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• W3096424211 abstract "Abstract Background Lung adenocarcinoma (LUAD) remains a crucial factor endangering human health. Gene-based clinical predictions could be of great help for cancer intervention strategies. Here, we tried to build a gene-based survival score (SS) for LUAD via analyzing multiple transcriptional datasets. Methods We first acquired differentially expressed genes between tumors and normal tissues from intersections of four LUAD datasets. Next, survival-related genes were preliminarily unscrambled by univariate Cox regression and further filtrated by LASSO regression. Then, we applied PCA to establish a comprehensive SS based on survival-related genes. Subsequently, we applied four independent LUAD datasets to evaluate prognostic prediction of SS. Moreover, we explored associations between SS and clinicopathological features. Furthermore, we assessed independent predictive value of SS by multivariate Cox analysis and then built prognostic models based on clinical stage and SS. Finally, we performed pathway enrichments analysis and investigated immune checkpoints expression underlying SS in four datasets. Results We established a 13 gene-based SS, which could precisely predict OS and PFS of LUAD. Close relations were elicited between SS and canonical malignant indictors. Furthermore, SS could serve as an independent risk factor for OS and PFS. Besides, the predictive efficacies of prognostic models were also reasonable (C-indexes: OS, 0.7; PFS, 0.7). Finally, we demonstrated enhanced cell proliferation and immune escape might account for high clinical risk of SS. Conclusions We built a 13 gene-based SS for prognostic prediction of LUAD, which exhibited wide applicability and could contribute to LUAD management." @default.
• W3096424211 created "2020-11-09" @default.
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• W3096424211 date "2020-10-31" @default.
• W3096424211 modified "2023-09-26" @default.
• W3096424211 title "A gene-based survival score for lung adenocarcinoma by multiple transcriptional datasets analysis" @default.
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• W3096424211 doi "https://doi.org/10.1186/s12885-020-07473-1" @default.
• W3096424211 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7603718" @default.
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