An estrogen response-related signature predicts response to immunotherapy in melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37251404", "kind": "science-record", "revision": 3, "content_hash": "sha256:2994a009d9a649b8e54bcadfaa7fbc4d5c29a0c320fcd8f820003bc77e38939f", "created_at": "2026-11-01T19:01:58Z", "updated_at": "2026-11-01T22:09:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An estrogen response-related signature predicts response to immunotherapy in melanoma.", "doi": "10.3389/fimmu.2023.1109300", "pmid": "37251404", "authors": ["Min Lin", "Tian Du", "Xiaofeng Tang", "Ying Liao", "Lan Cao", "Yafang Zhang", "Wei Zheng", "Jianhua Zhou"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: Estrogen/estrogen receptor signaling influences the tumor microenvironment and affects the efficacy of immunotherapy in some tumors, including melanoma. This study aimed to construct an estrogen response-related gene signature for predicting response to immunotherapy in melanoma.\nMethods: RNA sequencing data of 4 immunotherapy-treated melanoma datasets and TCGA melanoma was obtained from open access repository. Differential expression analysis and pathway analysis were performed between immunotherapy responders and non-responders. Using dataset GSE91061 as the training group, a multivariate logistic regression model was built from estrogen response-related differential expression genes to predict the response to immunotherapy. The other 3 datasets of immunotherapy-treated melanoma were used as the validation group. The correlation was also examined between the prediction score from the model and immune cell infiltration estimated by xCell in the immunotherapy-treated and TCGA melanoma cases.\nResults: \"Hallmark Estrogen Response Late\" was significantly downregulated in immunotherapy responders. 11 estrogen response-related genes were significantly differentially expressed between immunotherapy responders and non-responders, and were included in the multivariate logistic regression model. The AUC was 0.888 in the training group and 0.654-0.720 in the validation group. A higher 11-gene signature score was significantly correlated to increased infiltration of CD8+ T cells (rho=0.32, p=0.02). TCGA melanoma with a high signature score showed a significantly higher proportion of immune-enriched/fibrotic and immune-enriched/non-fibrotic microenvironment subtypes (p<0.001)-subtypes with better response to immunotherapy-and significantly better progression-free interval (p=0.021).\nConclusion: In this study, we identified and verified an 11-gene signature that could predict response to immunotherapy in melanoma and was correlated with tumor-infiltrating lymphocytes. Our study suggests targeting estrogen-related pathways may serve as a combination strategy for immunotherapy in melanoma.", "claims": [{"id": "claim-method", "statement": "Considering estrogen response is a complex pathway involving many genes, we included all 11 genes and developed a multivariate logistic regression model for ICB response in melanoma.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37251404", "locator": "sections[10] \"Establishment and evaluation of an 11-gene estrogen response-related signature to predict immunotherapy response\" paragraph 2", "quote": "Considering estrogen response is a complex pathway involving many genes, we included all 11 genes and developed a multivariate logistic regression model for ICB response in melanoma."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91061 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100797 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78220 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62944 http://www.cbioportal.org/study/summary?id=skcm_dfci_2015.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37251404", "locator": "sections[14] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91061 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100797 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78220 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62944 http://www.cbioportal.org/study/summary?id=skcm_dfci_2015."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC was 0.888 in the training group and 0.654–0.720 in the validation group.", "kind": "quantitative", "target": {"value": 0.888, "unit": "auc", "tolerance": null, "source": "The AUC was 0.888 in the training group and 0.654–0.720 in the validation group."}, "evidence": [{"asset": "source:pmid:37251404", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The AUC was 0.888 in the training group and 0.654–0.720 in the validation group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC was 0.888 in the training group and 0.654–0.720 in the validation group.", "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:37251404"], "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:GSE100797"], "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:GSE100797"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37251404", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1109300", "sha256": "sha256:a5473ca38ca59eff5f1743dc5216f805755a349941c56bcac6ca7518716bb440", "bytes": 48946, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1109300", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10213284", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE100797", "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=GSE100797", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE100797", "version": null, "note": "Publicly available datasets were analyzed in this study. 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No tolerance is published because the paper states none."}], "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": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:37251404", "data:geo:GSE100797"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE100797"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:42:28Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14048, "completion_tokens": 3193, "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:37251404", "type": "paper", "label": "pmid:37251404"}, {"id": "source:pmid:37251404", "type": "manuscript", "label": "PMID 37251404 full text"}, {"id": "data:geo:GSE100797", "type": "dataset", "label": "GEO GSE100797"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37251404", "type": "paper", "label": "pmid:37251404"}, {"id": "source:pmid:37251404", "type": "manuscript", "label": "PMID 37251404 full text"}, {"id": "data:geo:GSE100797", "type": "dataset", "label": "GEO GSE100797"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37251404", "type": "extraction", "used": ["source:pmid:37251404"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE100797", "type": "accession_resolution", "used": ["data:geo:GSE100797"], "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:37251404", "parent_hash": "sha256:4f982185a478136beeda97a80cb244f717bc59439c18189f4b455134d0893469", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE100797 licence not read", "asset:data:geo:GSE100797 not downloaded", "asset:data:geo:GSE62944 licence not read", "asset:data:geo:GSE62944 not downloaded", "asset:data:geo:GSE78220 licence not read", "asset:data:geo:GSE78220 not downloaded", "asset:data:geo:GSE91061 licence not read", "asset:data:geo:GSE91061 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": 0.99, "evidence": ["ponse to immunotherapy in melanoma. Methods: RNA sequencing data of 4 immunotherapy-treated melanoma dat", "ods Patient data acquisition RNA sequencing (RNA-seq) data of immunotherapy-treated melanoma were", "terials and methods Patient data acquisition RNA sequencing (RNA-seq) data of immunotherapy-treated mela"], "order_basis": "paper_text_order:344", "id": "assay:1"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Considering estrogen response is a complex pathway involving many genes, we included all 11 genes and developed a multivariate logistic regression model for ICB response in melanoma."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}