Published online May 26, 2022. doi: 10.12998/wjcc.v10.i15.4737
Peer-review started: September 13, 2021
First decision: January 23, 2022
Revised: February 2, 2022
Accepted: April 2, 2022
Article in press: April 2, 2022
Published online: May 26, 2022
Processing time: 253 Days and 0.9 Hours
Metabolic reprogramming is a feature of tumour cells and is essential to support their rapid proliferation. The glycolytic activity of liver cancer cells is significantly higher than that of normal liver cells, and the rapidly proliferating tumour cells are powered by aerobic glycolysis. Lipid metabolism reprogramming enables tumour cells to meet their needs for highly proliferative growth and is an important driving force for the development of hepatocellular carcinoma (HCC).
To explore the influence of different metabolic subtypes of HCC and analyse their significance in guiding prognosis and treatment based on the molecular mechanism of glycolysis and fatty acid oxidation (FAO).
By downloading related data from public databases including the Cancer Genome Atlas (TCGA), the Molecular Signatures Database, and International Cancer Genome Consortium, we utilised unsupervised consensus clustering to divide TCGA Liver Hepatocellular Carcinoma samples into four metabolic subgroups and compared single nucleotide polymorphism, copy number variation, tumour microenvironment, and Genomics of Drug Sensitivity in Cancer and Tumour Immune Dysfunction and Exclusion between different metabolites. The differences and causes of survival and the clinical characteristics between them were analysed, and a prognostic model was established based on glycolysis and FAO genes. Combined with the clinical features, a Norman diagram was created to compare the pros and cons of each model.
In the four metabolic subgroups, with the increase in glycolytic expression, the median survival of patients showed the worst results, while FAO showed the best. When comparing the follow-up analysis of each group, we considered that the differences between them might be related to reactive oxygen species, somatic copy number variation of key genes, and immune microenvironment. It was also found that the FAO group and the low-risk group had better efficacy and response to immune checkpoint blockade treatment and anti-tumour drugs.
There are obvious differences in genes, chromosomes, and clinical characteristics between metabolic subgroups. The establishment of a prognostic model could predict patient prognosis and guide clinical treatment.
Core Tip: Through cluster analysis, we divided liver cancer samples into four subgroups and compared survival and molecular biological differences between different subtypes. The median survival of the fatty acid oxidation group was significantly better than that of the glycolytic group. We used Lasso regression to reduce dimensionality and selected RAE1 and CTNNB1 for modelling. We found that the model can identify patients with a better response to anti-cancer drugs, such as Sorafenib and Paclitaxel, which can also guide clinical treatment.