{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen D"],"funding":["Shanghai Shenkang Hospital Development Center","National Natural Science Foundation of China","Special Funds for the Basic Research and Development Program in the Central Non-profit Research Institutesof China"],"pagination":["125"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11044366"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["43(1)"],"pubmed_abstract":["Background
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.Methods
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 surviva"],"journal":["Journal of experimental & clinical cancer research : CR"],"pubmed_title":["Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication."],"pmcid":["PMC11044366"],"funding_grant_id":["SHDC2020CR5008","82073326","202303021212363","82273356"],"pubmed_authors":["Li H","Li J","Lu X","Liu P","Shen B","Chen D","Liu Y","Zang L","Zhai S","Fu D","Weng Y","Qi D","Lin J"],"additional_accession":[]},"is_claimable":false,"name":"Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication.","description":"Background
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.Methods
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 surviva","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2026-06-01T11:18:51.36Z","creation":"2026-04-08T11:47:43.518Z"},"accession":"S-EPMC11044366","cross_references":{"pubmed":["38664705"],"doi":["10.1186/s13046-024-03042-7"]}}