A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36246599", "kind": "science-record", "revision": 4, "content_hash": "sha256:1fa15b5738c8bbb05daf46d8fefc8db1ac6b85ce5e6b782c9f7c9a3d0c5ec8ba", "created_at": "2026-11-01T18:15:37Z", "updated_at": "2026-11-01T22:07:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening.", "doi": "10.3389/fgene.2022.942166", "pmid": "36246599", "authors": ["Min Chen", "Guang-Bo Wu", "Zhi-Wen Xie", "Dan-Li Shi", "Meng Luo"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy. Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs. Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability. Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.", "claims": [{"id": "claim-method", "statement": "Pearson’s correlation analysis was performed to determine the association between diagnostic genes and drug sensitivity.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36246599", "locator": "sections[9] \"Drug-sensitive analysis of the gene signatures\" paragraph 2", "quote": "Pearson’s correlation analysis was performed to determine the association between diagnostic genes and drug sensitivity."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36246599", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ANN diagnostic model based on MT1M, SPINK1, AKR1B10, and SLCO1B3 achieved ROC AUC values of 0.910 in the training cohort and 0.953 in the testing cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36246599", "locator": "sections[15] \"Construction and validation of the diagnostic model built on an artificial neural network\" paragraph 2", "quote": "An ROC curve was performed to detect whether the model could distinguish the HCC sample from the normal sample, and the area under the curve (AUC) was 0.910 (Figure 5B)."}, {"asset": "source:pmid:36246599", "locator": "sections[15] \"Construction and validation of the diagnostic model built on an artificial neural network\" paragraph 2", "quote": "In addition, the model worked well in the testing cohort as the AUC of ROC was 0.953 (Figure 5C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The ANN diagnostic model based on MT1M, SPINK1, AKR1B10, and SLCO1B3 achieved ROC AUC values of 0.910 in the training cohort and 0.953 in the testing cohort.", "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:36246599"], "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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