Establishment and analysis of the prediction model for cervical squamous cell carcinoma

Predict the Progression of Cervical Intraepithelial Neoplasia by a Novel Marker Folate Combine with FRα, p16 and Ki-67

Purpose

To study the expression of serum folate and red blood cell (RBC) folate, folate receptor α (FRα), p^16INK4a (p16), and Ki-67 at different levels of cervical intraepithelial neoplasia (CIN) and then analyze their role in the progression of CIN and their value as an early warning indicator of CIN progression.

Patients and methods

We randomly collected the data of patients at the Department of Gynecology in Guangxi Medical University Affiliated Cancer Hospital from January 2016 to December 2018: Normal controls (149 cases), CIN1 (150 cases), CIN2 (100 cases), and CIN3 (101 cases). [Ethical approval by Ethics Committee of the Second Hospital of Shanxi Medical University. (2013) No. (001–1)]. The expression of serum folate and RBC folate was detected by the chemiluminescence method, while the expression of FRα, p16, and Ki-67 was detected by Streptavidin-Perosidase (SP) immunohistochemistry.

Results

There was no statistically significant difference in serum folate levels between different grades of CIN (P=0.784), but the RBC folate levels were statistically significant (P=0.015), and there was a negative correlation between RBC folate levels and CIN lesion grades (P<0.05). The FRα, p16, and Ki-67 levels in the CIN group were significantly different from those in the normal control group (P <0.01), and a positive correlation was found (P <0.01); FRα positivity (P=0.000), Ki-67 positivity (P=0.000), and low-level RBC folate (P=0.000) were independent risk factors for the progression of CIN; these indicators were combined to establish a random forest (RF) model in which the Ki-67+FRα model was used as the early warning model of CIN progression.

Conclusion

RBC folate, FRα, p16, and Ki-67 can be used as valuable clinical test indicators for predicting the progression of CIN; the combined detection model of Ki-67+FRα can be used as an early warning model for predicting the progression of CIN.

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

Background

This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis.

Methods

Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets.

Results

A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained.

Conclusions

These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08412-4.

A novel prognostic prediction model based on seven immune-related RNAs for predicting overall survival of patients in early cervical squamous cell carcinoma

BACKGROUND

In this study, we aimed to mine immune-related RNAs expressed in early cervical squamous cell carcinoma to construct prognostic prediction models.

METHODS

The RNA sequencing data of 309 cervical squamous cell carcinoma (CSCC) cases, including data of individuals with available clinical information, were obtained from The Cancer Genome Atlas (TCGA) database. We included 181 early-stage CSCC tumor samples with clinical survival and prognosis information (training dataset). Then, we downloaded the GSE44001 gene expression profile data from the National Center for Biotechnology Information Gene Expression Omnibus (validation dataset). Gene ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to analyze the biological functions of differentially expressed immune-related genes (DEIRGs). We established protein-protein interactions and competing endogenous RNA networks using Cytoscape. Using the Kaplan-Meier method, we evaluated the association between the high- and low-risk groups and the actual survival and prognosis information. Our univariate and multivariate Cox regression analyses screened for independent prognostic factors.

RESULTS

We identified seven prognosis-related signature genes (RBAKDN, CXCL2, ZAP70, CLEC2D, CD27, KLRB1, VCAM1), the expression of which was markedly associated with overall survival (OS) in CSCC patients. Also, the risk score of the seven-gene signature discripted superior ability to categorize CSCC patients into high-risk and low-risk groups, with a observablydifferent OS in the training and validation datasets. We screened two independent prognostic factors (Pathologic N and prognostic score model status) that correlated significantly by univariate and multivariate Cox regression analyses in the TCGA dataset. To further explore the potential mechanism of immune-related genes, we observed associated essential high-risk genes with a cytokine-cytokine receptor interaction.

CONCLUSIONS

This study established an immune-related RNA signature, which provided a reliable prognostic tool and may be of great significance for determining immune-related biomarkers in CSCC.