Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39010084", "kind": "science-record", "revision": 4, "content_hash": "sha256:1b5e60856c3e762e504994b5da109e3ed80a438903c0d870c3627f938b119689", "created_at": "2026-11-01T20:29:05Z", "updated_at": "2026-11-01T22:54:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing.", "doi": "10.1186/s12967-024-05460-9", "pmid": "39010084", "authors": ["Zhaorui Cheng", "Shuangmei Li", "Shujun Yang", "Huibao Long", "Haidong Wu", "Xuxiang Chen", "Xiaoping Cheng", "Tong Wang"], "journal": "Journal of translational medicine", "year": 2024, "abstract": "INTRODUCTION: Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.\nMETHOD: The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.\nRESULTS: An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.\nCONCLUSION: The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.", "claims": [{"id": "claim-method", "statement": "The “limma” R package was used to detect differential expressed genes (DEGs) between HCC and normal tissues in the training set with |log2 fold change (FC)|> 1.5 and adjusted p < 0.05 as cutoff value.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39010084", "locator": "sections[5] \"The identification of differential expressed genes and ERSRGs\" paragraph 2", "quote": "The “limma” R package was used to detect differential expressed genes (DEGs) between HCC and normal tissues in the training set with |log2 fold change (FC)|> 1.5 and adjusted p < 0.05 as cutoff value."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39010084", "locator": "sections[34] \"notes\" paragraph 1", "quote": "All data generated or analyzed during this study are included in this published article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Through the intersection of 7 ERSRGs in RF analysis and 6 ERSRGs in SVM analysis, 6 ERSRGs including SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1 were finally determined for the construction of the ANN below (Fig. 4E).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39010084", "locator": "sections[20] \"The identification of ERS-related DEGs\" paragraph 2", "quote": "Through the intersection of 7 ERSRGs in RF analysis and 6 ERSRGs in SVM analysis, 6 ERSRGs including SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1 were finally determined for the construction of the ANN below (Fig. 4E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Through the intersection of 7 ERSRGs in RF analysis and 6 ERSRGs in SVM analysis, 6 ERSRGs including SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1 were finally determined for the construction of the ANN below (Fig. 4E).", "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:39010084"], "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:GSE121248"], "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:GSE121248"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39010084", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-024-05460-9", "sha256": 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