Comprehensive Analysis of Genomic Instability Derived lncRNAs Prognostic Signature and the Associated Tumor Microenvironment in Glioma.

Genomic instability is a prominent hallmark of cancer, and Long non-coding RNAs (LncRNAs) have been implicated in cancer biology. This study aimed to develop a prognostic model for glioma by focusing on genomic instability-associated lncRNAs (GILnc). A computational framework was implemented to identify GILnc, followed by immuno-scoring and immune cell infiltration analyses using the ESTIMATE and CIBERSORT algorithms. Putative drugs and downstream target proteins were predicted using the Connectivity Map (Cmap) and STITCH database. Cox regression analysis was employed for prognostic modeling, and a competitive endogenous RNA (ceRNA) network was constructed using the miRcode database, miRDB, miRTarBase, and TargetScan. A set of GILnc was successfully identified in glioma, showing a significant correlation with prognosis. Based on GILnc, a 16-gene prognostic model (GILncSig) was developed to assess risk scores for glioma patients. A clinical-accessible and high-performance nomogram was formulated by integrating independent clinical parameters. Furthermore, several putative drugs with potential anti-tumor effects in genomically unstable glioma were identified. This study contributes novel insights into GILnc in gliomas and presents the GILncSig prognostic model with robust independent predictive capabilities. These findings offer valuable implications for personalized treatment strategies in glioma. The putative drug predictions provide promising avenues for therapeutic intervention in glioma patients with genomic instability. Overall, our research advances the understanding of lncRNA involvement in cancer genomic instability and establishes a basis for future investigations in this field.