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pmid:33102522
Identification of a Novel Tumor Microenvironment-Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma.
Chao Ma · Huan Luo · Jing Cao · Xiangyu Zheng · Jinjun Zhang · Yanmin Zhang · Zongqiang Fu
Frontiers in molecular biosciences · 2020 doi:10.3389/fmolb.2020.571641
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Complete file
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Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient's outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients' outcomes for LUAD.\nMETHODS: Dataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment.\nRESULTS: An eight-gene signature was built, and it was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature.\nCONCLUSION: Identifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients' prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients.", "claims": [{"id": "claim-method", "statement": "DEGs were identified between high and low immune/stromal score groups using “limma” R package (Ritchie et al., 2015), with a cutoff of | log2(fold-change)| > 1 and false discovery rate (FDR) < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33102522", "locator": "sections[5] \"Identification of the Intersection Differentially Expressed Genes (DEGs) Among Immune and Stromal Scores\" paragraph 2", "quote": "DEGs were identified between high and low immune/stromal score groups using “limma” R package (Ritchie et al., 2015), with a cutoff of | log2(fold-change)| > 1 and false discovery rate (FDR) < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "≪b≫Publicly available datasets were analyzed in this study. 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