Lipid metabolism-related miRNAs with potential diagnostic roles in prostate cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36915125", "kind": "science-record", "revision": 4, "content_hash": "sha256:755e65133867117f42468a4b63f27f67f67bf3306b84e2fbf2d513932d61ea9a", "created_at": "2026-11-01T19:01:33Z", "updated_at": "2026-11-01T22:51:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lipid metabolism-related miRNAs with potential diagnostic roles in prostate cancer.", "doi": "10.1186/s12944-023-01804-4", "pmid": "36915125", "authors": ["Tianyuan Zhai", "Meng Dou", "Yubo Ma", "Hong Wang", "Fang Liu", "Liandong Zhang", "Tie Chong", "Ziming Wang", "Li Xue"], "journal": "Lipids in health and disease", "year": 2023, "abstract": "BACKGROUND: Prostate cancer (PCa), the second most prevalent solid tumor among men worldwide, has caused greatly increasing mortality in PCa patients. The effects of lipid metabolism on tumor growth have been explored, but the mechanistic details of the association of lipid metabolism disorders with PCa remain largely elusive.\nMETHODS: The RNA sequencing data of the GSE45604 and The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) datasets were extracted from the Gene Expression Omnibus (GEO) and UCSC Xena databases, respectively. The Molecular Signatures Database (MSigDB) was utilized to identify lipid metabolism-related genes. The limma R package was used to identify differentially expressed lipid metabolism-related genes (DE-LMRGs) and differentially expressed microRNAs (DEMs). Moreover, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and support vector machine-recursive feature elimination (SVM-RFE) were applied to select signature miRNAs and construct a lipid metabolism-related diagnostic model. The expression levels of selected differentially expressed lipid metabolism-related miRNAs (DE-LMRMs) in PCa and benign prostate hyperplasia (BPH) specimens were verified using quantitative real-time polymerase chain reaction (qRT‒PCR). Furthermore, a transcription factor (TF)-miRNA‒mRNA network was constructed. Eventually, Kaplan‒Meier (KM) curves were plotted to illustrate the associations between signature miRNA-related mRNAs and TFs and overall survival (OS) along with biochemical recurrence-free survival (BCR).\nRESULTS: Forty-seven LMRMs were screened based on the correlation analysis of 29 DE-LMRGs and 56 DEMs, in which 27 LMRMs were stably expressed in the GSE45604 dataset. Subsequently, receiver operating characteristic (ROC) curves and machine learning methods were employed to develop a lipid metabolism-related diagnostic signature, which may be of diagnostic value for PCa patients. qRT‒PCR results showed that all seven key DE-LMRMs were differentially expressed between PCa and BPH tissues. Eventually, a TF-miRNA‒mRNA network was constructed.\nCONCLUSIONS: These results suggested that 7 key diagnostic miRNAs were closely related to PCa pathological processes and provided new targets for the diagnosis and treatment of PCa. Moreover, CLIC6 and SCNN1A linked to miR-200c-3p had good prognostic potential and provided valuable insights into the pathogenesis of PCa.", "claims": [{"id": "claim-method", "statement": "To identify LMRMs, DEMs between 52 normal and 499 PCa samples of the TCGA-PRAD dataset were first screened using the limma R package, considering |log2FC|> 1 and P < 0.05 as the screening criteria [20]. Next, DE-LMRMs were identified by performing Pearson's correlation analysis between DEMs and DE-LMRGs, and correlation < -0.3 and P < 0.05 were considered screening criteria.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36915125", "locator": "sections[7] \"Identification of DE-lipid metabolism-related miRNAs (DE-LMRMs)\" paragraph 2", "quote": "To identify LMRMs, DEMs between 52 normal and 499 PCa samples of the TCGA-PRAD dataset were first screened using the limma R package, considering |log2FC|> 1 and P < 0.05 as the screening criteria [20]. Next, DE-LMRMs were identified by performing Pearson's correlation analysis between DEMs and DE-LMRGs, and correlation < -0.3 and P < 0.05 were considered screening criteria."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data used in this study were extracted from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45604) and TCGA-PRAD database (https://portal.gdc.cancer.gov).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36915125", "locator": "sections[25] \"Availability of data and materials\" paragraph 2", "quote": "The raw data used in this study were extracted from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45604) and TCGA-PRAD database (https://portal.gdc.cancer.gov)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "To validate the diagnostic signature's predictive ability, ROC curves based on the 7 key diagnostic miRNAs were plotted for the training set and validation set, suggesting that the AUCs were 0.99 and 1, respectively (Fig. 3H and I).