The role of necroptosis in cancer remains debated. In theory, activating necroptosis in tumor cells could boost anti-tumor immunity. CD8 + T cell activation by dying cells via antigen-presenting cells, results in anti-tumor immunity 15 16. According to one study, inoculating necrotic, apoptotic cancer cells induced effective anti-tumor immunity in an experimental mouse model 17.
With the advent of next-generation sequencing technology and the era of precision medicine, a new era of tumor therapeutic approaches has emerged 18. However, there are currently few useful biomarkers in HCC for effective early diagnosis and prediction of therapeutic outcomes. In this study, we attempted for the first time to use necroptosis-associated genes as a predictor of HCC.
Similar to our study, other prognostic models such as the immune-related gene signature and ferroptosis-related gene signature had AUC values of 0.663 and 0.668 for predicting 3-year OS of HCC, respectively 19 20 21 22 23. The prognostic model of Necroptosis-associated genes developed in our study outperforms the gene signatures described above.
This study screened 14 significant genes related to necroptosis by searching and summarizing previous literature, then systematically analyzed their expression in HCC tissues, yielding nine DNRGs. Although the three clusters formed by the consensus clustering analysis based on DNRGs differed only in tumor grade and gene expression profile, the KM survival analysis revealed significant differences in OS between the three clusters. During the cross-comparison investigation, we discovered 421 genes with differential expression across all three clusters. We believe that these genes are DSGs and that DSGs are the underlying cause of survival differences. The results of GO functional and KEGG pathway enrichment analyses showed that DSGs and peroxisome-related pathways have a close relationship. In patients with non-alcoholic fatty liver disease, PPAR-γ was negatively correlated with hepatic RIPK3 24. In one study, the dual peroxisome proliferator-activated receptor alpha/delta agonist elafibranor (ELA) significantly reduced liver lipids and non-alcoholic fatty liver disease activity score in a mouse model via steatosis, inflammation, and fibrosis 25. In addition, ELA most likely achieves this effect by decreasing necrosis (cleavage of RIP3) and apoptosis (cleavage of caspase 3) in the liver. These findings back up our research direction.
A prognostic model comprised of four DSGs was developed and validated in ICGC cohort to confirm the prognostic value of these DSGs. The findings revealed that tumor grade, stage, and OS differed markedly between risk subgroups and that independent prognostic analysis revealed that risk score was an independent predictor of OS. This study's prognostic model was made up of four DSGs. Two DSGs (LDHA and BOD1) were up-regulated in HCC tumor tissues, while two DSGs (GOT2 and MTHFS) were down-regulated.
LDHA is a critical glycolytic enzyme. Increased LDHA expression is associated with a poor prognosis in a variety of cancers 26 27. According to one study, inhibiting LDHA in pancreatic cancer cells had a tumor-suppressive effect 28 29. Faloppi et al. discovered that patients with advanced pancreatic cancer receiving sorafenib with low LDHA serum levels had a better prognosis 30. The results of this experiment were also confirmed in breast cancer, where the use of LDHA inhibitors significantly inhibited tumor cell growth and aggressiveness 31. BOD1 is a protein-coding gene required for cell mitosis, but its role in tumors has received little attention 32. BOD1 was highly expressed in HCC, and high BOD1 expression was associated with a poor prognosis. Several studies have reached similar conclusions to ours 33. However, studies on breast cancer revealed that BOD1 deletion destabilizes DNA replication, increasing the likelihood of tumorigenesis. 34. More research is required to determine the role of BOD1 in cancer. GOT2 catalyzes the reversible interconversion of oxaloacetate and glutamate to aspartate and α-ketoglutarate, which is important in amino acid metabolism and the tricarboxylic acid cycle 35 36. One study discovered that GOT2 expression was low in HCC and that low GOT2 expression was associated with a poor prognosis. In an in-vitro experiment, increasing GOT2 expression in HCC inhibited tumor cell growth 37. At the moment, we know very little about MTHFS gene. According to some studies, MTHFS can influence human fatty acid metabolism by influencing DNA methylation 38. To summarize, the four genes in the prognostic model can help us better understand HCC, but their role require further investigation.
The risk scores of various immune infiltrations differed. This suggests that there could be a link between risk scores and immune microenvironment. It is reasonable to think that necroptosis can influence the composition of tumor immune microenvironment. The immune microenvironment's composition has a significant impact on the efficacy of immune checkpoint inhibitors 39. Recent advancements in cancer therapy that target immune checkpoints, such as anti-CTLA4 antibodies, have demonstrated effective clinical outcomes 40 41. The expressions of CTLA4, HAVCR2, LAG3, and PDCD1 were positively correlated with risk scores in our study. Prognostic models can predict immune checkpoint expression levels and potentially guide immunotherapy decisions. According to GSEA analysis, a high-risk score was associated with impaired activity of type II IFN responses, which play an important role in tumor immune surveillance, anti-tumor immunity stimulation, and tumor elimination promotion 42 43 44. Increased Tfh cells, Treg cells, Th1 cells, Th2 cells, T cell co-stimulation, and T cell co-inhibition in the high-risk group indicated that the immune regulation function was disrupted. According to a recent study, Treg cells suppress anti-tumor immunity and are associated with poor clinical outcomes 45 46. As a result, it is reasonable to assume that the high-risk group's anti-tumor immunity is diminished, which may be a contributing factor to their poor prognosis.
Even though we used various methods to evaluate our model, there are some drawbacks and shortcomings. It is vulnerable to the biases inherent in this research paradigm because it is a retrospective study. Although immune checkpoint expression levels differed significantly between risk groups, we could not compare the corresponding checkpoint inhibitor IC50s due to a lack of relevant data.