Identification of key biomarkers and related immune cell infiltration in cervical cancer tissue based on bioinformatics analysis

Background Colorectal cancer (CRC) is a highly aggressive, high-incidence malignancy. CRC accounted for approximately one out of every ten cancer cases and deaths. Although miRNAs are often used for medical diagnostic purposes, their diagnostic effectiveness in CRC remains uncertain. Methods Therefore, from January 2016 to April 2024, we conducted a comprehensive search of China National Knowledge Internet (CNKI), PubMed, Cochrane Library, Web of Science (WoS) and other resources. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under the curve (AUC) and Fagan plot analysis were used to assess the overall test performance of machine learning approaches. Moreover, we evaluated the publication bias by the Deeks’funnel plot asymmetry test. Results Ultimately, a total of 23 publications were identified and incorporated into this meta-analysis. The aggregated diagnostic data were as follows: The sensitivity of the test was 0.83, with a 95% confidence interval of 0.81–0.84. The specificity was found to be 0.83 with a 95% confidence interval (CI) of 0.81–0.84. The PLR was 4.60 with a 95% CI of 3.77–5.62. The NLR was 0.22 with a 95% CI of 0.17–0.27. The DOR was 23.79 with a 95% CI of 16.26–34.81. The AUC was 0.90 with a 95% CI of 0.87–0.92. The Deek funnel plot suggests that publication bias has no statistical significance. The Fagan plot analysis that the positive probability is 50% and the nagative probability is 5%. Conclusion In summary, our results suggest the high accuracy of miRNAs in diagnosing CRC.

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Circular RNA Expression Profile in Cervical Cancer and Construction of the Circular RNA‑MicroRNA‑Messenger RNA Regulatory Network

Begliarzade,

Tamrazov,

Musaev

et al. 2024

Kreativnaâ hirurgiâ i onkologiâ

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Introduction. Cervical cancer (CC) remains the most common cancer in women worldwide. However, effective and specific biomarkers for the diagnosis and prognosis of cervical cancer are yet to be found. In recent years, the potential of circular RNAs (circRNAs) as new diagnostic, prognostic and therapeutic tools has received much attention. The current study involved an in-depth bioinformatics research to explore the circRNA-microRNA (miRNA)-messenger RNA (mRNA) regulatory network in order to identify important molecular processes and biological pathways supposedly associated with CC. Materials and methods. The study collected data on the expression of circRNA (GSE102686), miRNA (GSE30656) and mRNA of target genes (GSE9750), based on the Gene Expression Omnibus (GEO) database, in squamous cell carcinoma of the cervix samples and normal squamous epithelium of the cervix, dividing them into study and control groups. Protein-protein interaction (PPI), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to further understand the function of circRNAs for their target genes. Results. A total of 105 differentially expressed circular RNAs (DECs), 144 differentially expressed microRNAs (DEMs), and 539 differentially expressed target genes (DEGs) were identified for cervical cancer. Concurrently, functional enrichment analysis of GO and KEGG pathways was performed for DEGs. Subsequently, searching databases for circRNA, miRNA and mRNA target genes, as well as PPI network analysis and functional enrichment revealed 3 DECs with significantly high expression levels (hsa_circ_0000745, hsa_circ_0084927 and hsa_circ_0002762), 6 DEMs with reduced expression levels (hsa -miR-145, hsa-miR-876-3p, hsa-miR-1229, hsa-miR-182, hsa-miR-520h and hsa-miR-1252) and 9 key genes such as ANGPT2, COL11A1, MEST, KIF20A, CLN6, FNDC3B, USP18, DLGAP5 and CXCL9, suggesting a potentially significant role in cervical cancer. Conclusion.Understanding the circRNA-miRNA-mRNA regulatory network is of great importance for evaluating the oncogenesis of CC, as well as discoverying new circRNAs as the main regulatory molecules in this network. This is considered to be a new direction in the diagnosis and targeted therapy of cervical cancer.

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Identification of key biomarkers and related immune cell infiltration in cervical cancer tissue based on bioinformatics analysis

Cited by 3 publications

References 47 publications

Analysing the relevance of TGF-β and its regulators in cervical cancer to identify therapeutic and diagnostic markers

Analysing the relevance of TGF-β and its regulators in cervical cancer to identify therapeutic and diagnostic markers

Diagnostic Accuracy of miRNA to Identify Colorectal cancer: A Systematic Review and Meta-Analysis

Circular RNA Expression Profile in Cervical Cancer and Construction of the Circular RNA‑MicroRNA‑Messenger RNA Regulatory Network

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