Background: Currently, there are no reliable diagnostic and prognostic markers for Malignant Pleural Mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis.
Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted Gene Co-expression Network Analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions.
Results: A total of 430 upregulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered as hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC=0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 was associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways.
Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.
Keywords: Malignant pleural mesothelioma, WGCNA, TCGA, GEO, biomarker, LASSO.
[http://dx.doi.org/10.1183/16000617.0063-2016] [PMID: 27903668]
[http://dx.doi.org/10.6004/jnccn.2012.0006] [PMID: 22223867]
[http://dx.doi.org/10.1016/j.jfma.2018.07.013] [PMID: 30072200]
[http://dx.doi.org/10.1016/j.lungcan.2015.03.005] [PMID: 25863904]
[http://dx.doi.org/10.1016/S0140-6736(15)01238-6] [PMID: 26719230]
[http://dx.doi.org/10.1016/S1470-2045(18)30100-1] [PMID: 29508763]
[http://dx.doi.org/10.1016/j.molonc.2012.01.010] [PMID: 22356776]
[http://dx.doi.org/10.1093/jac/dkq523] [PMID: 21398306]
[http://dx.doi.org/10.1016/j.copbio.2007.11.005] [PMID: 18207385]
[http://dx.doi.org/10.1038/ng.2764] [PMID: 24071849]
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[http://dx.doi.org/10.1093/bioinformatics/bti623] [PMID: 16096348]
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[http://dx.doi.org/10.1016/j.ctrv.2014.10.007] [PMID: 25467107]
[http://dx.doi.org/10.1038/sj.onc.1209320] [PMID: 16407840]
[http://dx.doi.org/10.1073/pnas.1106136108] [PMID: 21628590]
[http://dx.doi.org/10.1016/j.yexcr.2005.09.020] [PMID: 16288740]
[http://dx.doi.org/10.1371/journal.pone.0026866] [PMID: 22096502]
[http://dx.doi.org/10.1016/j.lungcan.2011.05.024] [PMID: 21689861]
[http://dx.doi.org/10.1038/labinvest.2013.124] [PMID: 24145239]
[http://dx.doi.org/10.1158/1078-0432.CCR-07-1113] [PMID: 17875765]
[http://dx.doi.org/10.21037/atm-21-626] [PMID: 33850884]
[http://dx.doi.org/10.21037/atm-20-3219] [PMID: 33708887]
[http://dx.doi.org/10.1186/s12885-020-07463-3] [PMID: 33008389]
[http://dx.doi.org/10.1002/ijc.31800] [PMID: 30129654]