首页|Affiliated People's Hospital of Ningbo University Reports Findings in Pancreatic Cancer (A Machine Learning Method for a Blood Diagnostic Model of Pancreatic Ca ncer Based on microRNA Signatures)
Affiliated People's Hospital of Ningbo University Reports Findings in Pancreatic Cancer (A Machine Learning Method for a Blood Diagnostic Model of Pancreatic Ca ncer Based on microRNA Signatures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Oncology-Pancreatic Cancer is t he subject of a report. According to news reporting from Zhejiang, People's Repu blic of China, by NewsRx journalists, research stated, "This study aimed to cons truct a blood diagnostic model for pancreatic cancer (PC) using miRNA signatures by a combination of machine learning and biological experimental verification. Gene expression profiles of patients with PC and transcriptome normalization dat a were obtained from the Gene Expression Omnibus (GEO) database." The news correspondents obtained a quote from the research from the Affiliated P eople's Hospital of Ningbo University, "Using random forest algorithm, lasso reg ression algorithm, and multivariate cox regression analyses, the classifier of d ifferentially expressed miRNAs was identified based on algorithms and functional properties. Next, the ROC curve analysis was used to evaluate the predictive pe rformance of the diagnostic model. Finally, we analyzed the expression of two sp ecific miRNAs in Capan-1, PANC-1, and MIA PaCa-2 pancreatic cells using qRT-PCR. Integrated microarray analysis revealed that 33 common miRNAs exhibited signifi cant differences in expression profiles between tumor and normal groups (P value <0.05 and |logFC| > 0.3). Pathway analysis showed that differentially expressed miRNAs were related to P00059 p53 pathway, hsa04062 chemokine signaling pathway, and cancer-related pathways incl uding PC. In ENCORI database, the hsa-miR-4486 and hsa-miR-6075 were identified by random forest algorithm and lasso regression algorithm and introduced as majo r miRNA markers in PC diagnosis. Further, the receiver operating characteristic curve analysis achieved the area under curve score > 80%, showing good sensitivity and specificity of the two-miRNA signature model in P C diagnosis. Additionally, hsa-miR-4486 and hsa-miR-6075 genes expressions in th ree pancreatic cells were all up-regulated by qRT-PCR."
ZhejiangPeople's Republic of ChinaAs iaAlgorithmsBiomarkersCancerCyborgsDiagnostics and ScreeningEmerging TechnologiesGastroenterologyHealth and MedicineMachine LearningOncologyPancreasPancreatic CancerPancreatic Neoplasms
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