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

IF 3.1 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-02-11 DOI:10.1002/cam4.70634

Guansheng Chen, Wenjing Li, Jiayi Guo, Lingyu Liu, Yongjun Wang

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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.

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卵巢癌中与染色质重塑相关基因相关的预后特征的发展和验证

卵巢癌（OC）是妇科常见的恶性肿瘤，在妇科肿瘤中发病率和死亡率均居第三位。染色质重塑在不同的基因组位点完成特定的染色质凝聚，并在与癌症发展相关的各种过程相关的表观遗传调控中发挥重要作用。方法从Cancer Genome Atlas （TCGA）和Genotype-Tissue Expression （GTEx）数据库中筛选OC与对照样本的差异表达基因（DEGs），结合GeneCards数据库中获得的染色质重塑相关基因（CRRGs），鉴定差异表达的CRRGs （DECRRGs）。对DECRRGs进行富集分析和蛋白相互作用（PPI）网络。使用单变量Cox和最小绝对收缩和选择算子（Lasso）分析筛选OC的预后基因。建立基于预后基因的风险模型，采用Kaplan-Meier （KM）曲线分析不同风险组OC患者的生存率。最后，通过定量实时聚合酶链反应（qRT-PCR）和western blotting验证预后基因的表达水平。结果共获得7个与OC患者进展相关的潜在预后基因，包括ARID1B、ATRX、CHRAC1、HDAC1、INO80、MBD2和SS18。根据预后基因表达水平将OC患者分为高危组和低危组。生存分析表明，高危组患者死亡率较高，该预测模型可作为OC的独立预测指标。免疫相关分析显示，低风险患者更有可能从免疫治疗中获益。在本研究中，我们鉴定了7个预后基因，包括ARID1B、ATRX、CHRAC1、HDAC1、INO80、MBD2和SS18。总的来说，我们的发现为进一步理解OC发病和进展的潜在分子机制提供了基础。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.