Expression of HK2, PKM2, and PFKM Is Associated with Metastasis and Late Disease Onset in Breast Cancer Patients

Abstract

The reprogramming of energy metabolism is one of the hallmarks of cancer and is crucial for tumor progression. Altered aerobic glycolysis is a well-known characteristic of cancer cell metabolism. In the present study, the expression profiles of key metabolic genes (HK2, PFKM, and PKM2) were assessed in the breast cancer cohort of Pakistan using quantitative polymerase chain reaction (qPCR) and IHC. Expression patterns were correlated with molecular subtypes and clinical parameters in the patients. A significant upregulation of key glycolytic genes was observed in tumor samples in comparison to their adjacent controls (p < 0.0001). The expression of the studied glycolytic genes was significantly increased in late clinical stages, positive nodal involvement, and distant metastasis (p < 0.05). HK2 and PKM2 were found to be upregulated in luminal B, whereas PFKM was overexpressed in the luminal A subtype of breast cancer. The genes were positively correlated with the proliferation marker Ki67 (p < 0.001). Moreover, moderate positive linear correlations between HK2 and PKM2 (r = 0.476), HK2 and PFKM (r = 0.473), and PKM2 and PFKM (r = 0.501) were also observed (p < 0.01). These findings validate that the key regulatory genes in glycolysis can serve as potential biomarkers and/or molecular targets for breast cancer management. However, the clinical significance of these molecules needs to be further validated through in vitro and in vivo experiments.

Keywords: HK2; PFKM; PKM2; Warburg effect; aerobic glycolysis; breast cancer.

Figure 1

Association of HK2 gene expression with various clinicopathological parameters and molecular subtypes. Fold change of HK2 gene in (A) control vs. tumor tissues; (B) different age groups of disease onset; (C) menopausal status; (D) molecular subtypes of breast cancer; (E) tumor grade; (F) tumor stage; (G) tumor size; (H) nodal involvement; (I) metastasis. Significance level * p < 0.05, ** p < 0.001, *** p < 0.0001.

Figure 2

Association of PFKM gene expression with various clinicopathological parameters and molecular subtypes. Fold change of PFKM gene in (A) control vs. tumor tissues; (B) different age groups of disease onset; (C) menopausal status; (D) molecular subtypes of breast cancer; (E) tumor grade; (F) tumor stage; (G) tumor size; (H) nodal involvement; (I) metastasis. Significance level * p < 0.05, ** p < 0.001, *** p < 0.0001.

Figure 3

Association of PKM2 gene expression with various clinicopathological parameters and molecular subtypes. Fold change of PKM2 gene in (A) control vs. tumor tissues; (B) different age groups of disease onset; (C) menopausal status; (D) molecular subtypes of breast cancer; (E) tumor grade; (F) tumor stage; (G) tumor size; (H) nodal involvement; (I) metastasis. Significance level * p < 0.05, ** p < 0.001, *** p < 0.0001.

Figure 4

Immunostaining of representative breast tumor specimen compared with normal breast tissue. Protein expressions of HK2 (A,B), PFKM (C,D), and PKM (E,F) were found to be higher in the tumor tissues (B,D,F) in comparison to adjacent normal tissues (A,C,E) (scale: 600 µm).

Figure 5

Expression of glycolytic markers in BRCA cohort from TCGA (* p < 0.05).

Figure 6

Correlation between glycolytic genes at transcript level of (A) HK2 and PKM2 (r = 0.476, p < 0.01); (B) HK2 and PFKM (r = 0.473, p < 0.01); (C) PKM2 and PFKM (r = 0.501, p < 0.01) in breast cancer cohort.

Figure 6

Correlation between glycolytic genes at transcript level of (A) HK2 and PKM2 (r = 0.476, p < 0.01); (B) HK2 and PFKM (r = 0.473, p < 0.01); (C) PKM2 and PFKM (r = 0.501, p < 0.01) in breast cancer cohort.