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36915125", "locator": "sections[13] \"Identification and validation of key diagnostic miRNAs in PCa\" paragraph 2", "quote": "To validate the diagnostic signature's predictive ability, ROC curves based on the 7 key diagnostic miRNAs were plotted for the training set and validation set, suggesting that the AUCs were 0.99 and 1, respectively (Fig. 3H and I)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "To validate the diagnostic signature's predictive ability, ROC curves based on the 7 key diagnostic miRNAs were plotted for the training set and validation set, suggesting that the AUCs were 0.99 and 1, respectively (Fig. 3H and I).", "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:36915125"], "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:GSE45604"], "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:GSE45604"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36915125", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12944-023-01804-4", "sha256": "sha256:639b443c33980c43d81e77094a632789c4bc388cfc725e073939a1bacd9bcec4", "bytes": 35813, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12944-023-01804-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10012590", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE45604", "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=GSE45604", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE45604", "version": null, "note": "The raw data used in this study were extracted from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45604) and TCGA-PRAD database (https://portal.gdc.cancer.gov).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The raw data used in this study were extracted from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45604) and TCGA-PRAD database (https://portal.gdc.cancer.gov).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1110567", "GSM1110568", "GSM1110569", "GSM1110570", "GSM1110571", "GSM1110572", "GSM1110573", "GSM1110574", "GSM1110575", "GSM1110576", "GSM1110577", "GSM1110578", "GSM1110579", "GSM1110580", "GSM1110581", "GSM1110582", "GSM1110583", "GSM1110584", "GSM1110585", "GSM1110586", "GSM1110587", "GSM1110588", "GSM1110589", "GSM1110590", "GSM1110591", "GSM1110592", "GSM1110593", "GSM1110594", "GSM1110595", "GSM1110596", "GSM1110597", "GSM1110598", "GSM1110599", "GSM1110600", "GSM1110601", "GSM1110602", "GSM1110603", "GSM1110604", "GSM1110605", "GSM1110606", "GSM1110607", "GSM1110608", "GSM1110609", "GSM1110610", "GSM1110611", "GSM1110612", "GSM1110613", "GSM1110614", "GSM1110615", "GSM1110616", "GSM1110617", "GSM1110618", "GSM1110619", "GSM1110620", "GSM1110621", "GSM1110622", "GSM1110623", "GSM1110624", "GSM1110625", "GSM1110626"], "sample_count": 60, "group_sizes": null, "group_sizes_basis": null, "platform": "14613", "taxon": "synthetic construct; Homo sapiens", "title": "Identification of miRNAs specific for prostate cancer (PCa)", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE45604 not downloaded", "asset:data:geo:GSE45604 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": 1.0, "band": "verbatim", "quote": "To identify LMRMs, DEMs between 52 normal and 499 PCa samples of the TCGA-PRAD dataset were first screened using the limma R package, considering |log2FC|> 1 and P < 0.05 as the screening criteria [20]. Next, DE-LMRMs were identified by performing Pearson's correlation analysis between DEMs and DE-LMRGs, and correlation < -0.3 and P < 0.05 were considered screening criteria."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "KM"}}, {"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": 0.7, "band": "medium", "quote": "support vector machine"}}, {"id": "method-4", "family": "machine_learning", "specific": "gradient_boosting", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "gradient boosting"}}, {"id": "method-5", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson's correlation"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["GSE45604"], "resolved_accessions": ["GSE45604"], "statements": ["The raw data used in this study were extracted from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45604) and TCGA-PRAD database (https://portal.gdc.cancer.gov)."], "basis": "The paper names GSE45604 in GEO database 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:36915125", "data:geo:GSE45604"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE45604"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:33:25Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12099, "completion_tokens": 1642, "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:36915125", "type": "paper", "label": "pmid:36915125"}, {"id": "source:pmid:36915125", "type": "manuscript", "label": "PMID 36915125 full text"}, {"id": "data:geo:GSE45604", "type": "dataset", "label": "GEO GSE45604"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36915125", "type": "paper", "label": "pmid:36915125"}, {"id": "source:pmid:36915125", "type": "manuscript", "label": "PMID 36915125 full text"}, {"id": "data:geo:GSE45604", "type": "dataset", "label": "GEO GSE45604"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36915125", "type": "extraction", "used": ["source:pmid:36915125"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE45604", "type": "accession_resolution", "used": ["data:geo:GSE45604"], "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:36915125", "parent_hash": "sha256:5c6a4d7a9a7f933b41790fbfbd3a484797c25d2f09179c3df8f01060cda75212", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE45604 licence not read", "asset:data:geo:GSE45604 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": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ith PCa remain largely elusive. METHODS: The RNA sequencing data of the GSE45604 and The Cancer Genome A"], "order_basis": "paper_text_order:381", "id": "assay:1"}, {"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": 1.0, "evidence": ["To identify LMRMs, DEMs between 52 normal and 499 PCa samples of the TCGA-PRAD dataset were first screened using the limma R package, considering |log2FC|> 1 and P < 0.05 as the screening criteria [20]. Next, DE-LMRMs were identified by performing Pearson's correlation analysis between DEMs and DE-LMRGs, and correlation < -0.3 and P < 0.05 were considered screening criteria."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}