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

Abstract excerpt

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 clustering method was used to classify patients into two subtypes and analyze the survival differences, somatic...

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