Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis

Liyan Yu; Xuemei Liang; Jianwei Wang; Guangxiang Ding; Jinhai Tang; Juan Xue; Xin He; Jingxuan Ge; Xianzhang Jin; Zhiyi Yang; Xianwei Li; Hehuan Yao; Hongtao Yin; Wu Liu; Shengchen Yin; Bing Sun; Junxiu Sheng

doi:10.1155/2023/6782732

Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis

Genet Res (Camb). 2023 Jan 3:2023:6782732. doi: 10.1155/2023/6782732. eCollection 2023.

Authors

Liyan Yu¹, Xuemei Liang², Jianwei Wang³, Guangxiang Ding³, Jinhai Tang³, Juan Xue³, Xin He², Jingxuan Ge², Xianzhang Jin², Zhiyi Yang², Xianwei Li², Hehuan Yao², Hongtao Yin², Wu Liu², Shengchen Yin², Bing Sun², Junxiu Sheng³

Affiliations

¹ Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian 116044, Liaoning Province, China.
² Department of Thoracic Surgery, First Affiliated Hospital, Dalian Medical University, Dalian 116044, China.
³ Department of Radiation Oncology, First Affiliated Hospital, Dalian Medical University, Dalian 116044, China.

Abstract

Background: Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis.

Methods: From the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, cBioPortal, String and Cytoscape, and Timer tools.

Results: We found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis (P < 0.05). We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis (P < 0.05). On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients (P < 0.05). Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma.

Conclusions: The results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma.

MeSH terms

Adenocarcinoma of Lung* / genetics
Biomarkers, Tumor / genetics
Carcinoma, Non-Small-Cell Lung* / diagnosis
Carcinoma, Non-Small-Cell Lung* / genetics
Carcinoma, Non-Small-Cell Lung* / pathology
Computational Biology / methods
Gene Expression Profiling / methods
Gene Expression Regulation, Neoplastic
Humans
Lung Neoplasms* / genetics
Lung Neoplasms* / metabolism
Lung Neoplasms* / pathology
MicroRNAs*
Prognosis

Substances

Biomarkers, Tumor
MicroRNAs