首页|Zhejiang University School of Medicine Reports Findings in Liver Cancer (Potenti al crosstalk between Naive CD4+ T cells and SPP1+ Macrophages is associated with clinical outcome and therapeutic response in hepatocellular carcinoma)

Zhejiang University School of Medicine Reports Findings in Liver Cancer (Potenti al crosstalk between Naive CD4+ T cells and SPP1+ Macrophages is associated with clinical outcome and therapeutic response in hepatocellular carcinoma)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Liver Cance r is the subject of a report. According to news originating from Zhejiang, Peopl e's Republic of China, by NewsRx correspondents, research stated, "The highly he terogeneity of the tumor microenvironment (TME) in hepatocellular carcinoma (HCC ) results in diverse clinical outcomes and therapeutic responses. This study aim ed to investigate potential intercellular crosstalk and its impact on clinical o utcomes and therapeutic responses." Our news journalists obtained a quote from the research from the Zhejiang Univer sity School of Medicine, "Single-cell RNA sequencing (scRNA-seq), spatial transc riptomics (ST) and bulk RNA sequencing (RNA-seq) datasets were integrated to com prehensively analyze the intercellular interactions within the TME. Multiplex im munohistochemistry was conducted to validate the intercellular interactions. A m achine learning-based integrative procedure was used in bulk RNA-seq datasets to generate a risk model to predict prognosis and therapeutic responses. Survival analyses based on the bulk RNA-seq datasets revealed the negative impact of the naive Cluster of Differentiation 4 (CD4) T cells and Secreted Phosphoprotein 1 ( SPP1) macrophages on prognosis. Furthermore, their intricate intercellular cross talk and spatial colocalization were also observed by scRNA-seq and ST analyses. Based on this crosstalk, a machine learning model, termed the naive CD4 T cell and SPP1 macrophage prognostic score (TMPS), was established in the bulk-RNA seq datasets for prognostic prediction. The TMPS achieved C-index values of 0.785, 0.715, 0.692 and 0.857, respectively, across 4 independent cohorts. A low TMPS w as associated with a significantly increased survival rates, improved response t o immunotherapy and reduced infiltration of immunosuppressive cells, such as. re gulatory T cells. Finally, 8 potential sensitive drugs and 6 potential targets w ere predicted for patients based on their TMPS. The crosstalk between naive CD4 T cells and SPP1 macrophages play a crucial role in the TME."

ZhejiangPeople's Republic of ChinaAsiaCancerCarcinomasConnective Tissue CellsCyborgsDrugs and TherapiesE merging TechnologiesGeneticsHealth and MedicineImmunologyLiver CancerM achine LearningMacrophagesMononuclear Phagocyte SystemMyeloid CellsOncol ogyPhagocytes

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.30)