Lymph node metastasis determined miRNAs in esophageal squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39401765", "kind": "science-record", "revision": 4, "content_hash": "sha256:f169a346abe9c6f5d8fc359ee7414ddd8781f7e660e495eab49ff01e733c919b", "created_at": "2026-11-01T20:29:38Z", "updated_at": "2026-11-01T22:55:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lymph node metastasis determined miRNAs in esophageal squamous cell carcinoma.", "doi": "10.18632/aging.206122", "pmid": "39401765", "authors": ["Feng Wei", "Shufeng Bi", "Mengmeng Li", "Jia Yu"], "journal": "Aging", "year": 2024, "abstract": "PURPOSE: There is no golden noninvasive and effective technique to diagnose lymph node metastasis (LNM) for esophageal squamous cell carcinoma (ESCC) patients. Here, a classifier was proposed consisting of miRNAs to screen ESCC patients with LNM from the ones without LNM.\nMETHODS: miRNA expression and clinical data files of 93 ESCC samples were downloaded from TCGA as the discovery set and 119 ESCC samples with similar dataset GSE43732 as the validation set. Differentially expressed miRNAs (DE-miRNAs) were analyzed between patients with LNM and without LNM. LASSO regression was performed for selecting the DE-miRNA pair to consist the classifier. To validate the accuracy and reliability of the classifier, the SVM and AdaBoost algorithms were applied. The CCK-8 and wound healing assay were used to evaluate the role of the miRNA in ESCC cells.\nRESULT: There were 43 DE miRNAs between the LNM+ group and LNM- group. Among them, miR-224-5p, miR-99a-5p, miR-100-5p, miR-34c-5p, miR-503-5p, and miR-452-5p were identified by LASSO to establish the classifier. SVM and AdaBoost showed that the model could classify the ESCC patients with LNM from the ones without LNM precisely and reliably in 2 data sets. miR-224-5p in the classifier as the top contributor to discriminate the two groups of patients based on AdaBoost, promoted ESCC cell proliferation and migration in vitro.\nCONCLUSION: The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully.", "claims": [{"id": "claim-method", "statement": "The LASSO regression was applied to identify DE-miRNAs for establishing the classifier within the TCGA set by glmnet package of R.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39401765", "locator": "sections[8] \"Construction of the classifier\" paragraph 2", "quote": "The LASSO regression was applied to identify DE-miRNAs for establishing the classifier within the TCGA set by glmnet package of R."}], "state": "complete"}, {"id": "claim-data-access", "statement": "93 ESCC tissue samples with the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/; IlluminaHiseq platform) as a discovery group to establish the classifier that was applied to discriminate the ESCC patients with LNM from the ones without LNM", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39401765", "locator": "sections[6] \"Collection and preparation of the data\" paragraph 2", "quote": "93 ESCC tissue samples with the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/; IlluminaHiseq platform) as a discovery group to establish the classifier that was applied to discriminate the ESCC patients with LNM from the ones without LNM"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39401765", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully.", "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:39401765"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE43732"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE43732"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39401765", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.206122", "sha256": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE43732"], "resolved_accessions": ["GSE43732"], "statements": ["93 ESCC tissue samples with the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/; IlluminaHiseq platform) as a discovery group to establish the classifier that was applied to discriminate the ESCC patients with LNM from the ones without LNM"], "basis": "The paper names GSE43732 in National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:39401765", "data:geo:GSE43732"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE43732"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:34:58Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 11847, "completion_tokens": 1607, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39401765", "type": "paper", "label": "pmid:39401765"}, {"id": "source:pmid:39401765", "type": "manuscript", "label": "PMID 39401765 full text"}, {"id": "data:geo:GSE43732", "type": "dataset", "label": "GEO GSE43732"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39401765", "type": "paper", "label": "pmid:39401765"}, {"id": "source:pmid:39401765", "type": "manuscript", "label": "PMID 39401765 full text"}, {"id": "data:geo:GSE43732", "type": "dataset", "label": "GEO GSE43732"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39401765", "type": "extraction", "used": ["source:pmid:39401765"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE43732", "type": "accession_resolution", "used": ["data:geo:GSE43732"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39401765", "parent_hash": "sha256:0b33bfd912b9866d2988f66d7455027b4fc0fc40ec6d7ceb63814e8873f498e5", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE43732 licence not read", "asset:data:geo:GSE43732 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["VM and AdaBoost algorithms were applied. The CCK-8 and wound healing assay were used to evaluat"], "order_basis": "paper_text_order:798", "id": "assay:1"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["MATERIALS AND METHODS Cell culture and transfection The human esophageal cancer cell line TE-1 w", "bator in the closed culture. TE-1 cells were transfected with miR-224-5p mimics and negative controls", "ense was 5′-AAACGGAACCACUAGUGACUUGAUU-3′. GP-Transfected mate (GenePharma, Suzhou, China) as the cell", "mate (GenePharma, Suzhou, China) as the cell transfection reagent was transfected with miRNA using the"], "order_basis": "paper_text_order:1594", "id": "assay:2"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["l Sciences, Beijing, China). TE-1 cells were cultured in a DMEM medium containing 10% fetal bovine se"], "order_basis": "paper_text_order:1755", "id": "assay:3"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The LASSO regression was applied to identify DE-miRNAs for establishing the classifier within the TCGA set by glmnet package of R."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}