Dataset Information

Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study.

ABSTRACT:

Background

The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), the fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary to discover the hub gene, key pathways, and the exact underlying mechanisms involved in developing this disease.

Objective

This study aims to use gene expression patterns and protein-protein interaction (PPI) network analysis to identify key pathways and druggable hub genes in CC.

Materials and methods

In this in silico analysis, 2 microarray gene expression datasets; GSE63514 (104 cancer and 24 normal samples), and GSE9750 (42 cancer and 24 normal samples) were extracted from gene expression omnibus to identify common differentially expressed genes between them. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed via the Enrichr database. STRING 12.0 database and CytoHubba plugin in Cytoscape 3.9.1 software were implemented to create and analyze the PPI network. Finally, druggable hub genes were screened.

Results

Based on the degree method, 10 key genes were known as the hub genes after the screening of PPI networks by the CytoHubba plugin. NCAPG, KIF11, TTK, PBK, MELK, ASPM, TPX2, BUB1, TOP2A, and KIF2C are the key genes, of which 5 genes (KIF11, TTK, PBK, MELK, and TOP2A) were druggable.

Conclusion

This research provides a novel vision for designing therapeutic targets in patients with CC. However, these findings should be verified through additional experiments.

SUBMITTER: Asadzadeh A

PROVIDER: S-EPMC10698354 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study.

Asadzadeh Azizeh A Ghorbani Nafiseh N Dastan Katayoun K

International journal of reproductive biomedicine 20231001 10

<h4>Background</h4>The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), the fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary to discover the hub gene, key pathways, and the exact underlying mechanisms involved in developing this disease.<h4>Objective</h4>This study aims to use gene expression patterns and protein-protein interaction (PPI) network analysis to identify key pathways and druggable ...[more]

PMID: 38077941

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Project description:BackgroundBasal-like breast cancer (BLBC) is the most aggressive subtype of breast cancer (BC) and links to poor outcomes. As the molecular mechanism of BLBC has not yet been completely discovered, identification of key pathways and hub genes of this disease is an important way for providing new insights into exploring the mechanisms of BLBC initiation and progression.ObjectiveThe aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis.Methods and resultsThe differential expressed genes (DEGs) including 40 up-regulated and 21 down-regulated DEGs were identified between GSE25066 and GSE21422 microarrays, and these DEGs were significantly enriched in the terms related to oncogenic or suppressive roles in BLBC progression. In addition, KEGG pathway and GSEA (Gene Set Enrichment Analysis) enrichment analyses were performed for DEGs between the basal type and non-basal-type breast cancer from GSE25066 microarray. These DEGs were enriched in pathways such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism signaling and TNF signaling pathway. Moreover, the protein-protein interaction (PPI) network was constructed with those 61 DEGs using the Cytoscape software, and the biological significance of putative modules was established using MCODE. The module 1 was found to be closely related with a term of mitosis regulation and enriched in cell cycle pathway, and thus confirmed the pathological characteristic of BLBC with a high mitotic index. Furthermore, prediction values of the top 10 hub genes such as CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A were validated using Oncomine and Kaplan-Meier plotter.ConclusionOur results suggest the intriguing possibility that the hub genes and modules in the PPI network contributed to in-depth knowledge about the molecular mechanism of BLBC, paving a way for more accurate discovery of potential treatment targets for BLBC patients.

Project description:Abstract AbstractBackground: Type II diabetes mellitus (T2DM), characterized by fasting hyperglycemia and impaired insulin secretion and action, is a global health burden. Despite the advances in this field, the mechanism underlying T2DM is far from clear. Objective: The present study sheds light upon a systematic evaluation of the genes, pathways, and interaction networks underlying T2DM with the aid of bioinformatics. Methods: Two Gene Expression Omnibus microarray datasets: GSE148961 and GSE26168 were selected for this study. The common differentially expressed genes (DEG) were sorted by the cutoff |logFC|≥1.0 for the first dataset and |logFC|≥0.263 for the second. Gene Ontology (GO), functional enrichment, and protein-protein interaction (PPI) network were analyzed in Search Tool for the Retrieval of Interacting Genes (STRING). The MCODE and CytoHubba plugins in Cytoscape (v3.7.2) were used to identify gene clusters and top hub genes, respectively. Top 10 nodes were ranked in CytoHubba according to MCC, DMNC, MNC, Degree, and EPC methods, and genes common in at least 3 methods were selected as top nodes. Results: 88 common DEGs were identified by Venn diagram (http://bioinformatics.psb.ugent.be/cgi-bin/liste/Venn/calculate_venn.htpl). GO analysis had 91 significantly enriched biological processes, including regulated exocytosis, secretion, vesicle mediated transport, antibacterial humoral response, and neutrophil degranulation. 4 molecular functions- fibrinogen binding, fibronectin binding, lipopolysaccharide binding, and Extracellular matrix binding; and 38 cellular components, including secretory vesicle, endomembrane system, and adherens junction were significant. The PPI network was highly significant (p-value < 0.001) at medium confidence (0.400) with 88 nodes, 140 edges, and an average node degree of 3.18. The MCODE plugin revealed two clusters, the former with 14 nodes, 73 edges, and a score of 9.733, and the latter with 6 nodes, 14 edges, and a score of 4.000. 9 candidate genes: ELANE, DEFA4, BPI, MPO, LTF, CAMP, OLFM4, LCN2, and VCL were identified, amongst which ELANE, LCN2, and MPO are associated with T2DM pathogenesis, while BPI and LTF have protective effects. OLFM4 deletion has been observed to improve glucose tolerance in mice models. Conclusion: This study provides a comprehensive analysis of genes, pathways, and functions which may be pivotal in T2DM pathogenesis and may represent potential therapeutic targets.

Dataset Information

Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study.

Background

Objective

Materials and methods

Results

Conclusion

Publications

Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study.

Similar Datasets

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