"Mononuclear phagocyte system-related multi-omics features yield head and neck squamous cell carcinoma subtypes with distinct overall survival, drug, and immunotherapy responses"

作者全名："Zhang, Cong; Deng, Jielian; Li, Kangjie; Lai, Guichuan; Liu, Hui; Zhang, Yuan; Xie, Biao; Zhong, Xiaoni"

作者地址："[Zhang, Cong; Deng, Jielian; Li, Kangjie; Lai, Guichuan; Liu, Hui; Zhang, Yuan; Xie, Biao; Zhong, Xiaoni] Chongqing Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Yixue Rd, Chongqing 400016, Peoples R China"

通信作者："Xie, B; Zhong, XN (通讯作者)，Chongqing Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Yixue Rd, Chongqing 400016, Peoples R China."

来源：JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001149853000001

JCR分区：Q2

影响因子：3.6

年份：2024

卷号：150

期号：2

开始页：　

结束页：　

文献类型：Article

关键词：Head and neck squamous carcinoma; Mononuclear phagocyte system; Machine learning; Prognosis; Immunotherapy; Drug response

摘要："BackgroundRecent research reported that mononuclear phagocyte system (MPS) can contribute to immune defense but the classification of head and neck squamous cell carcinoma (HNSCC) patients based on MPS-related multi-omics features using machine learning lacked.MethodsIn this study, we obtain marker genes for MPS through differential analysis at the single-cell level and utilize ""similarity network fusion"" and ""MoCluster"" algorithms to cluster patients' multi-omics features. Subsequently, based on the corresponding clinical information, we investigate the prognosis, drugs, immunotherapy, and biological differences between the subtypes. A total of 848 patients have been included in this study, and the results obtained from the training set can be verified by two independent validation sets using ""the nearest template prediction"".ResultsWe identified two subtypes of HNSCC based on MPS-related multi-omics features, with CS2 exhibiting better predictive prognosis and drug response. CS2 represented better xenobiotic metabolism and higher levels of T and B cell infiltration, while the biological functions of CS1 were mainly enriched in coagulation function, extracellular matrix, and the JAK-STAT signaling pathway. Furthermore, we established a novel and stable classifier called ""getMPsub"" to classify HNSCC patients, demonstrating good consistency in the same training set. External validation sets classified by ""getMPsub"" also illustrated similar differences between the two subtypes.ConclusionsOur study identified two HNSCC subtypes by machine learning and explored their biological difference. Notably, we constructed a robust classifier that presented an excellent classifying prediction, providing new insight into the precision medicine of HNSCC."

基金机构：National Youth Science Foundation Project

基金资助正文："We are very grateful to the TCGA and GEO database and other public resources for providing us with a research foundation. In addition, we greatly appreciate the inspiration and insightful assistance provided by the R package ""MOVICS"" for our classifier ""getMPsub""."