首页|First Affiliated Hospital of Guangxi Medical University Reports Findings in Head and Neck Cancer (Machine learning-based identification of a consensus immune-de rived gene signature to improve head and neck squamous cell carcinoma therapy an d ...)
First Affiliated Hospital of Guangxi Medical University Reports Findings in Head and Neck Cancer (Machine learning-based identification of a consensus immune-de rived gene signature to improve head and neck squamous cell carcinoma therapy an d ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Head and Ne ck Cancer is the subject of a report. According to news originating from Guangxi , People’s Republic of China, by NewsRx correspondents, research stated, “Head a nd neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor-node-metastasis staging system does not satisfy the requirements for screening treatment-sensit ive patients.” Our news journalists obtained a quote from the research from the First Affiliate d Hospital of Guangxi Medical University, “Thus, an ideal biomarker leading to p recise screening and treatment of HNSCC is urgently needed. Ten machine learning algorithms-Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partia l least squares regression for Cox (plsRcox), random survival forest (RSF), gene ralized boosted regression modelling (GBM), supervised principal components (Sup erPC), and survival support vector machine (survival-SVM)-as well as 85 algorith m combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS). Based on the expression profiles of three cohorts compr ising 719 patients with HNSCC, we identified 236 consensus prognostic genes, whi ch were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several c ore clinical features and 51 previously reported signatures, enhanced the predic tive capacity of the CIDGS to a level which was markedly superior to that of oth er signatures. Notably, patients with low CIDGS displayed fewer genomic alterati ons and higher immune cell infiltrate levels, as well as increased sensitivity t o immunotherapy and other therapeutic agents, in addition to receiving better pr ognoses. The survival times of HNSCC patients with high CIDGS, in particular, we re shorter. Moreover, CIDGS enabled accurate stratification of the response to i mmunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showe d potential as therapeutic agents in HNSCC patients with high CIDGS.”
GuangxiPeople’s Republic of ChinaAsi aCancerCarcinomasCyborgsDrugs and TherapiesEmerging TechnologiesGene ticsHead and Neck CancerHealth and MedicineImmunotherapyMachine LearningOncologySquamous Cell CarcinomaTherapy
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