Development and Validation of Prognostic Characteristics Associated With Chromatin Remodeling-Related Genes in Ovarian Cancer

Abstract

Background: Ovarian cancer (OC) is a prevalent malignant tumor in the field of gynecology, exhibiting the third highest incidence rate and the highest mortality rate among gynecological tumors. Chromatin remodeling accomplishes specific chromatin condensation at distinct genomic loci and plays an essential role in epigenetic regulation associated with various processes related to cancer development.

Methods: Differentially expressed genes (DEGs) between OC and control samples were screened from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, combined with chromatin remodeling-related genes (CRRGs) obtained from the GeneCards database to identify differentially expressed CRRGs (DECRRGs). Enrichment analysis and protein-protein interaction (PPI) network were performed on the DECRRGs. Prognostic genes of OC were screened using univariate Cox and least absolute shrinkage and selection operator (Lasso) analyses. A risk model based on prognostic genes was developed, and the survival probability of OC patients in different risk groups was analyzed by Kaplan-Meier (KM) curve. Finally, the expression levels of prognostic genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting.

Results: In total, 7 potential prognostic genes associated with the progression of OC patients were obtained, including ARID1B, ATRX, CHRAC1, HDAC1, INO80, MBD2, and SS18. Based on the expression level of prognostic genes, OC patients were divided into high-risk group and low-risk group. Survival analysis indicated that patients classified into the high-risk group had higher mortality rates, which enables this prediction model to be utilized as an independent predictor of OC. Immunocorrelation analysis showed that low-risk patients were more likely to benefit from immunotherapy.

Conclusion: In this study, we have identified 7 prognostic genes, including ARID1B, ATRX, CHRAC1, HDAC1, INO80, MBD2, and SS18. Overall, our findings provided a foundation for further comprehension of the potential molecular mechanisms underlying OC pathogenesis and progression.

Keywords: chromatin remodeling; immunocorrelation; ovarian cancer; prognostic genes.

FIGURE 1

Identification of DEGs (A) Volcano plot of DEGs in OC; (B) heatmap of DEGs in OC; (C) heatmap of 57 DECRRGs; (D) venn map of DEGs associated with chromatin remodeling; (E) chromosome circle diagram of DECRRGs. DECRRGs, differentially expressed chromatin remodeling‐related genes; DEGs, differentially expressed genes; OC: ovarian cancer.

FIGURE 2

Enrichment analysis of DECRRGs (A) KEGG enrichment analysis; (B) GO enrichment analysis; (C) PPI network of DECRRGs. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein–protein interaction.

FIGURE 3

Screening of prognostic genes (A) univariate Cox regression analysis of forest map; (B, C) Regression coefficient path graph and verification curve of LASSO logistic regression algorithm; (D, E) Prognostic gene interaction network diagram. LASSO, least absolute shrinkage and selection operator.

FIGURE 4

KM curve of prognostic genes. Red curve represented high‐risk group and blue represented low‐risk group. KM, Kaplan–Meier.

FIGURE 5

Construction of risk model (A) Clinically relevant heatmap; (B) Triptych of risk scores in the training set and validation set; (C–E) KM curve, UMAP, tSNE in the training set and verification set.

FIGURE 6

Construction of a nomogram (A) Univariate cox forest map of clinical features; (B) Multivariate cox forest map of clinical features; (C) Clinical prognosis histogram; (D) Calibration curves of the nomogram to predict 1, 3, and 5‐year survival rates.

FIGURE 7

Difference enrichment analysis of high and low risk groups (A) Volcano plot of DEGs in high and low risk groups; (B) Heatmap of DEGs in high and low risk groups; (C) GSEA‐hallmark enrichment analysis; (D) GSVA analyzed the differential KEGG pathway. GSEA, gene set enrichment analysis; GSVA, gene set variation analysis.

FIGURE 8

Analysis of immune infiltration (A) Histograms of cell infiltration differences in high and low‐risk groups; (B) Box plots of cell infiltration differences among high and low‐risk groups; (C) Heat map of immune cell infiltration and prognostic genes; (D) Lollipop chart in risk score with cell infiltration.

FIGURE 9

Immunotherapy response in high‐and low‐risk groups and IC₅₀ analysis of drug sensitivity. (A) difference analysis; (B) ESTIMATE score difference analysis; (C) immune checkpoint analysis; (D) IC₅₀ analysis of drug sensitivity. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. ns, not significant; TIDE, tumor immune dysfunction and exclusion.

FIGURE 10

The results of qRT‐PCR and Western blotting. (A) The results of qRT‐PCR ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001. (B) The results of Western blotting in CHRAC1. The molecular weight of GAPDH is 36 kDa, and the molecular weight of CHARC1 is 15 kDa. ****p < 0.0001. The experiment was conducted with biological replicates (N = 3). qRT‐PCR, quantitative real‐time polymerase chain reaction.