biostudies-literatureChen DShanghai Shenkang Hospital Development CenterNational Natural Science Foundation of ChinaSpecial Funds for the Basic Research and Development Program in the Central Non-profit Research Institutesof China125https://www.ebi.ac.uk/biostudies/studies/S-EPMC11044366biostudies-literatureUnknown43(1)<h4>Background</h4>Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.<h4>Methods</h4>Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survivaJournal of experimental & clinical cancer research : CRPan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication.PMC11044366SHDC2020CR50088207332620230302121236382273356Li HLi JLu XLiu PShen BChen DLiu YZang LZhai SFu DWeng YQi DLin JfalsePan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication.<h4>Background</h4>Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.<h4>Methods</h4>Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and surviva2024-01-01T00:00:00Z2024 Apr2026-06-01T11:18:51.36Z2026-04-08T11:47:43.518ZS-EPMC110443663866470510.1186/s13046-024-03042-7