Stemness Subtypes and Scoring System Predict Prognosis and Efficacy of Immunotherapy in Soft Tissue Sarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35464409", "kind": "science-record", "revision": 3, "content_hash": "sha256:efa169a0397fbef7470710afc19f1b570bacc04847c165c9ee08b5333f9253fd", "created_at": "2026-11-01T18:14:20Z", "updated_at": "2026-11-01T21:27:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Stemness Subtypes and Scoring System Predict Prognosis and Efficacy of Immunotherapy in Soft Tissue Sarcoma.", "doi": "10.3389/fimmu.2022.796606", "pmid": "35464409", "authors": ["Hui-Yun Gu", "Wen-Qiang Qu", "Hai-Heng Peng", "Yi-Feng Yu", "Zhe-Zhen Jiang", "Bai-Wen Qi", "Ai-Xi Yu"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Tumor stemness has been reported to play important roles in cancers. However, a comprehensive analysis of tumor stemness remains to be performed to investigate the specific mechanisms and practical values of stemness in soft tissue sarcomas (STS). Here, we applied machine learning to muti-omic data of patients from TCGA-SARC and GSE21050 cohorts to reveal important roles of stemness in STS. We demonstrated limited roles of existing mRNAsi in clinical application. Therefore, based on stemness-related signatures (SRSs), we identified three stemness subtypes with distinct stemness, immune, and metabolic characteristics using consensus clustering. The low-stemness subtype had better prognosis, activated innate and adaptive immunity (e.g., infiltrating B, DC, Th1, CD8+ T, activated NK, gamma delta T cells, and M1 macrophages), more enrichment of metabolic pathways, more sites with higher methylation level, higher gene mutations, CNA burdens, and immunogenicity indicators. Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype. Besides, low-stemness subtype and low-SPi patients could benefit from immunotherapy. The predictive value of SPi in immunotherapy was more accurate after the addition of MSI into SPi. MSIlowSPilow patients might be more sensitive to immunotherapy. In conclusion, we highlighted mechanisms and practical values of the stemness in STS. We also recommended the combination of MSI and SPi which is a promising tool to predict prognosis and achieve precise treatments of immunotherapy in STS.", "claims": [{"id": "claim-method", "statement": "The “ConsensusClusterPlus” package (29) was used to perform the consensus clustering (K-means) algorithm, which was repeated 50 times to obtain reliable stemness subtypes for STS.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35464409", "locator": "sections[5] \"Molecular Subtypes Based on Prognostic SRSs\" paragraph 2", "quote": "The “ConsensusClusterPlus” package (29) was used to perform the consensus clustering (K-means) algorithm, which was repeated 50 times to obtain reliable stemness subtypes for STS."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in this work can be acquired from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE21050, the GDC portal (https://portal.gdc.cancer.gov/), and the UCSC Xena browser (https://gdc.xenahubs.net).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35464409", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "All data used in this work can be acquired from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE21050, the GDC portal (https://portal.gdc.cancer.gov/), and the UCSC Xena browser (https://gdc.xenahubs.net)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35464409", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35464409"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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