Immune-related gene signature to predict TACE refractoriness in patients with hepatocellular carcinoma based on artificial neural network.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36685822", "kind": "science-record", "revision": 4, "content_hash": "sha256:f60229f3e0356dd9f68f80c9752a2a0af8a9713f8b92809dd6935bd02d2db0e2", "created_at": "2026-11-01T19:01:14Z", "updated_at": "2026-11-01T22:51:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune-related gene signature to predict TACE refractoriness in patients with hepatocellular carcinoma based on artificial neural network.", "doi": "10.3389/fgene.2022.993509", "pmid": "36685822", "authors": ["Qingyu Xu", "Chendong Wang", "Guowen Yin"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Transarterial chemoembolization (TACE) is the standard treatment option for intermediate-stage hepatocellular carcinoma (HCC), while response varies among patients. This study aimed to identify novel immune-related genes (IRGs) and establish a prediction model for TACE refractoriness in HCC patients based on machine learning methods. Methods: Gene expression data were downloaded from GSE104580 dataset of Gene Expression Omnibus (GEO) database, differential analysis was first performed to screen differentially expressed genes (DEGs). The least absolute shrinkage and selection operator (LASSO) regression analysis was performed to further select significant DEGs. Weighted gene co-expression network analysis (WGCNA) was utilized to build a gene co-expression network and filter the hub genes. Final signature genes were determined by the intersection of LASSO analysis results, WGCNA results and IRGs list. Based on the above results, the artificial neural network (ANN) model was constructed in the training cohort and verified in the validation cohort. Receiver operating characteristics (ROC) analysis was used to assess the prediction accuracy. Correlation of signature genes with tumor microenvironment scores, immune cells and immune checkpoint molecules were further analyzed. The tumor immune dysfunction and exclusion (TIDE) score was used to evaluate the response to immunotherapy. Results: One hundred and forty-seven samples were included in this study, which was randomly divided into the training cohort (n = 103) and validation cohort (n = 44). In total, 224 genes were identified as DEGs. Further LASSO regression analysis screened out 25 genes from all DEGs. Through the intersection of LASSO results, WGCNA results and IRGs list, S100A9, TREM1, COLEC12, and IFIT1 were integrated to construct the ANN model. The areas under the curves (AUCs) of the model were .887 in training cohort and .765 in validation cohort. The four IRGs also correlated with tumor microenvironment scores, infiltrated immune cells and immune checkpoint genes in various degrees. Patients with TACE-Response, lower expression of COLEC12, S100A9, TREM1 and higher expression of IFIT1 had better response to immunotherapy. Conclusion: This study constructed and validated an IRG signature to predict the refractoriness to TACE in patients with HCC, which may have the potential to provide insights into the TACE refractoriness in HCC and become the immunotherapeutic targets for HCC patients with TACE refractoriness.", "claims": [{"id": "claim-method", "statement": "The differentially expressed genes (DEGs) between TACE-Nonresponders with TACE-Responders were acquired with Linear Model for Microarray (LIMMA) package to analyze genes expression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36685822", "locator": "sections[4] \"Identification of differentially expressed genes\" paragraph 2", "quote": "The differentially expressed genes (DEGs) between TACE-Nonresponders with TACE-Responders were acquired with Linear Model for Microarray (LIMMA) package to analyze genes expression."}], "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=GSE104580.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36685822", "locator": "sections[23] \"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=GSE104580."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Prediction accuracy of the model had an AUC of .887 (95% CI, .810–.949) in training cohort and .762 (95% CI, .612–.883) in the validation cohort (Figure 4B), suggesting that the ANN is relatively stable in predicting TACE refractoriness.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Prediction accuracy of the model had an AUC of .887 (95% CI, .810–.949) in training cohort and .762 (95% CI, .612–.883) in the validation cohort (Figure 4B), suggesting that the ANN is relatively stable in predicting TACE refractoriness."}, "evidence": [{"asset": "source:pmid:36685822", "locator": "sections[16] \"Construction and evaluation of the ANN model\" paragraph 4", "quote": "Prediction accuracy of the model had an AUC of .887 (95% CI, .810–.949) in training cohort and .762 (95% CI, .612–.883) in the validation cohort (Figure 4B), suggesting that the ANN is relatively stable in predicting TACE refractoriness."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Prediction accuracy of the model had an AUC of .887 (95% CI, .810–.949) in training cohort and .762 (95% CI, .612–.883) in the validation cohort (Figure 4B), suggesting that the ANN is relatively stable in predicting TACE refractoriness.", "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:36685822"], "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. 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