Identification of potential prognostic biomarkers among gene models for coiled-coil domain-containing family members in hepatocellular carcinoma elucidates their influence on the hypoxia pathway and immune microenvironment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38196528", "kind": "science-record", "revision": 3, "content_hash": "sha256:c3be5b9e2daa02acb6a6bc27c0e35331edc217d4018c15a520cad8417a1d8584", "created_at": "2026-11-01T19:20:06Z", "updated_at": "2026-11-01T22:11:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential prognostic biomarkers among gene models for coiled-coil domain-containing family members in hepatocellular carcinoma elucidates their influence on the hypoxia pathway and immune microenvironment.", "doi": "10.21037/jgo-23-652", "pmid": "38196528", "authors": ["Jing Li", "Dafang Zhang", "Hao Liu", "Danhua Wang", "Chunxiao Pan", "Tiago Biachi de Castria", "Shaffer R S Mok", "Zegao Zhou", "Changqing Yan"], "journal": "Journal of gastrointestinal oncology", "year": 2023, "abstract": "Background: The family of coiled-coil domain-containing (CCDC) proteins participates in a wide range of physiological functions and plays a pivotal role in governing the invasion and metastasis of malignant tumor cells. Nonetheless, the precise mechanism governing the interaction among the immune microenvironment, hypoxia pathway, and proliferation in hepatocellular carcinoma (HCC) remains elusive. In this study, our objective was to identify the prognostic significance of CCDC family genes in HCC.\nMethods: We conducted an analysis of RNA-seq data from HCC patients sourced from The Cancer Genome Atlas (TCGA) database. Our analysis involved comparing the expression profiles of 168 CCDC family genes between tumor and normal tissues to identify differentially expressed genes (DEGs). The prognostic value of these genes was verified using overall survival (OS) data from TCGA-LIHC patients, employing Univariate and multivariate Cox proportional hazards regression models and Kaplan-Meier plots. Subsequently, we constructed a prognostic signature known as the CCDC score and validated it using additional datasets (ICGC-LIRI-JP and GSE14520). Additionally, we performed functional enrichment analysis and conducted an assessment of the tumor immune microenvironment (TIME).\nResults: We identified 34 DEGs of the CCDC family. Among them, six DEGs (CCDC6/22/51/59/132/134) were upregulated and associated with poor prognosis. Higher CCDC score was an independent predictor of poor OS in TCGA-HCC patients (P<0.001, HR =2.37), which was validated in the ICGC-LIRI-JP (P=0.021, HR =2.15) and GSE14520 (P=0.002, HR =2.23) datasets. Functional enrichment analysis showed that hypoxia pathway genes were enriched in the high CCDC score group. Furthermore, immune microenvironment analysis demonstrated that high CCDC score was associated with a suppressed TIME caused by the extrinsic immune escape.\nConclusions: The CCDC score, derived from six CCDC genes, exhibits remarkable expression levels in liver cancer and holds promise as an independent prognostic indicator. Our bioinformatics analysis revealed a high CCDC score is strongly associated with activation of the hypoxia pathway and an immunosuppressive tumor microenvironment in HCC. This profound finding may serve as a cornerstone for innovative targeted drug therapies and pave the way for further investigations into the underlying mechanisms of CCDC-related carcinogenesis in liver cancer.", "claims": [{"id": "claim-method", "statement": "Multivariate analysis with the Cox proportional hazards model was performed to predict the OS for HCC.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38196528", "locator": "sections[7] \"Prognostic model construction using CCDC score\" paragraph 2", "quote": "Multivariate analysis with the Cox proportional hazards model was performed to predict the OS for HCC."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene expression profiles of 371 HCC patients were downloaded and merged using the GDC-RNASeq-tool (https://gdc.cancer.gov/content/gdc-rnaseq-tool) from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) portal.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38196528", "locator": "sections[4] \"Study design and datasets\" paragraph 2", "quote": "Gene expression profiles of 371 HCC patients were downloaded and merged using the GDC-RNASeq-tool (https://gdc.cancer.gov/content/gdc-rnaseq-tool) from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) portal."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, in multivariate Cox regression analysis, high CCDC score was identified as an independent predictor of poor OS in TCGA [hazard ratio (HR) =2.13, 95% confidence interval (CI): 1.42–3.2, P<0.001; Figure 2C].", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Furthermore, in multivariate Cox regression analysis, high CCDC score was identified as an independent predictor of poor OS in TCGA [hazard ratio (HR) =2.13, 95% confidence interval (CI): 1.42–3.2, P<0.001; Figure 2C]."}, "evidence": [{"asset": "source:pmid:38196528", "locator": "sections[14] \"The establishment of a 6-CCDC gene prognosis model\" paragraph 2", "quote": "Furthermore, in multivariate Cox regression analysis, high CCDC score was identified as an independent predictor of poor OS in TCGA [hazard ratio (HR) =2.13, 95% confidence interval (CI): 1.42–3.2, P<0.001; Figure 2C]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, in multivariate Cox regression analysis, high CCDC score was identified as an independent predictor of poor OS in TCGA [hazard ratio (HR) =2.13, 95% confidence interval (CI): 1.42–3.2, P<0.001; Figure 2C].", "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:38196528"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38196528", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jgo-23-652", "sha256": 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"Gene expression data of human hepatocellular carcinoma (HCC)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE14520 not downloaded", "asset:data:geo:GSE14520 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Multivariate analysis with the Cox proportional hazards model was performed to predict the OS for HCC."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, 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Methods: We conducted an analysis of RNA-seq data from HCC patients sourced from The Canc", "Seq-tool (https://gdc.cancer.gov/content/gdc-rnaseq-tool) from The Cancer Genome Atlas (TCGA; ht", "nts were downloaded and merged using the GDC-RNASeq-tool (https://gdc.cancer.gov/content/gdc-rna"], "order_basis": "paper_text_order:720", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["the OS status of the candidate gene set, the microarray data and clinical information of ICGC-LIRI-J"], "order_basis": "paper_text_order:3237", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Multivariate analysis with the Cox proportional hazards model was performed to predict the OS for HCC."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}