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Identification of an Immune Gene-Associated Prognostic Signature and Its Association With a Poor Prognosis in Gastric Cancer Patients.

ABSTRACT: The immune response plays a critical role in gastric cancer (GC) development, metastasis, and treatment. A better understanding of the tumor-immune system interactions in gastric cancer may provide promising diagnostic, prognostic, and therapeutic biomarkers for patients with this disease. In the present study, we aimed to identify a prognostic signature of GC through a comprehensive bioinformatics analysis on the tumor-immune interactions as well as the molecular characteristics. We firstly identified two immunophenotypes and immunological characteristics by employing multiple algorithms, such as the single sample Gene Sets Enrichment Analysis and Cell type Identification By Estimating Relative Subsets of RNA Transcripts. Next, we developed a six-immune-gene signature as a promising independent prognostic biomarker for GC using Lasso Cox regression and verified it via the external validation set and systematically correlated the immune signature with GC clinicopathologic features and genomic characteristics. Finally, a nomogram was successfully constructed based on the immune signature and clinical characteristics and showed a high potential for GC prognosis prediction. This study may shed light on the treatment strategies for GC patients from the perspective of immunology.

SUBMITTER: Guan X

PROVIDER: S-EPMC7898907 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of an Immune Gene-Associated Prognostic Signature and Its Association With a Poor Prognosis in Gastric Cancer Patients.

Guan Xiaoqing X Xu Zhi-Yuan ZY Chen Runzhe R Qin Jiang-Jiang JJ Cheng Xiang-Dong XD

Frontiers in oncology 20210208

The immune response plays a critical role in gastric cancer (GC) development, metastasis, and treatment. A better understanding of the tumor-immune system interactions in gastric cancer may provide promising diagnostic, prognostic, and therapeutic biomarkers for patients with this disease. In the present study, we aimed to identify a prognostic signature of GC through a comprehensive bioinformatics analysis on the tumor-immune interactions as well as the molecular characteristics. We firstly ide ...[more]

PMID: 33628738

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Project description:BackgroundThe tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC).MethodsTranscriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database.ResultsGene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717-0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates.ConclusionsIn this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

Project description:Background: Lung adenocarcinoma (LUAD) is an exceedingly diverse disease, making prognostication difficult. Inflammatory responses in the tumor or the tumor microenvironment can alter prognosis in the process of the ongoing cross-talk between the host and the tumor. Nonetheless, Inflammatory response-related genes' prognostic significance in LUAD, on the other hand, has yet to be determined. Materials and Methods: The clinical data as well as the mRNA expression patterns of LUAD patients were obtained from a public dataset for this investigation. In the TCGA group, a multigene prognostic signature was built utilizing LASSO Cox analysis. Validation was executed on LUAD patients from the GEO cohort. The overall survival (OS) of low- and high-risk cohorts was compared utilizing the Kaplan-Meier analysis. The assessment of independent predictors of OS was carried out utilizing multivariate and univariate Cox analyses. The immune-associated pathway activity and immune cell infiltration score were computed utilizing single-sample gene set enrichment analysis. GO keywords and KEGG pathways were explored utilizing gene set enrichment analysis. Results: LASSO Cox regression analysis was employed to create an inflammatory response-related gene signature model. The high-risk cohort patients exhibited a considerably shorter OS as opposed to those in the low-risk cohort. The prognostic gene signature's predictive ability was demonstrated using receiver operating characteristic curve analysis. The risk score was found to be an independent predictor of OS using multivariate Cox analysis. The functional analysis illustrated that the immune status and cancer-related pathways for the two-risk cohorts were clearly different. The tumor stage and kind of immune infiltrate were found to be substantially linked with the risk score. Furthermore, the cancer cells' susceptibility to anti-tumor medication was substantially associated with the prognostic genes expression levels. Conclusion: In LUAD, a new signature made up of 8 inflammatory response-related genes may be utilized to forecast prognosis and influence immunological state. Inhibition of these genes could also be used as a treatment option.

Dataset Information

Identification of an Immune Gene-Associated Prognostic Signature and Its Association With a Poor Prognosis in Gastric Cancer Patients.

Publications

Identification of an Immune Gene-Associated Prognostic Signature and Its Association With a Poor Prognosis in Gastric Cancer Patients.

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