Construction and validation of a novel IGFBP3-related signature to predict prognosis and therapeutic decision making for Hepatocellular Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37397026", "kind": "science-record", "revision": 4, "content_hash": "sha256:f3b9245f3e642d6d0801535368dd2ae599b08c782bfdb95c83a3464d062e3567", "created_at": "2026-11-01T19:02:09Z", "updated_at": "2026-11-01T22:52:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a novel IGFBP3-related signature to predict prognosis and therapeutic decision making for Hepatocellular Carcinoma.", "doi": "10.7717/peerj.15554", "pmid": "37397026", "authors": ["Jianlin Chen", "Wanzhen Zhuang", "Yu Xia", "Xiaoqing Yin", "Mingshu Tu", "Yi Zhang", "Liangming Zhang", "Hengbin Huang", "Songgao Zhang", "Lisheng You", "Yi Huang"], "journal": "PeerJ", "year": 2023, "abstract": "Background: IGFBP3 plays a pivotal role in carcinogenesis by being anomalously expressed in some malignancies. However, the clinical value of IGFBP3 and the role of IGFBP3-related signature in HCC remain unclear.\nMethods: Multiple bioinformatics methods were used to determine the expression and diagnostic values of IGFBP3. The expression level of IGFBP3 was validated by RT-qPCR and IHC. A IGFBP3-related risk score (IGRS) was built via correlation analysis and LASSO Cox regression analysis. Further analyses, including functional enrichment, immune status of risk groups were analyzed, and the role of IGRS in guiding clinical treatment was also evaluated.\nResults: IGFBP3 expression was significantly downregulated in HCC. IGFBP3 expression correlated with multiple clinicopathological characteristics and demonstrated a powerful diagnostic capability for HCC. In addition, a novel IGRS signature was developed in TCGA, which exhibited good performance for prognosis prediction and its role was further validated in GSE14520. In TCGA and GSE14520, Cox analysis also confirmed that the IGRS could serve as an independent prognostic factor for HCC. Moreover, a nomogram with good accuracy for predicting the survival of HCC was further formulated. Additionally, enrichment analysis showed that the high-IGRS group was enriched in cancer-related pathways and immune-related pathways. Additionally, patients with high IGRS exhibited an immunosuppressive phenotype. Therefore, patients with low IGRS scores may benefit from immunotherapy.\nConclusions: IGFBP3 can act as a new diagnostic factor for HCC. IGRS signature represents a valuable predictive tool in the prognosis prediction and therapeutic decision making for Hepatocellular Carcinoma.", "claims": [{"id": "claim-method", "statement": "To evaluate the diagnostic significance of IGFBP3, ROC analysis was performed based on the TCGA cohort (Discovery cohort).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37397026", "locator": "sections[13] \"Diagnostic value of IGFBP3 and its relevance to clinical features\" paragraph 3", "quote": "To evaluate the diagnostic significance of IGFBP3, ROC analysis was performed based on the TCGA cohort (Discovery cohort)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed are available in the Supplemental Files and at the following sites:- The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository, search term: TCGA-LIHC).- International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Japanese liver cancer (ICGC-LIRI-JP) cohort (https://dcc.icgc.org/projects/LIRI-JP).- CPTAC database (http://proteomics.cancer.gov/). To collate the data, go to https://cprosite.ccr.cancer.gov/. Then, in brief, select “Liver Cancer” from the drop-down menu of “Tumor Types”, “IGFBP3” from the drop-down menu of “Gene” on the page, click “Submit” for analysis, and then click “Export Data” to obtain protein expression profile data.- NCBI GEO: GSE62254, GSE54236, GSE14520 (GPL3921), and GSE76427.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37397026", "locator": "sections[22] \"Additional Information and Declarations\" paragraph 5", "quote": "The datasets analyzed are available in the Supplemental Files and at the following sites:- The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository, search term: TCGA-LIHC).- International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Japanese liver cancer (ICGC-LIRI-JP) cohort (https://dcc.icgc.org/projects/LIRI-JP).- CPTAC database (http://proteomics.cancer.gov/). To collate the data, go to https://cprosite.ccr.cancer.gov/. Then, in brief, select “Liver Cancer” from the drop-down menu of “Tumor Types”, “IGFBP3” from the drop-down menu of “Gene” on the page, click “Submit” for analysis, and then click “Export Data” to obtain protein expression profile data.