Dataset Information

Ahypoxia-related signature enhances the prediction of the prognosis in hepatocellular carcinoma patients and correlates with sorafenib treatment response

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the leading cancer death and is the primary malignancy of the liver. Tumor hypoxia is the stressor that is involved in tumorigenesis and significantly increased the aggressiveness of HCC. Here, we systematically analyzed the expression profiles and prognostic values of 84 hypoxia associated genes in HCC. mRNA expression of 84 hypoxia associated genes and clinical parameters of HCC patients were downloaded from TCGA, GSE14520, GSE109211 and ICGC. Consensus clustering analysis was performed for unsupervised classes on the basis of 84 hypoxia associated genes. Univariate and LASSO analysis were used to develop the risk signature. A risk signature was developed, including the expression of APEX1, ATR, CTSA, DNAJC5, ENO1, EPO, HMOX1, LDHA, NDRG1, and PER1, and found to be significantly related with OS and DFS of HCC patients. We stratified HCC patients into the high-risk group and low-risk group by means of the risk signature. Patients of high-risk group had shorter OS and DFS, while that of the low-risk group had longer OS and DFS. The risk signature showed better predictive efficiency than the TNM staging in predicting OS and DFS. Also, macrophage M0 cells, regulatory T cells, and neutrophils were found to be significantly enriched in patients of high-risk group. Next, we validated the discrimination and prognostic value of the risk signature in GSE14520 and the ICGC HCC cohort. Finally, significantly lower risk scores were found in sorafenib treatment responders of GSE109211 cohort, and the AUC for predicting sorafenib treatment response was 0.881. In conclusion, a risk signature developed with the expression of 10 hypoxia associated genes improved the prognosis prediction of HCC and correlated with sorafenib treatment response.

SUBMITTER: Jiang H

PROVIDER: S-EPMC7791514 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundN6-methyladenosine (m6A) modification plays an essential role in diverse key biological processes and may take part in the development and progression of hepatocellular carcinoma (HCC). Here, we systematically analyzed the expression profiles and prognostic values of 13 widely reported m6A modification-related genes in HCC.MethodsThe mRNA expression of 13 m6A modification-related genes and clinical parameters of HCC patients were downloaded from TCGA, ICGC, GSE109211, and GSE78220. Univariate and LASSO analyses were used to develop risk signature. Time-dependent ROC was performed to assess the predictive accuracy and sensitivity of risk signature.ResultsFTO, YTHDC1, YTHDC2, ALKBH5, KIAA1429, HNRNPC, METTL3, RBM15, YTHDF2, YTHDF1, and WTAP were significantly overexpressed in HCC patients. YTHDF1, HNRNPC, RBM15, METTL3, and YTHDF2 were independent prognostic factors for OS and DFS in HCC patients. Next, a risk signature was also developed and validated with five m6A modification-related genes in TCGA and ICGC HCC cohort. It could effectively stratify HCC patients into high-risk patients with shorter OS and DFS and low-risk patients with longer OS and DFS and showed good predictive efficiency in predicting OS and DFS. Moreover, significantly higher proportions of macrophages M0 cells, neutrophils, and Tregs were found to be enriched in HCC patients with high risk scores, while significantly higher proportions of memory CD4 T cells, gamma delta T cells, and naive B cells were found to be enriched in HCC patients with low scores. Finally, significantly lower risk scores were found at sorafenib treatment responders and anti-PD-1 immunotherapy responders compared to that in nonresponders, and anti-PD-1 immunotherapy-treated patients with lower risk scores had better OS than patients with higher risk scores.ConclusionA risk signature developed with the expression of 5 m6A-related genes could improve the prediction of prognosis of HCC and correlated with sorafenib treatment and anti-PD-1 immunotherapy response.

Project description:Immune checkpoint genes (ICGs), the foundation of immunotherapy, are involved in the incidence and progression of hepatocellular carcinoma (HCC). Cuproptosis is characterized by copper-induced cell death, and this novel cell death pathway has piqued the interest of researchers in recent years. It is worth noting that there is little information available in the literature to determine the relationship between cuproptosis and anti-tumor immunity. We identified 39 cuproptosis-related ICGs using ICGs co-expressed with cuproptosis-related genes. A prognostic risk signature was constructed using the Cox regression and the least absolute shrinkage and selection operator analysis methods. The signature was built using the Cancer Genome Atlas (TCGA)-Liver Hepatocellular Carcinoma database. The TCGA and International Cancer Genome Consortium cohorts were classified into two groups; the low- and high-risk groups were determined using a prognostic signature comprised of five genes. The multivariate Cox regression analysis revealed that the signature could independently predict overall survival. Furthermore, the level of immune infiltration analysis revealed the robustness of the prognostic signature-immune cell infiltration relationship observed for Tregs, macrophages, helper T cells, and naive B cells. Both groups showed significant differences in immune checkpoint expression levels. The gene enrichment analysis was used for characterization, and the results revealed that enriching various pathways such as PI3K-AKT-mTOR signaling, glycolysis, Wnt/beta-catenin signaling, and unfolded protein response could potentially influence the prognosis of patients with HCC and the level of immune infiltration. The sensitivity of the two groups of patients to various drug-targeted therapy methods and immunotherapy was analyzed. In conclusion, the findings presented here lay the foundation for developing individualized treatment methods for HCC patients. The findings also revealed that studying the cuproptosis-based pathway can aid in the prognosis of HCC patients. It is also possible that cuproptosis contributes to developing anti-tumor immunity in patients.

Project description:BackgroundAn immune-related gene signature (IGS) was established for discriminating prognosis, predicting benefit of immunotherapy, and exploring therapeutic options in hepatocellular carcinoma (HCC).MethodsBased on Immune-related hub genes and The Cancer Genome Atlas (TCGA) LIHC dataset (n = 363), an immune-related gene signature (IGS) was established by least absolute shrinkage and selection operator (LASSO) analysis. The prognostic significance and clinical implications of IGS were verified in International Cancer Genome Consortium (ICGC) and Chinese HCC (CHCC) cohorts. The molecular and immune characteristics and the benefit of immune checkpoint inhibitor (ICI) therapy in IGS-defined subgroups were analyzed. In addition, by leveraging the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing datasets, we determined the potential therapeutic agents for high IGS-risk patients.ResultsThe IGS was constructed based on 8 immune-related hub genes with individual coefficients. The IGS risk model could robustly predict the survival of HCC patients in TCGA, ICGC, and CHCC cohorts. Compared with 4 previous established immune genes-based signatures, IGS exhibited superior performance in survival prediction. Additionally, for immunological characteristics and enriched pathways, a low-IGS score was correlated with IL-6/JAK/STAT3 signaling, inflammatory response and interferon α/γ response pathways, low TP53 mutation rate, high infiltration level, and more benefit from ICI therapy. In contrast, high IGS score manifested an immunosuppressive microenvironment and activated aggressive pathways. Finally, by in silico screening potential compounds, vindesine, ispinesib and dasatinib were identified as potential therapeutic agents for high-IGS risk patients.ConclusionsThis study developed a robust IGS model for survival prediction of HCC patients, providing new insights into integrating tailored risk stratification with precise immunotherapy and screening potentially targeted agents.

Project description:Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC. Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness. Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs ( LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8+ T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group. Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.

Project description:Background:The prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes. Methods:Six gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed. Results:A twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P?<?0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes. Conclusions:We identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

Dataset Information

Ahypoxia-related signature enhances the prediction of the prognosis in hepatocellular carcinoma patients and correlates with sorafenib treatment response

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets