Introduction: Esophageal cancer (EC) is an aggressive malignancy with increasing incidence and poor prognosis. Histologically, it is divided into two main types: adenocarcinoma (EAC) and squamous cell carcinoma (ESCC). Treatment approaches, which range from combined modality therapy for localized cancer to systemic therapy for advanced stages, are continually evolving. Therapeutic advancements are increasingly based on high-resolution genomic screening of individual tumors.
The hexokinase (HK) enzyme family plays a key role in metabolic reprogramming of cancerous cells. Hexokinase 2 (HK2) is the most active isoform, and its overexpression in neoplastic cells drives them toward aerobic glycolysis. Genetic deletion of HK2 in mouse models has been shown to inhibit tumor growth. Beyond its enzymatic function, HK2 binds to mitochondria, regulating autophagy and inhibiting cell death. Recent findings suggest HK2's localization at mitochondria-associated membranes (MAMs) is crucial for tumor progression by connecting metabolic and survival pathways. Disrupting these functions by targeting HK2's subcellular location presents a promising anti-tumor strategy.
Methods: This study employed a multi-faceted approach to investigate the relationship between esophageal cancer and the HK2 enzyme. Clinical data and imaging from 72 patients with EC who underwent PET-FDG scans were initially analyzed. The expression of glucose transporter 1 (Glut1) and hexokinase type II (HK-II) proteins was evaluated by immunohistochemical analysis of resected tissue specimens, with their correlation to FDG uptake meticulously measured.
Furthermore, bioinformatics methods were utilized to analyze genomic and epidemiological data. For the analysis of whole-exome sequencing (WES) data from nine patients with esophageal squamous cell carcinoma (ESCC) in Iran, MEGAJIN software was employed to determine the sequence, alignment, and identification of germline mutations in candidate genes. Epidemiological data regarding the geographical distribution and shared risk factors of EC, gastric, and lung cancers in Iran were also analyzed using BYM and SC statistical models within the OpenBUGS and R software environments.
Results: The multi-faceted analysis of clinical, genomic, and epidemiological data provided several key findings regarding the role of the Hexokinase 2 (HK2) enzyme and the prevalence of Esophageal Cancer (EC).
1. Clinical and Metabolic Findings:
Analysis of clinical data from 72 EC patients revealed a significant positive correlation (p<0.05) between high fluorodeoxyglucose (FDG) uptake on PET-FDG scans and the expression levels of both Hexokinase II (HK-II) and Glucose transporter 1 (Glut1) proteins. This finding indicates that HK-II expression is directly associated with the heightened glucose metabolism observed in EC tumors, positioning it as a strong candidate for a metabolic biomarker.
2. Genomic and Bioinformatic Findings:
Using the MEGAJIN bioinformatics software, we conducted a comprehensive analysis of whole-exome sequencing (WES) data from nine patients with esophageal squamous cell carcinoma (ESCC) in Iran. This analysis successfully identified and confirmed the presence of specific germline mutations in genes previously associated with familial ESCC, including KCNJ12/KCNJ18 and GPRIN2. These results demonstrate the value of bioinformatic tools in identifying genetic predispositions within specific populations.
3. Epidemiological and Geographical Findings:
Our spatial analysis of epidemiological data concerning EC, gastric, and lung cancers in Iran, conducted using BYM and SC statistical models, showed a notable geographical pattern. The results demonstrated a higher prevalence and mortality risk for these cancers in the northern regions of the country. This finding suggests the presence of shared environmental or genetic risk factors that may be more prominent in this area.
Conclusion: In conclusion, our findings establish a strong link between the expression of hexokinase 2 (HK2) and glucose metabolism in esophageal cancer. The significant correlation between high FDG uptake in PET scans and HK2 expression highlights its potential as a predictive biomarker for EC. Furthermore, the application of bioinformatics tools, particularly through the use of MEGAJIN software, has been instrumental in providing new insights into the genetic underpinnings and geographical prevalence of this disease in Iran. This study underscores that a deeper understanding of the metabolic and genetic drivers of esophageal cancer is crucial for developing more effective, targeted therapies and improving patient outcomes.
Keywords: Esophageal Cancer
Hexokinase 2 (HK2)
Biomarker
Bioinformatics