Bioinformatics Analysis of Key Genes and Pathways of Cervical Cancer

Chen, Huan; Wang, Xi; Jia, Huanhuan; Yin, Tao; Zhou, Hong; Wang, Mingyuan; Wang, Xin; Fang, Xiaoling

doi:10.2147/ott.s281533

Cited by 9 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jinhui Liu et al [ 33 ] showed that RMI2 was a novel key gene in CESC. Huan Chen et al [ 65 ] showed that the KNTC1 gene may be related to the pathophysiology of cervical cancer and may be one of the markers for the early diagnosis of cervical precancerous lesions. The abnormal expression of GINS2 can inhibit cell proliferation and tumorigenicity, as well as cell migration and invasion [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

2021

BMC Cancer

View full text Add to dashboard Cite

Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

show abstract

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

2021

BMC Cancer

View full text Add to dashboard Cite

show abstract

“…A recent study indicated that silencing KNTC1 with shRNA inhibited cell viability and caused apoptosis in esophageal squamous cell carcinoma [ 45 ]. Moreover, relevant bioinformatics publications demonstrated that KNTC1 was associated with a poor outcome in patients with hepatocellular carcinoma and cervical cancer [ 46 , 47 ]. However, there has been no research on the mechanism of action of KNTC1 in patients with LUAD.…”

Section: Discussionmentioning

confidence: 99%

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

et al. 2022

View full text Add to dashboard Cite

Objective Lung adenocarcinoma (LUAD) is one of the major subtypes of lung cancer that is associated with poor prognosis. The aim of this study was to identify useful biomarkers to enhance the treatment and diagnosis of LUAD. Methods GEO2R was used to identify common up-regulated differentially expressed genes (DEGs) in the GSE32863, GSE40791, and GSE75037 datasets. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis as well as construction of the protein-protein interaction (PPI) network, while the molecular complex detection (MCODE) plug-in was employed to filter important subnetworks. The expression levels of the hub genes and their prognostic values were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The timer algorithm was utilized to determine the correlation between immune cell infiltration and the expression levels of hub genes in LUAD tissues. In addition, the hub gene mutation landscape and the correlation analysis with tumor mutational burden (TMB) score were evaluated using maftools package and ggstatsplot package in R software, respectively. Results We identified 156 common up-regulated DEGs, with gene ontology and pathway enrichment analysis indicating that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1. The seven hub genes were highly expressed in LUAD tissues and were associated with poor prognosis. These hub genes were negatively correlated with most immune cells. The somatic mutation landscape showed that AURKB, CDC20, CENPF, and KNTC1 had mutations and were positively correlated with TMB scores. Conclusions Our findings demonstrate that increased expression of seven hub genes is associated with poor prognosis for LUAD patients. Additionally, the TMB score indicates that the high expression of hub gene increases immune cell infiltration in patients with lung adenocarcinoma which may significantly improve response to immunotherapy.

show abstract

“…Through BI analysis on PPI, [24] found RPS21, FOXO1, BIRC5, POLR2H, RPL22L1, and NPM1 as the prostate cancer key genes. CDK1, CCND1, PCNA [20], CDK2, AND VEGFA [25], PLOD2, DSG2, SPP1, CXCL8, MCM5, HLTF, and KLF4 [26], KNTC1 [27], CDKN2A, IL1R2 and RFC4 [28], and AKT1 ' [29] have been reported to have some significance in cause or prognosis of cervical cancer. The different roles of CDK1, CCNB1, ITGB1, FN1, MMP9 and STAT1 are also being discussed in the literature [30].…”

Section: Key Gene Identificationmentioning

confidence: 99%

Discovering Disease Genes in PPI Networks: A Bridge from Centrality to Communities

Khan,

Shams Malick,

Cherifi

2023

Preprint

View full text Add to dashboard Cite

Targeted therapies have become pivotal in modern clinical oncology, driven by a molecularlevel understanding of cancer’s intricacies, its progression, and innovative research and technology. Personalized and targeted treatments hinge on identifying key genes, hub genes, or biomarkers. Protein-protein interaction (PPI) networks are instrumental in understanding the molecular basis of diseases. While existing literature has identified significant genes based on network centrality, investigations based on community-aware centrality have been notably absent. This omission matters because disease networks frequently display modular structures, necessitating a new perspective. This study bridges the gap between network centrality and community-based investigations. Indeed, in modular networks, node influence can be categorized into two types: local impact within its community, determined by intra-community connections, and global effect on nodes in other communities, established through inter-community links. This concept extends conventional centrality measures to networks with a community structure. Initially, we performed a comparative analysis of seven PPI networks related to cancer and noncancerous conditions. We explore the correlation between classical network centralities and their equivalents at the global (inter-community) and local (intra-community) levels. Notably, we consistently observed a high correlation between network degree and local degree centrality in all PPIs, leading us to select local degree centrality for further investigation. Pronounced modularity characterizes prostate and cervical disease networks. Consequently, we investigate these networks to identify key genes at the local community level and validate them by examining their expression levels. Variations in gene expression between cancerous and non-cancerous tissues bolster our findings. We identify a novel set of genes as potential key players in prostate and cervical cancer. Specifically, in cervical cancer, the top genes at the mesoscopic level include AKT1, CDK2, BRCA1, VEGFA, SRC, PSMD14, MRPL3, TP53, and NUP37. Meanwhile, the top genes identified in prostate cancer are FOS, TP53, UBA52, HLA-B, TSPO, and CD19. Although we focus on cancer data, our methodology’s versatility makes it applicable to other disease networks, opening avenues to identify key genes as potential drug targets.

show abstract

Bioinformatics Analysis of Key Genes and Pathways of Cervical Cancer

Cited by 9 publications

References 44 publications

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

Discovering Disease Genes in PPI Networks: A Bridge from Centrality to Communities

Contact Info

Product

Resources

About