Integrative analysis unveils ECM signatures and pathways driving hepatocellular carcinoma progression: A multi-omics approach and prognostic model development.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38568083", "kind": "science-record", "revision": 3, "content_hash": "sha256:fcc85fdf544eef4437207559c581b19617b5f97439935e7f72c6b51ebc457907", "created_at": "2026-11-01T19:20:32Z", "updated_at": "2026-11-01T22:11:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative analysis unveils ECM signatures and pathways driving hepatocellular carcinoma progression: A multi-omics approach and prognostic model development.", "doi": "10.1111/jcmm.18230", "pmid": "38568083", "authors": ["Zhen Liu", "Pengfei Zhao"], "journal": "Journal of cellular and molecular medicine", "year": 2024, "abstract": "Liver hepatocellular carcinoma (LIHC) is a highly lethal form of cancer that is among the deadliest cancer types globally. In terms of cancer-related mortality rates, liver cancer ranks among the top three, underscoring the severity of this disease. Insufficient analysis has been conducted to fully understand the potential value of the extracellular matrix (ECM) in immune infiltration and the prognostic stratification of LIHC, despite its recognised importance in the development of this disease. The scRNA-seq data of GSE149614 was used to conduct single-cell analysis on 10 LIHC samples. CellChat scores were calculated for seven cell populations in the descending cohort to investigate cellular communication, while PROGENy scores were calculated to determine tumour-associated pathway scores in different cell populations. The pathway analysis using GO and KEGG revealed the enrichment of ECM-associated genes in the pathway, highlighting the potential role of the ECM in LIHC development. By utilizing the TCGA-LIHC cohort, an ECM-based prognostic model for LIHC was developed using Lasso regression. Immune infiltration scores were calculated using two methods, and the performance of the ECM-related risk score was evaluated using an independent cohort from the CheckMate study. To determine the precise expression of ECM-associated risk genes in LIHC, we evaluated hepatocellular carcinoma cell lines using a range of assays, including Western blotting, invasion assays and Transwell assays. Using single-cell transcriptome analysis, we annotated the spatially-specific distribution of major immune cell types in single-cell samples of LIHC. The main cell types identified and annotated included hepatocytes, T cells, myeloid cells, epithelial cells, fibroblasts, endothelial cells and B cells. The utilisation of cellchat and PROGENy analyses enabled the investigation and unveiling of signalling interactions, protein functionalities and the prominent influential pathways facilitated by the primary immune cell types within the LIHC. Numerous tumour pathways, including PI2K, EGFR and TGFb, demonstrated a close correlation with the involvement of ECM in LIHC. Moreover, an evaluation was conducted to assess the primary ECM-related functional changes and biological pathway enrichment in LIHC. Differential genes associated with ECM were identified and utilised to create prognostic models. The prognostic stratification value of these models for LIHC patients was confirmed through validation in multiple databases. Furthermore, through immune infiltration analysis, it was discovered that ECM might be linked to the irregular expression and regulation of numerous immune cells. Additionally, histone acetylation was mapped against gene mutation frequencies and differential expression profiles. The prognostic stratification efficacy of the ECM prediction model constructed in the context of PD-1 inhibitor therapy was also examined, and it exhibited strong stratification performance. Cellular experiments, including Western blotting, invasion and Transwell assays, revealed that ECM-associated risk genes have a promoting effect on the development of LIHC. The creation of biomarkers for LIHC using ECM-related genes unveiled substantial correlations with immune microenvironmental infiltration and functional mutations in various tumour pathways. This enlightens us to the possibility that the influence of ECM on tumours may extend beyond simply promoting the fibrotic process and the stromal composition of tumours.", "claims": [{"id": "claim-method", "statement": "Next, a prognostic model was constructed by selecting prognostically significant genes from the 22 genes based on Least absolute shrinkage and selection operator (Lasso) regression using TCGA‐LIHC cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38568083", "locator": "sections[6] \"Biological construction and clinical validation of prognostic risk models for LIHC with ECM‐related genes\" paragraph 2", "quote": "Next, a prognostic model was constructed by selecting prognostically significant genes from the 22 genes based on Least absolute shrinkage and selection operator (Lasso) regression using TCGA‐LIHC cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All datasets generated for this study are included in the article material, including TCGA‐LIHC, GSE14520 and GSE149614.