Evidence map›Paper›PMID 35116249›Full record

ArticleTranslational cancer research2021

A bioinformatics analysis to evaluate the prognostic value of stemness-related genes in gastric cancer.

Yu-Jie Lu, Lian Lian, Xiao-Ming Shen, Ying Li, Sheng-Jun Ji, Wen-Jie Wang, Yi Yang, Ying Wang, Wei-Ming Duan

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Article in Translational cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yu-Jie Lu *Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Lian Lian *Department of Oncology, Suzhou Xiangcheng People's Hospital, Suzhou, China.
Xiao-Ming Shen *Department of Oncology, Suzhou Xiangcheng People's Hospital, Suzhou, China.
Ying LiDepartment of Radio-Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
Sheng-Jun JiDepartment of Radio-Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
Wen-Jie WangDepartment of Radio-Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
Yi YangDepartment of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, China.
Ying WangDepartment of Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
Wei-Ming DuanDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to identify potential stemness-related targets in gastric cancer (GC) in order to support the development of new treatment strategies and improve patient survival.

methodsUsing the edgeR package, we identified stemness-related differentially expressed genes (DEGs) using GSE112631 and the stemness-related signaling pathways in the Gene Set Enrichment Analysis (GSEA) database. Lasso-penalized Cox regression analysis and multivariate Cox regression analysis tested by Akaike Information Criterion (AIC) were used to screen out survival genes in order to construct a prognostic model. We verified the accuracy of our prognostic model using a nomogram and receiver operating characteristic (ROC) curve analysis. Patients were divided into two groups based on the median risk score, and functional enrichment analysis was used to explore the differences between the two groups.

resultsEight genes were selected to establish a prognostic model of The Cancer Genome Atlas (TCGA) and a validation model of the GSE84437 dataset from the Genome Expression Omnibus (GEO). In both models, we found that the low risk score group had better overall survival (OS) than the high-risk score group. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the two risk groups were totally different.

conclusionsWe used eight stemness-related genes to build a prognostic model. The high-risk score group had a worse prognosis compared to the low-risk score group.

Indexed as

Gastric cancer (GC)Genome Expression Omnibus (GEO)prognosisstemnessThe Cancer Genome Atlas (TCGA)

Identifiers

PMID35116249
PMCPMC8798931

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