A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39749665", "kind": "science-record", "revision": 3, "content_hash": "sha256:ac675ef56dd47bad47980e2ef46df7a2c987d1a63b22688115ef4d9dcd3c425c", "created_at": "2026-11-01T20:30:05Z", "updated_at": "2026-11-01T22:13:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma.", "doi": "10.1093/bib/bbae686", "pmid": "39749665", "authors": ["Ren Wang", "Qiumei Liu", "Wenhua You", "Huiyu Wang", "Yun Chen"], "journal": "Briefings in bioinformatics", "year": 2024, "abstract": "Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti-PD-1/PD-L1 outcomes centered on the cGAS-STING pathway in HCC. We identified classical pathways highly correlated with cGAS-STING pathway and constructed transformer survival model preserving raw structure of pathways. We also developed explainable XGBoost model for predicting anti-PD-1/PD-L1 outcomes using SHAP algorithm. We trained and validated transformer survival model on pan-cancer cohort and tested it on three independent HCC cohorts. Using 0.5 as threshold across cohorts, we divided each HCC cohort into two groups and calculated P values with log-rank test. TCGA-LIHC: C-index = 0.750, P = 1.52e-11; ICGC-LIRI-JP: C-index = 0.741, P = .00138; GSE144269: C-index = 0.647, P = .0233. We trained and validated [area under the receiver operating characteristic curve (AUC) = 0.777] XGBoost model on immunotherapy datasets and tested it on GSE78220 (AUC = 0.789); we also tested XGBoost model on HCC anti-PD-L1 cohort (AUC = 0.719). Our deep learning model and XGBoost model demonstrate potential in predicting survival risks and anti-PD-1/PD-L1 outcomes in HCC. We deployed these two prediction models to the GitHub repository and provided detailed instructions for their usage: deep learning survival model, https://github.com/mlwalker123/CSP_survival_model; XGBoost immunotherapy model, https://github.com/mlwalker123/CSP_immunotherapy_model.", "claims": [{"id": "claim-method", "statement": "We utilized ROC curve to assess the diagnostic capability of continuous variable. The method used for testing comparisons between two independent datasets in this study was the Mann–Whitney U test. All correlation analyses mentioned in this study were conducted using Spearman correlation analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39749665", "locator": "sections[14] \"Statistics\" paragraph 2", "quote": "We utilized ROC curve to assess the diagnostic capability of continuous variable. The method used for testing comparisons between two independent datasets in this study was the Mann–Whitney U test. All correlation analyses mentioned in this study were conducted using Spearman correlation analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data utilized in this study can be downloaded in the public databases. The names, applications, and downloading methods of all datasets are explicitly detailed in the table (Table 1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39749665", "locator": "sections[28] \"Data availability\" paragraph 2", "quote": "All data utilized in this study can be downloaded in the public databases. The names, applications, and downloading methods of all datasets are explicitly detailed in the table (Table 1)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The cGAS-STING pathway ssGSEA score distinguished HCC from normal liver tissue with an AUC of 0.756 (P value = 4.3e−09).", "kind": "quantitative", "target": {"value": 4.0, "unit": "p-value", "tolerance": null, "source": "We compared the ssGSEA scores of the cGAS-STING pathway between normal liver tissue samples and HCC samples (P value = 4.3e−09) (Fig. 2b)."}, "evidence": [{"asset": "source:pmid:39749665", "locator": "sections[17] \"The diagnostic value of the cGAS-STING pathway and screening of related classical pathways\" paragraph 2", "quote": "We calculated the single sample gene set enrichment analysis (ssGSEA) scores of the cGAS-STING pathway in normal liver tissue samples and HCC samples from the TCGA-LIHC cohort and evaluated the ability of these scores to distinguish HCC from normal liver tissue using receiver operating characteristic (ROC) curve [area under the ROC curve (AUC) = 0.756] (Fig. 2a)."}, {"asset": "source:pmid:39749665", "locator": "sections[17] \"The diagnostic value of the cGAS-STING pathway and screening of related classical pathways\" paragraph 2", "quote": "We compared the ssGSEA scores of the cGAS-STING pathway between normal liver tissue samples and HCC samples (P value = 4.3e−09) (Fig. 2b)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The cGAS-STING pathway ssGSEA score distinguished HCC from normal liver tissue with an AUC of 0.756 (P value = 4.3e−09).", "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:39749665"], "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:GSE144269"], "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:GSE144269"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39749665", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bib/bbae686", "sha256": "sha256:91bfc754994b752d032259f2f6c8132b7ab104df94c3166e233bb3a8c63f0af3", "bytes": 92540, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bib/bbae686", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11695900", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE144269", "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=GSE144269", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE144269", "version": null, "note": "All data utilized in this study can be downloaded in the public databases. 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