Abstract 3292: Revisiting TCGA data identifies key genes in lung adenocarcinoma

Abstract

Abstract Although the incidence rate of lung cancer has declined over the past few years, it remains the leading cause of cancer death worldwide. The Cancer Genome Atlas has provided invaluable resources for cancer research. The development of next generation sequencing technology and evolution of data analysis tools make it possible to interpret the underlying mechanism of oncogenesis. In current study, normalized FPKM data from 572 samples derived from patients with lung adenocarcinoma (LUAD) from TCGA was used to do weighted gene co-expression network analysis (WGCNA) to identify gene modules associated with lung adenocarcinoma. Raw counts data was used to identify differentially expressed genes in LUAD by using DESeq2 in R (version 3.4.2). WGCNA results shows that magenta (r = 0.47, p <0.001)), black (r = 0.40, p <0.001)) and yellow (r = -0.88, p <0.001) modules are top 3 modules that associated with LUAD and cancer stage. Gene Ontology over-representation analysis shows that genes in magenta modules are involved with transcript elongation; genes in black modules are enriched in cell meiosis related biological processes, including chromosome segregation, nuclear division, DNA replication and mitotic nuclear division et al; genes in yellow module are enriched in angiogenesis, leukocyte migration and hemostasis. It is noteworthy that genes in magenta and black modules are upregulated and in yellow modules are downregulated. GOLM1 and MUC4 in magenta module play certain roles in cancer development. Several genes in MEGA family, including MAGEA3, MAGEA6, MEGEA16 and MEGEA1, and IGF2BP1 in black module have been reported to be overexpressed in various kinds of tumors. However, whether these in silicon discovered genes can be used to distinguish LUAD from controls still needs to be further evaluated. Keywords: lung adenocarcinoma; TCGA; gene expression analysis; bioinformatics; cancer biomarker Citation Format: Jianxiang Shi, Mengtao Xing, Chenglin Luo, Xiao Wang, Liping Dai, Jianying Zhang. Revisiting TCGA data identifies key genes in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3292.