首页|Khon Kaen University Reports Findings in Bioinformatics (Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma us ing bioinformatics and machine learning)
Khon Kaen University Reports Findings in Bioinformatics (Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma us ing bioinformatics and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biotechnology-Bioinf ormatics is the subject of a report. According to news reporting out of Khon Kae n, Thailand, by NewsRx editors, research stated, "Renal cell carcinoma (RCC), ac counting for 90% of all kidney cancer, is categorized into clear c ell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the cu rrent NCCN Guidelines. Thus, the classification will be associated with therapeu tic implications." Financial support for this research came from Khon Kaen University. Our news journalists obtained a quote from the research from Khon Kaen Universit y, "This study aims to identify novel biomarkers to differentiate ccRCC from non -ccRCC using bioinformatics and machine learning. The gene expression profiles o f ccRCC and non-ccRCC subtypes (including papillary RCC (pRCC) and chromophobe R CC (chRCC)), were obtained from TCGA. Differential expression genes (DEGs) were identified, and specific DEGs for ccRCC and non-ccRCC were explored using a Venn diagram. Gene Ontology and pathway enrichment analysis were performed using DAV ID. The top ten expressed genes in ccRCC were then selected for machine learning analysis. Feature selection was operated to identify a minimum highly effective gene set for constructing a predictive model. The expression of best-performing gene set was validated on tissue samples from RCC patients using immunohistoche mistry techniques. Subsequently, machine learning models for diagnosing RCC were developed using H-scores. There were 910, 415, and 835 genes significantly spec ific for DEGs in ccRCC, pRCC, and chRCC, respectively. Specific DEGs in ccRCC en riched in PD-1 signaling, immune system, and cytokine signaling in the immune sy stem, whereas TCA cycle and respiratory, signaling by insulin receptor, and meta bolism were enriched in chRCC. Feature selection based on Decision Tree Classifi er revealed that the model with two genes, including NDUFA4L2 and DAT, had an ac curacy of 98.89%. Supervised classification models based on H-score of NDUFA4L2, and DAT revealed that Decision Tree models showed the best perform ance with 82 % accuracy and 0.9 AUC. NDUFA4L2 expression was associ ated with lymphovascular invasion, pathologic stage and pT stage in ccRCC."
Khon KaenThailandAsiaBioinformatic sBiomarkersBiotechnologyCancerCarcinomasCyborgsDiagnostics and Scree ningEmerging TechnologiesGeneticsHealth and MedicineInformation Technolo gyKidneyMachine LearningNephrologyOncology
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