Original Article
TMEM213 as a novel prognostic and predictive biomarker for patients with lung adenocarcinoma after curative resection: a study based on bioinformatics analysis
Abstract
Background: Lung cancer is the leading cause of cancer-related death worldwide. Few effective biomarkers for lung adenocarcinoma have been adapted for clinical practice to assist in prognosis evaluation and treatment plan implementation. Our study’s goal was to find a new biological marker associated with the prognosis of lung adenocarcinoma after curative resection and the benefit of adjuvant chemotherapy (ACT).
Methods: Using the clinical information and RNA-Seq expression from The Cancer Genome Atlas (TCGA) database, prognostic genes were screened out and analyzed by Subpopulation Treatment Effect Pattern Plot (STEPP) in GSE42127 to filter out the drug-related gene. The relationship between the gene expression and clinicopathological parameters was assessed in the TCGA database. The prognostic significance was evaluated by Cox proportional hazards (PHs) regression analysis with 1,000 bootstrap replications. Gene set enrichment analysis (GSEA) was performed using high-throughput RNA sequencing data in TCGA and functional gene sets derived from the Molecular Signatures Database (MSigDB).
Results: A total of 297 prognostic genes were analyzed by STEPP in GSE42127. The results indicated a beneficial effect of adjuvant paclitaxel-carboplatin in patients with high TMEM213 expression. Its expression correlated with gender (P=0.013), and Kaplan-Meier analysis showed that patients with high TMEM213 expression had significantly longer overall survival (OS) (P=0.014, 0.027, and 0.000). Multivariate analysis showed TMEM213 to be an independent predictor for improved OS of patients (P=0.020), and the result was verified with the bootstrapping methodology and online “Kaplan-Meier Plotter” database analysis. Moreover, enriched pathway analysis indicated that TMEM213 expression was associated with the two gene sets of KEGG_DRUG_METABOLISM_CYTOCHROME_P450 and KEGG_ABC_TRANSPORTERS.
Conclusions: Based on bioinformatics analysis, we found that TMEM213 expression independently predicted better OS for lung adenocarcinoma. Patients in the high TMEM213 group appear to benefit more from adjuvant paclitaxel-carboplatin, but this needs further validation.
Methods: Using the clinical information and RNA-Seq expression from The Cancer Genome Atlas (TCGA) database, prognostic genes were screened out and analyzed by Subpopulation Treatment Effect Pattern Plot (STEPP) in GSE42127 to filter out the drug-related gene. The relationship between the gene expression and clinicopathological parameters was assessed in the TCGA database. The prognostic significance was evaluated by Cox proportional hazards (PHs) regression analysis with 1,000 bootstrap replications. Gene set enrichment analysis (GSEA) was performed using high-throughput RNA sequencing data in TCGA and functional gene sets derived from the Molecular Signatures Database (MSigDB).
Results: A total of 297 prognostic genes were analyzed by STEPP in GSE42127. The results indicated a beneficial effect of adjuvant paclitaxel-carboplatin in patients with high TMEM213 expression. Its expression correlated with gender (P=0.013), and Kaplan-Meier analysis showed that patients with high TMEM213 expression had significantly longer overall survival (OS) (P=0.014, 0.027, and 0.000). Multivariate analysis showed TMEM213 to be an independent predictor for improved OS of patients (P=0.020), and the result was verified with the bootstrapping methodology and online “Kaplan-Meier Plotter” database analysis. Moreover, enriched pathway analysis indicated that TMEM213 expression was associated with the two gene sets of KEGG_DRUG_METABOLISM_CYTOCHROME_P450 and KEGG_ABC_TRANSPORTERS.
Conclusions: Based on bioinformatics analysis, we found that TMEM213 expression independently predicted better OS for lung adenocarcinoma. Patients in the high TMEM213 group appear to benefit more from adjuvant paclitaxel-carboplatin, but this needs further validation.
