The role of IQCB1 in liver cancer: a bioinformatics analysis

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.

Methods: 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).

Results: 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.

Conclusions: 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.

Keywords: IQCB1; diagnosis and prognosis; half-maximal inhibitory concentration (IC50); immunity infiltration and checkpoint; liver hepatocellular carcinoma (LIHC).

Figure 1

The expression of IQCB1 in patients with LIHC. (A) The expression of IQCB1 in pan-cancer. (B) The expression of IQCB1 between normal tissues and tumor tissues in LIHC. (C) The paired analysis of IQCB1 between normal tissues and liver cancer tissues in the TCGA database. (D) Diagnostic ROC curve of IQCB1 in LIHC. (E) Protein expression of IQCB1 in HCC from the UALCAN database. (F) The expression of IQCB1 in GSE25097 database. (G) The paired analysis of IQCB1 in GSE25097 database. *, P<0.05; **, P<0.01; ***, P<0.001. TPM, transcripts per million; ns, no significance; TPR, true positive rate; FPR, false positive rate; AUC, area under the curve; CI, confidence interval; AN, adjacent non-tumor; HLT, healthy liver tissue; LIHC, liver hepatocellular carcinoma; TCGA, The Cancer Genome Atlas; ROC, receiver operating characteristic; HCC, hepatocellular carcinoma; UALCAN, University of ALabama at Birmingham CANcer data analysis Portal.

Figure 2

The relationship between IQCB1 and clinical pathology and prognosis. (A-J) The relationship between IQCB1 and pathologic stage, pathologic T stage, AFP, HBV, HCV, pathologic N stage, pathologic M stage, gender, age, and BMI. (K) IQCB1 versus MKI67 correlation scatter diagram. (L-N) Impact of IQCB1 expression on OS, PFI, DSS. (O-Q) The impact of IQCB1 on OS in stage I, stage III, stage T1. *, P<0.05; **, P<0.01; ***, P<0.001. ns, no significance; TPM, transcripts per million; HBV, hepatitis B virus; HCV, hepatitis C virus; BMI, body mass index; HR, hazard ratio; AFP, alpha-fetoprotein; OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval.

Figure 3

Establishment of a nomogram combined with clinical characteristics. (A) Time-dependent ROC curve of IQCB1. (B) Prognostic line chart for IQCB1. (C) Prognostic calibration curve for IQCB1. TPR, true positive rate; FPR, false positive rate; AUC, area under the curve; ROC, receiver operating characteristic.

Figure 4

The PPI network and functional enrichment analysis of IQCB1. (A) The PPI network of IQCB1. (B) GO/KEGG pathway enrichment for IQCB1 and closed interact genes. (C) The top 5 hub genes of PPI network. (D) IQCB1 with pathway activity or inhibition. GnRH, gonadotropin-releasing hormone; cGMP, cyclic guanosine monophosphate; cAMP, cyclic adenosine monophosphate; BP, biological process; CC, cellular composition; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; GO, Gene Ontology.

Figure 5

Hub gene construction of prognosis model. (A,B) Selection of prognostic genes was performed through LASSO regression analysis. (C) The risk score, survival time distributions and gene expression heat map of genes in the TCGA-LIHC cohort. (D) Kaplan-Meier survival analysis of the OS between the two risk groups in the TCGA-LIHC cohort. The median survival time of the high- and low-risk groups was 2.5 and 6.7 years, respectively. (E) The ROC curves of the risk scoring model predicting OS of 1-year, 3-year, and 5-year in the TCGA-LIHC cohort. (F) AUC curves of genes. (G) Decision curve analysis in OS of 3-year. HR, hazard ratio; CI, confidence interval; AUC, area under the curve; OS, overall survival; LASSO, least absolute shrinkage and selection operator; TCGA, The Cancer Genome Atlas; LIHC, liver hepatocellular carcinoma; ROC, receiver operating characteristic.

Figure 6

The correlation between IQCB1 expression and immune infiltration. (A) 24 immune cells and IQCB1 expression levels. (B-D) Spearman correlation between IQCB1 and Th17 cells, DC, Th2 cells. (E-G) Correlation between high- and low-IQCB1 expression and the infiltration levels of Th17 cells, DC, Th2 cells. (H) immune checkpoints and IQCB1 expression levels. (I-K) Spearman correlation between IQCB1 and CD276, PDCD1, CTLA4. (L-N) Correlation between high- and low-IQCB1 expression and CD276, PDCD1, CTLA4. *, P<0.05; **, P<0.01; ***, P<0.001. ns, no significance; aDC, activated dendritic cells; DC, dendritic cells; iDC, immature dendritic cells; NK, natural killer cells; pDC, plasmacytoid DC; Tcm, T central memory; Tem, T effector memory; TFH, T follicular helper; Th, T-helper; Tgd, T gamma delta; TPM, transcripts per million; Treg, regulatory T cells.

Figure 7

IC50 of 12 drugs analysis of IQCB1 in LIHC cohort. IC50, half-maximal inhibitory concentration; LIHC, liver hepatocellular carcinoma.

Figure 8

The expression of IQCB1 in TME in single-cell datasets. (A-C) The expression of IQCB1 in LIHC_GSE140228_10X, LIHC_GSE140228_Smartseq2, and LIHC_GSE98638. (D) The heatmap of the average expression of IQCB1 across datasets. (E-G) The distribution of expression of IQCB1 in LIHC_GSE140228_10X, LIHC_GSE140228_Smartseq2, and LIHC_GSE98638. LIHC, liver hepatocellular carcinoma; B, B cells; CD4Tconv, conventional CD4 T cells; CD8T, CD8 T cells; CD8Tex, exhausted CD8 T cells; DC, dendritic cells; ILC, innate lymphoid cells; Mast, mast cells; Mono/Macro, monocytes or macrophages; NK, natural killer cells; plasma, plasma cells; Tprolif, proliferating T cells; Treg, regulatory T cells; TME, tumor microenvironment.