IntroductionLung adenocarcinoma (LAC) accounts for more than a half of non-small cell lung cancer with high morbidity and mortality. Progression of treatment has not accelerated the improvement of its prognosis. Hence, it is an urgent need to develop novel biomarkers for its early diagnosis and treatment.Materials and methodsIn this study, we proposed to identify LAC survival-related genes through comprehensive analysis of large-scale gene expression profiles. LAC gene expression data sets were obtained from The Cancer Genome Atlas (TCGA). Identification of differentially expressed genes (DEGs) in LAC compared with adjacent normal lung tissues was first performed followed by univariate Cox regression analysis to obtain genes that are significantly associated with LAC survival (SurGenes). Then, we conducted sure independence screening (SIS) for SurGenes to identify more reliable genes and the prognostic signature for LAC survival prediction. Another two lung cancer data sets from TCGA and Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.ResultsA total of 20 genes were obtained, which were significantly associated with the overall survival (OS) of LAC patients. The prognostic signature, a weighted linear combination of the 20 genes, could successfully separate LAC samples with high OS from those with low OS and had robust predictive performance for survival (training set: p-value <2.2×10−16; testing set: p-value =2.04×10−5, area under the curve (AUC) =0.615). Combined with GEO data set, we obtained four genes, that is, FUT4, SLC25A42, IGFBP1, and KLHDC8B that are found in both the prognostic signature and DEGs of LAC in GEO data set.DiscussionThe prognostic signature combined with multi-gene expression profiles provides a moderate OS prediction for LAC and should be helpful for appropriate treatment method selection.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with high morbidity and mortality rates. Numerous diagnosis and treatment methods have been proposed, and the prognosis of NSCLC has improved to a certain extent. However, the mechanisms of NSCLC remain largely unknown, and additional studies are required. In the present study, the RNA sequencing dataset of NSCLC was downloaded from the Gene Expression Omnibus (). The clean reads obtained from the raw data were mapped to the University of California Santa Cruz human genome (hg19), based on TopHat, and were assembled into transcripts via Cufflink. The differential expression (DE) and differential alternative splicing (DAS) genes were screened out through Cuffdiff and rMATS, respectively. The significantly enriched gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways were obtained through the Database of Annotation, Visualization and Integrated Discovery (DAVID). Different numbers of DE and DAS genes were identified in different types of NSCLC samples, but a number of common functions and pathways were obtained, including biological processes associated with abnormal immune and cell activity. GO terms and pathways associated with substance metabolism, including the insulin signaling pathway and oxidative phosphorylation, were enriched in DAS genes rather than DE genes. Integrated analysis of differential expression and alternative splicing may be helpful in understanding the mechanisms of NSCLC, in addition to its early diagnosis and treatment.