Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data.
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ABSTRACT:
SUBMITTER: Liu S
PROVIDER: S-EPMC8109113 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHepatocellular carcinoma (HCC) is a major cause of cancer death in the world. The aim of this study was to establish a new model to predict the prognosis of HCC.Materials and methodsThe mRNA, miRNA and lncRNA expression profiles of early (stage I-II) and late (stage III-IV) stage HCC patients were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) were identified between early and late stage HCC. Key molecules associated with the prognosis, and important immune cell types in HCC were identified. The nomogram based on incorporating age, gender, stage, and all important factors was constructed to predict the survival of HCC.ResultsA total of 1516 DEmRNAs, 97 DEmiRNAs and 87 DElncRNAs were identified. A DElncRNA-DEmiRNA-DEmRNA regulatory network including 78 mRNAs, 50 miRNAs and 1 lncRNA was established. Among the regulatory network, 11 molecules were significantly correlated with the prognosis of HCC based on Lasso regression analysis. Then, Preadipocytes and 3 survival-associated DEmRNAs were identified as crucial biomarkers. Subsequently, a nomogram with a differentiation degree of 0.758, including 1 immune cell, 11 mRNAs and 3 miRNAs, was generated.ConclusionOur study constructed a model by incorporating clinical information, significant biomarkers and immune cells to predict the survival of HCC, which achieved a good performance.
| S-EPMC10281275 | biostudies-literature
Project description:Tumor mutation burden (TMB) has been associated with prognosis in various malignancies, but it has yet to be elucidated in hepatocellular carcinoma (HCC). We aimed to investigate the prognostic effects of TMB and its relationship with immune infiltration through multiple databases and whole-exome sequencing, so as to establish a panel model capable of predicting prognosis. The results demonstrated that the prognosis of high TMB group was worse than that of low TMB group, with a cutoff TMB value of 4.9. Enrichment analysis demonstrated that differentially expressed genes were mainly related to T cell activation, cell membrane localization and matrix composition. Tumor immune infiltration analysis revealed the infiltrations of Th2, Th17, and Tgd were up-regulated in high TMB group, while those of Tr1, MAIT, and DC were up-regulated in low TMB group. TMB-Infiltration model fit well with the actual survival observation, with a C-index 0.785 (0.700-0.870), which verified in ICGC-LIRI-JP was 0.650 (0.553-0.747). Additionally, these screened immune genes performed well in predicting tumor vascular invasion with a C-index of 0.847 (0.778-0.916). Overall, these results indicated that patients with high mutation frequency of immune-related genes and high TMB were prone to have worse prognosis and relapse after radical treatment.
| S-EPMC8034943 | biostudies-literature
Project description:Introduction: Tumor mutation burden (TMB) is an emerging biomarker for immunotherapy of hepatocellular carcinoma (HCC), but its value for clinical application has not been fully revealed.Materials and methods: We used the Wilcox test to identify the differentially expressed immune-related genes (DEIRGs) in groups with high and low TMB as well as screened the immune gene pairs related to prognosis using univariate Cox regression analysis. A LASSO Cox regression prognostic model was developed by combining The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) with the International Cancer Genome Consortium (ICGC) LIRI-JP cohort, and internal (TCGA, ICGC) and external (GSE14520) validation analyses were conducted on the predictive value of the model. We also explored the relationship between the prognostic model and tumor microenvironment via the ESTIMATE algorithm and performed clinical correlation analysis by the chi-square test, revealing its underlying molecular mechanism with the help of Gene Set Enrichment Analysis (GSEA).Results: The prognostic model consisting of 15 immune gene pairs showed high predictive value for short- and long-term survival of HCC in three independent cohorts. Based on univariate multivariate Cox regression analysis, the prognostic model could be used to independently predict the prognosis in each independent cohort. The immune score, stromal score, and estimated score values were lower in the high-risk group than in the low-risk group. As shown by the chi-square test, the prognostic model exhibited an obvious correlation with the tumor stage [American Joint Committee on Cancer tumor-node-metastasis (AJCC-TNM) (p < 0.001), Barcelona Clinic Liver Cancer (BCLC) (p = 0.003)], histopathological grade (p = 0.033), vascular invasion (p = 0.009), maximum tumor diameter (p = 0.013), and background of liver cirrhosis (p < 0.001). GSEA revealed that the high-risk group had an enrichment of many oncology features, including the cell cycle, mismatch repair, DNA replication, RNA degradation, etc.Conclusion: Our research developed and validated a reliable prognostic model associated with TMB for HCC, which may help to further enrich the therapeutic targets of HCC.
| S-EPMC7691640 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure.MethodThe mutation data was analyzed by employing "maftools" package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package "limma". Gene ontology (GO) enrichment analysis was implemented with "clusterProfiler", "enrichplot" and "ggplot2" packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3.ResultsTMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction.ConclusionCollectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC.
