Dataset Information

Stromal-Immune Score-Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer.

ABSTRACT: Background: A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the gastric cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to develop a stromal-immune score-based gene signature in gastric cancer. Methods: Stromal and immune scores were estimated from transcriptomic profiles of a gastric cancer cohort from TCGA using the ESTIMATE algorithm. A robust partial likelihood-based Cox proportional hazard regression model was applied to select prognostic genes and to construct a stromal-immune score-based gene signature. Two independent datasets from GEO were used for external validation. Results: Favorable overall survivals were found in patients with high stromal score (p = 0.014) and immune score (p = 0.045). Forty-five stromal-immune score-related differentially expressed genes were identified. Using a robust partial likelihood-based Cox proportional hazard regression model, a gene signature containing SOX9, LRRC32, CECR1, and MS4A4A was identified to develop a risk stratification model. Multivariate analysis revealed that the stromal-immune risk score was an independent prognostic factor (p = 0.018). Based on the risk stratification model, the cohort was classified into three groups yielding incremental survival outcomes (log-rank test p = 0.0004). A nomogram integrating the risk stratification model and clinicopathologic factors was developed. Calibration and decision curves showed a better performance and net benefits for the nomogram. Similar findings were validated in two independent cohorts. Conclusion: The stromal-immune score-based gene signature represents a prognosis stratification tool in gastric cancer.

SUBMITTER: Wang H

PROVIDER: S-EPMC6861210 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Stromal-Immune Score-Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer.

Wang Hao H Wu Xiaosheng X Chen Yiming Y

Frontiers in oncology 20191112

<b>Background:</b> A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the gastric cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to develop a stromal-immune score-based gene signature in gastric cancer. <b>Methods:</b> Stromal and immune scores were estimated from transcriptomic profiles of a gastric cancer cohort from TCGA using ...[more]

PMID: 31781506

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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.

Dataset Information

Stromal-Immune Score-Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer.

Publications

Stromal-Immune Score-Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer.

Similar Datasets

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