Identification of prognostic markers by weighted gene co‐expression network analysis in non-small cell lung cancer

ABSTRACT

Non-small cell lung cancer (NSCLC) is one of the fatal tumors and is associated with a poor prognosis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify the proportions of 22 types of immune cells. Weighted gene co-expression network analysis (WGCNA) was established from the GSE37745 data, and key modules correlating most with CD8⁺ T cell infiltration were determined. Genes that manifested a high module connectivity in the key module were identified as hub genes. Three bioinformatics online databases were used to evaluate hub gene expression levels in tumor and normal tissues. Finally, survival analysis was conducted for these hub genes. In this study, we chose four hub genes (AURKB, CDC20, TPX2 and KIF2C) based on the comprehensive bioinformatics analyses. All hub genes were overexpressed in tumor tissue, and high expression of AURKB, CDC20, TPX2, and KIF2C correlated with the poor prognosis of these patients. In vitro experiments confirmed that CDC20 knockdown inhibited cell proliferation and growth. The above results indicated that AURKB, CDC20, TPX2, and KIF2C are potential CD8⁺ T cell infiltration-related biomarkers and therapeutic targets.

KEYWORDS:

• Weighted gene co-expression network analysis
• CIBERSORT
• Non-small cell lung cancer

Abbreviations

NSCLC, Non-small cell lung cancer; WGCNA, weighted gene co-expression network analysis; TCGA, The Cancer Genome Atlas; HPA, The Human Protein Atlas; SCLC, small cell lung cancer; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; CIBERSORT,Cell type Identification by Estimating Relative Subsets of RNA Transcripts; TIL, tumor infiltrating lymphocytes; GEO, the Gene Expression Omnibus; MEs, module eigengenes; GO, gene ontology; KEGG, kyoto encyclopedia of genes; MM, Module-Membership; GS, Gene-Significance; PPI, protein–protein interaction; CV, coefficient of variation.

Acknowledgements

The authors extend their appreciation to the Scientific Research and Technology Development Program of Guangxi (NO. AB18221080) for the financial support.

Contribution

BLC and WQL designed the study. BLC, XWX, FFL and WQL acquired and analyzed the data. BLC and FFL contributed to data analysis and manuscript preparation. All authors contributed toward data analysis, drafting, and revising the paper, and agree to be accountable for all aspects of the work.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Highlights

(1) AURKB, CDC20, TPX2 and KIF2C are potential underlying prognostic factors in NSCLC.

(2) Comprehensive bioinformatics methods can explore factors related to prognosis.

(3) Genes related to CD8⁺ T cells play an important role in prognosis of NSCLC.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by Guangxi key research and development program No. (GK) AB18221080.