ArticleScientific data2023
Stemness signature and targeted therapeutic drugs identification for Triple Negative Breast Cancer.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 12 citations in OpenAlex.
- Integrating single-cell RNA and bulk RNA sequencing data to identify prognostic genes associated with pyrimidine metabolism in triple-negative breast cancer by machine learning algorithm combinations.Discover oncology · 2026Article
- PKC-eta promotes breast cancer metastasis by regulating the Hippo-YAP signaling pathway.Signal transduction and targeted therapy · 2026Article
- Cancer stem cell heterogeneity‑inspired therapeutics for enhancing tumor treatment outcomes (Review).International journal of oncology · 2026Review
- A novel lncRNA-mediated signaling axis governs cancer stemness and splicing reprogramming in hepatocellular carcinoma with therapeutic potential.Journal of experimental & clinical cancer research : CR · 2025Article
- Machine Learning-Based Prognostic Signature in Breast Cancer: Regulatory T Cells, Stemness, and Deep Learning for Synergistic Drug Discovery.International journal of molecular sciences · 2025Article
- Developing angiogenesis-related prognostic biomarkers and therapeutic strategies in bladder cancer using deep learning and machine learning.Scientific reports · 2025Article
- Stemness of Cancer: A Study of Triple-negative Breast Cancer From a Neuroscience Perspective.Stem cell reviews and reports · 2025Review
- A DNA Nanomachine Modulates the Stemness-Associated Signaling Pathways for Overcoming Chemoresistance by Temporally Programming Drug Release.Research (Washington, D.C.) · 2025Article
- Transcriptome analysis of ovarian cancer uncovers association between tumor-related inflammation/immunity and patient outcome.Frontiers in pharmacology · 2025Article
- The prognostic marker KRT81 is involved in suppressing CD8 + T cells and predicts immunotherapy response for triple-negative breast cancer.Cancer biology & therapy · 2024Article
- Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments.Journal of translational medicine · 2024Article
- Stemness signature and targeted therapeutic drugs identification for Triple Negative Breast Cancer.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and carries the worst prognosis, characterized by the lack of progesterone, estrogen, and HER2 gene expression. This study aimed to analyze cancer stemness-related gene signature to determine patients' risk stratification and prognosis feature with TNBC. Here one-class logistic regression (OCLR) algorithm was applied to compute the stemness index of TNBC patients. Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort. We diagnosed the expression level of prognostic genes signature in the tumor immune microenvironment, analyzed the TNBC scRNA-seq GSE176078 dataset, and further validated the expression level of prognostic genes using the HPA database. Finally, the small molecular compounds targeted at the anti-tumor effect of predictive genes were screened by molecular docking; this novel stemness-based prognostic genes signature study could facilitate the prognosis of patients with TNBC and thus provide a feasible therapeutic target for TNBC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.