Comprehensive analysis of HOX family genes in endometrial cancer

Abstract

Background Endometrial cancer (EC) is one of the most prevalent malignancies in the female population. Homeoboxes (HOXs) are a large family of transcription factors that have a variety of functions in biological processes (BPs), including developmental differentiation, and their dysregulated expression has been implicated in tumorigenesis. However, the involvement of HOXs in EC has received little attention. Thus, we aimed to identify the potential role of HOXs in EC from a multi-omics perspective through bioinformatics analysis. Methods We obtained transcriptome, mutation, and methylation data and the corresponding clinical data for normal and tumor tissues from The Cancer Genome Atlas (TCGA) database. Abnormal expression of HOXs in EC was identified via differential analysis, and the diagnostic value of HOXs in EC was assessed with the receiver operating characteristic (ROC) method. Univariate and multivariate Cox regression models were employed to evaluate the predictive efficacy of HOXs in EC. Methylation and mutation analyses revealed epigenetic and genetic sequence alterations in HOXs. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the altered immune microenvironment in EC. Moreover, the gene activity and pathway enrichment of downstream key HOX genes were revealed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis in EC. Results HOXs were found to be linked to the growth of EC and potentially playing a role in establishing the tumor immune microenvironment in patients with EC. HOXB9 was found to be a vital prognostic molecule in patients with EC and is expected to contribute to a novel treatment approach. Conclusions We used bioinformatics techniques to clarify the potential role of HOXs from a multi-omics perspective, and our findings provide a foundation for future investigations into the molecular mechanisms of HOXs in EC.

Correlation between Complement C1q A Chain (C1QA) and Macrophages in the Progression of Carotid Atherosclerosis

“…The level of immune infiltration in each sample was determined using the ssGSEA algorithm based on the GSE100927 and GSE43292 datasets ( 22 ). The case group of the dataset was divided into high and low expression groups based on the expression level of C1QA , and an immune cell expression box plot was generated.…”