Molecular subtypes based on immune-related genes predict the prognosis for hepatocellular carcinoma patients

Single-cell transcriptomic analysis reveals efferocytosis signature predicting immunotherapy response in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a substantial global health challenge owing to its high mortality rate and limited therapeutic options. We aimed to develop an efferocytosis-related gene signature (ER.Sig) and conduct a transcriptomic analysis to predict the prognosis and immunotherapeutic responses of patients with HCC.

METHODS

Single-cell RNA sequencing data and bulk RNA sequencing data were obtained from public databases. Based on single-sample gene set enrichment analysis and Weighted Gene Co-expression Network analyses, efferocytosis-related genes (ERGs) were selected at both the single-cell and bulk transcriptome levels. A machine-learning framework employing ten different algorithms was used to develop the ER.Sig. Subsequently, a multi-omics approach (encompassing genomic analysis, single-cell transcriptomics, and bulk transcriptomics) was employed to thoroughly elucidate the prognostic signatures.

RESULTS

Analysis of the HCC single-cell transcriptomes revealed significant efferocytotic activity in macrophages, endothelial cells, and fibroblasts within the HCC microenvironment. We then constructed a weighted co-expression network and identified six modules, among which the brown module (168 genes) was most highly correlated with the efferocytosis score (cor = 0.84). Using the univariate Cox regression analysis, 33 prognostic ERGs were identified. Subsequently, a predictive model was constructed using 10 machine-learning algorithms, with the random survival forest model showing the highest predictive performance. The final model, ER.Sig, comprised nine genes and demonstrated robust prognostic capabilities across multiple datasets. High-risk patients exhibited greater intratumoral heterogeneity and higher TP53 mutation frequencies than did low-risk patients. Immune landscape analysis revealed that compared with high-risk patients, low-risk patients exhibited a more favorable immune environment, characterized by higher proportions of CD8+ T and B cells, tumor microenvironment score, immunophenoscore, and lower Tumor Immune Dysfunction and Exclusion scores, indicating better responses to immunotherapy. Additionally, an examination of an independent immunotherapy cohort (IMvigor210) demonstrated that low-risk patients exhibited more favorable responses to immunotherapy and improved prognoses than did their high-risk counterparts.

CONCLUSIONS

The developed ER.Sig effectively predicted the prognosis of patients with HCC and revealed significant differences in tumor biology and treatment responses between the risk groups.

Development and validation of an Immune-related Gene-based model for predicting prognosis and immunotherapy outcomes in hepatocellular carcinoma patients

Predicting disease prognosis and the efficacy of immunotherapy presents a significant challenge in the treatment of hepatocellular carcinoma (HCC). By analyzing transcriptome sequencing data from 69 patients and identifying differentially expressed immune genes, a prognostic index named the immune-related gene prognostic index (IRGPI) was established by Lasso-Cox regression. The IRGPI, which consists of six key genes, was found to be a significant predictor of poor prognosis in patients with high IRGPI scores. The model's predictive accuracy was confirmed via receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values of 0.85, 0.779, and 0.857 for 1-, 3-, and 5-year survival predictions, respectively. Additionally, patients with high IRGPI scores had increased levels of Treg cells and neutrophils, advanced tumor staging, microvascular invasion grading, and immune checkpoint expression. The IRGPI was also effective in predicting the efficacy of immunotherapy in the IMvigor210 dataset, demonstrating its potential as a valuable tool for assessing patient prognosis and guiding immunotherapy strategies in HCC.

Pharmacological mechanisms of carvacrol against hepatocellular carcinoma by network pharmacology and molecular docking

BACKGROUND

Preclinical studies have demonstrated that carvacrol possesses various biological and pharmacological properties, including anti-hepatocellular carcinoma (HCC) effects. However, the molecular basis of its therapeutic action on HCC remains unclear.

OBJECTIVE

The aim of this study was to investigate and further validate the multi-target therapeutic mechanism of carvacrol against HCC.

METHODS

The chemical structure of carvacrol was obtained from the PubChem database, and its potential targets were identified using SwissTargetPrediction, HERB, and BATMAN-TCM. HCC-specific genes were screened from the TCGA-LIHC cohort. The therapeutic targets of carvacrol against HCC were determined through the intersection of these datasets. Subsequently, a multivariate Cox regression prognostic model was established. Molecular docking was performed to analyze the interactions between carvacrol and its therapeutic targets. Additionally, molecular dynamics simulations were conducted to validate the molecular docking results using Discovery Studio 2019 software.

