Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma

Abstract

Background. The aim of this study was to identify prognostic fatty acid metabolism lncRNAs and potential molecular targeting drugs in uveal melanoma through integrated bioinformatics analysis. Methods. In the present study, we obtained the expression matrix of 309 FAM-mRNAs and identified 225 FAM-lncRNAs by coexpression network analysis. We then performed univariate Cox analysis, LASSO regression analysis, and cross-validation and finally obtained an optimized UVM prognosis prediction model composed of four PFAM-lncRNAs (AC104129.1, SOS1-IT1, IDI2-AS1, and DLGAP1-AS2). Results. The survival curves showed that the survival time of UVM patients in the high-risk group was significantly lower than that in the low-risk group in the train cohort, test cohort, and all patients in the prognostic prediction model ( $P<0.05$ ). We further performed risk prognostic assessment, and the results showed that the risk scores of the high-risk group in the train cohort, test cohort, and all patients were significantly higher than those of the low-risk group ( $P<0.05$ ), patient survival decreased and the number of deaths increased with increasing risk scores, and AC104129.1, SOS1-IT1, and DLGAP1-AS2 were high-risk PFAM-lncRNAs, while IDI2-AS1 were low-risk PFAM-lncRNAs. Afterwards, we further verified the accuracy and the prognostic value of our model in predicting prognosis by PCA analysis and ROC curves. Conclusion. We identified 24 potential molecularly targeted drugs with significant sensitivity differences between high- and low-risk UVM patients, of which 13 may be potential targeted drugs for high-risk patients. Our findings have important implications for early prediction and early clinical intervention in high-risk UVM patients.