Analysis and validation of genes joint expression in Crohn's disease and cervical cancer based on GEO database.

Abstract

Abstract Background: In recent years, numerous studies have demonstrated an increased incidence of cervical cancer in women with Crohn's disease (CD). This paper aims to delve into the underlying mechanism of this association. Methods: Gene expression profiles for Crohn's disease (GSE95095 and GSE186582) and cervical cancer (GSE63514 and GSE63678) were obtained from the GEO database. Heterozygotes (DEGs) were searched for in experimental and control groups for both diseases. Gene modules for Crohn's disease and cervical cancer were also analyzed using the WGCNA method. Machine learning (LASSO logistic regression algorithm & RF method) was applied to screen the characterized genes in the two diseases. And the transcription factors related to the characterized genes were predicted. Finally, it was validated by Western Blot (WB) and immunohistochemistry experiments. Results: From the pool of differential genes in both disease groups, we identified a total of 60 co-expressed genes. Using the WGCNA method, we found 11 key modular genes that were common to both diseases. Machine learning screening allowed us to identify a shared biomarker for both diseases: CXCR4. Furthermore, we predicted MYC as its transcriptional regulator. Finally, to validate our findings, we conducted immunohistochemistry and protein immunoblotting experiments, which confirmed that CXCR4 exhibits a higher expression level in cervical cancer. Conclusion: This study screened a gene co-expressed in Crohn's disease and cervical cancer based on machine learning: CXCR4, which is expected to be a potential biomarker for both diseases.