Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with lower-grade glioma
To comprehensively evaluate the immune cell-related lncRNA, 115 purified cell lines from 19 major immune cell types from 16 datasets were collected by searching for literature from 2007 to 2022. TIIClncRNAs were further found to be significantly upregulated in 115 immune cell lines and downregulated in 10 LGG cell lines.
101 combinations of 10 machine learning algorithms, including Lasso, Ridge, stepwise Cox, CoxBoost, random survival forest (RSF), elastic network (Enet), partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modeling (GBM), and survival support vector machine (survival-SVM) based on 10-folds cross-validation were further used to screen out the most valuable TIIClnc signature with the highest C-index.
10 machine learning algorithms, including Lasso, Ridge, stepwise Cox, CoxBoost, random survival forest (RSF), elastic network (Enet), partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modeling (GBM), and survival support vector machine (survival-SVM)
For a comprehensive comparison of the performance of the TIIClnc signature with other signatures, the published signatures over the past 10 years were systematically retrieved. Ultimately, 95 signatures (including mRNA and lncRNA signatures) were enrolled in this study. These 95 signatures were closely related to different biological features, including immunotherapy response, immune infiltration, autophagy, ferroptosis, pyroptosis, stemness, epithelial-mesenchymal transition, hypoxia, glycolysis, epigenetics, N6-methyladenosine, and aging. Notably, the TIIClnc signature displayed better performance regarding C-index in 4 datasets than almost all models.