Mechanism investigation and experiment validation of capsaicin on uterine corpus endometrial carcinoma

Abstract

Background: Using bioinformatics analysis and experimental operations, we intend to analyze the potential mechanism of action of capsaicin target gene GATA1 in the treatment of uterine corpus endometrial carcinoma (UCEC) and develop a prognostic model for the disease to validate this model.Methods: By obtaining capsaicin and UCEC-related DR-DEGs, the prognosis-related gene GATA1 was screened. The survival analysis was conducted via establishing high and low expression groups of GATA1. Whether the GATA1 could be an independent prognostic factor for UCEC, it was also validated. The therapeutic mechanism of capsaicin-related genes in UCEC was further investigated using enrichment analysis and immune methods as well as in combination with single-cell sequencing data. Finally, it was validated by cell experiments.Results: GATA1, a high-risk gene associated with prognosis, was obtained by screening. Kaplan-Meier analysis showed that the survival of the high expression group was lower than that of low expression group. ROC curves showed that the prediction effect of the model was good and stable (1-year area under curve (AUC): 0.601; 2-years AUC: 0.575; 3-years AUC: 0.610). Independent prognosis analysis showed that the GATA1 can serve as an independent prognostic factor for UCEC. Enrichment analysis showed that “neuroactive Ligand - receptor interaction and TYPE I DIABETES MELLITUS” had a significant enrichment effect. Single-cell sequencing showed that the GATA1 was significantly expressed in mast cells. Cell experiments showed that the capsaicin significantly reduced the UCEC cell activity and migration ability, as well as inhibited the expression of GATA1.Conclusion: This study suggests that the capsaicin has potential value and application prospect in the treatment of UCEC. It provides new genetic markers for the prognosis of UCEC patients.

Deciphering the molecular landscape: integrating single-cell transcriptomics to unravel myofibroblast dynamics and therapeutic targets in clear cell renal cell carcinomas

“…We utilized the NormalizeData function within the R package Seurat to standardize the curated single-cell data ( 27 ). Subsequently, we computed the variance and standard deviation for each gene through the FindVariableFeatures function, selecting the top 2000 genes as highly variable based on the genes’ average expression and the extent of their dispersion ( 28 , 29 ).…”

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer

“…For extra validation, we also used an independent external dataset (GSE140082). GO/KEGG enrichment analysis 31 – 33 of DEGs was performed using the clusterProfiler R package 34 , 35 , and we used gene set enrichment analysis (GSEA) to investigate significantly enriched pathways in the high-risk and low-risk groups. Additionally, we contrasted m6A-related genes, immune activity, survival rates, and other factors.…”

Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients

“…Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was also performed to find pathways associated with DEGs. [ 35 ] Based on the “c2.cp.kegg.symbols.gmt” and “c5.go.symbols.gmt” gene sets in the Molecular Signatures Database, we performed a gene set enrichment analysis (GSEA) on the whole genome to study the potential mechanism by which RNA editing site may exert their effects in cervical carcinoma. [ 36 , 37 ]…”