SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38571554", "kind": "science-record", "revision": 5, "content_hash": "sha256:9f1dd76846b5044e67951a3b1dc90b1cf15769223a45202735d5efd0249077e9", "created_at": "2026-11-01T19:20:32Z", "updated_at": "2026-11-01T22:54:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach.", "doi": "10.1016/j.bbrep.2024.101693", "pmid": "38571554", "authors": ["Zengjian Tian", "Shilong Yu", "Ruizhi Cai", "Yinghui Zhang", "Qilun Liu", "Yongzhao Zhu"], "journal": "Biochemistry and biophysics reports", "year": 2024, "abstract": "Objective: Using bioinformatics machine learning methods, our research aims to identify the potential key genes associated with Lung adenocarcinoma (LUAD).\nMethods: We obtained two gene expression profiling microarrays (GSE68571 and GSE74706) from the public Gene Expression Omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI). The purpose was to identify Differentially Expressed Genes (DEGs) between the lung adenocarcinoma group and the healthy control group. The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover. To evaluate the performance, ROC curves were plotted. We performed immuno-infiltration analysis using CIBERSORT. Finally, we validated the key genes through qRT-PCR and Western-blot verification, then downregulated MMP17 gene expression, upregulated SH3GL2 gene expression, and performed CCK8 experiments.\nResults: A total of 32 Differentially Expressed Genes (DEGs) were identified. Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods. In Lung adenocarcinoma cells, the expression of MMP17 was observed to be elevated compared to normal lung epithelial cells in the control group (P < 0.05). In contrast, a down-regulation of SH3GL2 was found in Lung adenocarcinoma cells (P < 0.05). Finally, we downregulated MMP17 and upregulated SH3GL2 gene expression, then the CCK8 showed that the proliferation of both lung cancer cells was inhibited.\nConclusion: SH3GL2 and MMP17 are expected to be potential biomarkers for Lung adenocarcinoma.", "claims": [{"id": "claim-method", "statement": "The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38571554", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Specifically, scRNA-seq data (GSE68571) and microarray expression profiles (GSE74706) of lung adenocarcinoma samples were accessed(Beer et al., 2002, Marwitz et al., 2016).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38571554", "locator": "sections[6] \"Downloading and organising data\" paragraph 2", "quote": "Specifically, scRNA-seq data (GSE68571) and microarray expression profiles (GSE74706) of lung adenocarcinoma samples were accessed(Beer et al., 2002, Marwitz et al., 2016)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38571554", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods.", "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:38571554"], "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:GSE139294"], "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:GSE139294"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38571554", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.bbrep.2024.101693", "sha256": 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