Multi-omics analysis at epigenomics and transcriptomics levels reveals prognostic subtypes of lung squamous cell carcinoma

In this study, we identified prognostic biomarkers for lung squamous cell carcinoma (LUSC) by integrating multiple sets of DNA copy number variants (CNV) and methylation variant (MET) data, and performing qPCR and immunohistochemical identification. We examined the expression of CNV and MET in 368 L...

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Veröffentlicht in:Biomedicine & pharmacotherapy 2020-05, Vol.125, p.109859-109859, Article 109859
Hauptverfasser: Xu, Yong, She, Yunlang, Li, Yaqiang, Li, Hao, Jia, Zihao, Jiang, Gening, Liang, Leilei, Duan, Liang
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Sprache:eng
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Zusammenfassung:In this study, we identified prognostic biomarkers for lung squamous cell carcinoma (LUSC) by integrating multiple sets of DNA copy number variants (CNV) and methylation variant (MET) data, and performing qPCR and immunohistochemical identification. We examined the expression of CNV and MET in 368 LUSC patients. Gene expression associated with DNA copy number or DNA methylation was identified and four LUSC gene subtypes were defined based on these correlations. The prognosis overall survival (OS) of the iC1 subtype was significantly lower than that in the iC2 and iC4 subtypes. We assessed the immune scores of each subtype and found that the six immune cell scores of the iC3 subtype were significantly higher than the other subtypes (p < 0.01). Three genes associated with prognosis, NFE2L2, ASAH2, and RIMBP2, were identified by comparing the expression of CNV and MET in subtypes. Analysis of mutational differences between subtypes revealed a group of genes with significant mutations between the iC1 and iC4 subtypes. The number of mutations in the NFE2L2 gene in LUSC was significantly higher than that in other genes, and the gene was prognostic. The number of mutations was significantly higher in the best iC4 subtype than the iC1 subtype with the worst prognosis; the other two genes, ASAH2 and RIMBP2, were only found in the worst prognosis of the iC1 subtype. This comprehensive multi-omics analysis of genomics, epigenomics, and transcriptomics data provides new insights into the molecular mechanisms of LUSC and may be helpful in identifying biomolecular markers for early disease diagnosis.
ISSN:0753-3322
1950-6007
DOI:10.1016/j.biopha.2020.109859