- NCBI GEO: GSE62254, GSE54236, GSE14520 (GPL3921), and GSE76427."}], "state": "complete"}, {"id": "claim-target-result", "statement": "IGFBP3 exhibited powerful diagnostic ability for HCC with an AUC of 0.927 (95% CI [0.902–0.951]).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "As showed in Fig. 3I, IGFBP3 exhibited powerful diagnostic ability for HCC with an AUC of 0.927 (95% CI [0.902–0.951])."}, "evidence": [{"asset": "source:pmid:37397026", "locator": "sections[13] \"Diagnostic value of IGFBP3 and its relevance to clinical features\" paragraph 3", "quote": "As showed in Fig. 3I, IGFBP3 exhibited powerful diagnostic ability for HCC with an AUC of 0.927 (95% CI [0.902–0.951])."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "IGFBP3 exhibited powerful diagnostic ability for HCC with an AUC of 0.927 (95% CI [0.902–0.951]).", "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:37397026"], "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:GSE54236"], "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:GSE54236"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37397026", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.15554", "sha256": "sha256:27206f47b2e96fdcaa25ff510789d633dc2ceb59719a7bda0897587730a3a5b2", "bytes": 35109, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.15554", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10312159", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE54236", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54236", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE54236", "version": null, "note": "The datasets analyzed are available in the Supplemental Files and at the following sites:- The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository, search term: TCGA-LIHC).- International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Japanese liver cancer (ICGC-LIRI-JP) cohort (https://dcc.icgc.org/projects/LIRI-JP).- CPTAC database (http://proteomics.cancer.gov/). To collate the data, go to https://cprosite.ccr.cancer.gov/. Then, in brief, select “Liver Cancer” from the drop-down menu of “Tumor Types”, “IGFBP3” from the drop-down menu of “Gene” on the page, click “Submit” for analysis, and then click “Export Data” to obtain protein expression profile data.- NCBI GEO: GSE62254, GSE54236, GSE14520 (GPL3921), and GSE76427.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed are available in the Supplemental Files and at the following sites:- The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository, search term: TCGA-LIHC).- International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Japanese liver cancer (ICGC-LIRI-JP) cohort (https://dcc.icgc.org/projects/LIRI-JP).- CPTAC database (http://proteomics.cancer.gov/). To collate the data, go to https://cprosite.ccr.cancer.gov/. Then, in brief, select “Liver Cancer” from the drop-down menu of “Tumor Types”, “IGFBP3” from the drop-down menu of “Gene” on the page, click “Submit” for analysis, and then click “Export Data” to obtain protein expression profile data.- NCBI GEO: GSE62254, GSE54236, GSE14520 (GPL3921), and GSE76427.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1310570", "GSM1310571", "GSM1310572", "GSM1310573", "GSM1310574", "GSM1310575", "GSM1310576", "GSM1310577", "GSM1310578", "GSM1310579", "GSM1310580", "GSM1310581", "GSM1310582", "GSM1310583", "GSM1310584", "GSM1310585", "GSM1310586", "GSM1310587", "GSM1310588", "GSM1310589", "GSM1310590", "GSM1310591", "GSM1310592", "GSM1310593", "GSM1310594", "GSM1310595", "GSM1310596", "GSM1310597", "GSM1310598", "GSM1310599", "GSM1310600", "GSM1310601", "GSM1310602", "GSM1310603", "GSM1310604", "GSM1310605", "GSM1310606", "GSM1310607", "GSM1310608", "GSM1310609", "GSM1310610", "GSM1310611", "GSM1310612", "GSM1310613", "GSM1310614", "GSM1310615", "GSM1310616", "GSM1310617", "GSM1310618", "GSM1310619", "GSM1310620", "GSM1310621", "GSM1310622", "GSM1310623", "GSM1310624", "GSM1310625", "GSM1310626", "GSM1310627", "GSM1310628", "GSM1310629", "GSM1310630", "GSM1310631", "GSM1310632", "GSM1310633", "GSM1310634", "GSM1310635", "GSM1310636", "GSM1310637", "GSM1310638", "GSM1310639", "GSM1310640", "GSM1310641", "GSM1310642", "GSM1310643", "GSM1310644", "GSM1310645", "GSM1310646", "GSM1310647", "GSM1310648", "GSM1310649", "GSM1310650", "GSM1310651", "GSM1310652", "GSM1310653", "GSM1310654", "GSM1310655", "GSM1310656", "GSM1310657", "GSM1310658