FGF/FGFR-related lncRNAs based classification predicts prognosis and guilds therapy in gastric cancer

Background: Precision classification greatly contributes to the individual treatment of gastric cancer (GC). Fibroblast growth factor (FGF)/receptor (FGFR) and long non-coding RNAs (lncRNAs) served as biomarkers for prognosis and treatment response. Hence, identifying FGF/FGFR-related lncRNAs is of importance to precision medicine in GC. Methods: : STAD cohort from TCGA was used to identify FGF/FGFR-related lncRNAs. Cox analysis was employed to select lncRNAs with prognosis signature and establish risk score (RS) model. Than the molecular and tumor microenvironment characteristics of GC was estimated in RS groups. Finally, a comprehensive investigation was carried out to identify subclass-specific therapy. Results: : We identified some FGF/FGFR-related lncRNAs and established a four-lncRNAs ( FGF10-AS1 , MIR2052HG , POU6F2-AS2 and DIRC1 ) RS model. Low RS group displayed high tumor mutation burden and infiltration of immune cells, as well as more sensitivity to immunotherapy or chemotherapy. High RS group showed high infiltration of stromal cells and more oncogenic signatures. In addition, two promising targeted drugs (Panobinostat and Tivantinib) was identified as specific agents for high RS group. Conclusions: : Our study not only put forward a novel personalized prognostication classification model according to FGF/FGFR-related lncRNAs, but also provided new strategy for subclass-specific precision treatment in GC.