J Cancer 2020; 11(8):2150-2157. doi:10.7150/jca.38211 This issue

Research Paper

Bioinformatics analysis and identification of potential genes related to pathogenesis of cervical intraepithelial neoplasia

Xue Zhang1*, Jian Bai2*, Cheng Yuan1, Long Long1, Zhewen Zheng1, Qingqing Wang1, Fengxia Chen1, Yunfeng Zhou1✉

1. Department of Radiation and Medical Oncology, Zhongnan Hospital, Wuhan University, Wuhan, Hubei 430071, P.R. China
2. Department of Gastrointestinal Surgery and Department of Gastric and Colorectal Surgical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
*Contributed equally to this work.

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Zhang X, Bai J, Yuan C, Long L, Zheng Z, Wang Q, Chen F, Zhou Y. Bioinformatics analysis and identification of potential genes related to pathogenesis of cervical intraepithelial neoplasia. J Cancer 2020; 11(8):2150-2157. doi:10.7150/jca.38211. Available from https://www.jcancer.org/v11p2150.htm

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The aim of this study was to explore and identify the key genes and signal pathways contributing to cervical intraepithelial neoplasia (CIN). The gene expression profiles of GSE63514 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened performing with packages in software R. After Gene ontology terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyzing, and Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA) was used to analyze these genes. Then sub-modules were subsequently analyzed base CIN grade, and protein-protein interaction (PPI) network of DEGs were constructed. 537 DEGs were screened in total, consisting 331 up-regulated genes and 206 down-regulated genes in CIN samples compared to normal samples. The most DEGs were enriched in chromosomal region in cellular component (CC), organelle fission inbiological process (BP) and ATPase activity in molecular function (MF). KEGG pathway enrichment analyzing found the DEGs were mainly concentrated in 10 pathways. The results of GSEA mainly enriched in 4 functional sets: E2F-Targets, G2M-Checkpoint, Mitotic-Spindle and Spermatogenesis. A total of 6 modules were identified by WCGNA. Subsequently, grey module was the highest correlation (Cor=0.78, P=5e-22) and 31 genes were taken as candidate hub genes for CIN high grade risk (weighted correlation coefficients >0.80). Finally, diagnostic analysis showed that in addition to CCDC7, the expression levels of the remaining 13 DEGs have a high diagnostic value (AUC>0.8 and P<0.05). These findings provided a new sight into the understanding of molecular functions for CIN.

Keywords: cervical intraepithelial neoplasia, bioinformatical analysis, microarray, differentially expressed genes