Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.

IF 3.2 4区 医学 Q3 IMMUNOLOGY
Min Zhou, Jie Pi, Yuzi Zhao
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引用次数: 0

Abstract

Summary: Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to identify molecular subtypes of OV through integrative multi-omics analysis and construct machine learning-based prognostic models for predicting the efficacy of immunotherapy. In here, the mutation, copy number variation, RNA sequencing expression profiles, and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Multi-omics data were stratified using the MOVICS package, identifying different molecular subtypes. Our analysis identified 2 molecular subtypes (CS1 and CS2) with significant survival differences. Transcriptional regulatory network analysis revealed differential activation of transcription factors such as FOXA1 and GATA3 in CS1, whereas AR and ESR2 were enriched in CS2. A robust prognostic signature comprising 5 key genes was developed through the integration of 10 machine learning algorithms, demonstrating high predictive power across data sets. Immune cell infiltration analysis revealed that anti-tumor immune cells were more abundant in low-risk groups, whereas pro-tumor immune cells predominated in high-risk groups. Furthermore, low-risk patients exhibited better immunotherapy responses and higher tumor mutational burden (TMB). In conclusion, our findings underscore the potential of multi-omics integration in unveiling novel OV subtypes and constructing predictive models that inform personalized treatment strategies. Future research should focus on validating these findings in larger cohorts to enhance OV management through targeted therapeutic approaches.

综合多组学分析揭示卵巢癌分子亚型并构建预后模型。
总结:卵巢癌(OV)仍然是最致命的妇科恶性肿瘤。本研究的目的是通过综合多组学分析鉴定OV的分子亚型,并构建基于机器学习的预测模型来预测免疫治疗的疗效。在这里,突变、拷贝数变异、RNA测序表达谱和临床信息从癌症基因组图谱(TCGA)和基因表达Omnibus (GEO)数据库中获得。使用MOVICS包对多组学数据进行分层,确定不同的分子亚型。我们的分析确定了两种分子亚型(CS1和CS2)具有显著的生存差异。转录调控网络分析显示,FOXA1和GATA3等转录因子在CS1中存在差异激活,而AR和ESR2在CS2中富集。通过整合10种机器学习算法,开发了包含5个关键基因的稳健预后签名,展示了跨数据集的高预测能力。免疫细胞浸润分析显示,低危组抗肿瘤免疫细胞较多,高危组促肿瘤免疫细胞较多。此外,低风险患者表现出更好的免疫治疗反应和更高的肿瘤突变负担(TMB)。总之,我们的研究结果强调了多组学整合在揭示新型OV亚型和构建预测模型方面的潜力,这些预测模型可为个性化治疗策略提供信息。未来的研究应侧重于在更大的队列中验证这些发现,以通过靶向治疗方法加强OV管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Immunotherapy
Journal of Immunotherapy 医学-免疫学
CiteScore
6.90
自引率
0.00%
发文量
79
审稿时长
6-12 weeks
期刊介绍: Journal of Immunotherapy features rapid publication of articles on immunomodulators, lymphokines, antibodies, cells, and cell products in cancer biology and therapy. Laboratory and preclinical studies, as well as investigative clinical reports, are presented. The journal emphasizes basic mechanisms and methods for the rapid transfer of technology from the laboratory to the clinic. JIT contains full-length articles, review articles, and short communications.
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