0.28024 × expression of ITIH1) + (−0.32831 × expression of A1BG) + (−0.25993 × expression of ANG) (Figure 5e). Based on the prognostic RS model, the RS of each HCC sample in the training and test cohorts was calculated, and it was found that the OS of the low-risk group was significantly better than that of the high-risk group (Figure 5f, training cohort: P < 0.0001; Figure 5h, test cohort: P < 0.05). Furthermore, the areas under the receiver operating characteristic (ROC) curves for predicting 1-year, 3-year and 5-year OS were 0.702, 0.646, and 0.656, respectively, in the training cohort (Figure 5g), and 0.61, 0.544, and 0.56, respectively, in the test cohort (Figure 5i). The clinicopathological characteristics of HCC patients in the TCGA training cohort and ICGC validating cohorts are summarized (Table 1).

Figure 5 Generation, assessment, and confirmation of the HDRG-based prognostic risk score model. (a–b) Based on weighted correlation network analysis, 11 modules were accessed with a soft threshold = 10. (c) Correlation analysis between modules and clinicopathological data. (d) Variable filtering using Boruta, red represents low related genes, yellow represents uncertain genes and green represents high related genes. (e) Multivariate Cox regression analysis of differentially expressed HDRGs. (f) Kaplan–Meier analysis between the low-risk group and the high-risk group in the training cohort. (g) In the training cohort, the areas under the ROC curves for predicting 1-year, 3-year and 5-year OS. (h) Kaplan–Meier analysis between the low-risk group and the high-risk group in the validating cohort. (i) In the validating cohort, the areas under the ROC curves for predicting 1-year, 3-year, and 5-year OS.
Figure 5

Generation, assessment, and confirmation of the HDRG-based prognostic risk score model. (a–b) Based on weighted correlation network analysis, 11 modules were accessed with a soft threshold = 10. (c) Correlation analysis between modules and clinicopathological data. (d) Variable filtering using Boruta, red represents low related genes, yellow represents uncertain genes and green represents high related genes. (e) Multivariate Cox regression analysis of differentially expressed HDRGs. (f) Kaplan–Meier analysis between the low-risk group and the high-risk group in the training cohort. (g) In the training cohort, the areas under the ROC curves for predicting 1-year, 3-year and 5-year OS. (h) Kaplan–Meier analysis between the low-risk group and the high-risk group in the validating cohort. (i) In the validating cohort, the areas under the ROC curves for predicting 1-year, 3-year, and 5-year OS.

Table 1

Clinicopathological characteristics of patients with HCC

Variable TCGA Training cohort (n = 371) ICGC Validating cohort (n = 228)
n (%) n (%)
Age (years)
 ≥60 201 (54.18) 184 (80.70)
 <60 170 (45.82) 44 (19.30)
Sex
 Male 250 (67.39) 168 (73.68)
 Female 121 (32.61) 60 (26.32)
Stage
 I − 36 (15.79)
 II − 103 (45.18)
 III − 70 (30.70)
 IV − 19 (8.33)
T stage
 T1 181 (48.79) −
 T2 94 (25.34) −
 T3 80 (21.56) −
 T4 13 (3.50) −
 TX 1 (0.27) −
 Unknown 2 (0.54) −
N stage
 N0 252 (67.92) −
 N1 4 (1.08) −
 NX 114 (30.73) −
 Unknown 1 (0.27) −
M stage
 M0 266 (71.70) −
 M1 4 (1.08) −
 MX 101 (27.22) −

3.6 Expression, validation, and prognosis of eight RS-HDRGs

The expression levels of the eight RS-HDRGs across 16 clusters are shown in the figure below, with the high expression levels observed in clusters 4, 8, and 12 (most of the cells in clusters 4/8/12 belonging to State 3) (Figure 6a and b). The expression patterns of these eight RS-HDRGs were found to be consistent with the above-mentioned marker genes of HCC cells, and they were all highly expressed in HCC cells. These findings suggest that these eight RS-HDRGs have the potential to serve as biomarkers of HCC cells (Figure 6c). Kaplan–Meier analysis of these eight RS-HDRGs indicated that high expression of FMO3, ALDH5A1, HSD17B6, APOC4, PON1, ITIH1, A1BG, and ANG were associated with better OS (Figure 7a–h).

Figure 6 Expression and validation of eight RS-HDRGs. (a) and (b) Expression levels of eight RS-HDRGs in 16 clusters. (c) Expression levels of eight RS-HDRGs in all cells from seven HCC samples.
Figure 6

Expression and validation of eight RS-HDRGs. (a) and (b) Expression levels of eight RS-HDRGs in 16 clusters. (c) Expression levels of eight RS-HDRGs in all cells from seven HCC samples.

Figure 7 Prognostic analysis of eight RS-HDRGs based on TCGA database. (a–h) Kaplan–Meier analysis of FMO3, ALDH5A1, HSD17B6, APOC4, PON1, ITIH1, A1BG, and ANG.
Figure 7

Prognostic analysis of eight RS-HDRGs based on TCGA database. (a–h) Kaplan–Meier analysis of FMO3, ALDH5A1, HSD17B6, APOC4, PON1, ITIH1, A1BG, and ANG.

