Drug repositioning and biomarkers in low-grade glioma via bioinformatics approach. (2019) - Islam, Tania; Rahman, Md Rezanur; Shuvo, Md Abu Hena; Shahjaman, Md; Islam, Md Rafiqul; Karim, Md Rezaul
Abstract: Aim: Diffuse low-grade glioma (LGG) patients have a tendency to develop glioblastoma multiforme within 5–10 years, with only 15 months life expectancy. The present study aimed to decode a diagnostic and prognostic gene signature in LGG and identify repositioned potential candidate biomarker therapeutics. Methods: We have utilized a publicly available transcriptomics dataset to identify differentially expressed genes (DEGs) using Limma. The functional annotation of the DEGs was performed to identify Gene Ontology and pathways. The protein-protein interaction (PPI) network of the (DEGs) encoding proteins was analyzed utilizing STRING via NetworkAnalyst. The ROC analysis and survival analysis of the hub gene was also performed in R and SurvExpress-a biomarker validation tool. We have utilized the L1000CDS2 to identify candidate small molecules or drugs to target the diagnostic and prognostic biomarkers. Results: We have identified 238 genes as DEGs in LGG compared to non-LGG samples involved in the biological process of signaling and metabolic systems. We then identified molecular pathways enriched by the DEGs including the VEGF signaling pathway, Fc epsilon RI signaling pathway, and p38 signaling mediated by MAPKAP kinases. We identified hub proteins (MAPK11, MRPL1, RAF1, CDC20, NOP2, TUBB4B, CTNND1, PWP2, FGR, BMP2) from PPI analysis. The ROC and survival analysis of the hub genes revealed that these biomarkers can be utilized as diagnostic and prognostic biomarker signatures in LGG. Several novel candidate drugs were repositioned; among them penfluridol, perheiline maleate is used for purposes other than cancer. Other repositioned drugs: AG14361, NCGC00185094-01, BRD-K61717269, C8273, BRD-A28970875, KIN001-127, BYL719, JW-7-24-1, HG-6-64-01, radicicol, MK-2206, and WZ-4-14 have not been described in the literature for treatment purposes. Conclusions: Our analysis of LGG datasets identified 10 hub genes as diagnostic and prognostic signatures, and the candidate repositioned drugs linked to these biomarkers. These biomolecules and repositioned drugs may be utilized as biomarkers for diagnosis and prognosis, and for development of a treatment strategy for LGG.