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

Abstract

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.

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

“…To explore the potential mechanisms of different prognoses between high-risk and low-risk subgroups, the ‘limma’ R package was utilized to determine differentially expressed genes (DEGs, |log2FC| > 1 and FDR < 0.05) between the subgroups. Functional enrichment analysis of DEGs was further carried out to assess discrepancy in biological processes (BP), cellular components (CC), molecular functions (MF), and signaling pathways between the high-risk and low-risk subgroups [ 31 ]. In addition, we performed single-sample gene set enrichment analysis (ssGSEA) based on the ‘gsva’ R package to evaluate disparity in immune cells and immune-related functions between these subgroups [ 32 ].…”

Maintaining the Balance of Intestinal Flora through the Diet: Effective Prevention of Illness

“…Depression is associated with psychological problems that can cause changes in the structure of the gut microbiome [31]. Depression is one of the most well-known mental and psychological diseases, and is influenced by intestinal microflora changes [32]. Depression involves hypothalamus-pituitary-adrenal function, the monoamine neurotransmitter and its receptors, and related hormone levels, which is associated with changes in the gut microbiota [33].…”

A role of TTI1 in the colorectal cancer by promoting proliferation

“…However, transcription analysis presents hundreds of DEGs, raising the level of challenges and complexity to search the target gene. Moreover, co-expression clustering is a conventional method to extract genetic interactions and highlight common features of DEGs in CRC ( 7 , 8 ). Weighted gene co-expression network analysis (WGCNA) is regarded as a systematic biological strategy to screen out modules with higher genetic correlations ( 9 ).…”