An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38404588", "kind": "science-record", "revision": 3, "content_hash": "sha256:2d80cae02df0e349eb92b47949bacf43a5571f8a4c2acacb68fc4256f11917ad", "created_at": "2026-11-01T19:20:21Z", "updated_at": "2026-11-01T22:11:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.", "doi": "10.3389/fimmu.2024.1228235", "pmid": "38404588", "authors": ["Yongjia Cui", "Weixuan Zhang", "Wenping Lu", "Yaogong Feng", "Xiaoqing Wu", "Zhili Zhuo", "Dongni Zhang", "Yichi Zhang"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.\nMethods: 420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.\nResults: 64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.\nConclusion: Our study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.", "claims": [{"id": "claim-method", "statement": "With the stepCOX (forward) combined with Ridge algorithm analysis, based on the smallest AIC area, we identified 36 important lncRNAs ( Figure 3B ). We used multivariate Cox regression analysis to select 20 exosome-related lncRNAs that were independently associated with overall survival (OS) ( Figure 3C ; Table 2 ).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38404588", "locator": "sections[13] \"Establishment of exosome-related lncRNA signature (ERLS) based on machine learning\" paragraph 2", "quote": "With the stepCOX (forward) combined with Ridge algorithm analysis, based on the smallest AIC area, we identified 36 important lncRNAs ( Figure 3B ). We used multivariate Cox regression analysis to select 20 exosome-related lncRNAs that were independently associated with overall survival (OS) ( Figure 3C ; Table 2 )."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38404588", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author/s."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38404588", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model.", "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:38404588"], "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:GSE102073"], "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:GSE102073"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38404588", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1228235", "sha256": "sha256:5140750941bcf3ecce6bd964055c78c14ebe9c9df6c70036a75630470bd48fd6", "bytes": 42903, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2024.1228235", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10884316", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE102073", "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=GSE102073", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE102073", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author/s.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/ Supplementary Materials , further inquiries can be directed to the corresponding author/s.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2722881", "GSM2722883", "GSM2722885", "GSM2722888", "GSM2722890", "GSM2722892", "GSM2722894", "GSM2722896", "GSM2722898", "GSM2722900", "GSM2722902", "GSM2722905", "GSM2722907", "GSM2722909", "GSM2722911", "GSM2722913", "GSM2722915", "GSM2722917", "GSM2722920", "GSM2722921", "GSM2722923", "GSM2722926", "GSM2722928", "GSM2722930", "GSM2722932", "GSM2722934", "GSM2722936", "GSM2722938", "GSM2722940", "GSM2722942", "GSM2722944", "GSM2722946", "GSM2722948", "GSM2722950", "GSM2722952", "GSM2722955", "GSM2722957", "GSM2722959", "GSM2722961", "GSM2722963", "GSM2722966", "GSM2722968", "GSM2722970", "GSM2722972", "GSM2722974", "GSM2722976", "GSM2722978", "GSM2722980", "GSM2722982", "GSM2722984", "GSM2722986", "GSM2722988", "GSM2722990", "GSM2722992", "GSM2722994", "GSM2722996", "GSM2722998", "GSM2723000", "GSM2723002", "GSM2723004", "GSM2723006", "GSM2723008", "GSM2723011", "GSM2723013", "GSM2723014", "GSM2723016", "GSM2723019", "GSM2723021", "GSM2723023", "GSM2723026", "GSM2723027", "GSM2723029", "GSM2723031", "GSM2723033", "GSM2723035", "GSM2723038", "GSM2723040", "GSM2723042", "GSM2723044", "GSM2723046", "GSM2723048", "GSM2723050", "GSM2723052", "GSM2723054", "GSM2723056"], "sample_count": 85, "group_sizes": null, "group_sizes_basis": null, "platform": "16791", "taxon": "Homo sapiens", "title": "Molecular analysis of high-grade serous ovarian carcinoma with and without associated serous tubal intra-epithelial carcinoma [RNA-Seq]", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE102073 not downloaded", "asset:data:geo:GSE102073 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "With the stepCOX (forward) combined with Ridge algorithm analysis, based on the smallest AIC area, we identified 36 important lncRNAs ( Figure 3B ). 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