Development and validation of a hypoxia-immune-based microenvironment gene signature for risk stratification in gastric cancer.
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ABSTRACT:
SUBMITTER: Liu Y
PROVIDER: S-EPMC7226948 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
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Development and validation of a hypoxia-immune-based microenvironment gene signature for risk stratification in gastric cancer.
Liu Yifan Y Wu Jianhua J Huang Weiwei W Weng Shaowen S Wang Baochun B Chen Yiming Y Wang Hao H
Journal of translational medicine 20200514 1
<h4>Background</h4>Increasing evidences have found that the clinical importance of the interaction between hypoxia and immune status in gastric cancer microenvironment. However, reliable prognostic signatures based on combination of hypoxia and immune status have not been well-established. This study aimed to develop a hypoxia-immune-based gene signature for risk stratification in gastric cancer.<h4>Methods</h4>Hypoxia and immune status was estimated with transcriptomic profiles for a discovery ...[more]
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Project description:BackgroundIncreasing evidence showed that the clinical significance of the interaction between hypoxia and immune status in tumor microenvironment. However, reliable biomarkers based on the hypoxia and immune status in triple-negative breast cancer (TNBC) have not been well established. This study aimed to explore a gene signature based on the hypoxia and immune status for predicting prognosis, risk stratification, and individual treatment in TNBC.MethodsHypoxia-related genes (HRGs) and Immune-related genes (IRGs) were identified using the weighted gene co-expression network analysis (WGCNA) method and the single-sample gene set enrichment analysis (ssGSEA Z-score) with the transcriptomic profiles from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Then, prognostic hypoxia and immune based genes were identified in TNBC patients from the METABRIC (N = 221), The Cancer Genome Atlas (TCGA) (N = 142), and GSE58812 (N = 107) using univariate cox regression model. A robust hypoxia-immune based gene signature for prognosis was constructed using the least absolute shrinkage and selection operator (LASSO) method. Based on the cross-cohort prognostic hypoxia-immune related gene signature, a comprehensive index of hypoxia and immune was developed and two risk groups with distinct hypoxia-immune status were identified. The prognosis value, hypoxia and immune status, and therapeutic response in different risk groups were analyzed. Furthermore, a nomogram was constructed to predict the prognosis for individual patients, and an independent cohort from the gene expression omnibus (GEO) database was used for external validation.ResultsSix cross-cohort prognostic hypoxia-immune related genes were identified to establish the comprehensive index of hypoxia and immune. Then, patients were clustered into high- and low-risk groups based on the hypoxia-immune status. Patients in the high-risk group showed poorer prognoses to their low-risk counterparts, and the nomogram we constructed yielded favorable performance to predict survival and risk stratification. Besides, the high-risk group had a higher expression of hypoxia-related genes and correlated with hypoxia status in tumor microenvironment. The high-risk group had lower fractions of activated immune cells, and exhibited lower expression of immune checkpoint markers. Furthermore, the ratio of complete response (CR) was greatly declined, and the ratio of breast cancer related events were significantly elevated in the high-risk group.ConclusionThe hypoxia-immune based gene signature we constructed for predicting prognosis was developed and validated, which may contribute to the optimization of risk stratification for prognosis and personalized treatment in TNBC patients.
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Project description:Introduction: Increasing evidences have shown that hypoxia and the immune microenvironment play vital roles in the development of osteosarcoma. However, reliable gene signatures based on the combination of hypoxia and the immune status for prognostic prediction of osteosarcoma have so far not been identified. Methods: The individual hypoxia and immune status of osteosarcoma patients were identified with transcriptomic profiles of a training cohort from the TARGET database using ssGSEA and ESTIMATE algorithms, respectively. Lasso regression and stepwise Cox regression were performed to develop a hypoxia-immune-based gene signature. An independent cohort from the GEO database was used for external validation. Finally, a nomogram was constructed based on the gene signature and clinical features to improve the risk stratification and to quantify the risk assessment for individual patients. Results: Hypoxia and the immune status were significantly associated with the prognosis of osteosarcoma patients. Seven hypoxia- and immune-related genes (BNIP3, SLC38A5, SLC5A3, CKMT2, S100A3, CXCL11 and PGM1) were identified to be involved in our prognostic signature. In the training cohort, the prognostic signature discriminated high-risk patients with osteosarcoma. The hypoxia-immune-based gene signature proved to be a stable and predictive method as determined in different datasets and subgroups of patients. Furthermore, a nomogram based on the prognostic signature was generated to optimize the risk stratification and to quantify the risk assessment. Similar results were validated in an independent GEO cohort, confirming the stability and reliability of the prognostic signature. Conclusion: The hypoxia-immune-based prognostic signature might contribute to the optimization of risk stratification for survival and personalized management of osteosarcoma patients.
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Project description:Background: Tumor immune microenvironment plays a vital role in tumorigenesis and progression of gastric cancer (GC), but potent immune biomarkers for predicting the prognosis have not been identified yet. Methods: At first, RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) were mined to identify an immune-risk signature using least absolute shrinkage and selection operator (LASSO) regression and multivariate stepwise Cox regression analyses. Furthermore, the risk score of each sample was calculated, and GC patients were divided into high-risk group and low-risk group based on their risk scores. Subsequently, the performance of this signature, including the correlation with overall survival (OS), clinical features, immune cell infiltration, and immune response, has been tested in GC data from TCGA database and Gene Expression Omnibus (GSE84437), respectively. Results: An immune signature composed of four genes (MAGED1, ACKR3, FZD2, and CTLA4) was constructed. The single sample gene set enrichment analysis (ssGSEA) indicated that activated CD4+/CD8+ T cell, activated dendritic cell, and effector memory CD8+ T cell prominently increased in the low-risk group, showing relatively high immune scores and low stromal scores. Further GSEA analysis indicated that TGF-β, Ras, and Rap1 pathways were activated in the high-risk group, while Th17/Th1/Th2 differentiation, T cell receptor and PD-1/PD-L1 checkpoint pathways were activated in the low-risk group. Low-risk patients presented higher tumor mutation burden (TMB) and expression of HLA-related genes. The immune-associated signature showed an excellent predictive ability for 2-, 3-, and 5-year OS in GC. Conclusion: The immune-related prognosis model contributes to predicting the prognosis of GC patients and providing valuable information about their response to immunotherapy using integrated bioinformatics methods.
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