Lei Zhang, Bo Yang, Huiting Xiao, Lu Sun, Wenting He, Ying Chen
{"title":"利用panoptoosis相关基因预测卵巢癌患者的免疫治疗反应和预后","authors":"Lei Zhang, Bo Yang, Huiting Xiao, Lu Sun, Wenting He, Ying Chen","doi":"10.1155/humu/7108361","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A PANoptosis-related risk model for OC was constructed based on eight genes (<i>PIK3CG</i>, <i>CAMK2A</i>, <i>CD38</i>, <i>NFKB1</i>, <i>PSMA4</i>, <i>PSMA8</i>, <i>PSMB1</i>, and <i>STAT4</i>). 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, <i>PIK3CG</i>, <i>CAMK2A</i>, <i>NFKB1</i>, <i>PSMA4</i>, and <i>PSMB1</i> were upregulated in OC cell lines, and knockdown of <i>PIK3CG</i> notably suppressed the proliferative, migratory, and invasive capabilities of OC cells.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The PRG model established in this study may contribute to the assessment of immunotherapeutic response and prognosis for OC patients.</p>\n </section>\n </div>","PeriodicalId":13061,"journal":{"name":"Human Mutation","volume":"2025 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/humu/7108361","citationCount":"0","resultStr":"{\"title\":\"Prediction of Immunotherapy Response and Prognostic Outcomes for Patients With Ovarian Cancer Using PANoptosis-Related Genes\",\"authors\":\"Lei Zhang, Bo Yang, Huiting Xiao, Lu Sun, Wenting He, Ying Chen\",\"doi\":\"10.1155/humu/7108361\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>A PANoptosis-related risk model for OC was constructed based on eight genes (<i>PIK3CG</i>, <i>CAMK2A</i>, <i>CD38</i>, <i>NFKB1</i>, <i>PSMA4</i>, <i>PSMA8</i>, <i>PSMB1</i>, and <i>STAT4</i>). 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. 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Prediction of Immunotherapy Response and Prognostic Outcomes for Patients With Ovarian Cancer Using PANoptosis-Related Genes
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.
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.
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