Open Access

Bioinformatics and functional analyses of key genes in smoking‑associated lung adenocarcinoma

  • Authors:
    • Dajie Zhou
    • Yilin Sun
    • Yanfei Jia
    • Duanrui Liu
    • Jing Wang
    • Xiaowei Chen
    • Yujie Zhang
    • Xiaoli Ma
  • View Affiliations

  • Published online on: August 7, 2019     https://doi.org/10.3892/ol.2019.10733
  • Pages: 3613-3622
  • Copyright: © Zhou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Smoking is one of the most important factors associated with the development of lung cancer. However, the signaling pathways and driver genes in smoking‑associated lung adenocarcinoma remain unknown. The present study analyzed 433 samples of smoking‑associated lung adenocarcinoma and 75 samples of non‑smoking lung adenocarcinoma from the Cancer Genome Atlas database. Gene Ontology (GO) analysis was performed using the Database for Annotation, Visualization and Integrated Discovery and the ggplot2 R/Bioconductor package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the R packages RSQLite and org.Hs.eg.db. Multivariate Cox regression analysis was performed to screen factors associated with patient survival. Kaplan‑Meier and receiver operating characteristic curves were used to analyze the potential clinical significance of the identified biomarkers as molecular prognostic markers for the five‑year overall survival time. A total of 373 differentially expressed genes (DEGs; |log2‑fold change|≥2.0 and P<0.01) were identified, of which 71 were downregulated and 302 were upregulated. These DEGs were associated with 28 significant GO functions and 11 significant KEGG pathways (false discovery rate <0.05). Two hundred thirty‑eight proteins were associated with the 373 differentially expressed genes, and a protein‑protein interaction network was constructed. Multivariate regression analysis revealed that 7 mRNAs, cytochrome P450 family 17 subfamily A member 1, PKHD1 like 1, retinoid isomerohydrolase RPE65, neurotensin receptor 1, fetuin B, insulin‑like growth factor binding protein 1 and glucose‑6‑phosphatase catalytic subunit, significantly distinguished between non‑smoking and smoking‑associated adenocarcinomas. Kaplan‑Meier analysis demonstrated that patients in the 7 mRNAs‑high‑risk group had a significantly worse prognosis than those of the low‑risk group. The data obtained in the current study suggested that these genes may serve as potential novel prognostic biomarkers of smoking‑associated lung adenocarcinoma.

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October 2019
Volume 18 Issue 4

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
APA
Zhou, D., Sun, Y., Jia, Y., Liu, D., Wang, J., Chen, X. ... Ma, X. (2019). Bioinformatics and functional analyses of key genes in smoking‑associated lung adenocarcinoma. Oncology Letters, 18, 3613-3622. https://doi.org/10.3892/ol.2019.10733
MLA
Zhou, D., Sun, Y., Jia, Y., Liu, D., Wang, J., Chen, X., Zhang, Y., Ma, X."Bioinformatics and functional analyses of key genes in smoking‑associated lung adenocarcinoma". Oncology Letters 18.4 (2019): 3613-3622.
Chicago
Zhou, D., Sun, Y., Jia, Y., Liu, D., Wang, J., Chen, X., Zhang, Y., Ma, X."Bioinformatics and functional analyses of key genes in smoking‑associated lung adenocarcinoma". Oncology Letters 18, no. 4 (2019): 3613-3622. https://doi.org/10.3892/ol.2019.10733