Integrating bioinformatics and machine learning to uncover lncRNA LINC00269 as a key regulator in Parkinson's disease via pyroptosis pathways - LiLi Ma; Yue Liu; Yajing Ren; Na Mi; Jing Fang; Rui Bao; Xiuzhi Xu; Hongjia Zhang; Ying Tang (European Journal of Medical Research)
Abstract Background Pyroptosis, a specific type of programmed cell death, which has become a significant factor to Parkinson's disease (PD). Concurrently, long non-coding RNAs (lncRNAs) have garnered attention for their regulatory roles in neurodegenerative disorders. This study was designed to ascertain the key lncRNAs in pyroptosis pathways of PD and elucidate their regulatory mechanisms. Methods Employing a combination of bioinformatics and machine learning, we analyzed PD data sets GSE133347 and GSE110716. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) recognized different lncRNAs. Through various algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Weighted Gene Co-expression Network Analysis (WGCNA), we recognized LINC01606 and LINC00269, which are key factors during the emergence and development of PD. Furthermore, experimental validation was conducted in PD mouse models to confirm these bioinformatics findings. Results The analysis showed that there were a large number of apoptosis-related gene expression changes in Parkinson's syndrome, for example, CASP1 and GSDME were up-regulated, and CASP9 and AIM2 were down-regulated. Among the lncRNAs, LINC01606 and LINC00269 were identified as potential modulators of pyroptosis. Notably, LINC00269 was observed to be significantly downregulated in the brain tissues of a PD mouse model, supporting its involvement in PD. The study also highlighted potential interactions of these lncRNAs with genes like ONECUT2, PRLR, CTNNA3, and LRP2. Conclusions This study identifies LINC00269 as a potential contributor to pyroptosis pathways in PD. While further investigation is required to fully elucidate its role, these findings provide new insights into PD pathogenesis and suggest potential avenues for future research on diagnostic and therapeutic targets. The study underscores the importance of integrating bioinformatics with experimental validation in neurodegenerative disease research.