Prognostic hub gene CBX2 drives a cancer stem cell-like phenotype in HCC revealed by multi-omics and multi-cohorts.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37980163", "kind": "science-record", "revision": 3, "content_hash": "sha256:1ed5579aa9c611a689941a8b2f2e87a4eee93d55a44ef617d272f2980ebe0993", "created_at": "2026-11-01T19:02:53Z", "updated_at": "2026-11-01T22:10:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic hub gene CBX2 drives a cancer stem cell-like phenotype in HCC revealed by multi-omics and multi-cohorts.", "doi": "10.18632/aging.205173", "pmid": "37980163", "authors": ["Qingren Meng", "Qian Zhou", "Xi Chen", "Jun Chen"], "journal": "Aging", "year": 2023, "abstract": "Hepatocellular carcinoma (HCC) is a malignant tumor with a high prevalence and fatality rate. CBX2 has been demonstrated to impact the development and advancement of various cancers, albeit it has received limited attention in relation to HCC. In this study, CBX2 and CEP55 were screened out with the refined triple regulatory networks constructed by total RNA-seq datasets (TCGA-LIHC, GSE140845) and a robust prognostic model. Aberrantly higher expression levels of CBX2 and CEP55 in HCC may be caused by CNV alterations, promoter hypo-methylation, open chromatin accessibility, and greater active marks such as H3K4me3, H3K4me1, and H3K27ac. Functionally, CBX2, which was highly correlated with CD44, shaped a cancer stem cell-like phenotype by positively regulating cell-cycle progression, proliferation, invasion, metastasis, wound healing, and radiation resistance, revealed by combining bulk RNA-seq and scRNA-seq datasets. CBX2 knockdown validated its role in affecting the cell cycle. Importantly, we revealed CBX2 could activate gene by cooperating with co-regulators or not rather than a recognizer of the repressive mark H3K27me3. For instance, we uncovered CBX2 bound to promoter of CTNNB1 and CEP55 to augment their expressions. CBX2 showed a highly positive correlation with CEP55 at pan-cancer level. In addition, CBX2 and CEP55 may enhance extracellular matrix reprograming via cancer-associated fibroblast. Surprisingly, patients with high expression of CBX2 or CEP55 exhibited a higher response to immunotherapy, indicating that CBX2 and CEP55 may be promising therapeutic targets for HCC patients.", "claims": [{"id": "claim-method", "statement": "Prognostic variables were chosen using the randomForest algorithm from the randomForestRSC package and the LASSO algorithm from the “glmnet” package. Candidates for prognostic DERNA served as the regression’s independent variable, and overall survival and overall status in TCGA-LIHC cohorts served as the response variables. Further PH testing was done on chosen prognostic RNAs. On the chosen, a multivariate Cox regression analysis was conducted and the ggforest package was used to visualize the results. RMS R package was used to confirm nomograms as well. The prognostic model’s reliability was verified both internally and externally. By randomly dividing the TCGA-LIHC into a training cohort and a validation cohort 50 times, internal validation was carried out. And for external validation, we used the ICGC-LIRI-JR expression profile. Calibration curves, the concordance index (C-index), and the time-dependent ROC curve were used to assess the prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37980163", "locator": "sections[6] \"Survival analysis, construction of prognosis model, and validation for HCC\" paragraph 3", "quote": "Prognostic variables were chosen using the randomForest algorithm from the randomForestRSC package and the LASSO algorithm from the “glmnet” package. Candidates for prognostic DERNA served as the regression’s independent variable, and overall survival and overall status in TCGA-LIHC cohorts served as the response variables. Further PH testing was done on chosen prognostic RNAs. On the chosen, a multivariate Cox regression analysis was conducted and the ggforest package was used to visualize the results. RMS R package was used to confirm nomograms as well. The prognostic model’s reliability was verified both internally and externally. By randomly dividing the TCGA-LIHC into a training cohort and a validation cohort 50 times, internal validation was carried out. And for external validation, we used the ICGC-LIRI-JR expression profile. Calibration curves, the concordance index (C-index), and the time-dependent ROC curve were used to assess the prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The source data and public datasets used in this manuscript are uploaded as Supplementary Materials. The code used in this project has been uploaded to https://github.com/mengqingren/ceRNA.CBX2. All data used in this study are available as mentioned in methods, and all source data are available from corresponding authors upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37980163", "locator": "sections[16] \"Data and code available statement\" paragraph 2", "quote": "The source data and public datasets used in this manuscript are uploaded as Supplementary Materials. The code used in this project has been uploaded to https://github.com/mengqingren/ceRNA.CBX2. All data used in this study are available as mentioned in methods, and all source data are available from corresponding authors upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A five-gene prognostic model including CPEB3, ANXA10, CEP55, CBX2, and DUXAP8 was created with a C-index of 0.7.