Establishment of multifactor predictive models for the occurrence and progression of cervical intraepithelial neoplasia
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RESEARCH ARTICLE
Open Access
Establishment of multifactor predictive models for the occurrence and progression of cervical intraepithelial neoplasia Mengjie Chen† , He Wang† , Yuejuan Liang , Mingmiao Hu
and Li Li*
Abstract Background: To study the risk factors involved in the occurrence and progression of cervical intraepithelial neoplasia (CIN) and to establish predictive models. Methods: Genemania was used to build a gene network. Then, the core gene-related pathways associated with the occurrence and progression of CIN were screened in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) experiments were performed to verify the differential expression of the identified genes in different tissues. R language was used for predictive model establishment. Results: A total of 10 genes were investigated in this study. A total of 30 cases of cervical squamous cell cancer (SCC), 52 cases of CIN and 38 cases of normal cervix were enrolled. Compared to CIN cases, the age of patients in the SCC group was older, the number of parities was greater, and the percentage of patients diagnosed with CINII+ by TCT was higher. The expression of TGFBR2, CSKN1A1, PRKCI and CTBP2 was significantly higher in the SCC groups. Compared to patients with normal cervix tissue, the percentage of patients who were HPV positive and were diagnosed with CINII+ by TCT was significantly higher. FOXO1 expression was significantly higher in CIN tissue, but TGFBR2 and CTBP2 expression was significantly lower in CIN tissue. The significantly different genes and clinical factors were included in the models. Conclusions: Combination of clinical and significant genes to establish the random forest models can provide references to predict the occurrence and progression of CIN. Keywords: Cervical intraepithelial neoplasia, Cervical cancer, Random forest model, Bioinformatics
Background Cervical cancer is a female malignant tumor, and it has the second highest morbidity rate and the third highest mortality rate in the world [1]. Cervical intraepithelial neoplasia (CIN) is a precancerous lesion that precedes invasive cervical cancer. Persistent high-risk human papillomavirus (HPV) infection is one of the main causes * Correspondence: [email protected] † Mengjie Chen and He Wang are co-first author. Guangxi Medical University affiliated Cancer Hospital, NO.71 Hedi Road Qingxiu Square, Nanning City, Guangxi Province, China
of cervical cancer and CIN, but individual genes and other clinical factors also have an important impact on the progression of CIN [2]. Cervical cytology, HPV testing, colposcopy and cervical biopsy histopathology are widely used clinically to screen for CIN. The occurrence and outcome of CIN are closely related to genes, vaginal microecology, environment and other factors. CIN is classified into CINI, CINII, and CINIII grades. Sixty percent of CINI grades can regress spontaneously, and only 10 and 1% of them progress to CINIII and cervical invasive carci
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