Transcriptional profiles reveal histologic origin and prognosis across 33 The Cancer Genome Atlas tumor types.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37969389", "kind": "science-record", "revision": 3, "content_hash": "sha256:c4b932b20f26e18c10adbca30cc633a1c0fadae56bb67444a7d61611b3b968a7", "created_at": "2026-11-01T19:02:53Z", "updated_at": "2026-11-01T21:31:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptional profiles reveal histologic origin and prognosis across 33 The Cancer Genome Atlas tumor types.", "doi": "10.21037/tcr-23-234", "pmid": "37969389", "authors": ["Hui Xiao", "Liang Hu", "Qi Tan", "Jinping Jia", "Ping Xie", "Junai Li", "Minghua Wang"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: In recent years, with the development of transcriptome sequencing, the molecular characteristics of tumors are gradually revealed. Because of the complexity of tumor transcriptome, there is a need to look for the molecular signatures which can be used to evaluate the tissue origin and cell stemness of tumors in order to promote the diagnosis and treatment of tumors.\nMethods: Tumor tissue-specific gene sets (TTSGs) consisting of 200 genes were selected using RNA expression data of 9,875 patients from 33 tumor types. t-distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction and visualization of TTSGs in each tumor type. To evaluate oncogenic dedifferentiation and loss of cell stemness, Euclidean distance from each sample to a human embryo single-cell RNA-seq dataset (GSE36552) of TTSGs was calculated as TTSGs index indicating dissimilarity of tumors and embryo. TTSGs index was evaluated for prognosis in each tumor type. Two published signature indexes, the mRNA signature index (mRNAsi) and CIBERSORT, were compared to assess the correlation between the TTSGs index with cell stemness and immune microenvironment. Finally, the difference of prognosis, immune microenvironment and radiotherapy outcomes were compared between patients with high and low TTSGs index.\nResults: In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm. Clusters of squamous-cell carcinoma were adjacent to each other revealing similar histologic origin. Basal-like breast cancer was separated from luminal and HER-2-amplified subtypes and closed to squamous-cell carcinoma. TTSGs index was related to overall survival outcomes in cancers derived from liver, thyroid, brain, cervical and kidney. There was a positive correlation between mRNAsi and TTSGs index in pan-kidney and pan-neuronal cancers. Furthermore, cell fractions of M2 macrophages and total leukocytes increased in the group with higher TTSGs index. Patients with higher TTSGs index had longer overall survival time and less radiation therapy resistance compared to patients with lower TTSGs index.\nConclusions: The signature of TTSGs is related to tumor expression features that distinguish tumors of different histologic origin using t-SNE. The signature also relates to prognosis of certain kinds of tumors.", "claims": [{"id": "claim-method", "statement": "T-test, log-rank test, kNN and visualization of results was performed using R.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37969389", "locator": "sections[10] \"Statistical analysis and available software\" paragraph 2", "quote": "T-test, log-rank test, kNN and visualization of results was performed using R."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA-seq whole-transcriptome expression data for all 33 cancer cohorts from TCGA project (https://tcga-data.nci.nih.gov/tcga/), including 9,875 tumors and 721 normal solid tissues, were obtained via the Genomic Data Commons (GDC) client tool.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37969389", "locator": "sections[4] \"Data preparation\" paragraph 2", "quote": "RNA-seq whole-transcriptome expression data for all 33 cancer cohorts from TCGA project (https://tcga-data.nci.nih.gov/tcga/), including 9,875 tumors and 721 normal solid tissues, were obtained via the Genomic Data Commons (GDC) client tool."}], "state": "complete"}, {"id": "claim-target-result", "statement": "All 33 tumor types in TCGA were separated into isolated clusters by t-SNE and confirmed by the kNN algorithm.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37969389", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "All 33 tumor types in TCGA were separated into isolated clusters by t-SNE and confirmed by the kNN algorithm.", "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:37969389"], "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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