The role of IQCB1 in liver cancer: a bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39430824", "kind": "science-record", "revision": 4, "content_hash": "sha256:76183ee58856cabb06cf09d987a18773733dc2fb953159d2276a0519599604e2", "created_at": "2026-11-01T20:29:41Z", "updated_at": "2026-11-01T22:55:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The role of IQCB1 in liver cancer: a bioinformatics analysis.", "doi": "10.21037/tcr-24-110", "pmid": "39430824", "authors": ["Dongmei Han", "Bin Ling", "Caihong Wu", "Hao Jin"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Liver hepatocellular carcinoma (LIHC) is a prevalent malignancy globally, exhibiting substantial incidence and mortality rates. Early diagnosis and prevention of metastasis are crucial for the benefit of patients with liver cancer. The present study aimed to elucidate the involvement of IQCB1 in liver cancer through the utilization of bioinformatics.\nMethods: The samples utilized in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Initially, the TCGA-LIHC dataset was employed to examine the expression of IQCB1, and its validation was performed on the GSE25097 dataset. Subsequently, Kaplan-Meier (KM) analysis was conducted to evaluate the prognostic significance of IQCB1 in LIHC, and its correlation with clinical pathological features was also investigated. Furthermore, a protein-protein interaction (PPI) network consisting of 20 proteins associated with IQCB1 was constructed using data from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. A risk model was formulated to assess the prognostic significance and its prognostic value was compared to that of IQCB1 in isolation. Furthermore, an examination was conducted to explore the correlation between IQCB1 and immune infiltration, along with the involvement of immunological checkpoints. A drug sensitivity assessment of IQCB1 was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Additionally, the Tumor Immune Single-cell Hub (TISCH) database was utilized to investigate the association between IQCB1 and the tumor microenvironment (TME).\nResults: The expression of IQCB1 was observed to be significantly elevated in tumor samples. Furthermore, patients with high expression levels of IQCB1 demonstrated a poorer prognosis. Additionally, IQCB1 exhibited significant correlations with MKI67, hepatitis B virus (HBV), hepatitis C virus (HCV), and alpha-fetoprotein (AFP). GO and KEGG analyses revealed enrichment of multiple signaling pathways. Subsequently, an investigation was conducted to examine the association between IQCB1 and the activity of ten signaling pathways related to tumor development. A positive correlation was observed between IQCB1 expression and T-helper 2 (Th2) cells, whereas a negative correlation was observed between IQCB1 expression and Th17 cells. Furthermore, a positive association was found between IQCB1 and immune checkpoints, particularly with CD276. Analysis of single-cell data from the TISCH database revealed widespread expression of IQCB1 in the TME. Additionally, screening revealed that among 12 drugs related to IQCB1, a subset of 10 drugs demonstrated negative correlations, whereas two drugs exhibited positive correlations.\nConclusions: IQCB1 has the potential to function as a diagnostic and prognostic molecular marker, and its association with immune infiltration and checkpoint mechanisms has been observed.", "claims": [{"id": "claim-method", "statement": "Subsequently, Kaplan-Meier (KM) analysis was conducted to evaluate the prognostic significance of IQCB1 in LIHC, and its correlation with clinical pathological features was also investigated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39430824", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Subsequently, Kaplan-Meier (KM) analysis was conducted to evaluate the prognostic significance of IQCB1 in LIHC, and its correlation with clinical pathological features was also investigated."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov) for the TCGA-LIHC project, transcripts per million (TPM) formatted data was extracted, and data processing was performed by log2(value+1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39430824", "locator": "sections[4] \"Data collection and expression analysis\" paragraph 2", "quote": "The RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov) for the TCGA-LIHC project, transcripts per million (TPM) formatted data was extracted, and data processing was performed by log2(value+1)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "IQCB1 was a significant independent predictor of overall survival in LIHC (P=0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "Notably, IQCB1 emerged as a significant independent predictor of OS, with a P value of 0.001, as presented in Table 1."}, "evidence": [{"asset": "source:pmid:39430824", "locator": "sections[12] \"The prognostic value of IQCB1 gene\" paragraph 2", "quote": "Notably, IQCB1 emerged as a significant independent predictor of OS, with a P value of 0.001, as presented in Table 1."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "IQCB1 was a significant independent predictor of overall survival in LIHC (P=0.001).", "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:39430824"], "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:GSE25097"], "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:GSE25097"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39430824", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-24-110", "sha256": 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