RESULTS

A total of 223 carvacrol targets and 882 HCC-specific genes were identified. Fifteen therapeutic targets of carvacrol against HCC were obtained, including CA2, AR, ALB, AURKA, ALPL, EPHX2, BCHE, IL1RN, AGRN, CRP, DMGDH, APOA1, SOX9, HPX, and CHKA. The prognostic model accurately and independently predicted survival outcomes. AGRN and AURKA were significantly associated with HCC overall survival. Molecular docking and molecular dynamics simulations demonstrated that carvacrol exhibited strong potential for stable binding to the therapeutic targets AGRN and AURKA.

CONCLUSION

Our findings elucidate the multi-target mechanism of action of carvacrol against HCC, providing a foundation for future research on its application in HCC management.

The immunological landscape of CCL26High invasive oral squamous cell carcinoma

Background

Our previous study demonstrated that CCL26 secreted by cancer-associated fibroblasts (CAF) promoted the invasive phenotype of oral squamous cell carcinoma (OSCC), however, more comprehensive clinical expression patterns of CCL26 and its role in immunotherapy remains ambiguous.

Methods

CCL26 levels in different cancer and normal tissues were analyzed and validated in 67 OSCC patients through immunohistochemical staining (IHC). The clinical spatial distribution pattern of CCL26 in tumor microenvironment was determined, and its clinical outcomes were investigated. We also determined the invasive phenotype of tumor cells with distinct CCL26 level and explored its immune checkpoint and immunocytes relevance by differentially expressed gene (DEG) analysis, GSEA, and GO analysis. We collected peripheral blood from 28 OSCC patients to assess the percentage and absolute number of lymphocytes by flow cytometry.

Results

CCL26 was upregulated in HNSC and preferentially high-expressed on CAFs and tumor cells in OSCC patients, which exhibits a trend toward decreased overall survival. CCL26high OSCC had a characteristic of tumor invasive phenotype with upregulated CLDN8/20 and reduced keratin KRT36, which was significantly associated with EMT markers (CDH1, CDH2, VIM, SNAI2). In addition, CCL26high OSCC was found to be associated with immunoglobulin mediated immune response, B cell mediated immunity et al. Indeed, immune checkpoint molecules (PD-L1, PD-L2, et al.) also decreased in CCL26high OSCC. However, CCL26 did not affect T/B/NK lymphocytes in peripheral blood of OSCC patients.

Conclusion

CCL26 could regulate Immune balance and promote invasiveness of OSCC, which gave a new insight into a potential immunotherapy strategy.

Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma

Background

A novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset.

Methods

This prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells in vitro.

Results

We constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells in vitro, and increased the percentage of apoptotic W038 cells.

Conclusions

The analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene GBP1P1 might serve as prognostic biomarker for OC.

Multilayered insights: a machine learning approach for personalized prognostic assessment in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a complex malignancy, and precise prognosis assessment is vital for personalized treatment decisions.

Objective

This study aimed to develop a multi-level prognostic risk model for HCC, offering individualized prognosis assessment and treatment guidance.

Methods

By utilizing data from The Cancer Genome Atlas (TCGA) and the Surveillance, Epidemiology, and End Results (SEER) database, we performed differential gene expression analysis to identify genes associated with survival in HCC patients. The HCC Differential Gene Prognostic Model (HCC-DGPM) was developed through multivariate Cox regression. Clinical indicators were incorporated into the HCC-DGPM using Cox regression, leading to the creation of the HCC Multilevel Prognostic Model (HCC-MLPM). Immune function was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), and immune cell infiltration was assessed. Patient responsiveness to immunotherapy was evaluated using the Immunophenoscore (IPS). Clinical drug responsiveness was investigated using drug-related information from the TCGA database. Cox regression, Kaplan-Meier analysis, and trend association tests were conducted.

Results

Seven differentially expressed genes from the TCGA database were used to construct the HCC-DGPM. Additionally, four clinical indicators associated with survival were identified from the SEER database for model adjustment. The adjusted HCC-MLPM showed significantly improved discriminative capacity (AUC=0.819 vs. 0.724). External validation involving 153 HCC patients from the International Cancer Genome Consortium (ICGC) database verified the performance of the HCC-MLPM (AUC=0.776). Significantly, the HCC-MLPM exhibited predictive capacity for patient response to immunotherapy and clinical drug efficacy (P < 0.05).

