Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39297018", "kind": "science-record", "revision": 3, "content_hash": "sha256:8bc29e07557651e021419ee427de8c87483f3f8e62758d3d788ad889ddcf1f87", "created_at": "2026-11-01T20:29:28Z", "updated_at": "2026-11-01T22:12:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma.", "doi": "10.1155/2024/8217215", "pmid": "39297018", "authors": ["Dongqian Li", "Qian Bao", "Shiqi Ren", "Haoxiang Ding", "Chengfeng Guo", "Kai Gao", "Jian Wan", "Yao Wang", "MingYan Zhu", "Yicheng Xiong"], "journal": "Genetics research", "year": 2024, "abstract": "Background: Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated.\nMethods: This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes.\nResults: In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples.\nConclusion: The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.", "claims": [{"id": "claim-method", "statement": "Through the LASSO Cox regression model, the candidate genes were further narrowed down using the LASSO Cox regression model to develop prognostic gene signatures.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39297018", "locator": "sections[7] \"2.5. Establishment and Verification of a Prognostic Gene Signature\" paragraph 2", "quote": "Through the LASSO Cox regression model, the candidate genes were further narrowed down using the LASSO Cox regression model to develop prognostic gene signatures."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets (TCGA-HCC; GSE76427) analyzed in this study were obtained from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov) database and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39297018", "locator": "sections[29] \"Data Availability\" paragraph 2", "quote": "The datasets (TCGA-HCC; GSE76427) analyzed in this study were obtained from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov) database and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, three genes (ETV4, PBK, and SLC2A1) were identified to generate the gene signature (Figures 4(a) and 4(b)).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39297018", "locator": "sections[19] \"3.4. Construction of Prognostic Gene Signature\" paragraph 2", "quote": "Finally, three genes (ETV4, PBK, and SLC2A1) were identified to generate the gene signature (Figures 4(a) and 4(b))."}, {"asset": "source:pmid:39297018", "locator": "sections[19] \"3.4. Construction of Prognostic Gene Signature\" paragraph 4", "quote": "The corresponding AUC values predicting 5-, 3-, and 1-year OS rates across were 0.716, 0.677, and 0.726, respectively (Figure 4(e))."}, {"asset": "source:pmid:39297018", "locator": "sections[19] \"3.4. Construction of Prognostic Gene Signature\" paragraph 5", "quote": "The corresponding AUC values to predict the 5-, 3-, and 1-year OS were 0.641, 0.628, and 0.753, respectively (Figure 4(h))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, three genes (ETV4, PBK, and SLC2A1) were identified to generate the gene signature (Figures 4(a) and 4(b)).", "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:39297018"], "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:GSE76427"], "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:GSE76427"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39297018", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2024/8217215", "sha256": 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