ArticleJournal of thoracic disease2021
Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma.
Article in Journal of thoracic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study.Current cancer drug targets · 2025Article
- Development of a cuproptosis-related signature for prognosis prediction in lung adenocarcinoma based on WGCNA.Translational lung cancer research · 2023Article
- Article
- Weighted Gene Co-Expression Network Analysis and Treatment Strategies of Tumor Recurrence-Associated Hub Genes in Lung Adenocarcinoma.Frontiers in genetics · 2021Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of the present study was to predict candidate genes with prognostic information for lung adenocarcinoma (LUAD).
methodsWeighted correlation network analysis (WGCNA) was utilized to build the co-expression network of deferentially expressed genes (DEGs) in GSE32863. Key genes were identified as the intersecting genes of the modules of WGCNA and DEGs. Kaplan-Meier plotter was employed to conduct survival analysis. Enrichment analysis was performed. The expression of key genes in LUAD was validated. Then, we performed
resultsA total of 1,587 DEGs in GSE32863 were identified, including 649 up-regulated genes and 938 down-regulated genes. In coexpression analysis, there were 1,271 hubgenes from the modules that were chosen for further analysis. 15 key genes were identified as the intersecting genes of the modules of WGCNA and DEGs. The expressions of dynein light chain roadblock-type 2 (DYNLRB2) and mouse homolog of ß1 spectrin (SPTBN1) were lower in LUAD, and were associated with survival time of LUAD patients. GSEA results showed that high expressed DYNLRB2 and SPTBN1 were enriched in Drug metabolism cytochrome P450, Cardiac muscle contraction, Retinol metabolism. Down-regulated DYNLRB2 and SPTBN1 were associated with Homologous recombination, Progesterone mediated oocyte maturation, Base excision repair. The
conclusionsOur study suggested that DYNLRB2 and SPTBN1 might be potential tumor suppressor genes and could serve as biomarkers for predicting the prognosis of LUAD patients.
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