Conclusion

This study offers comprehensive insights into HCC prognosis and develops predictive models to enhance patient outcomes. The evaluation of immune function, immune cell infiltration, and clinical drug responsiveness enhances our comprehension and management of HCC.

Novel molecular hepatocellular carcinoma subtypes and RiskScore utilizing apoptosis-related genes

Hepatocellular carcinoma (HCC) is the third leading cause of global cancer-related deaths. Despite immunotherapy offering hope for patients with HCC, only some respond to it. However, it remains unclear how to pre-screen eligible patients. Our study aimed to address this issue. In this study, we identified 13 prognostic genes through univariate Cox regression analysis of 87 apoptosis-related genes. Subsequently, these 13 genes were analyzed using ConsensusClusterPlus, and patients were categorized into three molecular types: C1, C2, and C3. A prognostic model and RiskScore were constructed using Lasso regression analysis of 132 significant genes identified between C1 and C3. We utilized quantitative polymerase chain reaction to confirm the model's transcript level in Huh7 and THLE2 cell lines. Both molecular subtypes and RiskScores effectively predicted patients benefiting from immunotherapy. Cox regression analysis revealed RiskScore as the most significant prognosis factor, suggesting its clinical application potential and providing a foundation for future experimental research.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.

Chronic stress promotes gastric cancer progression via the adrenoceptor beta 2/PlexinA1 pathway

Chronic stress is a common emotional disorder in cancer patients. Chronic stress promotes progression of gastric cancer (GC) and leads to poor outcomes. However, the underlying mechanisms remain not clear. Herein, we explored the possible mechanisms of chronic stress in GC progression. The Cancer Genome Atlas (TCGA) datasets were analyzed for differentially expressed genes. Clinical data of GC were evaluated for their association with PlexinA1 using TCGA and Kaplan-Meier-plotter databases. Chronic stress of GC patients was evaluated using the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Chronic unpredictable mild stress (CUMS) was used to induce chronic stress in mice. Gastric xenograft tumor was constructed using the sewing method. Chronic stress-like behaviors were assessed using light/dark box and tail suspension tests. Protein expression was detected using immunohistochemistry and Western blot analysis. Analyses of TCGA and the Kaplan-Meier-plotter databases showed that patients with high levels of PlexinA1 in GC had worse overall survival than those with low levels of PlexinA1. A total of 36 GC patients were enrolled in the study, and about 33% of the patients had chronic stress. Compared with patients without chronic stress, higher expression levels of adrenoceptor beta 2 and PlexinA1 were observed in patients with chronic stress. The tumor size in mice under CUMS was significantly increased compared with the control mice. Adrenoceptor beta 2, PlexinA1, N-cadherin, and alpha-smooth muscle actin, as well as Ki67 were highly expressed in the tumors of CUMS group. However, E-cadherin was lowly expressed in the tumors of CUMS group. Importantly, chemical sympathectomy with 6-hydroxydopamine or treatment with a selective β2 adrenergic receptor antagonist (ICI118,551) could reverse these effects. Our findings suggest that chronic stress plays an important role in GC progression and there is a potential for blocking the epinephrine-β2AR/PlexinA1 pathway in the treatment of GC.

G-protein signaling of oxytocin receptor as a potential target for cabazitaxel-resistant prostate cancer

Although the treatment armamentarium for patients with metastatic prostate cancer has improved recently, treatment options after progression on cabazitaxel (CBZ) are limited. To identify the mechanisms underlying CBZ resistance and therapeutic targets, we performed single-cell RNA sequencing of circulating tumor cells (CTCs) from patients with CBZ-resistant prostate cancer. Cells were clustered based on gene expression profiles. In silico screening was used to nominate candidate drugs for overcoming CBZ resistance in castration-resistant prostate cancer. CTCs were divided into three to four clusters, reflecting intrapatient tumor heterogeneity in refractory prostate cancer. Pathway analysis revealed that clusters in two cases showed up-regulation of the oxytocin (OXT) receptor-signaling pathway. Spatial gene expression analysis of CBZ-resistant prostate cancer tissues confirmed the heterogeneous expression of OXT-signaling molecules. Cloperastine (CLO) had significant antitumor activity against CBZ-resistant prostate cancer cells. Mass spectrometric phosphoproteome analysis revealed the suppression of OXT signaling specific to CBZ-resistant models. These results support the potential of CLO as a candidate drug for overcoming CBZ-resistant prostate cancer via the inhibition of OXT signaling.