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37980163", "locator": "sections[19] \"Construction and validation of the CBX2/CEP55-featured prognostic model\" paragraph 2", "quote": "DUXAP8 and CBX2 were found to be independent prognostic genes by multivariate Cox analysis, and a prognostic model with a C-index of 0.7 was created (Figure 2B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A five-gene prognostic model including CPEB3, ANXA10, CEP55, CBX2, and DUXAP8 was created with a C-index of 0.7.", "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:37980163"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37980163", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205173", "sha256": "sha256:efbe8c35035ac173fb380c8af7da219f991a816f7cee4f06cd9496fa2f010fbe", "bytes": 56590, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.205173", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10713423", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "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": "Prognostic variables were chosen using the randomForest algorithm from the randomForestRSC package and the LASSO algorithm from the “glmnet” package. Candidates for prognostic DERNA served as the regression’s independent variable, and overall survival and overall status in TCGA-LIHC cohorts served as the response variables. Further PH testing was done on chosen prognostic RNAs. On the chosen, a multivariate Cox regression analysis was conducted and the ggforest package was used to visualize the results. RMS R package was used to confirm nomograms as well. The prognostic model’s reliability was verified both internally and externally. By randomly dividing the TCGA-LIHC into a training cohort and a validation cohort 50 times, internal validation was carried out. And for external validation, we used the ICGC-LIRI-JR expression profile. Calibration curves, the concordance index (C-index), and the time-dependent ROC curve were used to assess the prognostic model."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-5", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The source data and public datasets used in this manuscript are uploaded as Supplementary Materials. The code used in this project has been uploaded to https://github.com/mengqingren/ceRNA.CBX2. All data used in this study are available as mentioned in methods, and all source data are available from corresponding authors upon request."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:37980163"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:59:24Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15645, "completion_tokens": 1483, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37980163", "type": "paper", "label": "pmid:37980163"}, {"id": "source:pmid:37980163", "type": "manuscript", "label": "PMID 37980163 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37980163", "type": "paper", "label": "pmid:37980163"}, {"id": "source:pmid:37980163", "type": "manuscript", "label": "PMID 37980163 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37980163", "type": "extraction", "used": ["source:pmid:37980163"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:37980163", "parent_hash": "sha256:01f83d102aba8172f2507bedd368637b25c81163068a342e58995f01aa6b0860", "fork_of": null, "generation": 2}, "gaps": ["execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ple regulatory networks constructed by total RNA-seq datasets (TCGA-LIHC, GSE140845) and a robust", "ation resistance, revealed by combining bulk RNA-seq and scRNA-seq datasets. CBX2 knockdown valid"], "order_basis": "paper_text_order:428", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ance, revealed by combining bulk RNA-seq and scRNA-seq datasets. CBX2 knockdown validated its role"], "order_basis": "paper_text_order:981", "id": "assay:2"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["ng bulk RNA-seq and scRNA-seq datasets. CBX2 knockdown validated its role in affecting the cell cyc"], "order_basis": "paper_text_order:1006", "id": "assay:3"}, {"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": ["Prognostic variables were chosen using the randomForest algorithm from the randomForestRSC package and the LASSO algorithm from the “glmnet” package. Candidates for prognostic DERNA served as the regression’s independent variable, and overall survival and overall status in TCGA-LIHC cohorts served as the response variables. Further PH testing was done on chosen prognostic RNAs. On the chosen, a multivariate Cox regression analysis was conducted and the ggforest package was used to visualize the results. RMS R package was used to confirm nomograms as well. The prognostic model’s reliability was verified both internally and externally. By randomly dividing the TCGA-LIHC into a training cohort and a validation cohort 50 times, internal validation was carried out. And for external validation, we used the ICGC-LIRI-JR expression profile. Calibration curves, the concordance index (C-index), and the time-dependent ROC curve were used to assess the prognostic model."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}