3.7 Potential therapeutic drugs analysis of seven RS-HDRGs

This study screened potential therapeutic drugs for seven of the RS-HDRGs based on RNAactDrug database. The results showed that FMO3 was associated with drugs such as Lapatinib, Dovitinib (TKI258), and Docetaxel. ALDH5A1 was associated with drugs such as Carboxyphthalatoplatinum and Mansonone F. HSD17B6 was associated with drugs such as Selumetinib (AZD6244) and PD-0325901. PON1 was associated with drugs such as Dovitinib (TKI258), Docetaxel, and PD-0325901. ITIH1 was associated with drugs such as Lapatinib, Trametinib, and Selumetinib. A1BG was associated with drugs such as Refametinib, Trametinib, and Selumetinib. ANG was associated with L-685458 (Figure 8).

Figure 8 Compound-target network of seven RS-HDRGs of HCC. The yellow ones represent the seven RS-HDRGs, respectively. The blue ones represent molecularly targeted drugs, respectively. HCC: hepatocellular carcinoma.
Figure 8

Compound-target network of seven RS-HDRGs of HCC. The yellow ones represent the seven RS-HDRGs, respectively. The blue ones represent molecularly targeted drugs, respectively. HCC: hepatocellular carcinoma.

4 Discussion

In recent years, researchers have increasingly recognized that ITH in HCC is closely linked to disease progression, classification, treatment, and prognosis [37]. While CSCs are considered to be the main drivers of ITH, some studies have investigated the differentiation of HCC CSCs [38,39]. However, there were very few studies on the differentiation status of HCC cells. However, few studies have explored the differentiation status of HCC cells and their relationship with immunotherapy, molecular-targeted drugs, and clinical prognosis. Therefore, in this study, we further explored the heterogeneity of HCC cells by analyzing their differentiation trajectory. Our research results revealed that HCC cells could be classified into three distinct differentiation states, and we identified important HDRGs associated with cell subgroups. Importantly, the classification of HCC patients based on the differentiation state of HCC cells played a crucial role in immunotherapy, identifying potential molecularly targeted drugs, and clinical prognosis for HCC patients.

The scRNA-Seq technology provides a unique perspective for studying ITH [40]. ITH means that within a tumor, all tumor cells do not have a single set of tumor molecular characteristics, but have rather their own relatively specific molecular characteristics or differentiation status [41]. In this study, we identified 16 cell clusters in the scRNA-Seq data obtained from seven HCC samples, which were consistent with previous works of literature [42]. Using Gene Set Enrichment Analysis and correlation analysis, we found that genes associated with State 1 were closely related to phosphoric ester hydrolase activity, genes associated with State 2 were involved in eukaryotic initiation factor 4E binding, translation regulator activity, and ribosomes, and genes associated with State 3 were involved in oxidoreductase activity and metabolism. These findings suggest that the differentiation status of HCC cells can reveal the intratumoral heterogeneity of HCC and is related to intratumoral metabolism pathways. This information may provide a reference basis for individualized treatment of HCC patients.

In recent years, immunotherapy has emerged as a promising treatment for tumors. By blocking the immune escape mechanisms of tumors and restoring the killing functions of immune cells, it offers new possibilities for the treatment and even cure of cancers [43]. Studies have shown that immune checkpoint inhibitors, such as programmed death-1 antibodies, programmed death ligand-1 (PD-L1) antibodies, and (CTLA-4) antibodies, have demonstrated certain efficacy in the clinical treatment of HCC [4,44,45]. Clinical studies on immunotherapy for HCC, from second-line to first-line therapy, and perioperative therapy are also underway, with promising prospects for the future of HCC immunotherapy. However, the clinical guidance value of PD-L1, which is a widely used biomarker for different tumors, is not uniform. Studies have shown that the positive expression rate of PD-L1 in HCC tumor cells is no more than 10% [46]. Therefore, it is important to search for effective predictive markers for immunotherapy of tumor. In this study, we found that HCC could be divided into three classes based on HDRGs, and we observed a correlation between the different classes and OS as well as the expression of ICGs. These relationships are similar to previous studies [47]. Thus, based on the above findings, we believed that the characteristics of HCC cell differentiation status can serve as an effective predictor of HCC immunotherapy. Immune checkpoint inhibitors have the potential to break through the bottleneck of advanced HCC treatment and improve clinical efficacy by screening for the dominant population and optimizing drug combinations in the future. This area is worthy of further research and exploration.