CDK1-SRC Interaction-Dependent Transcriptional Activation of HSP90AB1 Promotes Antitumor Immunity in Hepatocellular Carcinoma

This study aimed to analyze multiomics data and construct a regulatory network involving kinases, transcription factors, and immune genes in hepatocellular carcinoma (HCC) prognosis. The researchers used transcriptomic, proteomic, and clinical data from TCGA and GEO databases to identify immune genes associated with HCC. Statistical analysis, meta-analysis, and protein-protein interaction analyses were performed to identify key immune genes and their relationships. In vitro and in vivo experiments validated the CDK1-SRC-HSP90AB1 network's effects on HCC progression and antitumor immunity. A prognostic risk model was developed using clinicopathological features and immune infiltration. The immune genes LPA, BIRC5, HSP90AB1, ROBO1, and CCL20 were identified as the key prognostic factors. The CDK1-SRC-HSP90AB1 network promoted HCC cell proliferation and migration, with HSP90AB1 being transcriptionally activated by the CDK1-SRC interaction. Manipulating SRC or HSP90AB1 reversed the effects of CDK1 and SRC on HCC. The CDK1-SRC-HSP90AB1 network also influenced HCC tumor formation and antitumor immunity. Overall, this study highlights the importance of the CDK1-SRC-HSP90AB1 network as a crucial immune-regulatory network in the HCC prognosis.

Combining WGCNA and machine learning to construct immune-related EMT patterns to predict HCC prognosis and immune microenvironment

Hepatocellular carcinoma (HCC) is a malignancy with a very high mortality rate. Because of its high heterogeneity, there is an urgent need to find biomarkers that accurately predict prognosis. Epithelial-mesenchymal transition (EMT) is closely associated with frequent recurrence and high mortality of HCC. Therefore, it is necessary to comprehensively analyze the prognostic value and immunological properties of EMT gene in HCC. In our study, we performed bioinformatics analysis of the TCGA and ICGC liver cancer cohorts and identified the module genes of immune-associated EMTs (iEMT) by Weighted Gene Co-Expression Network Analysis (WGCNA). Further we used machine learning (support vector machines-recursive feature elimination and Lasso) to identify three central iEMT genes (ARMC9, ADAM15 and STC2) and construct iEMT_score. Subsequently, in the training and validation cohorts, it was demonstrated that the overall survival (OS) of patients in the high iEMT_score group was worse than that of patients in the low iEMT_score group. Based on this, we have constructed a nomogram that is easy for clinicians to use. In addition, our study explored differences in pathway enrichment, immunological properties, and sensitivity to common chemotherapy and targeted drugs in different subgroups of iEMT_score. Finally, we showed through in vitro experiments that knockdown of ARMC9 could significantly inhibit the proliferation, migration and invasion of HCC cells BEL7402. Taken together, our findings suggest that iEMT_score is an excellent biomarker for predicting prognosis and provide some new insights for personalized treatment of HCC patients.

Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates

Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.

Comprehensive Analysis to Identify the Epithelial-Mesenchymal Transition-Related Immune Signatures as a Prognostic and Therapeutic Biomarkers in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with the high rates of the morbidity and mortality due to the lack of the effective prognostic model for prediction.

Aim: To construct a risk model composed of the epithelial–mesenchymal transition (EMT)-related immune genes for the assessment of the prognosis, immune infiltration status, and chemosensitivity.

Methods: We obtained the transcriptome and clinical data of the HCC samples from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) databases. The Pearson correlation analysis was applied to identify the differentially expressed EMT-related immune genes (DE-EMTri-genes). Subsequently, the univariate Cox regression was introduced to screen out the prognostic gene sets and a risk model was constructed based on the least absolute shrinkage and selection operator-penalized Cox regression. Additionally, the receiver operating characteristic (ROC) curves were plotted to compare the prognostic value of the newly established model compared with the previous model. Furthermore, the correlation between the risk model and survival probability, immune characteristic, and efficacy of the chemotherapeutics were analyzed by the bioinformatics methods.

