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Journal of Zhejiang University SCIENCE B
ISSN 1673-1581(Print), 1862-1783(Online), Monthly
2025 Vol.26 No.12 P.1192-1215
Constructing a PANoptosis-based prognostic signature to evaluate the immune landscape and therapeutic response in clear cell renal cell carcinoma
Abstract: ObjectiveTo identify pyroptosis, apoptosis, and necroptosis (PANoptosis)-related genes (PRGs) in clear cell renal cell carcinoma (ccRCC) for patient stratification and prognosis prediction.
MethodsWe used differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify ccRCC-specific PRGs. A prognostic model, the PANoptosis-index (PANI), was constructed using least absolute shrinkage and selection operator (LASSO) and Cox regression. The PANI model, comprising PRGs, was validated through single-cell RNA-sequencing (scRNA-seq), immunohistochemistry, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Patient cohorts were categorized into high- and low-PANI groups, and the model’s performance was appraised using various metrics. External validation was performed with the E-MTAB-1980 dataset. Functional and gene set enrichment analyses distinguished biological differences between groups. Mutational landscapes and tumor immune microenvironments were compared. Sensitivity to immunotherapy and antineoplastic drugs was also predicted using PANI. The effects of Z-DNA-binding protein 1 (ZBP1) on cell proliferation and migration were assessed by cell counting kit-8 (CCK-8) and Transwell assays.
ResultsWe identified five PRGs (ZBP1, tumor necrosis factor superfamily protein 14 (TNFSF14), cyclin-dependent kinase inhibitor 3 (CDKN3), parathyroid hormone-like hormone (PTHLH), and heme-oxygenase 1 (HMOX1)) constituting PANI, independently associated with ccRCC patient prognosis. The PANI-based nomogram, integrated with clinical factors, demonstrated high predictive accuracy for prognosis. High-PANI patients exhibited distinct co-mutation patterns in ccRCC driver genes and lower survival probabilities, with an enriched immune-related functional profile, indicating an activated immune environment. These patients also showed increased sensitivity to immunotherapy and antineoplastic drugs. The knockdown of ZBP1, a key PRG in the PANI, significantly reduced ccRCC cell proliferation and migration.
ConclusionsPANI provides precise prognosis and immunotherapy response predictions for ccRCC patients, facilitating individualized treatment strategies.
Key words: Pyroptosis, apoptosis, and necroptosis (PANoptosis); Clear cell renal cell carcinoma (ccRCC); Prognosis; Tumor immune microenvironment; Immunotherapy response
1浙江大学医学院附属第四医院泌尿外科,浙江大学国际医学院,浙江大学国际健康医学研究院,中国义乌市,322000
2浙江省肿瘤医院泌尿外科,中国科学院杭州医学研究所,中国杭州市,310022
3哈尔滨医科大学附属第四医院泌尿外科,中国哈尔滨市,150081
4浙江大学医学院附属第四医院放射科,浙江大学国际医学院,浙江大学国际健康医学研究院,中国义乌市,322000
5贵州省人民医院泌尿外科,中国贵阳市,550002
6西湖大学医学院附属杭州市第一人民医院泌尿外科,中国杭州市,310006
7浙江中医药大学第四临床医学院,中国杭州市,310006
摘要:目的:识别与肾透明细胞癌(ccRCC)中泛凋亡(PANoptosis)相关的基因(PRGs),以用于患者分层和预后评估。方法:通过差异表达分析与加权基因共表达网络分析(WGCNA)筛选ccRCC特异的PRGs,并利用LASSO和Cox回归构建预后模型,即PANoptosis指数(PANI)。该模型在单细胞RNA测序(scRNA-seq)、免疫组化和实时荧光定量逆转录聚合酶链反应(RT-qPCR)中得到验证。根据PANI将患者队列分为高低两组,使用多种指标评估该模型性能,并在E-MTAB-1980数据集中进行外部验证。通过功能和基因集富集分析揭示两组间的生物学差异,并比较了突变景观和肿瘤免疫微环境。进一步基于PANI预测患者对免疫疗法和抗肿瘤药物的敏感性。此外,通过CCK-8和Transwell实验评估ZBP1对ccRCC细胞增殖和迁移能力的影响。结果:筛选出ZBP1、TNFSF14、CDKN3、PTHLH和HMOX1共5个PRGs构建PANI模型,该模型与ccRCC患者预后独立相关。通过与临床因素的结合,基于PANI的Nomogram显示出高度的预测准确性。高PANI患者在ccRCC驱动基因中表现出独特的共突变模式和较低的生存概率,且免疫相关功能特征丰富,提示其肿瘤免疫微环境处于激活状态;该组患者对免疫疗法和抗肿瘤药物的敏感性也有所增加。敲低ZBP1能显著抑制ccRCC细胞的增殖和迁移能力。结论:PANI可作为ccRCC患者预后和免疫疗法反应预测的有效工具,有助于制定个体化治疗策略。
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DOI:
10.1631/jzus.B2400132
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On-line Access:
2025-12-31
Received:
2024-03-07
Revision Accepted:
2024-11-29
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2025-12-31