", "GSM1310659", "GSM1310660", "GSM1310661", "GSM1310662", "GSM1310663", "GSM1310664", "GSM1310665", "GSM1310666", "GSM1310667", "GSM1310668", "GSM1310669", "GSM1310670", "GSM1310671", "GSM1310672", "GSM1310673", "GSM1310674", "GSM1310675", "GSM1310676", "GSM1310677", "GSM1310678", "GSM1310679", "GSM1310680", "GSM1310681", "GSM1310682", "GSM1310683", "GSM1310684", "GSM1310685", "GSM1310686", "GSM1310687", "GSM1310688", "GSM1310689", "GSM1310690", "GSM1310691", "GSM1310692", "GSM1310693", "GSM1310694", "GSM1310695", "GSM1310696", "GSM1310697", "GSM1310698", "GSM1310699", "GSM1310700", "GSM1310701", "GSM1310702", "GSM1310703", "GSM1310704", "GSM1310705", "GSM1310706", "GSM1310707", "GSM1310708", "GSM1310709", "GSM1310710", "GSM1310711", "GSM1310712", "GSM1310713", "GSM1310714", "GSM1310715", "GSM1310716", "GSM1310717", "GSM1310718", "GSM1310719", "GSM1310720", "GSM1310721", "GSM1310722", "GSM1310723", "GSM1310724", "GSM1310725", "GSM1310726", "GSM1310727", "GSM1310728", "GSM1310729", "GSM1310730"], "sample_count": 161, "group_sizes": null, "group_sizes_basis": null, "platform": "6480", "taxon": "Homo sapiens", "title": "Prospective gene expression analysis of  human RNA samples from Hepatocellular Carcinoma in relation with growth rate and survival", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE62254", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62254", "version": null, "note": "The datasets analyzed are available in the Supplemental Files and at the following sites:- The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository, search term: TCGA-LIHC).- International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Japanese liver cancer (ICGC-LIRI-JP) cohort (https://dcc.icgc.org/projects/LIRI-JP).- CPTAC database (http://proteomics.cancer.gov/). To collate the data, go to https://cprosite.ccr.cancer.gov/. Then, in brief, select “Liver Cancer” from the drop-down menu of “Tumor Types”, “IGFBP3” from the drop-down menu of “Gene” on the page, click “Submit” for analysis, and then click “Export Data” to obtain protein expression profile data.- NCBI GEO: GSE62254, GSE54236, GSE14520 (GPL3921), and GSE76427.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed are available in the Supplemental Files and at the following sites:- The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository, search term: TCGA-LIHC).- International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Japanese liver cancer (ICGC-LIRI-JP) cohort (https://dcc.icgc.org/projects/LIRI-JP).- CPTAC database (http://proteomics.cancer.gov/). To collate the data, go to https://cprosite.ccr.cancer.gov/. Then, in brief, select “Liver Cancer” from the drop-down menu of “Tumor Types”, “IGFBP3” from the drop-down menu of “Gene” on the page, click “Submit” for analysis, and then click “Export Data” to obtain protein expression profile data.- NCBI GEO: GSE62254, GSE54236, GSE14520 (GPL3921), and GSE76427.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1523727", "GSM1523728", "GSM1523729", "GSM1523744", "GSM1523745", "GSM1523746", "GSM1523747", "GSM1523748", "GSM1523765", "GSM1523768", "GSM1523769", "GSM1523770", "GSM1523771", "GSM1523772", "GSM1523773", "GSM1523774", "GSM1523775", "GSM1523776", "GSM1523777", "GSM1523778", "GSM1523779", "GSM1523780", "GSM1523781", "GSM1523782", "GSM1523783", "GSM1523784", "GSM1523785", "GSM1523786", "GSM1523787", "GSM1523788", "GSM1523789", "GSM1523790", "GSM1523791", "GSM1523792", "GSM1523793", "GSM1523794", "GSM1523795", "GSM1523796", "GSM1523797", "GSM1523798", "GSM1523799", "GSM1523800", "GSM1523801", "GSM1523802", 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To collate the data, go to https://cprosite.ccr.cancer.gov/. Then, in brief, select “Liver Cancer” from the drop-down menu of “Tumor Types”, “IGFBP3” from the drop-down menu of “Gene” on the page, click “Submit” for analysis, and then click “Export Data” to obtain protein expression profile data.- NCBI GEO: GSE62254, GSE54236, GSE14520 (GPL3921), and GSE76427.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed are available in the Supplemental Files and at the following sites:- The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository, search term: TCGA-LIHC).- International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/) Japanese liver cancer (ICGC-LIRI-JP) cohort (https://dcc.icgc.org/projects/LIRI-JP).- CPTAC database (http://proteomics.cancer.gov/). To collate the data, go to https://cprosite.ccr.cancer.gov/. 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