ArticleStem cell research & therapy2022
StemSC: a cross-dataset human stemness index for single-cell samples.
Article in Stem cell research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Integrated Genetic Screening Identifies FEN1 as a Selective Vulnerability and Temozolomide Resistance Mediator in Glioblastoma Stem Cells.Cancer research communications · 2026Article
- Comprehensive molecular characterization of high-stemness gastric cancer cells using single-cell transcriptomics, spatial mapping, and machine learning.NPJ precision oncology · 2025Article
- Single-cell multi-omics and machine learning for dissecting stemness in cancer.Briefings in bioinformatics · 2025Review
- Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.Genes & development · 2025Review
- Application of artificial intelligence-based stemness index in cancer.Frontiers in oncology · 2025Review
- Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response.Genomics, proteomics & bioinformatics · 2024Article
- Targeting axonal guidance dependencies in glioblastoma with ROBO1 CAR T cells.Nature medicine · 2024Article
- Aberrant activation of TGF-β/ROCK1 enhances stemness during prostatic stromal hyperplasia.Cell communication and signaling : CCS · 2024Article
- Single-cell multi-omics analysis of lineage development and spatial organization in the human fetal cerebellum.Cell discovery · 2024Article
- Prostate cancer as a dedifferentiated organ: androgen receptor, cancer stem cells, and cancer stemness.Essays in biochemistry · 2022Article
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11 authors.
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Abstract
backgroundStemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these methods showed low negative correlations with differentiation time and can't leverage the existing experimentally validated stem cells to recognize the stem-like cells.
methodsHere, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the stemness-related genes by selecting the genes significantly related to differentiation time. Then, we used 13 RNA-seq datasets from both the bulk and single-cell embryonic stem cell (ESC) samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples.
resultsWe validated the StemSC by its higher negative correlations with differentiation time in eight normal datasets and its higher positive correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. Besides, the robust of StemSC to batch effect enabled us to leverage the existing experimentally validated cancer stem cells to recognize the stem-like cells in other independent tumor datasets. And the recognized stem-like tumor cells had fewer interactions with anti-tumor immune cells. Further survival analysis showed the immunotherapy-treated patients with high stemness had worse survival than those with low stemness.
conclusionsStemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.
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