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. 2023 Sep 14:2023:6626279.
doi: 10.1155/2023/6626279. eCollection 2023.

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Parinaz Tabrizi-Nezhadi  1 , Habib MotieGhader  1   2 , Masoud Maleki  1 , Soner Sahin  2 , Sajjad Nematzadeh  3 , Mahsa Torkamanian-Afshar  4
Affiliations

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Parinaz Tabrizi-Nezhadi et al. ScientificWorldJournal. .

Abstract

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The workflow diagram of the project. In this project, a protein-protein interaction (PPI) network analysis and miRNA-mRNA regulatory network were utilized to discover RNA-based biomarkers in cervical cancer. (a, b) First, a transcriptome dataset for normal and CC samples was gathered from the GEO database with the accession number GSE63514. (c) Next, a PPI network was constructed for the DEG (p_value <0.05) between normal and CC groups thanks to the STRING database. (d) Then, a significant protein module containing 16 proteins was extracted from the PPI network. (e) Subsequently, a miRNA-mRNA regulatory network was reconstructed. (f) Consequently, four cancer driver genes (MCM2, MCM10, POLA1, and TONSL) and two miRNAs (miR-193b-3p and miR-615-3p) were introduced as potential candidate biomarkers for CC patients.
Figure 2
Figure 2
PPI network for the primary gene list. The degree of the nodes indicates their interactions. This network has 290 nodes (proteins) and 381 edges (interactions). CDK1 has the highest interactions in the network. Green nodes indicate regular ones, and red nodes indicate cervical cancer driver genes based on the DriverDBv3 [25] report.
Figure 3
Figure 3
The significant protein module extracted from the PPI network. Green and red color nodes indicate regular and driver genes, respectively.
Figure 4
Figure 4
miRNA-mRNA regulatory network. Circle and diamond nodes indicate genes and miRNAs, respectively. As well as, red shapes indicate cancer driver genes. High-degree miRNAs are miR-193b-3p and miR-615-3p. This network contains 262 miRNAs and 16 target genes.
See this image and copyright information in PMC

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