首页|Second Affiliated Hospital of Guilin Medical University Reports Findings in Mach ine Learning (Integrating machine learning and multi-omics analysis to develop a n asparagine metabolism immunity index for improving clinical outcome and drug . ..)
Second Affiliated Hospital of Guilin Medical University Reports Findings in Mach ine Learning (Integrating machine learning and multi-omics analysis to develop a n asparagine metabolism immunity index for improving clinical outcome and drug . ..)
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New research on Machine Learning is th e subject of a report. According to news reporting originating in Guangxi, Peopl e's Republic of China, by NewsRx journalists, research stated, "Lung adenocarcin oma (LUAD) is a malignancy affecting the respiratory system. Most patients are d iagnosed with advanced or metastatic lung cancer due to the fact that most of th eir clinical symptoms are insidious, resulting in a bleak prognosis." The news reporters obtained a quote from the research from the Second Affiliated Hospital of Guilin Medical University, "Given that abnormal reprogramming of as paragine metabolism (AM) has emerged as an emerging therapeutic target for anti- tumor therapy. However, the clinical significance of abnormal reprogramming of A M in LUAD patients is unclear. In this study, we collected 864 asparagine metabo lism-related genes (AMGs) and used a machine-learning computational framework to develop an asparagine metabolism immunity index (AMII) for LUAD patients. Throu gh the utilization of median AMII scores, LUAD patients were segregated into eit her a low-AMII group or a high-AMII group. We observed outstanding performance o f AMII in predicting survival prognosis in LUAD patients in the TCGA-LUAD cohort and in three externally independently validated GEO cohorts (GSE72094, GSE37745 , and GSE30219), and poorer prognosis for LUAD patients in the high-AMII group. The results of univariate and multivariate analyses showed that AMII can be used as an independent risk factor for LUAD patients. In addition, the results of C- index analysis and decision analysis showed that AMII-based nomograms had a robu st performance in terms of accuracy of prognostic prediction and net clinical be nefit in patients with LUAD. Excitingly, LUAD patients in the low-AMII group wer e more sensitive to commonly used chemotherapeutic drugs."
GuangxiPeople's Republic of ChinaAsiaAmino AcidsAsparagineBasic Amino AcidsCyborgsDiamino Amino AcidsDru gs and TherapiesEmerging TechnologiesImmunologyMachine LearningNeutral A mino AcidsRisk and Prevention
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