A novel peroxisome-related gene signature predicts clinical prognosis and is associated with immune microenvironment in low-grade glioma.

Low-grade glioma (LGG), a common primary tumor, mainly originates from astrocytes and oligodendrocytes. Increasing evidence has shown that peroxisomes function in the regulation of tumorigenesis and development of cancer. However, the prognostic value of peroxisome-related genes (PRGs) in LGG has not been reported. Therefore, it is necessary to construct a prognostic risk model for LGG patients based on the expression profiles of peroxisome-related genes. Our study mainly concentrated on developing a peroxisome-related gene signature for overall survival (OS) prediction in LGG patients. First, according to these peroxisome-related genes, all LGG patients from The Cancer Genome Atlas (TCGA) database could be divided into two subtypes. Univariate Cox regression analysis was used to find prognostic peroxisome-related genes in TCGA_LGG dataset, and least absolute shrinkage and selection operator Cox regression analysis was employed to establish a 14-gene signature. The risk score based on the signature was positively associated with unfavorable prognosis. Then, multivariate Cox regression incorporating additional clinical characteristics showed that the 14-gene signature was an independent predictor of LGG. Time-dependent ROC curves revealed good performance of this prognostic signature in LGG patients. The performance about predicting OS of LGG was validated using the GSE107850 dataset derived from the Gene Expression Omnibus (GEO) database. Furethermore, we constructed a nomogram model based on the gene signature and age, which showed a better prognostic power. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that neuroactive ligand-receptor interaction and phagosome were enriched and that the immune status was decreased in the high-risk group. Finally, cell counting kit-8 (CCK8) were used to detect cell proliferation of U251 and A172 cells. Inhibition of ATAD1 (ATPase family AAA domain-containing 1) and ACBD5 (Acyl-CoA binding-domain-containing-5) expression led to significant inhibition of U251 and A172 cell proliferation. Flow cytometry detection showed that ATAD1 and ACBD5 could induce apoptosis of U251 and A172 cells. Therefore, through bioinformatics methods and cell experiments, our study developed a new peroxisome-related gene signature that migh t help improve personalized OS prediction in LGG patients.

Peroxisome-related genes in hepatocellular carcinoma correlated with tumor metabolism and overall survival.

BACKGROUND AND AIM: A prominent hallmark of tumors is aberrant lipid metabolism, and various peroxisome-related genes (PRGs) are associated with aberrant tumoral metabolic signaling. However, the influence of PRGs on the prognosis of hepatocellular carcinoma (HCC) patients remains debatable. Thus, the current study was designed to evaluate the effect of PRGs on HCC and construct a prognostic model for predicting survival. METHODS: We initially acquired HCC-related gene expression profiles from the Cancer Genome Atlas and International Cancer Genome Consortium databases. We then utilized Cox analysis and Lasso regression to identify suitable PRGs for the risk model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of PRGs. Single-sample gene set enrichment analysis (ssGSEA) was conducted to confirm the relationship between PRGs and immunity. RESULTS: Four PRGs were correlated with HCC patient survival: 2 risk genes (MPV17, and ABCD1) and 2 protective genes (ACSL1 and ACSL6). We derived risk scores based on PRGs to construct a predictive model that could accurately predict overall survival (OS) among HCC patients. Furthermore, GO and KEGG analyses revealed that these PRGs were potentially involved in lipid metabolism and ferroptosis in HCC. Moreover, ssGSEA results demonstrated that high PRG scores were associated with immune suppressor activation, which caused the suppression of immune effectors (CD8+ T-cells, B cells, and NK cells) and the attenuation of the immune-mediated antitumor effect. CONCLUSION: PRGs act as key regulators in tumorigenesis and tumor progression by affecting lipid synthesis and utilization, which we used to predict the outcome of HCC patients. Moreover, PRGs have been shown to promote tumoral immune resistance by serving as a vital bridge between metabolism and immunity. Thus, a personalized treatment approach targeting PRGs would clinically benefit patients by blocking the interaction between tumor metabolism and immunity.