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38568083", "locator": "sections[24] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "All datasets generated for this study are included in the article material, including TCGA‐LIHC, GSE14520 and GSE149614."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using Lasso regression on 22 ECM-related genes in the TCGA-LIHC cohort, the study constructed a seven-gene prognostic model that separated patients with significantly different survival outcomes (p < 0.01) and achieved an AUC of 0.705 in the independent GSE14520 cohort.", "kind": "quantitative", "target": {"value": 0.705, "unit": "auc", "tolerance": null, "source": "The results of ROC curve showed that signature in the testing cohort had good test efficiency (AUC = 0.705)."}, "evidence": [{"asset": "source:pmid:38568083", "locator": "sections[17] \"Biological construction and clinical validation of prognostic risk models for LIHC with ECM‐associated genes\" paragraph 2", "quote": "Using the TCGA‐LIHC cohort, we selected seven prognostic ECM genes from 22 genes using Lasso regression and constructed a prognostic model."}, {"asset": "source:pmid:38568083", "locator": "sections[17] \"Biological construction and clinical validation of prognostic risk models for LIHC with ECM‐associated genes\" paragraph 2", "quote": "The analysis indicated that high‐risk group patients, with higher ECM expression, had poorer survival rates (p < 0.01) compared to those in the low‐risk group, who exhibited significantly longer survival times."}, {"asset": "source:pmid:38568083", "locator": "sections[17] \"Biological construction and clinical validation of prognostic risk models for LIHC with ECM‐associated genes\" paragraph 4", "quote": "The results of ROC curve showed that signature in the testing cohort had good test efficiency (AUC = 0.705)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using Lasso regression on 22 ECM-related genes in the TCGA-LIHC cohort, the study constructed a seven-gene prognostic model that separated patients with significantly different survival outcomes (p < 0.01) and achieved an AUC of 0.705 in the independent GSE14520 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:38568083"], "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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The scRNA-seq data of GSE149614 was used to conduct single"], "order_basis": "paper_text_order:620", "id": "assay:1"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ell lines using a range of assays, including Western blotting, invasion assays and Transwell assays. Using", "performance. Cellular experiments, including Western blotting, invasion and Transwell assays, revealed tha", "stical analysis of intergroup differences in Western blot, colony formation assay and Transwell assay,", "SPP1 in HepG2 and SNU‐387 cell lines through Western blot. (B) Quantitative analysis of the results of"], "order_basis": "paper_text_order:1563", "id": "assay:2"}, {"role": "data_generation", "definition_id": "amplicon_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["s Conventional second‐generation sequencing (NGS) analyses the genome of a group of cells, in"], "order_basis": "paper_text_order:3814", "id": "assay:3"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["e PD‐1 inhibitor nivolumab. Cell culture and transfection The hepatocellular carcinoma HepG2 and SNU‐3", "of culture medium. Twenty‐four hours before transfection, all groups were switched to serum‐free medi", "μL of Opti‐MEM medium (Thermo, USA), and the transfection reagent Lipofectamine 3000 (Thermo, USA) was", "in 125 μL of Opti‐MEM medium. The siRNA and transfection reagent mixture was incubated in the dark at"], "order_basis": "paper_text_order:14043", "id": "assay:4"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["emperature cell incubator (37°C, 5% CO2) and cultured in high‐glucose Dulbecco's modified eagle mediu", "nd transfection The hepatocellular carcinoma HepG2 and SNU‐387O2 cell lines were obtained from", "ted that as the culture time increased, both HepG2 (Figure 6C) and SNU‐387 (Figure 6D) cell lin", "ation of the knockdown efficiency of SPP1 in HepG2 and SNU‐387 cell lines through Western blot."], "order_basis": "paper_text_order:14085", "id": "assay:5"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["were employed. Statistical analysis for the CCK8 assay was performed using two‐way ANOVA. A s", "epatocellular carcinoma cell lines using the CCK8 assay. The results indicated that as the cul", "absorbance of three groups of HepG2 cells in CCK8 experiments. (D) Time‐dependent changes in a", "sorbance of three groups of SNU‐387 cells in CCK8 experiments. (E) Photographs of colony forma"], "order_basis": "paper_text_order:16104", "id": "assay:6"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["ANOVA"], "order_basis": "consumes_earlier_output", "id": "assay:7"}]}