Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma.

ABSTRACT: Rationale: Accumulating evidence demonstrated that long noncoding RNAs (lncRNAs) involved in the regulation of the immune system and displayed a cell-type-specific pattern in immune cell subsets. Given the vital role of tumor-infiltrating lymphocytes in effective immunotherapy, we explored the tumor-infiltrating immune cell-associated lncRNA (TIIClncRNA) in low-grade glioma (LGG), which has never been uncovered yet. Methods: This study utilized a novel computational framework and 10 machine learning algorithms (101 combinations) to screen out TIIClncRNAs by integratively analyzing the sequencing data of purified immune cells, LGG cell lines, and bulk LGG tissues. Results: The established TIIClnc signature based on the 16 most potent TIIClncRNAs could predict outcomes i

SUBMITTER: Zhang N 

PROVIDER: S-EPMC9373811 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature