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

ABSTRACT: 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.

SUBMITTER: Yang Y

PROVIDER: S-EPMC8273615 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying an effective marker for predicting the prognosis and therapeutic response is extremely meaningful. Angiogenesis-related genes (ARGs) play important roles in the tumor progression and immune-suppressive microenvironment formation.MethodsThe differential expressed ARGs associated with the prognosis of HCC were identified in the TCGA dataset. Univariate Cox and least absolute shrinkage selection operator (LASSO) regression were applied to construct a ARGs Scoring model. The prognostic value of the ARGs Scoring model was assessed by Cox regression, Kaplan-Meier (KM) and ROC curve analyses. Then the model was further validated in an external dataset, ICGC dataset. The patients were split into two groups based on the ARGs Score and the clinical features were compared. TIMER, CIBERSORT and xCell algorithms were utilized to analyze the correlation between the ARGs Score and tumor immune microenvironment (TIME). Furthermore, we analyzed the efficacy of the model in predicting the therapeutic response for immunotherapy, targeted therapy and TACE treatment in different cohorts.ResultsA total of 97 differential expressed ARGs were identified relating to the prognosis of HCC patients from the TCGA dataset. Then the ARGs Scoring model based on a 9-gene signature was constructed using the Cox and LASSO regression analyses. Higher ARGs Score had a poor clinical outcome and was considered to be an independent prognostic predictor for HCC in the multivariate Cox analysis. The ARGs Score was related to the enrichment of various immune cells, such as CD4+ T cells, Treg, macrophage, neutrophil and dendritic cells, exhibiting a more immunosuppressive phenotype. Higher ARGs Score was correlated with higher expression of immune checkpoint genes and poor response to immunotherapy. Furthermore, higher ARGs Score indicated poor therapeutic response in the sorafenib and TACE treatment cohorts, individually.ConclusionsThe ARGs Scoring model exhibited robust predictive value for the prognosis and TIME for HCC patients. Higher ARGs Score indicated poor therapeutic response of the immunotherapy, sorafenib and TACE treatment. The ARGs Scoring model could be used as a biomarker to help physicians to develop more individualized treatment for HCC patients.

Project description:BackgroundCopper (Cu) metabolism is strongly associated with liver disease. Cuproptosis is a novel format of cell death, and cuproptosis-related genes (CRGs) were identified. However, the role of CRGs in Hepatocellular Carcinoma (HCC) remains unknown.MethodThe mRNA transcriptome profiling data, somatic mutation data, and copy number gene level data of The Cancer Genome Atlas-Liver Hepatocellular Carcinoma project (TCGA-LIHC) were downloaded for subsequent analysis. Molecular characterization analysis of CRGs, including differential gene expression analysis, mutation analysis, copy number variation (CNV) analysis, Kaplan-Meier analysis, and immune regulator prioritization analysis, was implemented. The nonnegative matrix factorization (NMF) approach was used to identify the CRG-related molecular subtypes. Principal component analysis was adopted to verify the robustness and reliability of the molecular subtype. The least absolute shrinkage and selection operator regression analysis was performed to construct the prognostic signature based on differentially expressed genes between molecular subtypes. The survival characteristics of the molecular subtype and the signature were analyzed. The Gene Set Variation Analysis was performed for functional annotation. The immune landscape analysis, including immune checkpoint gene analysis, single sample gene set enrichment analysis, tumor immune dysfunction and exclusion (TIDE) analysis, immune infiltration cell, and tumor mutation burden analysis (TMB), was conducted. The ability of the signature to predict conventional anti-HCC agent responses was evaluated. The signature was validated in the LIRI-JP cohort and the IMvigor210 cohort.ResultA total of 13 CRGs are differentially expressed between the tumor and normal samples, while the mutation of CRGs in HCC is infrequent. The expression of CRGs is associated with the CNV level. Fourteen CRGs are associated with the prognosis of HCC. Two clusters were identified and HCC patients were divided into 2 groups with a cutoff risk score value of 1.570. HCC patients in the C1 cluster and high-risk have a worse prognosis. The area under the receiver operating characteristic curve for predicting 1-, 2-, and 3-year overall survival is 0.775, 0.768, and 0.757 in the TCGA-LIHC cohort, and 0.811, 0.741, and 0.775 in the LIRI-JP cohort. Multivariate Cox regression analysis indicates that the signature is an independent prognostic factor. Pathways involved in metabolism and gene stability and immune infiltration cells are significantly enriched. Immune checkpoint genes are highly expressed in the C1 cluster. TMB is positively correlated with the risk score. HCC patients in the high-risk group are more likely to benefit from conventional anti-HCC agents and immune checkpoint inhibitor therapies.ConclusionThe molecular characterization of CRGs in HCC is presented in this study, and a successful prognostic signature for HCC based on the cuproptosis-related molecular subtype was constructed.

Project description:BackgroundImmune cell infiltration in the tumor microenvironment (TME) affects tumor initiation, patients' prognosis and immunotherapy strategies. However, their roles and interactions with genomics and molecular processes in hepatocellular carcinoma (HCC) still have not been systematically evaluated.MethodsWe performed unsupervised clustering of total 1000 HCC samples including discovery and validation group from available public datasets. Immune heterogeneity of each subtype was explored by multi-dimension analysis. And a support vector machine (SVM) model based on multi-omics signatures was trained and tested. Finally, we performed immunohistochemistry to verify the immune role of signatures.ResultsWe defined three immune subtypes in HCC, with diverse clinical, molecular, and genomic characteristics. Cluster1 had worse prognosis, better anti-tumor characteristics and highest immune scores, but also accompanied by immunosuppression and T cell dysfunction. Meanwhile, a better anti-PD1/CTLA4 immunotherapeutic response was predicted in cluster1. Cluster2 was enriched in TAM-M2 and stromal cells, indicating immunosuppression. Cluster3, with better prognosis, had lowest CD8 T cell but highest immune resting cells. Further, based on genomic signatures, we developed an SVM classifier to identify the patient's immunological status, which was divided into Type A and Type B, in which Type A had poorer prognosis, higher T cell dysfunction despite higher T cell infiltration, and had better immunotherapeutic response. At the same time, MMP9 may be a potential predictor of the immune characteristics and immunotherapeutic response in HCC.ConclusionsOur work demonstrated 3 immune clusters with different features. More importantly, multi-omics signatures, such as MMP9 was identified based on three clusters to help us recognize patients with different prognosis and responses to immunotherapy in HCC. This study could further reveal the immune status of HCC and provide potential predictors for immune checkpoint treatment response.

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

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