Identification of subgroups along the glycolysis-cholesterol synthesis axis and the development of an associated prognostic risk model

Abstract

Background Skin cutaneous melanoma (SKCM) is one of the most highly prevalent and complicated malignancies. Glycolysis and cholesterogenesis pathways both play important roles in cancer metabolic adaptations. The main aims of this study are to subtype SKCM based on glycolytic and cholesterogenic genes and to build a clinical outcome predictive algorithm based on the subtypes. Methods A dataset with 471 SKCM specimens was downloaded from The Cancer Genome Atlas (TCGA) database. We extracted and clustered genes from the Molecular Signatures Database v7.2 and acquired co-expressed glycolytic and cholesterogenic genes. We then subtyped the SKCM samples and validated the efficacy of subtypes with respect to simple nucleotide variations (SNVs), copy number variation (CNV), patients’ survival statuses, tumor microenvironment, and proliferation scores. We also constructed a risk score model based on metabolic subclassification and verified the model using validating datasets. Finally, we explored potential drugs for high-risk SKCM patients. Results SKCM patients were divided into four subtype groups: glycolytic, cholesterogenic, mixed, and quiescent subgroups. The glycolytic subtype had the worst prognosis and MGAM SNV extent. Compared with the cholesterogenic subgroup, the glycolytic subgroup had higher rates of DDR2 and TPR CNV and higher proliferation scores and MK167 expression levels, but a lower tumor purity proportion. We constructed a forty-four-gene predictive signature and identified MST-321, SB-743921, Neuronal Differentiation Inducer III, romidepsin, vindesine, and YM-155 as high-sensitive drugs for high-risk SKCM patients. Conclusions Subtyping SKCM patients via glycolytic and cholesterogenic genes was effective, and patients in the glycolytic-gene enriched group were found to have the worst outcome. A robust prognostic algorithm was developed to enhance clinical decisions in relation to drug administration.

The Genetic Architecture of Vitamin D Deficiency among an Elderly Lebanese Middle Eastern Population: An Exome-Wide Association Study

“…In our study, the top genetic predisposition was the novel MGAM locus, rs141064014, with no indication of LD. The MGAM encodes the maltase-glucoamylase protein belonging to the glycoside hydrolase family 31 that breaks down complex carbohydrates in the small intestine [ 33 ]. In elderly individuals, reduced MGAM activity may exacerbate age-related changes in the digestive system, leading to reduced intestinal and hepatic enzymes, intestinal motility, and malabsorption of nutrients [ 34 ].…”

Genetic Architecture of Vitamin D Deficiency Among an Elderly Lebanese Middle Eastern Population: An Exome-wide Association Study

“…In our study, the top genetic predisposition was the novel MGAM locus, rs141064014, with no indication of LD. The MGAM encodes the maltase-glucoamylase protein belonging to the glycoside hydrolase family 31 that breaks down complex carbohydrates in the small intestine [33]. In elderly individuals, reduced MGAM activity may exacerbate agerelated changes in the digestive system, leading to reduced intestinal and hepatic enzymes, intestinal motility, and malabsorption of nutrients [34].…”

Creatine kinase mitochondrial 2 promotes the growth and progression of colorectal cancer via enhancing Warburg effect through lactate dehydrogenase B

“…Hence, enzymes and proteins related to glucometabolic may play an important role in tumor generation. Tumors are classified into four metabolic subtypes based on genomic and transcriptomic alterations, finding that the glycolytic subtype has the worst prognosis compared to other subgroups ( Zhang et al, 2021 ). Lactate dehydrogenase (LDH), one of the key enzymes in glycolysis, is encoded by the lactate dehydrogenase A and B ( LDHA and LDHB ) genes.…”