Bicheng Ye, Hongsheng Ji, Meng Zhu, Anbang Wang, Jingsong Tang, Yong Liang, Qing Zhang
{"title":"单细胞测序揭示新型增殖细胞类型:肾细胞癌预后和治疗反应的关键因素。","authors":"Bicheng Ye, Hongsheng Ji, Meng Zhu, Anbang Wang, Jingsong Tang, Yong Liang, Qing Zhang","doi":"10.1007/s10238-024-01424-x","DOIUrl":null,"url":null,"abstract":"<p><p>Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as \"Prol,\" was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.</p>","PeriodicalId":10337,"journal":{"name":"Clinical and Experimental Medicine","volume":"24 1","pages":"167"},"PeriodicalIF":3.2000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11272756/pdf/","citationCount":"0","resultStr":"{\"title\":\"Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response.\",\"authors\":\"Bicheng Ye, Hongsheng Ji, Meng Zhu, Anbang Wang, Jingsong Tang, Yong Liang, Qing Zhang\",\"doi\":\"10.1007/s10238-024-01424-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as \\\"Prol,\\\" was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.</p>\",\"PeriodicalId\":10337,\"journal\":{\"name\":\"Clinical and Experimental Medicine\",\"volume\":\"24 1\",\"pages\":\"167\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11272756/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical and Experimental Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10238-024-01424-x\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical and Experimental Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10238-024-01424-x","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response.
Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as "Prol," was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.
期刊介绍:
Clinical and Experimental Medicine (CEM) is a multidisciplinary journal that aims to be a forum of scientific excellence and information exchange in relation to the basic and clinical features of the following fields: hematology, onco-hematology, oncology, virology, immunology, and rheumatology. The journal publishes reviews and editorials, experimental and preclinical studies, translational research, prospectively designed clinical trials, and epidemiological studies. Papers containing new clinical or experimental data that are likely to contribute to changes in clinical practice or the way in which a disease is thought about will be given priority due to their immediate importance. Case reports will be accepted on an exceptional basis only, and their submission is discouraged. The major criteria for publication are clarity, scientific soundness, and advances in knowledge. In compliance with the overwhelmingly prevailing request by the international scientific community, and with respect for eco-compatibility issues, CEM is now published exclusively online.