Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34572898", "kind": "science-record", "revision": 7, "content_hash": "sha256:f99faad1799ed9fe84af81050bd9c8c406ec0a56f1ba034f557c35c886126b92", "created_at": "2026-11-01T15:41:54Z", "updated_at": "2026-11-01T22:04:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning.", "doi": "10.3390/cancers13184671", "pmid": "34572898", "authors": ["André Marquardt", "Laura-Sophie Landwehr", "Cristina L Ronchi", "Guido di Dalmazi", "Anna Riester", "Philip Kollmannsberger", "Barbara Altieri", "Martin Fassnacht", "Silviu Sbiera"], "journal": "Cancers", "year": 2021, "abstract": "Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several \"multiple-omics\" studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier followed by Cox regression analysis was used to estimate overall survival (OS) using IBM SPSS v 26 for Windows.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34572898", "locator": "sections[8] \"2.2.4. Plots and Statistical Analysis\" paragraph 2", "quote": "Kaplan–Meier followed by Cox regression analysis was used to estimate overall survival (OS) using IBM SPSS v 26 for Windows."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The results shown here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga (accessed on 8 August 2019) and the European Network for the Study of Adrenal Tumors/ENSAT (ensat.org, accessed on 8 August 2019). The original data can be explored through the Broad Institute GDAC FireBrowse portal (http://firebrowse.org/?cohort=ACC, accessed on 8 August 2019) and the platform EGA (https://www.ebi.ac.uk/ega/home, accession number EGAS00001004533), respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34572898", "locator": "sections[21] \"Data Availability Statement\" paragraph 2", "quote": "The results shown here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga (accessed on 8 August 2019) and the European Network for the Study of Adrenal Tumors/ENSAT (ensat.org, accessed on 8 August 2019). The original data can be explored through the Broad Institute GDAC FireBrowse portal (http://firebrowse.org/?cohort=ACC, accessed on 8 August 2019) and the platform EGA (https://www.ebi.ac.uk/ega/home, accession number EGAS00001004533), respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:34572898"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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