Identification of feature genes for smoking-related lung adenocarcinoma based on gene expression profile data

Abstract

This study aimed to identify the genes and pathways associated with smoking-related lung adenocarcinoma. Three lung adenocarcinoma associated datasets (GSE43458, GSE10072, and GSE50081), the subjects of which included smokers and nonsmokers, were downloaded to screen the differentially expressed feature genes between smokers and nonsmokers. Based on the identified feature genes, we constructed the protein–protein interaction (PPI) network and optimized feature genes using closeness centrality (CC) algorithm. Then, the support vector machine (SVM) classification model was constructed based on the feature genes with higher CC values. Finally, pathway enrichment analysis of the feature genes was performed. A total of 213 down-regulated and 83 up-regulated differentially expressed genes were identified. In the constructed PPI network, the top ten nodes with higher degrees and CC values included ANK3, EPHA4, FGFR2, etc. The SVM classifier was constructed with 27 feature genes, which could accurately identify smokers and nonsmokers. Pathways enrichment analysis for the 27 feature genes revealed that they were significantly enriched in five pathways, including proteoglycans in cancer (EGFR, SDC4, SDC2, etc.), and Ras signaling pathway (FGFR2, PLA2G1B, EGFR, etc.). The 27 feature genes, such as EPHA4, FGFR2, and EGFR for SVM classifier construction and cancer-related pathways of Ras signaling pathway and proteoglycans in cancer may play key roles in the progression and development of smoking-related lung adenocarcinoma.

Heterogeneous impact of smoking on major salivary gland cancer according to histopathological subtype: A case‐control study

“…26 Previous studies reported that smoking was associated with increased mutation burdens of multiple distinct mutational signatures. [33][34][35][36][37] Significantly positive associations between smoking and M-SGCs other than MEC might indicate that smoking induces nonspecific and variable genetic alterations, leading to carcinogenesis of ADCA and SDC.…”

Differential protein‐coding gene and long noncoding RNA expression in smoking‐related lung squamous cell carcinoma

“…Previous studies have explored the relationship between differentially expressed genes (DEGs) and cigarette smoking. For instance, EPHA4, FGFR2, and EGFR are differentially expressed in lung cancer tissues in cigarette smokers . Additionally, the polymorphism of P73 is suggested to be highly associated with susceptibility to smoking‐related lung cancer .…”

“…For instance, EPHA4, FGFR2, and EGFR are differentially expressed in lung cancer tissues in cigarette smokers. 5 Additionally, the polymorphism of P73 is suggested to be highly associated with susceptibility to smoking-related lung cancer. 6 Moreover, transcription factors (TFs) have been identified in lung SCC.…”

Classification and Regression Analysis of Lung Tumors from Multi-level Gene Expression Data

“…We compared our features with other studies and [2] identified some transcriptomes to differentiate people with different smoking habits and their features on microarray data of whole blood with our features ends up with empty set. PLA2G1B of [22] is common with our features where they used microarray data of lung adenocarcinoma patients. Methylation 450K and 27K data were used in smoking related studies.…”

“…Several transcriptome-based studies have been reported in the literature, including the irreversible effect of tobacco [1], identifying differentially expressed genes between smokers and non-smokers [6], [7], [22] and differentiating smokers from non-smokers [20] or current smokers from others [24]. Methylation data has been used in studies to show how smoking could be identified using single [3], [5] or multiple methylation sites [35], and to infer the effects of maternal smoking on new-borns [27].…”