文献类型：期刊文献

英文题名：Cytokine expression patterns: A single-cell RNA sequencing and machine learning based roadmap for cancer classification

作者：Ren, Zhixiang[1];Ren, Yiming[1];Liu, Pengfei[5];Xu, Huan[2,3,4]

机构：[1]Peng Cheng Lab, Shenzhen 518055, Guangdong, Peoples R China;[2]Anhui Univ Sci & Technol, Sch Publ Hlth, Hefei 231131, Anhui, Peoples R China;[3]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China;[5]Sun Yatsen Univ, Sch Comp Sci & Engn, Guangzhou 528406, Guangdong, Peoples R China

年份：2024

卷号：109

外文期刊名：COMPUTATIONAL BIOLOGY AND CHEMISTRY

收录：;EI(收录号：20240715558021);WOS:【SCI-EXPANDED(收录号:WOS:001184761800001)】；

基金：This work was funded by National Natural Science Foundation of China [grant No. 42177417] . The project was supported by the Peng Cheng Laboratory and Peng Cheng Cloud -Brain (CPU: Intel Xeon Platinum 8268) . This work was also funded by Shanghai Frontiers Science. Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission) . The authors appreciate Yue Zhou from Peng Cheng Laboratory for the technical advice.

语种：英文

外文关键词：Tumor immune microenvironment; Cancer classification; Machine learning; Single-cell RNA sequencing

摘要：Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification. The dataset, including 39 cancers, is constructed by integrating 684 tumor scRNA-seq samples from multiple public repositories. After screening and processing, the dataset retains only the expression data of immune cells. With a machine learning classification model, unique cytokine expression patterns are identified for various cancer categories and pioneering applied to cancer classification with an accuracy rate of 78.01%. Our method will not only boost the understanding of cancer-type-specific immune modulations in TIME but also serve as a crucial reference for future diagnostic and therapeutic research in cancer immunity.