Machine learning-identified stemness features and constructed stemness-related subtype with prognosis, chemotherapy, and immunotherapy responses for non-small cell lung cancer patients

• Published in: Stem cell research & therapy Vol. 14; no. 1; pp. 1 - 15
• Main Authors: Liu, Mingshan, Zhou, Ruihao, Zou, Wei, Yang, Zhuofan, Li, Quanjin, Chen, Zhiguo, jiang, Lei, Zhang, Jingtao
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 07.09.2023; BioMed Central; BMC
• Subjects: Algorithms; Analysis; B cells; Cancer; Cancer stem cell; Cancer therapies; Chemotherapy; Clustering; Consensus clustering; Copy number; Datasets; Genes; Genetic aspects; Genomes; Genomics; Immune checkpoint; Immunotherapy; Learning algorithms; Lung cancer; Lung cancer, Non-small cell; Lung cancer, Small cell; Machine learning; Medical prognosis; Microenvironments; Mutation; Non-small cell lung cancer; Non-small cell lung carcinoma; Patients; Radiation therapy; Regression analysis; Small cell lung carcinoma; Stem cells; Stemness index; Thoracic surgery; Taiwan

Aim This study aimed to explore a novel subtype classification method based on the stemness characteristics of patients with non-small cell lung cancer (NSCLC). Methods Based on the Cancer Genome Atlas database to calculate the stemness index (mRNAsi) of NSCLC patients, an unsupervised consensus clu...