Dataset Information

Potential Prognostic Value and Mechanism of Stromal-Immune Signature in Tumor Microenvironment for Stomach Adenocarcinoma.

ABSTRACT: Stomach adenocarcinoma (STAD) is one of the most common malignancies. But the molecular mechanism is unknown. In this study, we downloaded the transcriptional profiles and clinical data of 344 STAD and 30 normal samples from The Cancer Genome Atlas (TCGA) database. Stromal and immune scores of STAD were calculated by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, and association of stromal/immune scores with tumor differentiation/T/N/M stage and survival was investigated. The differentially expressed genes (DEGs) between high and low score groups (based on media) were identified, and prognostic genes over-/underexpressed in both STAD and stromal/immune signature were retrieved. Furthermore, proportions of 22 infiltrating immune cells for the cohort from TCGA were estimated by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm, and association of 22 immune cells with tumor differentiation/T/N/M stage and survival was investigated. Next, coexpression analysis of 22 immune cells and intersection genes over-/underexpressed in both STAD and stromal signature was conducted. We found high stromal and immune scores and macrophage infiltration predicting poor tumor differentiation and severe local invasion, obtained a list of prognostic genes based on stromal-immune signature, and explored the interaction of collagen, chemokines such as CXCL9, CXCL10, and CXCL11, and macrophage through coexpression analysis and may provide novel prognostic biomarkers and immunotherapeutic targets for STAD.

SUBMITTER: Zhu X

PROVIDER: S-EPMC7335387 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Potential Prognostic Value and Mechanism of Stromal-Immune Signature in Tumor Microenvironment for Stomach Adenocarcinoma.

Zhu Xinying X Xie Xiaoli X Zhao Qingchao Q Zhang Lixian L Li Changjuan C Zhao Dongqiang D

BioMed research international 20200626

Stomach adenocarcinoma (STAD) is one of the most common malignancies. But the molecular mechanism is unknown. In this study, we downloaded the transcriptional profiles and clinical data of 344 STAD and 30 normal samples from The Cancer Genome Atlas (TCGA) database. Stromal and immune scores of STAD were calculated by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, and association of stromal/immune scores with tumor differentiation ...[more]

PMID: 32685487

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Project description:Background: Immune and stromal cells in the tumor microenvironment (TME) significantly contribute to the prognosis of lung adenocarcinoma; however, the TME-related immune prognostic signature is unknown. The aim of this study was to develop a novel immune prognostic model of the TME in lung adenocarcinoma. Methods: First, the immune and stromal scores among lung adenocarcinoma patients were determined using the ESTIMATE algorithm in accordance with The Cancer Genome Atlas (TCGA) database. Differentially expressed immune-related genes (IRGs) between high and low immune/stromal score groups were analyzed, and a univariate Cox regression analysis was performed to identify IRGs significantly correlated with overall survival (OS) among patients with lung adenocarcinoma. Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures. Gene set enrichment analysis was performed to analyze the mechanisms underlying these immune prognostic signatures. Finally, the functions of hub IRGs were further analyzed to delineate the potential prognostic mechanisms in comprehensive TCGA datasets. Results: In total, 702 intersecting differentially expressed IRGs (589 upregulated and 113 downregulated) were screened. Univariate Cox regression analysis revealed that 58 significant differentially expressed IRGs were correlated with patient prognosis in the training cohort, of which three IRGs (CLEC17A, INHA, and XIRP1) were identified through LASSO regression analysis. A robust prognostic model was generated on the basis of this three-IRG signature. Furthermore, functional enrichment analysis of the high-risk-score group was performed primarily on the basis of metabolic pathways, whereas analysis of the low-risk-score group was performed primarily on the basis of immunoregulation and immune cell activation. Finally, hub IRGs CLEC17A, INHA, and XIRP1 were considered novel prognostic biomarkers for lung adenocarcinoma. These hub genes had different mutation frequencies and forms in lung adenocarcinoma and participated in different signaling pathways. More importantly, these hub genes were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, B cells, and neutrophils. Conclusions: The robust novel TME-related immune prognostic signature effectively predicted the prognosis of patients with lung adenocarcinoma. Further studies are required to further elucidate the regulatory mechanisms of these hub IRGs in the TME and to develop new treatment strategies.

Project description:The main challenge in treating stomach adenocarcinoma (STAD) is chemotherapy resistance, which is characterized by changes in the immune microenvironment. Disulfidptosis, a novel form of programmed cell death, is involved in STAD but its mechanism is not fully understood. Long non-coding RNAs (LncRNAs) may play a role in regulating disulfidptosis and influencing the immune microenvironment and chemotherapy resistance in STAD. This study aims to establish disulfidptosis-related lncRNA (DRL) features and explore their significance in the immune microenvironment and chemotherapy resistance in STAD patients. By analyzing RNA sequencing and clinical data from STAD patients and extracting disulfidptosis-related genes, we identified DRLs through co-expression, single-factor and multi-factor Cox regression, and Lasso regression analyses. We also investigated differences in the immune microenvironment, immune function, immune checkpoint gene expression, and chemotherapy resistance between different risk groups using various algorithms. A prognostic risk model consisting of 2 DRLs was constructed, with a strong predictive value for patient survival, outperforming other clinical-pathological factors in predicting 3-year and 5-year survival. Immune-related analysis revealed a strong positive correlation between T cell CD4+ cells and risk score across all algorithms, and higher expression of immune checkpoint genes in the high-risk group. In addition, high-risk patients showed better sensitivity to Erlotinib, Oxaliplatin, and Gefitinib. Furthermore, three novel molecular subtypes of STAD were identified based on the 2-DRLs features, with evaluation of the immune microenvironment and chemotherapy drug sensitivity for each subgroup, which holds significant implications for achieving precise treatment in STAD. Overall, our 2-DRLs prognostic model demonstrates high predictive value for patient survival in STAD, potentially providing new targets for individualized immune and chemical therapy.

Project description:N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.

Dataset Information

Potential Prognostic Value and Mechanism of Stromal-Immune Signature in Tumor Microenvironment for Stomach Adenocarcinoma.

Publications

Potential Prognostic Value and Mechanism of Stromal-Immune Signature in Tumor Microenvironment for Stomach Adenocarcinoma.

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