Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37907850", "kind": "science-record", "revision": 2, "content_hash": "sha256:2910151a1a9d987c782cbd8671688d865c5c895950e9fadd77394076633d0620", "created_at": "2026-11-01T19:02:47Z", "updated_at": "2026-11-01T21:31:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity.", "doi": "10.1186/s12885-023-11415-y", "pmid": "37907850", "authors": ["Yangwei Wang", "Tong Yu", "Jiaping Chen", "Rong Zhao", "Mingxin Diao", "Peiyuan Mei", "Shiwen He", "Wenlin Qiu", "Guanchao Ye", "Lijuan Jiang", "Han Xiao", "Yongde Liao"], "journal": "BMC cancer", "year": 2023, "abstract": "Lung adenocarcinoma (LUAD) is a common type of malignant tumor with poor prognosis and high mortality. In our previous studies, we found that estrogen is an important risk factor for LUAD, and different estrogen statuses can predict different prognoses. Therefore, in this study, we constructed a prognostic signature related to estrogen reactivity to determine the relationship between different estrogen reactivities and prognosis. We downloaded the LUAD dataset from The Cancer Genome Atlas (TCGA) database, calculated the estrogen reactivity of each sample, and divided them into a high-estrogen reactivity group and a low-estrogen reactivity group. The difference in overall survival between the groups was significant. We also analyzed the status of immune cell infiltration and immune checkpoint expression between the groups. We analyzed the differential gene expression between the groups and screened four key prognostic factors by the least absolute shrinkage and selection operator (LASSO) regression and univariable and multivariable Cox regression. Based on the four genes, a risk signature was established. To a certain extent, the receiver operating characteristic (ROC) curve showed the predictive ability of the risk signature, which was further verified using the GSE31210 dataset. We also determined the role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predictive nomogram combining the risk signature with other clinical characteristics. In conclusion, our four-gene prognostic signature based on estrogen reactivity had prognostic value and can provide new insights into the development of treatment strategies for LUAD.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to narrow down the prognosis-related DEGs using the “glmnet” package [38]. Then univariable and multivariable Cox regression analyses were conducted to determine the signature genes and calculate the corresponding regression coefficients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37907850", "locator": "sections[10] \"Construction and validation of a novel prognostic signature\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to narrow down the prognosis-related DEGs using the “glmnet” package [38]. Then univariable and multivariable Cox regression analyses were conducted to determine the signature genes and calculate the corresponding regression coefficients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed for this study can be found in the TCGA (TCGA-LUAD, https://portal.gdc.cancer.gov) and GEO (GSE31210, https://www.ncbi.nlm.nih.gov/gds) databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37907850", "locator": "sections[28] \"Data availability\" paragraph 2", "quote": "The datasets analyzed for this study can be found in the TCGA (TCGA-LUAD, https://portal.gdc.cancer.gov) and GEO (GSE31210, https://www.ncbi.nlm.nih.gov/gds) databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The one-year, three-year, and five-year OS AUC values were 0.7, 0.66, and 0.69, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37907850", "locator": "sections[19] \"A novel prognostic signature for the prognostic prediction of LUAD\" paragraph 4", "quote": "The one-year, three-year, and five-year OS AUC values were 0.7, 0.66, and 0.69, respectively, which showed that the accuracy of risk signature needs to be improved (Fig. 7B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The one-year, three-year, and five-year OS AUC values were 0.7, 0.66, and 0.69, respectively.", "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:37907850"], "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:GSE31210"], "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:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37907850", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-023-11415-y", "sha256": "sha256:01b9a3faba33faef0bffb3a056f63e624db10ee3551f2461ce8477e0188499ea", "bytes": 53182, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-023-11415-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10619241", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31210", "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=GSE31210", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31210", "version": null, "note": "The datasets analyzed for this study can be found in the TCGA (TCGA-LUAD, https://portal.gdc.cancer.gov) and GEO (GSE31210, 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Additionally, we developed a predictive nom", "role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predicti", "in another study [36]. Ovariectomized (OVX) mice were intraperitoneally injected with PBS or E2 (1"], "order_basis": "paper_text_order:1463", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to narrow down the prognosis-related DEGs using the “glmnet” package [38]. Then univariable and multivariable Cox regression analyses were conducted to determine the signature genes and calculate the corresponding regression coefficients."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}