Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35157609", "kind": "science-record", "revision": 6, "content_hash": "sha256:479aa6487fd38883d72f09523d991557a845a2395add8e9c9e134a011f551241", "created_at": "2026-11-01T17:36:55Z", "updated_at": "2026-11-01T22:48:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy.", "doi": "10.18632/aging.203894", "pmid": "35157609", "authors": ["Tiegang Li", "Weiqi Wang", "Wenqiang Gan", "Silin Lv", "Zifan Zeng", "Yufang Hou", "Zheng Yan", "Rixin Zhang", "Min Yang"], "journal": "Aging", "year": 2022, "abstract": "Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.", "claims": [{"id": "claim-method", "statement": "The DEGs between Control and HT without LVH, as well as between HT with LVH and HT without LVH, were screened by R package ‘limma’. WGCNA was conducted to identify gene sets (modules) associated with hypertensive LVH using R package ‘WGNCA’. Least absolute shrinkage and selection operator (LASSO) regression was employed to construct a multi-molecule panel signature with 142 genes to discriminate HT without LVH from HT with LVH using ‘glmnet’ package of R software. Key gene subset was screened out with ten-fold cross-validation through SVM module by R package ‘e1071’.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35157609", "locator": "sections[13] \"Identification of differentially expressed genes (DEGs)\" paragraph 2", "quote": "The DEGs between Control and HT without LVH, as well as between HT with LVH and HT without LVH, were screened by R package ‘limma’."}, {"asset": "source:pmid:35157609", "locator": "sections[14] \"Construction of weighted gene co-expression network analysis (WGCNA) and identification of modules related to HT with LVH\" paragraph 2", "quote": "WGCNA was conducted to identify gene sets (modules) associated with hypertensive LVH using R package ‘WGNCA’."}, {"asset": "source:pmid:35157609", "locator": "sections[16] \"Construction of the risk assessment model for HT with LVH through LASSO regression\" paragraph 2", "quote": "Least absolute shrinkage and selection operator (LASSO) regression was employed to construct a multi-molecule panel signature with 142 genes to discriminate HT without LVH from HT with LVH using ‘glmnet’ package of R software."}, {"asset": "source:pmid:35157609", "locator": "sections[17] \"Feature selection by SVM-RFE\" paragraph 2", "quote": "Key gene subset was screened out with ten-fold cross-validation through SVM module by R package ‘e1071’."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Microarray gene expression profiles of peripheral blood leukocytes from GSE74144 were obtained from GEO (https://www.ncbi.nlm.nih.gov/geo/) based on the GPL13497 platform, which contained 8 healthy individuals (Control), 14 hypertensive patients without LVH (HT without LVH), and 14 hypertensive patients with LVH (HT with LVH).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35157609", "locator": "sections[12] \"mRNA dataset collection\" paragraph 2", "quote": "Microarray gene expression profiles of peripheral blood leukocytes from GSE74144 were obtained from GEO (https://www.ncbi.nlm.nih.gov/geo/) based on the GPL13497 platform, which contained 8 healthy individuals (Control), 14 hypertensive patients without LVH (HT without LVH), and 14 hypertensive patients with LVH (HT with LVH)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The results demonstrated that the area under ROC curve (AUC) values were 1.000, 1.000, 0.946, 0.954, 0.893, 0.944, 0.934, 0.934, and 0.913 for the risk score, LAPTM5, COPS6, SASH3, YPEL3, DDX47, QARS, NDUFS8, and CIC, respectively (Supplementary Figure 2).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35157609", "locator": "sections[6] \"Screening for candidate diagnostic biomarker\" paragraph 2", "quote": "The results demonstrated that the area under ROC curve (AUC) values were 1.000, 1.000, 0.946, 0.954, 0.893, 0.944, 0.934, 0.934, and 0.913 for the risk score, LAPTM5, COPS6, SASH3, YPEL3, DDX47, QARS, NDUFS8, and CIC, respectively (Supplementary Figure 2)."}, {"asset": "source:pmid:35157609", "locator": "sections[6] \"Screening for candidate diagnostic biomarker\" paragraph 4", "quote": "Ultimately, the gene biomarkers obtained by the two algorithms were overlapped, and one robust diagnostic-related gene, LAPTM5, was obtained for further analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The results demonstrated that the area under ROC curve (AUC) values were 1.000, 1.000, 0.946, 0.954, 0.893, 0.944, 0.934, 0.934, and 0.913 for the risk score, LAPTM5, COPS6, SASH3, YPEL3, DDX47, QARS, NDUFS8, and CIC, respectively (Supplementary Figure 2).", "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:35157609"], "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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WGCNA was conducted to identify gene sets (modules) associated with hypertensive LVH using R package ‘WGNCA’. Least absolute shrinkage and selection operator (LASSO) regression was employed to construct a multi-molecule panel signature with 142 genes to discriminate HT without LVH from HT with LVH using ‘glmnet’ package of R software. Key gene subset was screened out with ten-fold cross-validation through SVM module by R package ‘e1071’."], "order_basis": "analysis_only", "id": "assay:1"}]}