{"title":"鉴定和验证骨肉瘤的新型 PANoptosis 相关基因签名作为预后模型","authors":"Xianglin Peng, Wanchun Wang","doi":"10.29271/jcpsp.2024.11.1315","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes.</p><p><strong>Study design: </strong>Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024.</p><p><strong>Methodology: </strong>Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves.</p><p><strong>Results: </strong>This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators.</p><p><strong>Conclusion: </strong>The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways.</p><p><strong>Key words: </strong>PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.</p>","PeriodicalId":94116,"journal":{"name":"Journal of the College of Physicians and Surgeons--Pakistan : JCPSP","volume":"34 11","pages":"1315-1321"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.\",\"authors\":\"Xianglin Peng, Wanchun Wang\",\"doi\":\"10.29271/jcpsp.2024.11.1315\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes.</p><p><strong>Study design: </strong>Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024.</p><p><strong>Methodology: </strong>Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves.</p><p><strong>Results: </strong>This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators.</p><p><strong>Conclusion: </strong>The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways.</p><p><strong>Key words: </strong>PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.</p>\",\"PeriodicalId\":94116,\"journal\":{\"name\":\"Journal of the College of Physicians and Surgeons--Pakistan : JCPSP\",\"volume\":\"34 11\",\"pages\":\"1315-1321\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the College of Physicians and Surgeons--Pakistan : JCPSP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29271/jcpsp.2024.11.1315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the College of Physicians and Surgeons--Pakistan : JCPSP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29271/jcpsp.2024.11.1315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.
Objective: To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes.
Study design: Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024.
Methodology: Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves.
Results: This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators.
Conclusion: The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways.
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