Identification and validation of obesity related genes signature based on microenvironment phenotypes in prostate adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37788005", "kind": "science-record", "revision": 5, "content_hash": "sha256:8a27a5fd565e978fbaf787fd88a577d95eebcc379edf25bd0cb60ed559fd01df", "created_at": "2026-11-01T19:02:38Z", "updated_at": "2026-11-01T22:52:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of obesity related genes signature based on microenvironment phenotypes in prostate adenocarcinoma.", "doi": "10.18632/aging.205065", "pmid": "37788005", "authors": ["Linghui Liang", "Jinwei Shang", "Yuwei Zhang", "Yuxin Xu", "Yihui Zhouteng", "Jianxiang Wen", "Yuxin Zhao", "Ninghan Feng", "Ruizhe Zhao"], "journal": "Aging", "year": 2023, "abstract": "BACKGROUND: The role of obesity related genes (ORGs) in the immune checkpoint inhibitors (ICIs) treatment of prostate adenocarcinoma (PRAD) has not yet been proved by research.\nMETHODS: We comprehensively evaluated the ORGs patterns in PRAD based on tumor microenvironment (TME) phenotypes and immunotherapy efficacies. Then we constructed a ORGs risk score for prognosis and a ORGs signature for accurate prediction of TME phenotype and immunotherapy efficacy in order to evaluate individual patients.\nRESULTS: Two distinct ORGs patterns were generated. The two ORGs patterns were consistent with inflammatory and non-inflammatory TME phenotypes. ORGs patterns had an important role for predicting immunotherapy efficacies. Next, we constructed a ORGs risk score for predicting each patient's prognosis with high performance in TCGA-PRAD. The ORGs risk score could be well verified in the external cohorts including GSE70769 and GSE21034. Then, we developed a ORGs signature and found it was significantly positively correlated with tumor-infiltrating lymphocytes in TCGA-PRAD. We found that each patient in the high-risk ORGs signature group represented a non-inflamed TME phenotype on the single cell level. The patients with high ORGs signature had more sensitivity to immunotherapy. And those ORGs were verified.\nCONCLUSIONS: ORGs pattern depicts different TME phenotypes in PRAD. The ORGs risk score and ORGs signature have an important role for predicting prognosis and immunotherapy efficacies.", "claims": [{"id": "claim-method", "statement": "In the training set (TCGA-PRAD), we used univariate Cox analysis to analyze common DERs, and then used LASSO algorithm to screen the best candidate DERs. Next, we used multivariate Cox regression coefficient to select the best candidate DERs, and established ORGs model based on ORG RNA expression pattern and weighted using the formula: ORGs=Σβi * RNAi, where βi represented the expression pattern coefficient of the ‘i’th ORG RNA. Finally, logarithmic rank test and Kaplan-Meier analysis were employed to evaluate the prognostic predictive value of ORGs model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37788005", "locator": "sections[7] \"Construction and verification of ORGs model\" paragraph 2", "quote": "In the training set (TCGA-PRAD), we used univariate Cox analysis to analyze common DERs, and then used LASSO algorithm to screen the best candidate DERs. Next, we used multivariate Cox regression coefficient to select the best candidate DERs, and established ORGs model based on ORG RNA expression pattern and weighted using the formula: ORGs=Σβi * RNAi, where βi represented the expression pattern coefficient of the ‘i’th ORG RNA. Finally, logarithmic rank test and Kaplan-Meier analysis were employed to evaluate the prognostic predictive value of ORGs model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data of this study were from publicly available datasets. They could be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37788005", "locator": "sections[12] \"Data availability statement\" paragraph 2", "quote": "The data of this study were from publicly available datasets. They could be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/geo/."}], "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:37788005"], "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:GSE157703"], "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:GSE157703"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37788005", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205065", "sha256": "sha256:8e58888825790c8659e13c635ee14ec0ca2cf00d3f4776989f5d88739cfafb6a", "bytes": 47388, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.205065", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10599753", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE157703", "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=GSE157703", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE157703", "version": null, "note": "The data of this study were from publicly available datasets. They could be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/geo/.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data of this study were from publicly available datasets. They could be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/geo/.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4773521", "GSM4773522"], "sample_count": 2, "group_sizes": null, "group_sizes_basis": null, "platform": "20795", "taxon": "Homo sapiens", "title": "Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing", "resolution_checked_at": "2026-11-01T19:09:46Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE21034", "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=GSE21034", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21034", "version": null, "note": "The data of this study were from publicly available datasets. They could be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/geo/.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data of this study were from publicly available datasets. 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Next, we used multivariate Cox regression coefficient to select the best candidate DERs, and established ORGs model based on ORG RNA expression pattern and weighted using the formula: ORGs=Σβi * RNAi, where βi represented the expression pattern coefficient of the ‘i’th ORG RNA. Finally, logarithmic rank test and Kaplan-Meier analysis were employed to evaluate the prognostic predictive value of ORGs model."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Cox regression"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"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 correlation"}}, {"id": "method-6", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}, {"id": "method-7", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "χ2"}}], "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": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "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": ["GSE70769", "GSE21034", "GSE157703"], "resolved_accessions": ["GSE70769", "GSE21034", "GSE157703"], "statements": ["The data of this study were from publicly available datasets. They could be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/geo/."], "basis": "The paper names GSE70769, GSE21034, GSE157703 in GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["survival_km"], "assets": ["source:pmid:37788005", "data:geo:GSE157703"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE157703"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:55:06Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14142, "completion_tokens": 2593, "cached_tokens": 264}}, "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:37788005", "type": "paper", "label": "pmid:37788005"}, {"id": "source:pmid:37788005", "type": "manuscript", "label": "PMID 37788005 full text"}, {"id": "data:geo:GSE157703", "type": "dataset", "label": "GEO GSE157703"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37788005", "type": "paper", "label": "pmid:37788005"}, {"id": "source:pmid:37788005", "type": "manuscript", "label": "PMID 37788005 full text"}, {"id": "data:geo:GSE157703", "type": "dataset", "label": "GEO GSE157703"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37788005", "type": "extraction", "used": ["source:pmid:37788005"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE157703", "type": "accession_resolution", "used": ["data:geo:GSE157703"], "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:37788005", "parent_hash": "sha256:2c12cb5f098b89cb6017b655d12092780b50d30e7da4bb8fa6b32b22120ccf90", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE157703 licence not read", "asset:data:geo:GSE157703 not downloaded", "asset:data:geo:GSE21034 licence not read", "asset:data:geo:GSE21034 not downloaded", "asset:data:geo:GSE70769 licence not read", "asset:data:geo:GSE70769 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": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["China Center for Type Culture Collection and cultured in RPMI 1640 (Gibco, Grand Island, NY, USA), wh", "ml streptomycin (Invitrogen). They both were cultured in a humidified incubator at 37° C and 5%CO2. S"], "order_basis": "paper_text_order:1755", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In the training set (TCGA-PRAD), we used univariate Cox analysis to analyze common DERs, and then used LASSO algorithm to screen the best candidate DERs. Next, we used multivariate Cox regression coefficient to select the best candidate DERs, and established ORGs model based on ORG RNA expression pattern and weighted using the formula: ORGs=Σβi * RNAi, where βi represented the expression pattern coefficient of the ‘i’th ORG RNA. Finally, logarithmic rank test and Kaplan-Meier analysis were employed to evaluate the prognostic predictive value of ORGs model."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}