New Glance at the Role of TM6SF2 in Lipid Metabolism and Liver Cancer

Advances in genetic variation in metabolism-related fatty liver disease

Metabolism-related fatty liver disease (MAFLD) is the most common form of chronic liver disease in the world. Its pathogenesis is influenced by both environmental and genetic factors. With the upgrading of gene screening methods and the development of human genome project, whole genome scanning has been widely used to screen genes related to MAFLD, and more and more genetic variation factors related to MAFLD susceptibility have been discovered. There are genetic variants that are highly correlated with the occurrence and development of MAFLD, and there are genetic variants that are protective of MAFLD. These genetic variants affect the development of MAFLD by influencing lipid metabolism and insulin resistance. Therefore, in-depth analysis of different mechanisms of genetic variation and targeting of specific genetic variation genes may provide a new idea for the early prediction and diagnosis of diseases and individualized precision therapy, which may be a promising strategy for the treatment of MAFLD.

A Novel Integrated Metabolism-Immunity Gene Expression Model Predicts the Prognosis of Lung Adenocarcinoma Patients

Background: Although multiple metabolic pathways are involved in the initiation, progression, and therapy of lung adenocarcinoma (LUAD), the tumor microenvironment (TME) for immune cell infiltration that is regulated by metabolic enzymes has not yet been characterized.

Methods: 517 LUAD samples and 59 non-tumor samples were obtained from The Cancer Genome Atlas (TCGA) database as the training cohort. Kaplan-Meier analysis and Univariate Cox analysis were applied to screen the candidate metabolic enzymes for their role in relation to survival rate in LUAD patients. A prognostic metabolic enzyme signature, termed the metabolic gene risk score (MGRS), was established based on multivariate Cox proportional hazards regression analysis and was verified in an independent test cohort, GSE31210. In addition, we analyzed the immune cell infiltration characteristics in patients grouped by their Risk Score. Furthermore, the prognostic value of these four enzymes was verified in another independent cohort by immunohistochemistry and an optimized model of the metabolic-immune protein risk score (MIPRS) was constructed.

Results: The MGRS model comprising 4 genes (TYMS, NME4, LDHA, and SMOX) was developed to classify patients into high-risk and low-risk groups. Patients with a high-risk score had a poor prognosis and exhibited activated carbon and nucleotide metabolism, both of which were associated with changes to TME immune cell infiltration characteristics. In addition, the optimized MIPRS model showed more accurate predictive power in prognosis of LUAD.

Conclusion: Our study revealed an integrated metabolic enzyme signature as a reliable prognostic tool to accurately predict the prognosis of LUAD.