Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis

Background The mechanism of cancer occurrence and development could be understood with multi-omics data analysis. Discovering genetic markers is highly necessary for predicting clinical outcome of lung adenocarcinoma (LUAD). Methods Clinical follow-up information, copy number variation (CNV) data, single nucleotide polymorphism (SNP), and RNA-Seq were acquired from The Cancer Genome Atlas (TCGA). To obtain robust biomarkers, prognostic-related genes, genes with SNP variation, and copy number differential genes in the training set were selected and further subjected to feature selection using random forests. Finally, a gene-based prediction model for LUAD was validated in validation datasets. Results The study filtered 2071 prognostic-related genes and 230 genomic variants, 1878 copy deletions, and 438 significant mutations. 218 candidate genes were screened through integrating genomic variation genes and prognosis-related genes. 7 characteristic genes (RHOV, CSMD3, FBN2, MAGEL2, SMIM4, BCKDHB, and GANC) were identified by random forest feature selection, and many genes were found to be tumor progression-related. A 7-gene signature constructed by Cox regression analysis was an independent prognostic factor for LUAD patients, and at the same time a risk factor in the test set, external validation set, and training set. Noticeably, the 5-year AUC of survival in the validation set and training set was all > 0.67. Similar results were obtained from multi-omics validation datasets. Conclusions The study builds a novel 7-gene signature as a prognostic marker for the survival prediction of patients with LUAD. The current findings provided a set of new prognostic and diagnostic biomarkers and therapeutic targets.