Related Experiment Video
Updated: May 12, 2026
Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
Development and Validation of Prognostic Characteristics Associated With Chromatin Remodeling-Related Genes in
Guansheng Chen1, Wenjing Li1, Jiayi Guo1
1Department of Gynecology and Obstetrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
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.
