Pan-Cancer Analysis Reveals PPRC1 as a Novel Prognostic Biomarker in Ovarian Cancer and Hepatocellular Carcinoma

Abstract

Background and Objectives: As is well understood, peroxisome proliferator-activated receptor gamma cofactor-related 1 (PPRC1) plays a central role in the transcriptional control of the mitochondrial biogenesis and oxidative phosphorylation (OXPHOS) process, yet its critical role in pan-cancer remains unclear. Materials and Methods: In this paper, the expression levels of PPRC1 in different tumor tissues and corresponding adjacent normal tissues were analyzed based on four databases: The Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA), and Tumor Immune Estimation Resource (TIMER). Meanwhile, the prognostic value of PPRC1 was inferred using Kaplan–Meier plotter and forest-plot studies. In addition, the correlation between PPRC1 expression and tumor immune cell infiltration, immune checkpoints, and the tumor-stemness index was analyzed using TCGA and TIMER databases. Results: According to our findings, the expression level of PPRC1 was found to be different in different cancer types and there was a positive correlation between PPRC1 expression and prognosis in several tumor types. In addition, PPRC1 expression was found to be significantly correlated with immune cell infiltration, immune checkpoints, and the tumor-stemness index in both ovarian and hepatocellular carcinoma. Conclusions: PPRC1 demonstrated promising potential as a novel biomarker in pan-cancer due to its potential association with immune cell infiltration, expression of immune checkpoints, and the tumor-stemness index.

Construction of a Lung Adenocarcinoma Prognostic Model Utilizing Serine and Glycine Metabolism-Related Genes

“…To investigate the potential linkage between the constructed model and LUAD-related clinical data, the Wilcoxon test was employed to assess the differences in risk scores across various clinical factors. Then, regression analysis was carried out by utilizing the survival and forestplot R packages to investigate the independence of the risk scores and clinical indicators in predicting LUAD patient prognosis. Additionally, a nomogram and three calibration curves were generated to display the predicted 1-, 3-, and 5-year survival rates of LUAD individuals with different risk scores and clinical features as well as possible deviations between these predicted values and the actual values.…”