| S-EPMC8254981 | biostudies-literature
Project description:H2A family member Z (H2AFZ) is a highly conserved gene encoding H2A.Z.1, an isoform of histone variant H2A.Z, and is implicated in cancer. In this study, we report that overexpression of H2AFZ is associated with tumor malignancy and poor prognosis in HCC patients. Functional network analysis suggested that H2AFZ mainly regulates cell cycle signaling and DNA replication via pathways involving several cancer-related kinases and transcription factor E2F1. Further studies revealed that H2AFZ overexpression is regulated by TP53 mutation and led to an attenuation of rapid proliferation phenotype and aggressive behavior in HCC cells. Moreover, we found that H2AFZ was related to immune infiltrations and was co-expressed with immune checkpoint genes, including CD274 (PD-L1), CTLA-4, HAVCR2 (TIM3), LAG3, PDCD1 (PD-1), and TIGIT (VSIG9) in HCC, indicating that H2AFZ-overexpressed HCC patients may be sensitive to immune checkpoint blockades (ICBs). Integrated analysis suggested that H2AFZhigh/TP53mut patients had the shortest OS and PFS time, but most likely to respond to ICBs. These findings indicate that the H2AFZ possesses potential value as a novel prognostic indicator for HCC patients and is correlated with immune infiltration in HCC, laying a foundation for future study of HCC investigation and intervention.
| S-EPMC8573175 | biostudies-literature
Project description:Cervical squamous cell carcinoma is one of the most common causes of female cancer deaths worldwide. At present, immunotherapy using immune checkpoint blockade (ICB) has improved the prognosis of many cancer patients, and neoantigens generated by mutations may serve as potential biomarkers for predicting the outcome of ICB therapy. In this study, we identified missense mutations as the most frequent in landscapes of gene mutation in cervical squamous cell carcinoma (CESC) samples. Patients with higher tumor mutation burden (TMB) presented higher overall survival (OS). In addition, there was a significant correlation between the high TMB group and fractions of most immune cells. Univariate and multivariate Cox regression analyses identified five hub genes (IFNG, SERPINA3, CCL4L2, TNFSF15, and IL1R1) that were used to build a prognostic model. In the prognostic model, the low-risk group achieved better OS. Mutations in the five hub genes mainly affected the infiltration level of CD8+ T cells and dendritic cells. In conclusion, our study is valuable for exploring the role of TMB and its relationship with immune infiltration in CESC. Moreover, the prognosis model may help predict the sensitivity of patients to immunotherapy and provide underlying biomarkers for personalized immunotherapy.
| S-EPMC8631969 | biostudies-literature
Project description:IntroductionFemale breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.MethodsA total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.ResultsA 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.ConclusionMultidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.
| S-EPMC10040797 | biostudies-literature
Project description:The aim of the study was to evaluate the relationship between tumor mutational burden (TMB) and immune infiltration in ovarian cancer. We extracted somatic mutational data and gene expression profiles of ovarian cancer from The Cancer Genome Atlas (TCGA). The samples were separated into low and high TMB groups. Correlations between TMB and cancer prognosis were analyzed and immune cell infiltration in the high and low TMB subgroups was calculated using the CIBERSORT package software. High TMB was significantly related to an improved survival rate. We identified 4 TMB-related core genes that were significantly associated with prognosis. Furthermore, mutations in the 4 genes were associated with immune cell infiltration. We also found a high proportion of naive B cells and activated NK cells in the high TMB group, while increased proportions of memory B cells and plasma cells were found in the low TMB group. Overall, our study indicated that patients with a higher TMB level experienced a favorable survival outcome and this may influence immune infiltration in ovarian cancer. Furthermore, the 4 TMB-related core genes were highly correlated with prognosis and the level of immune cell infiltration.
| S-EPMC7642696 | biostudies-literature
Project description:Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking. Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing "maftools" package. Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA-miRNA-mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed. Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.
| S-EPMC8473911 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Recent studies reveal that tumor microenvironment (TME) components significantly affect HCC growth and progression, particularly the infiltrating stromal and immune cells. Thus, mining of TME-related biomarkers is crucial to improve the survival of patients with HCC. Public access of The Cancer Genome Atlas (TCGA) database allows convenient performance of gene expression-based analysis of big data, which contributes to the exploration of potential association between genes and prognosis of a variety of malignancies, including HCC. The "Estimation of STromal and Immune cells in MAlignant Tumors using Expression data" algorithm renders the quantification of the stromal and immune components in TME possible by calculating the stromal and immune scores. Differentially expressed genes (DEGs) were screened by dividing the HCC cohort of TCGA database into high- and low-score groups according to stromal and immune scores. Further analyses of functional enrichment and protein-protein interaction networks show that the DEGs are mainly involved in immune response, cell adhesion, and extracellular matrix. Finally, seven DEGs have significant association with HCC poor outcomes. These genes contain FABP3, GALNT5, GPR84, ITGB6, MYEOV, PLEKHS1, and STRA6 and may be candidate biomarkers for HCC prognosis.
| S-EPMC6885833 | biostudies-literature