ArticleBriefings in bioinformatics2021
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.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.
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Who cites it
98 citing papers in PubMed, 1 synthesis or guideline pooled it, 151 citations in OpenAlex.
- The Three Pillars of Glioblastoma: A Systematic Review and Novel Analysis of Multi-Omics and Clinical Data.Cells · 2024Pooled it
- Article
- A novel cuproptosis-related prognostic gene signature is identified by machine learning and integrative analyses in gliomas.Molecular and cellular biochemistry · 2026Article
- Single-Cell and Bulk Transcriptomic Integration Reveals a Stemness-Related Astrocyte Subpopulation for Prognostic Risk Stratification in Glioblastoma.Current medicinal chemistry · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- The resilient subset: cancer stem cells at the core of immunotherapy resistance.Immunotherapy advances · 2026Review
- Biomarkers for predicting immunotherapy response and resistance in glioblastoma.Frontiers in immunology · 2026Review
- Targeting Pan-Cancer Stemness: Core Regulatory lncRNAs as Novel Therapeutic Vulnerabilities.International journal of molecular sciences · 2025Article
- Single-cell multi-omics analysis reveals drivers of intrahepatic cholangiocarcinoma metastasis.BMC cancer · 2025Article
- Stemness-based gastric cancer classification by machine learning for precision diagnosis and treatment of gastric cancer.Journal of gastrointestinal oncology · 2025Article
- Development of an ADME gene signature for prognostic and therapeutic stratification in gastric cancer.Discover oncology · 2025Article
- Value of artificial intelligence in neuro-oncology.The Lancet. Digital health · 2025Review
- The Circadian Rhythm Gene Network Could Distinguish Molecular Profile and Prognosis for Glioblastoma.International journal of molecular sciences · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- LOXScientific reports · 2025Article
- Revolutionizing the treatment of intervertebral disc degeneration: an approach based on molecular typing.Journal of translational medicine · 2025Article
- Application of artificial intelligence-based stemness index in cancer.Frontiers in oncology · 2025Review
- Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.Cancer science · 2025Article
- A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer.Open medicine (Warsaw, Poland) · 2025Article
- An immune-related signature based on molecular subtypes for predicting the prognosis and immunotherapy efficacy of hepatocellular carcinoma.Frontiers in immunology · 2025Article
38 more citing papers are in PubMed but not listed here.
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10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.
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