AIMC Topic: Neoplastic Stem Cells
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Leukaemia Stem Cells and Their Normal Stem Cell Counterparts Are Morphologically Distinguishable by Artificial Intelligence.
Journal of cellular and molecular medicine
May 1, 2025
Leukaemia stem cells (LSCs) are a rare population among the bulk of leukaemia cells and are responsible for disease initiation, progression/relapse and insensitivity to therapies in numerous haematologic malignancies. Identification of LSCs and monit...
Multi-omics analysis and experiments uncover the link between cancer intrinsic drivers, stemness, and immunotherapy in ovarian cancer with validation in a pan-cancer census.
Frontiers in immunology
Jan 1, 2025
BACKGROUND: Although immune checkpoint inhibitors (ICIs) represent a substantial breakthrough in cancer treatment, it is crucial to acknowledge that their efficacy is limited to a subset of patients. The heterogeneity and stemness of cancer render it...
Establishment and Validation of the Novel Necroptosis-related Genes for Predicting Stemness and Immunity of Hepatocellular Carcinoma Machine-learning Algorithm.
Combinatorial chemistry & high throughput screening
Jan 1, 2025
BACKGROUND: Necroptosis, a recently identified mechanism of programmed cell death, exerts significant influence on various aspects of cancer biology, including tumor cell proliferation, stemness, metastasis, and immunosuppression. However, the role o...
Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.
Journal of cellular and molecular medicine
Jul 1, 2024
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intellig...
Deep Learning of Cancer Stem Cell Morphology.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Knowledge regarding cancer stem cell (CSC) morphology is limited, and more extensive studies are therefore required. Image recognition technologies using artificial intelligence (AI) require no previous expertise in image annotation. Herein, we descr...
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.
Briefings in bioinformatics
Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Survival Analysis
Temozolomide
Atlases as Topic
Antineoplastic Agents, Alkylating
Glioblastoma
Patient Selection
Tumor Microenvironment
Programmed Cell Death 1 Receptor
Treatment Outcome
Brain Neoplasms
Neoplastic Stem Cells
Humans
Male
Transcriptome
Aged
Prognosis
Middle Aged
Immunotherapy
Adult
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Machine Learning
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