Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36325528", "kind": "science-record", "revision": 4, "content_hash": "sha256:4f9f31fea39109c987bf352303efd28409a570c82218af328fa0b15a28f51dac", "created_at": "2026-11-01T18:15:45Z", "updated_at": "2026-11-01T22:08:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms.", "doi": "10.3389/fpsyt.2022.1009911", "pmid": "36325528", "authors": ["Zhe Wang", "Zhe Meng", "Che Chen"], "journal": "Frontiers in psychiatry", "year": 2022, "abstract": "Background: The prevalence of depression has been increasing worldwide in recent years, posing a heavy burden on patients and society. However, the diagnostic and therapeutic tools available for this disease are inadequate. Therefore, this research focused on the identification of potential biomarkers in the peripheral blood of patients with depression.\nMethods: The expression dataset GSE98793 of depression was provided by the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). Initially, differentially expressed genes (DEGs) were detected in GSE98793. Subsequently, the most relevant modules for depression were screened according to weighted gene co-expression network analysis (WGCNA). Finally, the identified DEGs were mapped to the WGCNA module genes to obtain the intersection genes. In addition, Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on these genes. Moreover, biomarker screening was carried out by protein-protein interaction (PPI) network construction of intersection genes on the basis of various machine learning algorithms. Furthermore, the gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and the prediction of the regulatory mechanism were collectively performed on the identified biomarkers. In addition, we also estimated the clinical diagnostic ability of the obtained biomarkers, and performed Mfuzz expression pattern clustering and functional enrichment of the most potential biomarkers to explore their regulatory mechanisms. Finally, we also perform biomarker-related drug prediction.\nResults: Differential analysis was used for obtaining a total of 550 DEGs and WGCNA for obtaining 1,194 significant genes. Intersection analysis of the two yielded 140 intersection genes. Biological functional analysis indicated that these genes had a major role in inflammation-related bacterial infection pathways and cardiovascular diseases such as atherosclerosis. Subsequently, the genes S100A12, SERPINB2, TIGIT, GRB10, and LHFPL2 in peripheral serum were identified as depression biomarkers by using machine learning algorithms. Among them, S100A12 is the most valuable biomarker for clinical diagnosis. Finally, antidepressants, including disodium selenite and eplerenone, were predicted.\nConclusion: The genes S100A12, TIGIT, SERPINB2, GRB10, and LHFPL2 in peripheral serum are viable diagnostic biomarkers for depression. and contribute to the diagnosis and prevention of depression in clinical practice.", "claims": [{"id": "claim-method", "statement": "We performed receiver operating characteristic (ROC) analysis in two datasets, GSE98793 (source set) and GSE76826 (validation set), and calculated area under the curve (AUC) values through the “pROC” package to evaluate whether potential biomarkers are good Distinguish between depression samples and control samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36325528", "locator": "sections[14] \"Prediction of clinical diagnostic ability of biomarkers\" paragraph 2", "quote": "We performed receiver operating characteristic (ROC) analysis in two datasets, GSE98793 (source set) and GSE76826 (validation set), and calculated area under the curve (AUC) values through the “pROC” package to evaluate whether potential biomarkers are good Distinguish between depression samples and control samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36325528", "locator": "sections[31] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:36325528"], "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:GSE201332"], "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:GSE201332"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36325528", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fpsyt.2022.1009911", "sha256": "sha256:98f0b2cd20a820d0f3db4e2565d3d8eaecdfec51b737d18184afe1fb8df462a3", "bytes": 