首页|Jinzhou Medical University Reports Findings in Bladder Cancer (LIG1 is a novel m arker for bladder cancer prognosis: evidence based on experimental studies, mach ine learning and single-cell sequencing)
Jinzhou Medical University Reports Findings in Bladder Cancer (LIG1 is a novel m arker for bladder cancer prognosis: evidence based on experimental studies, mach ine learning and single-cell sequencing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Bladder Can cer is the subject of a report. According to news reporting from Liaoning, Peopl e’s Republic of China, by NewsRx journalists, research stated, “Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19 q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins.” The news correspondents obtained a quote from the research from Jinzhou Medical University, “Despite this, the precise involvement of LIG1 in BLCA remains elusi ve. This pioneering investigation delves into the uncharted territory of LIG1’s impact on BLCA. Our primary objective is to elucidate the intricate interplay be tween LIG1 and BLCA, alongside exploring its correlation with various clinicopat hological factors. We retrieved gene expression data of para-carcinoma tissues a nd bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data we re processed using the ‘Seurat’ package. Differential expression analysis was th en performed with the ‘Limma’ package. The construction of scale-free gene co-ex pression networks was achieved using the ‘WGCNA’ package. Subsequently, a Venn d iagram was utilized to extract genes from the positively correlated modules iden tified by WGCNA and intersect them with differentially expressed genes (DEGs), i solating the overlapping genes. The ‘STRINGdb’ package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted K EGG and GO enrichment analyses to uncover the regulatory mechanisms and biologic al functions associated with the hub genes. A machine-learning diagnostic model was established using the R package ‘mlr3verse.’ Mutation profiles between the L IG1.high and LIG1.low groups were visualized using the BEST website. Survival an alyses within the LIG1.high and LIG1.low groups were performed using the BEST we bsite and the GENT2 website. Finally, a series of functional experiments were ex ecuted to validate the functional role of LIG1 in BLCA. Our investigation reveal ed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correl ating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1 ’s involvement in critical function such as the DNA replication, cellular senesc ence, cell cycle and the p53 signalling pathway. Notably, the mutational landsca pe of BLCA varied significantly between LIG1 and LIG1 groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immun e regulation within the BLCA microenvironment, thereby impacting prognosis. Subs equent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples.”
LiaoningPeople’s Republic of ChinaAs iaBiomarkersBladder CancerCancerCyborgsDiagnostics and ScreeningEmer ging TechnologiesGeneticsHealth and MedicineMachine LearningOncologyRi sk and Prevention
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