首页|Fujian Medical University Reports Findings in Lung Cancer (Identification of a n ovel ADCC-related gene signature for predicting the prognosis and therapy respon se in lung adenocarcinoma)

Fujian Medical University Reports Findings in Lung Cancer (Identification of a n ovel ADCC-related gene signature for predicting the prognosis and therapy respon se in lung adenocarcinoma)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology-Lung Cancer is the subject of a report. According to news originating from Fuzhou, People's Republic of China, by NewsRx correspondents, research stated, "Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematicall y compiled ADCC-associated genes to create prognostic signatures. In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with vario us cancer types from diverse cohorts were included." Our news journalists obtained a quote from the research from Fujian Medical Univ ersity, "R package ConsensusClusterPlus was utilized to classify patients into d ifferent subtypes. A number of machinelearning algorithms were used to construc t the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IO BR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools w ere used to analyze the CNV and SNV data. The Oncopredict package was used to pr edict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to p rocess single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associat ed cells. Through unsupervised clustering, two distinct subtypes of LUAD were id entified, each exhibiting distinct clinical characteristics. The ADCCRS, consist ed of 16 genes, was constructed by integrated machine-learning methods. The prog nostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in p redicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS posi tively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME m odulators. Cells representing high-ADCCRS phenotype exhibited more malignant fea tures. LUAD patients with high ADCCRS levels exhibited sensitivity to chemothera py and targeted therapy, while displaying resistance to immunotherapy. In pan-ca ncer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients."

FuzhouPeople's Republic of ChinaAsiaAdenocarcinomaCancerCyborgsDrugs and TherapiesEmerging TechnologiesG eneticsHealth and MedicineIm-munotherapyLung CancerLung Diseases and Cond itionsMachine LearningOncologyRisk and PreventionTherapy

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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年,卷(期):2024.(Apr.2)