43768, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fpsyt.2022.1009911", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9621316", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE201332", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201332", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE201332", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM6058641", "GSM6058642", "GSM6058643", "GSM6058644", "GSM6058645", "GSM6058646", "GSM6058647", "GSM6058648", "GSM6058649", "GSM6058650", "GSM6058651", "GSM6058652", "GSM6058653", "GSM6058654", "GSM6058655", "GSM6058656", "GSM6058657", "GSM6058658", "GSM6058659", "GSM6058660", "GSM6058661", "GSM6058662", "GSM6058663", "GSM6058664", "GSM6058665", "GSM6058666", "GSM6058667", "GSM6058668", "GSM6058669", "GSM6058670", "GSM6058671", "GSM6058672", "GSM6058673", "GSM6058674", "GSM6058675", "GSM6058676", "GSM6058677", "GSM6058678", "GSM6058679", "GSM6058680"], "sample_count": 40, "group_sizes": null, "group_sizes_basis": null, "platform": "32193", "taxon": "Homo sapiens", "title": "Peripheral whole blood: Healthy control and Major depression disorder patients", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE76826", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76826", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE76826", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2038455", "GSM2038456", "GSM2038457", "GSM2038458", "GSM2038459", "GSM2038460", "GSM2038461", "GSM2038462", "GSM2038463", "GSM2038464", "GSM2038465", "GSM2038466", "GSM2038467", "GSM2038468", "GSM2038469", "GSM2038470", "GSM2038471", "GSM2038472", "GSM2038473", "GSM2038474", "GSM2038475", "GSM2038476", "GSM2038477", "GSM2038478", "GSM2038479", "GSM2038480", "GSM2038481", "GSM2038482", "GSM2038483", "GSM2038484", "GSM2038485", "GSM2038486"], "sample_count": 32, "group_sizes": null, "group_sizes_basis": null, "platform": "17077", "taxon": "Homo sapiens", "title": "Blood transcriptomic markers in patients with late-onset major depressive disorder", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE98793", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98793", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE98793", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2612096", "GSM2612097", "GSM2612098", "GSM2612099", "GSM2612100", "GSM2612101", "GSM2612102", "GSM2612103", "GSM2612104", "GSM2612105", "GSM2612106", "GSM2612107", "GSM2612108", "GSM2612109", "GSM2612110", "GSM2612111", "GSM2612112", "GSM2612113", "GSM2612114", "GSM2612115", "GSM2612116", "GSM2612117", "GSM2612118", "GSM2612119", "GSM2612120", "GSM2612121", "GSM2612122", "GSM2612123", "GSM2612124", "GSM2612125", "GSM2612126", "GSM2612127", "GSM2612128", "GSM2612129", "GSM2612130", "GSM2612131", "GSM2612132", "GSM2612133", "GSM2612134", "GSM2612135", "GSM2612136", "GSM2612137", "GSM2612138", "GSM2612139", "GSM2612140", "GSM2612141", "GSM2612142", "GSM2612143", "GSM2612144", "GSM2612145", "GSM2612146", "GSM2612147", "GSM2612148", "GSM2612149", "GSM2612150", "GSM2612151", "GSM2612152", "GSM2612153", "GSM2612154", "GSM2612155", "GSM2612156", "GSM2612157", "GSM2612158", "GSM2612159", "GSM2612160", "GSM2612161", "GSM2612162", "GSM2612163", "GSM2612164", "GSM2612165", "GSM2612166", "GSM2612167", "GSM2612168", "GSM2612169", "GSM2612170", "GSM2612171", "GSM2612172", "GSM2612173", "GSM2612174", "GSM2612175", "GSM2612176", "GSM2612177", "GSM2612178", "GSM2612179", "GSM2612180", "GSM2612181", "GSM2612182", "GSM2612183", "GSM2612184", "GSM2612185", "GSM2612186", "GSM2612187", "GSM2612188", "GSM2612189", "GSM2612190", "GSM2612191", "GSM2612192", "GSM2612193", "GSM2612194", "GSM2612195", "GSM2612196", "GSM2612197", "GSM2612198", "GSM2612199", "GSM2612200", "GSM2612201", "GSM2612202", "GSM2612203", "GSM2612204", "GSM2612205", "GSM2612206", "GSM2612207", "GSM2612208", "GSM2612209", "GSM2612210", "GSM2612211", "GSM2612212", "GSM2612213", "GSM2612214", "GSM2612215", "GSM2612216", "GSM2612217", "GSM2612218", "GSM2612219", "GSM2612220", "GSM2612221", "GSM2612222", "GSM2612223", "GSM2612224", "GSM2612225", "GSM2612226", "GSM2612227", "GSM2612228", "GSM2612229", "GSM2612230", "GSM2612231", "GSM2612232", "GSM2612233", "GSM2612234", "GSM2612235", "GSM2612236", "GSM2612237", "GSM2612238", "GSM2612239", "GSM2612240", "GSM2612241", "GSM2612242", "GSM2612243", "GSM2612244", "GSM2612245", "GSM2612246", "GSM2612247", "GSM2612248", "GSM2612249", "GSM2612250", "GSM2612251", "GSM2612252", "GSM2612253", "GSM2612254", "GSM2612255", "GSM2612256", "GSM2612257", "GSM2612258", "GSM2612259", "GSM2612260", "GSM2612261", "GSM2612262", "GSM2612263", "GSM2612264", "GSM2612265", "GSM2612266", "GSM2612267", "GSM2612268", "GSM2612269", "GSM2612270", "GSM2612271", "GSM2612272", "GSM2612273", "GSM2612274", "GSM2612275", "GSM2612276", "GSM2612277", "GSM2612278", "GSM2612279", "GSM2612280", "GSM2612281", "GSM2612282", "GSM2612283", "GSM2612284", "GSM2612285", "GSM2612286", "GSM2612287"], "sample_count": 192, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gene expression analysis in whole blood samples obtained from donors diagnosed with MDD compared to healthy controls", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE201332 not downloaded", "asset:data:geo:GSE201332 licence not read", "asset:data:geo:GSE76826 not downloaded", "asset:data:geo:GSE76826 licence not read", "asset:data:geo:GSE98793 not downloaded", "asset:data:geo:GSE98793 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-2", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "random forest"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "SVM"}}, {"id": "method-4", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-5", "family": "model_calibration", "specific": "decision_curve", "label": "Model calibration / clinical utility", "dsl_operations": ["calibration_bins", "net_benefit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictions_and_outcomes"], "evidence": {"score": 0.8, "band": "high", "quote": "net benefit"}}, {"id": "method-6", "family": null, "specific": null, "label": "receiver operating characteristic (ROC) analysis", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We performed receiver operating characteristic (ROC) analysis in two datasets, GSE98793 (source set) and GSE76826 (validation set), and calculated area under the curve (AUC) values through the “pROC” package to evaluate whether potential biomarkers are good Distinguish between depression samples and control samples."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE98793", "GSE76826", "GSE201332"], "resolved_accessions": ["GSE98793", "GSE76826", "GSE201332"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."], "basis": "The paper names GSE98793, GSE76826, GSE201332 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:36325528", "data:geo:GSE201332"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE201332"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:14:04Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11164, "completion_tokens": 1310, "cached_tokens": 128}}, "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:36325528", "type": "paper", "label": "pmid:36325528"}, {"id": "source:pmid:36325528", "type": "manuscript", "label": "PMID 36325528 full text"}, {"id": "data:geo:GSE201332", "type": "dataset", "label": "GEO GSE201332"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36325528", "type": "paper", "label": "pmid:36325528"}, {"id": "source:pmid:36325528", "type": "manuscript", "label": "PMID 36325528 full text"}, {"id": "data:geo:GSE201332", "type": "dataset", "label": "GEO GSE201332"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36325528", "type": "extraction", "used": ["source:pmid:36325528"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE201332", "type": "accession_resolution", "used": ["data:geo:GSE201332"], "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:36325528", "parent_hash": "sha256:8d1665cceb156ada9e87cb6e95e94bb7e551a49dd8afb6c111fcaf5ae5bb0424", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE201332 licence not read", "asset:data:geo:GSE201332 not downloaded", "asset:data:geo:GSE76826 licence not read", "asset:data:geo:GSE76826 not downloaded", "asset:data:geo:GSE98793 licence not read", "asset:data:geo:GSE98793 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["limma"], "order_basis": "analysis_only", "id": "assay:1"}]}