ArticleFrontiers in oncology2024
Identification of potential biomarkers for lung adenocarcinoma: a study based on bioinformatics analysis combined with validation experiments.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Transmembrane mucin 21 drives lung adenocarcinoma growth by suppressing retinoic acid receptor β signaling.Journal of thoracic disease · 2026Article
- The flavonoid astragalin induces apoptosis in lung adenocarcinoma and correlates with a prognostic cell cycle gene signature associated with PANoptosis.Translational cancer research · 2026Article
- Identification of key genes as diagnostic biomarkers for lung adenocarcinoma using bioinformatics and machine learning.Discover oncology · 2026Article
- SUSD2 suppresses lung adenocarcinoma tumorigenesis by inducing autophagyEuropean journal of histochemistry : EJH · 2026Article
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11 authors.
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Abstract
Background: The prognosis for lung adenocarcinoma (LUAD) remains dismal, with a 5-year survival rate of <20%. Therefore, the purpose of this study was to identify potentially reliable biomarkers in LUAD by machine learning combination with Mendelian randomization (MR). Methods: TCGA-LUAD, GSE40791, and GSE31210 were employed this study. Key module differential genes were identified through differentially expressed analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, candidate biomarkers were derived from protein-protein interaction network (PPI) and machine learning. Ultimately, biomarkers were confirmed using MR analysis. In addition, immunohistochemistry was used to detect the expression levels of genes that have a causal relationship to LUAD in the LUAD group and the control group. Cell experiments were conducted to validate the effect of screening genes on proliferation, migration, and apoptosis of LUAD cells. The correlation between the screened genes and immune infiltration was determined by CIBERSORT algorithm. In the end, the gene-related drugs were predicted through the Drug-Gene Interaction database. Results: In total, 401 key module differential genes were obtained by intersecting of 5,702 differentially expressed genes (DEGs) and 406 key module genes. Thereafter, GIMAP6, CAV1, PECAM1, and TGFBR2 were identified. Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=-0.06), and it is a protective factor for LUAD. Subsequently, sensitivity analyses showed that there were no heterogeneity and horizontal pleiotropy in the univariate MR results, and the results were not overly sensitive to individual SNP loci, further validating the reliability of univariate Mendelian randomization (UVMR) results. However, no causal relationship was found between them by reverse MR analysis. Meanwhile, TGFBR2 expression was decreased in LUAD group through immunohistochemistry. TGFBR2 can inhibit proliferation and migration of lung adenocarcinoma cell line A549 and promote apoptosis of A549 cells. Immune infiltration analysis suggested a potential link between TGFBR2 expression and immune infiltration. Finally, Irinotecan and Hesperetin were predicted through DGIDB database. Conclusion: In this study, TGFBR2 was identified as a biomarker of LUAD, which provided a new idea for the treatment strategy of LUAD and may aid in the development of personalized immunotherapy strategies.
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