Prediction of Immunotherapy Response and Prognostic Outcomes for Patients With Ovarian Cancer Using PANoptosis-Related Genes

IF 3.7 2区医学 Q2 GENETICS & HEREDITY

Human Mutation Pub Date : 2025-10-16 DOI:10.1155/humu/7108361

Lei Zhang, Bo Yang, Huiting Xiao, Lu Sun, Wenting He, Ying Chen

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Abstract

Background

Ovarian cancer (OC) is a lethal malignancy often diagnosed at a late stage with frequent recurrence and immunotherapy resistance. PANoptosis is a novel programmed cell death regulating tumors and immunity. We constructed a prognostic model based on PANoptosis-related genes (PRGs) and evaluated its value for predicting immunotherapy response and survival in OC.

Methods

PRGs linked to OC prognosis were identified from public databases, followed by using the STRING database to develop a protein–protein interaction (PPI) network. The LASSO and multivariate Cox regression analyses were used to construct a risk model, and its predictive value was verified by survival analysis, receiver operator characteristic (ROC) curve, and nomogram. Next, we analyzed the immune microenvironment by combining CIBERSORT, MCP-counter, and ssGSEA algorithms and assessed the response of patients in different risk groups to immunotherapy using TIDE with immune phenotype score (IPS) methods. GSEA was performed to evaluate the activation status of biological pathways between patients in different risk groups. Finally, we verified the expression and potential biological functions of the key genes using quantitative reverse transcription-PCR (qRT-PCR), CCK-8, scratch, and transwell assays.

Results

A PANoptosis-related risk model for OC was constructed based on eight genes (PIK3CG, CAMK2A, CD38, NFKB1, PSMA4, PSMA8, PSMB1, and STAT4). The model could accurately evaluate the prognostic outcomes for OC patients, showing a high stability across different datasets. High-risk patients had lower immune cell infiltration, elevated TIDE, and reduced IPS, which suggested weaker immunotherapy responsiveness and therefore a worse prognosis. In addition, pathway analysis showed that the high-risk group was mainly enriched in tumor progression–related pathways. In vitro, PIK3CG, CAMK2A, NFKB1, PSMA4, and PSMB1 were upregulated in OC cell lines, and knockdown of PIK3CG notably suppressed the proliferative, migratory, and invasive capabilities of OC cells.

Conclusion

The PRG model established in this study may contribute to the assessment of immunotherapeutic response and prognosis for OC patients.

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利用panoptoosis相关基因预测卵巢癌患者的免疫治疗反应和预后

背景卵巢癌（OC）是一种晚期诊断的致命恶性肿瘤，经常复发和免疫治疗耐药。PANoptosis是一种调节肿瘤和免疫的新型程序性细胞死亡。我们构建了一个基于panoptoosis相关基因（PRGs）的预后模型，并评估了其预测OC免疫治疗反应和生存的价值。方法从公共数据库中识别与OC预后相关的PRGs，然后利用STRING数据库建立蛋白-蛋白相互作用（PPI）网络。采用LASSO和多变量Cox回归分析构建风险模型，并通过生存分析、ROC曲线和nomogram验证其预测价值。接下来，我们结合CIBERSORT、MCP-counter和ssGSEA算法分析了免疫微环境，并使用TIDE结合免疫表型评分（IPS）方法评估了不同风险组患者对免疫治疗的反应。采用GSEA评估不同风险组患者生物通路的激活状态。最后，我们利用定量逆转录pcr （qRT-PCR）、CCK-8、scratch和transwell实验验证了关键基因的表达和潜在的生物学功能。结果基于PIK3CG、CAMK2A、CD38、NFKB1、PSMA4、PSMA8、PSMB1、STAT4 8个基因构建了OC pantoposis相关风险模型。该模型可以准确评估OC患者的预后，在不同的数据集上表现出很高的稳定性。高危患者免疫细胞浸润较低，TIDE升高，IPS降低，提示免疫治疗反应性较弱，预后较差。此外，通路分析显示高危组主要富集肿瘤进展相关通路。在体外实验中，PIK3CG、CAMK2A、NFKB1、PSMA4和PSMB1在OC细胞系中表达上调，PIK3CG的下调显著抑制OC细胞的增殖、迁移和侵袭能力。结论本研究建立的PRG模型可用于评估卵巢癌患者的免疫治疗反应和预后。

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

Human Mutation 医学-遗传学

CiteScore

8.40

自引率

5.10%

发文量

190

审稿时长

2 months

期刊介绍： Human Mutation is a peer-reviewed journal that offers publication of original Research Articles, Methods, Mutation Updates, Reviews, Database Articles, Rapid Communications, and Letters on broad aspects of mutation research in humans. Reports of novel DNA variations and their phenotypic consequences, reports of SNPs demonstrated as valuable for genomic analysis, descriptions of new molecular detection methods, and novel approaches to clinical diagnosis are welcomed. Novel reports of gene organization at the genomic level, reported in the context of mutation investigation, may be considered. The journal provides a unique forum for the exchange of ideas, methods, and applications of interest to molecular, human, and medical geneticists in academic, industrial, and clinical research settings worldwide.