A pan-cancer analysis of the role of hexokinase II (HK2) in human tumors

Abstract

More and more evidence show that HK2 is closely related to tumors. But no pan-cancer analysis is available. This paper aimed to explore the potential roles of HK2 across thirty-three tumors based on the datasets of the cancer genome Atlas (TCGA) and gene expression omnibus. HK2 is highly expressed in most tumors and related to the progression of some tumors. HK2 expression was associated with the infiltration of T follicular helper cells for the TCGA tumors of uveal melanoma, breast invasive carcinoma (BRCA), breast invasive carcinoma-luminalA (BRCA-LumA), head and neck squamous cell carcinoma (HNSC), head and neck squamous cell carcinoma with HPV positive (HNSC-HPV⁺), and cancer-associated fibroblasts for the tumors of brain lower grade glioma and stomach adenocarcinoma. Our first pan-cancer study offers a relatively comprehensive understanding of the roles of HK2 in different tumors.

Figure 1

The expression level of HK2 gene in different tumors and pathological stages. (a) The expression status of the HK2 gene in different cancers or specific cancer subtypes was analyzed through TIMER2. * P < 0.05; ** P < 0.01; *** P < 0.001. (b) For the type of ACC, LGG, THYM, and UCS in the TCGA project, the corresponding normal tissues of the GTEx database were included as controls. The box plot data were supplied. ** P < 0.01. (c) The expression level of HK2 total protein based on the CPTAC dataset between normal tissue and primary tissue of breast cancer, KIRC, colon cancer, LUAD, ovarian cancer, and UCEC. *** P < 0.001. (d) The expression level of HK2 protein based on the HPA database between normal tissue and primary tissue of breast cancer, renal cancer, colon cancer, lung cancer, ovarian cancer, and endometrial cancer. (e) Based on the TCGA data, the expression levels of the HK2 gene were analyzed by the main pathological stages (stage I, stage II, stage III, and stage IV) of CESC, KICH, LIHC, ovarian cancer, and PAAD. Log2 (TPM + 1) was applied for the log-scale.

Figure 2

Correlation between HK2 gene expression and survival prognosis of cancers in TCGA. The GEPIA2 was used to perform overall survival (a) and disease-free survival (b) analyses of different tumors in TCGA by HK2 gene expression. The survival map and Kaplan–Meier curves with positive results are given.

Figure 3

Mutation feature of HK2 in different tumors of TCGA. The mutation features of HK2 for the TCGA tumors were analyzed using the cBioPortal tool. The alteration frequency with mutation type (a) and mutation site (b) are displayed. The mutation site (V431Cfs*26, L795Rfs*10, and A901Rfs*74) in the 3D structure of HK2 were also displayed (b). The potential correlation between mutation status and overall, disease-specific, disease-free, and progression-free survival of UCEC (c) was also analyzed using the cBioPortal tool.

Figure 4

Correlation analysis between HK2 expression and immune infiltration of cancer-associated fibroblasts and T follicular helper cells. Different algorithms were used to explore the potential correlation between the expression level of the HK2 gene and the infiltration level of cancer-associated fibroblasts and T follicular helper cells across all types of cancer to TCGA.

Figure 5

HK2-related gene enrichment analysis. (a) The STRING was used to obtain the available experimentally determined HK2-binding proteins. (b) Using the GEPIA2 approach, we obtained the top 100 HK2-correlated genes in TCGA projects and analyzed the expression correlation between HK2 and selected targeting genes, including ACTR3, BZW1, CPSF2, GSK3B, GSPT1, KCMF1, MAPK6, NAA50, PGK1, and PSMD12. (c) The corresponding heatmap data in the detailed cancer types are displayed. (d) An intersection analysis of the HK2-binding and correlated genes was conducted. (e) Based on the HK2-binding and interacted genes, a KEGG pathway analysis was performed.