What question did this study set out to answer?

To identify potential diagnostic biomarkers for cervical cancer using bioinformatics analysis.

March 15, 2026Open Access

MCM2, AURKA and MCM6: potential diagnostic biomarkers for cervical cancer

Key Points

To identify potential diagnostic biomarkers for cervical cancer using bioinformatics analysis.
Utilized Gene Expression Omnibus datasets to screen differentially expressed genes in cervical cancer tissues.
Conducted Gene Ontology and KEGG enrichment analysis on identified DEGs.
Constructed a protein-protein interaction network and selected hub genes using CytoHubba algorithms.
Assessed the diagnostic value of hub genes through ROC curve analysis and verified expression using GEPIA and HPA.
Analyzed correlation between hub gene expression and immune cell infiltration using TIMER database.
Identified 226 differentially expressed genes in cervical cancer tissues.
Selected MCM2, AURKA, and MCM6 as key hub genes highly expressed in CC tissues.
Confirmed diagnostic value of MCM2, AURKA, and MCM6 through ROC analysis.
Establishment of a correlation between expression of these genes and immune infiltration in the tumor microenvironment.

Abstract

• MCM2, AURKA and MCM6 are highly expressed in CC tissues. • MCM2, AURKA and MCM6 can be used as diagnostic biomarkers for CC. • MCM2, AURKA and MCM6 are correlated with immune infiltration. As a disease that seriously threatens the lives of women, there are serious limitations in the early screening of cervical cancer (CC). This study aimed to screen promising diagnostic markers for CC using bioinformatics analysis. The differentially expressed genes (DEGs) in CC tissues were screened by GSE46857 and GSE63514 datasets from Gene Expression Omnibus (GEO) database. Subsequently, these DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein-protein interaction (PPI) network of DEGs was constructed through STRING website, and the candidate hub genes were selected through 3 algorithms of CytoHubba plug-in. The expression of candidate hub genes was confirmed by GSE7803 and GSE9705 datasets to obtain the hub genes that were highly expressed in CC tissues. The receiver operating characteristic (ROC) curve was performed to assess the diagnostic value of the hub genes, and the expression of hub genes were verified through Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. The correlation between hub gene expression and immune infiltration of different types of cells was analyzed by Tumor Immune Estimation Resource (TIMER) database. A total of 226 DEGs were identified in CC tissues, and they were associated with various cell components, biological pathways, molecular functions and KEGG pathways. Nine candidate hub genes were selected through 3 algorithms of CytoHubba plug-in, and then PPI network mining was performed on them. Through the verification of GEO database, a total of 3 genes highly expressed in CC tissues were screened as key hub genes (MCM2, AURKA and MCM6). The ROC curve confirmed that MCM2, AURKA and MCM6 could diagnose CC, and GEPIA and HPA databases confirmed their high expression in CC tissues. TIMER database showed that the expression of MCM2, AURKA, and MCM6 was correlated with immune infiltration in the tumor microenvironment. MCM2, AURKA and MCM6 were overexpressed in CC tissues, which might be biomarkers for the diagnosis of CC.

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