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.

Keywords: UCEC; bioinformatics; capsaicin; experiment validation; mechanism investigation.

FIGURE 1

Workflow diagram for the design of this study.

FIGURE 2

Interaction relationship and enrichment analysis of drug-associated differential genes; (A) Volcanoes of 6,989 endometrial cancer differential genes; (B) Venny diagram of 19 drug-associated differential genes; (C) Heat map of 19 drug-differential genes (N represents normal group, T represents tumor group; red represents high gene expression, blue represents low gene expression, the depth of colour represents high or low expression); (D) PPI protein interaction network relationship diagram of DR-DEGs (red represents high level of interaction, green represents low level of interaction; (E) Analysis of the enrichment effect of DR-DEGs.

FIGURE 3

GATA1 prognosis and gene correlation analysis. (A) Forest plots of DR-DEGs associated with UCEC prognosis were obtained by univariate COX regression analysis; (B–K) GATA1 correlation with MTCYBP29, RN7SL51P, PPEF1-AS1, SLC4A9, TRY2P, CICP26, GAS2L1P1, LINC01570, LINC01584, LINC02209 correlation analysis; (L) Kaplan-Meier analysis plot of prognostic correlation between high and low GATA1 expression groups; (M) Time-dependent ROC plot.

FIGURE 4

GATA1 clinical correlation and independent prognostic analysis. (A) GATA1 expression in patients in the tumor group and in tumor and normal tissues; (B) GATA1 expression in patients in the normal and tumor groups; (C) GATA1 expression in samples from patients in different age groups; (D) GATA1 expression in samples from patients at different stages; (E,F) Univariate and multivariate Cox regression independent prognostic analyses; (G) Nomogram predicting 1-, 3- and 5-years survival rates in UCEC patients; (H) Columnar plot calibration curves for testing deviations between predicted and actual 1-, 3- and 5-years survival probabilities in nomogram.

FIGURE 5

Functional analysis of differential gene enrichment in high and low expression groups. (A) Heat map of differential genes in high and low expression groups; (B) Functional analysis of GO enrichment; (C) Functional analysis of KGGG enrichment; (D) Functional analysis of GSEA enrichment.

FIGURE 6

Tumor microenvironment and immune correlation analysis: (A) Distribution of stromal, immune, and total scores in tumor microenvironment between high and low expression groups; (B) Analysis of immune cell infiltration in high and low expression groups; (C) Correlation between GATA1 and immune cells for analysis; (D–F) Relationship between GATA1 and B cells naïve, Dendritic cells resting, and Macrophages M2 expression; (G) Box line plot of immune checkpoint gene scores in high and low expression groups; (H) Co-expression relationship between GATA1 and immune checkpoint genes.

FIGURE 7

Single-cell sequencing subgroups and cell trajectory analysis. (A) Nine major cell subgroups of UCEC tumor tissues were constructed by t-SNE analysis; (B) Visualization of Marker gene expression in the 9 cell subgroups; (C) Distribution of single-cell sequencing data from five UCEC patient samples in t-SNE analysis; (D,E) Glandular epithelial cells versus Tumor cells in cell trajectory analysis graphs; (F) Expression changes of DR-DEGs with cell trajectory.

FIGURE 8

Distribution of t-SNE expression of DR-DEGs in single cell sequencing data.

FIGURE 9

Expression relationship of GATA1 in cell grouping of Plasma B and mast cells; (A) Further clustering of Plasma B and mast cells into different subgroups; (B,C) Expression of GATA1 in different subgroups of Plasma B and mast cells.

FIGURE 10

Experimental verification. (A) Wound Healing Assay; (B,C) Transwell assay; (D) CCk8 cytotoxicity test; (E) q-PCR.