Results: Six DE-EMTri-genes were ultimately selected to construct the prognostic model. The area under the curve (AUC) values for 1-, 2-, and 3- year were 0.773, 0.721, and 0.673, respectively. Stratified survival analysis suggested that the prognosis of the low-score group was superior to the high-score group. Moreover, the univariate and multivariate analysis indicated that risk score [hazard ratio (HR) 5.071, 95% CI 3.050, 8.432; HR 4.396, 95% CI 2.624, 7.366; p < 0.001] and stage (HR 2.500, 95% CI 1.721, 3.632; HR 2.111, 95% CI 1.443, 3.089; p < 0.001) served as an independent predictive factors in HCC. In addition, the macrophages, natural killer (NK) cells, and regulatory T (Treg) cells were significantly enriched in the high-risk group. Finally, the patients with the high-risk score might be more sensitive to cisplatin, doxorubicin, etoposide, gemcitabine, and mitomycin C.

Conclusion: We established a reliable EMTri-genes-based prognostic signature, which may hold promise for the clinical prediction.

Molecular classification of hepatocellular carcinoma: prognostic importance and clinical applications

Hepatocellular carcinoma (HCC) is a lethal human malignancy with a very low overall and long-term survival rate. Poor prognostic outcomes are predominantly associated with HCC due to a huge landscape of heterogeneity found in the deadliest disease. However, molecular subtyping of HCC has significantly improved the knowledge of the underlying mechanisms that contribute towards the heterogeneity and progression of the disease. In this review, we have extensively summarized the current information available about molecular classification of HCC. This review can be of great significance for providing the insight information needed for development of novel, efficient and personalized therapeutic options for the treatment of HCC patients globally.

Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and heterogeneity. Genetic mutations caused by driver genes are important contributors to the formation of the tumor microenvironment. The purpose of this study is to discuss the expression of cancer driver genes in tumor tissues and their clinical value in predicting the prognosis of HCC.

Methods: All data were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) public databases. Differentially expressed and prognostic genes were screened by the expression distribution of the cancer driver genes and their relationship with survival. Candidate genes were subjected to functional enrichment and transcription factor regulatory network. We further constructed a prognostic signature and analyzed the survival outcomes and immune status between different risk groups.

Results: Most cancer driver genes are specifically expressed in cancer tissues. Driver genes may influence HCC progression through processes such as transcription, cell cycle, and T-cell receptor-related pathways. Patients in different risk groups had significant survival differences (p < 0.05), and risk scores showed high predictive efficacy (AUC>0.69). Besides, risk subgroups were also associated with multiple immune functions and immune cell content.

Conclusion: We confirmed the critical role of cancer driver genes in mediating HCC progression and the immune microenvironment. Risk subgroups contribute to the assessment of prognostic value in different patients and explain the heterogeneity of HCC.

Angiogenesis-Related Immune Signatures Correlate With Prognosis, Tumor Microenvironment, and Therapeutic Sensitivity in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the highly heterogeneous cancers that lacks an effective risk model for prognosis prediction. Therefore, we searched for angiogenesis-related immune genes that affected the prognosis of HCC to construct a risk model and studied the role of this model in HCC.

Methods: In this study, we collected the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. Pearson correlation analysis was performed to identify the association between immune genes and angiogenesis-related genes. Consensus clustering was applied to divide patients into clusters A and B. Subsequently, we studied the differentially expressed angiogenesis-related immune genes (DEari-genes) that affected the prognosis of HCC. The most significant features were identified by least absolute shrinkage and selection operator (LASSO) regression, and a risk model was constructed. The reliability of the risk model was evaluated in the TCGA discovery cohort and the ICGC validation cohort. In addition, we compared the novel risk model to the previous models based on ROC analysis. ssGSEA analysis was used for function evaluation, and pRRophetic was utilized to predict the sensitivity of administering chemotherapeutic agents.

Results: Cluster A patients had favorable survival rates. A total of 23 DEari-genes were correlated with the prognosis of HCC. A five-gene (including BIRC5, KITLG, PGF, SPP1, and SHC1) signature-based risk model was constructed. After regrouping the HCC patients by the median score, we could effectively discriminate between them based on the adverse survival outcome, the unique tumor immune microenvironment, and low chemosensitivity.

Conclusion: The five-gene signature-based risk score established by ari-genes showed a promising clinical prediction value.