Studies also reported that patients’ classifications through tumor differentiation-related genes can be used to predict OS in tumor patients. Several studies have reported that patient classifications based on tumor differentiation-related genes can be used to predict OS in cancer patients [47,48]. In this study, we identified eight RS HDRGs (namely, FMO3, ALDH5A1, HSD17B6, APOC4, PON1, ITIH1, A1BG, and ANG) and explored the impact of HDRGs on OS of patients with HCC. Survival analysis showed that patients with C2 HCC had a poor prognosis, while the survival rates of patients with C1 HCC and C3 HCC were better, indicating that patient classifications based on HDRGs can be used to predict the survival of HCC patients. Based on the above HDRGs, we constructed a risk-scoring model for predicting the survival of HCC patients. This RS model can help clinicians to predict the survival of HCC patients and provide new insights for clinical decision-makings. Flavin-containing monooxygenase 3 (FMO3) belongs to the flavin oxidase family and is an important liver microsomal enzyme involved in the oxidative metabolism of drugs, xenobiotics, and other chemicals in the body. One study showed that ectopic re-expression of FMO3 increased apoptosis and decreased cell viability in liver-derived cancer cell lines [49]. Its family member, FMO4, also represented a worse prognosis in HCC [50]. Aldehyde dehydrogenase 5 family member A1 (ALDH5A1) encodes succinate semialdehyde dehydrogenase, which is an enzyme involved in mitochondrial glutamate metabolism. ALDH5A1 is positively associated with prognosis in patients with ovarian cancer, particularly in those with serous ovarian cancer [51]. Hydroxysteroid 17-Beta Dehydrogenase 6 (HSD17B6) encodes a protein with both oxidoreductase and isonomerase activities involved in steroid metabolism. HSD17B6 expression is lower in HCC than in normal liver tissue, and it can inhibit the proliferation, migration, and invasion of HCC cells, and is related to tumor grading and staging. Low expression of HSD17B6 in HCC patients is associated with poorer OS [52,53]. And high expression of apolipoprotein C4 (APOC4) in HCC patients is associated with better OS [54]. Paraoxonase 1 (PON1), a member of the paraoxonase family, which encodes an enzymatic protein with lactonase and esterase activities. Down-regulation of PON1 may suggest that patients with liver cancer have difficulty in survival [55]. PON1 in serum can be used as a biomarker for early diagnosis of HCC and for the status of vascular invasion[56,57]. Compared with the corresponding normal tissues, the expression of Inter-alpha-trypsin inhibitor heavy chain 1 (ITIH1) is significantly reduced in HCC, and its downregulation adversely affects patients’ prognosis [58]. In this study, we identified eight RS HDRGs that were associated with better OS in HCC patients when they were highly expressed in HCC cells, and our findings were consistent with the earlier findings.

Most HCC patients are diagnosed in the middle or advanced stages when their tumors were found, which often precludes surgical resection as a treatment option. However, the combinations of molecularly targeted and immunotherapy can downstage advanced HCC and obtained the opportunity for surgery. Therefore, it is necessary to explore molecularly targeted drugs for patients with advanced HCC. In this study, we used the RNAactDrug database to identify drugs that may inhibit tumor progression by acting on RS-HDRGs, which could potentially improve the survival and prognosis of HCC patients. Based on these findings, we propose that the characteristics of HCC cell differentiation status can be used as an effective predictor of potential therapeutic drugs for HCC. This potential approach to improve the efficacy of HCC drug treatments is a current one among all feasible solutions. All these results were not found in previous studies.

One limitation of this study is that it is a retrospective analysis based on data from public databases. Therefore, the validity and practicality of our conclusions still require verification through further analysis of real-world data. In addition, while we identified potential therapeutic drugs for HCC based on our analysis of RS-HDRGs, further experimental studies are needed to validate their efficacy and safety.

5 Conclusions

This study uses bioinformatics algorithms to classify HCC cells into three subgroups based on the molecular typing of HDRGs, which can predict the immunotherapy response and prognosis of HCC patients and screen out potential molecular-targeted drugs. These findings provide important guidance for identifying subgroups of HCC tumors that may be susceptible to immunotherapy or potential molecular-targeted drugs. This will open a new perspective for individualized treatment strategies for HCC.

# Equal contribution.

Acknowledgements

The authors gratefully acknowledge the editors and reviewers for their suggestions on this study, as well as the support of these four funds.

  1. Funding information: The study was funded by Scientific research project of Health Commission of Hunan Province (No. 202204014556), Scientific research key project of Hunan Education Department (No. 21A0258), the Natural Science Foundation of Hunan Province (No. 2022JJ40400), the Natural Science Foundation of Hunan Province (Joint Project of Science and Health) (No. 2021JJ70039), and the Hunan Provincial Clinical Medical Technology Innovation Guide Project (No. 2020SK51817).

  2. Author contributions: H.P.J. and G.D.C. designed the project. J.Q., H.Y.W., and L.P.M. performed the data curation and analysis. J.Q., H.Y.W., X.L., and T.H. analyzed and interpreted the results. J.Q. and H.Y.W. drafted the manuscript. J.Y.H., J.L.L., and S.Q. reviewed and revised the manuscript. All authors read and approved the final manuscript.

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

  4. Supplementary Materials: Supplementary Figure 1. 30 important HDRGs were screened out by random forest algorithm. Supplementary Table 1. Correlation coefficients of drugs and genes.

  5. 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: 2022-12-14
Revised: 2023-05-30
Accepted: 2023-06-10
Published Online: 2023-08-08

© 2023 the author(s), published by De Gruyter

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

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  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0656
Keywords for this article
cell differentiation trajectory; hepatocellular carcinoma; immune checkpoint; overall survival; molecular-targeted drugs
Creative Commons
BY 4.0

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  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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