Identification of Critical miRNAs as Novel Diagnostic Markers for Laryngeal Squamous Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35909889", "kind": "science-record", "revision": 5, "content_hash": "sha256:1036980473f43f770118b918dfb3c03afa8f3110cc3f91baa47d7bb4dc0c3b2e", "created_at": "2026-11-01T18:15:00Z", "updated_at": "2026-11-01T22:49:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Critical miRNAs as Novel Diagnostic Markers for Laryngeal Squamous Cell Carcinoma.", "doi": "10.1155/2022/6858411", "pmid": "35909889", "authors": ["Kuo Luo", "Yanguang Zhao", "Hong Liu", "Jinhua Mo"], "journal": "Disease markers", "year": 2022, "abstract": "The prognosis of laryngeal squamous cell carcinoma (LSCC) patients remains poor, and early diagnosis can distinctly improve the long-term survival of LSCC patients. MicroRNAs (miRs) are a group of endogenous, noncoding, 18-24 nucleotide length single-strand RNAs and have been demonstrated to regulate the expression of many genes, thus modulating various cellular biological processes. In this study, we aimed to identify critical diagnostic miRNAs based on two machine learning algorithms. The GSE133632 dataset was acquired from the Gene Expression Omnibus (GEO) datasets, comprising LSCC tissular samples (57 specimens) and matched neighboring healthy mucosa tissular samples (57 specimens). Differentially expressed miRNAs (DEMs) were screened between 57 LSCC specimens and 57 normal specimens. The LASSO regression model and SVM-RFE analysis were carried out for the identification of critical miRNAs. ROC assays were applied to evaluate discriminatory ability. We identified 32 DEMs between LSCC specimens and normal specimens. Two machine learning algorithms confirmed that hsa-miR-615-3p, hsa-miR-4652-5p, hsa-miR-450a-5p, hsa-miR-196a-5p, hsa-miR-21-3p, hsa-miR-139-5p, and hsa-miR-424-5p were critical diagnostic factors. According to the ROC assays, seven miRNAs had an AUC value of >0.85 for LSCC. Taken together, our findings identified seven critical miRNAs in LSCC patients which can be used to diagnose LSCC patients with high sensitivity and specificity. These results must be verified by large-scale prospective studies.", "claims": [{"id": "claim-method", "statement": "Then, LASSO regressive analyses were utilized to study the DEMs so as to acquire the diagnosis miRNAs from LSCC sufferers. Another approach, like SVM-RFE, was utilized at the same time to select the miRNAs for LSCC diagnoses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35909889", "locator": "sections[10] \"3.2. Identification of Diagnostic miRNAs in LSCC\" paragraph 2", "quote": "Then, LASSO regressive analyses were utilized to study the DEMs so as to acquire the diagnosis miRNAs from LSCC sufferers. Another approach, like SVM-RFE, was utilized at the same time to select the miRNAs for LSCC diagnoses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35909889", "locator": "sections[14] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Seven overlapping diagnostic miRNAs were selected by LASSO regression and SVM-RFE.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35909889", "locator": "sections[10] \"3.2. Identification of Diagnostic miRNAs in LSCC\" paragraph 2", "quote": "The 7 overlapped miRNAs (hsa-miR-615-3p, hsa-miR-4652-5p, hsa-miR-450a-5p, hsa-miR-196a-5p, hsa-miR-21-3p, hsa-miR-139-5p, and hsa-miR-424-5p) between those 2 arithmetics were eventually chosen (Figure 2(c))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Seven overlapping diagnostic miRNAs were selected by LASSO regression and SVM-RFE.", "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:35909889"], "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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