Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer

Abstract

This study is aimed at exploring the potential mechanism of the PPAR signaling pathway in breast cancer (BRCA) and constructing a novel prognostic-related risk model. We used various bioinformatics methods and databases to complete our exploration in this research. Based on TCGA database, we use multiple extension packages based on the R language for data conversion, processing, and statistics. We use LASSO regression analysis to establish a prognostic-related risk model in BRCA. And we combined the data of multiple online websites, including GEPIA, ImmuCellAI, TIMER, GDSC, and the Human Protein Atlas database to conduct a more in-depth exploration of the risk model. Based on the mRNA data in TCGA database, we conducted a preliminary screening of genes related to the PPAR signaling pathway through univariate Cox analysis, then used LASSO regression analysis to conduct a second screening, and successfully established a risk model consisting of ten genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide breast cancer patients into high- and low-risk groups with significant prognostic differences ( P = 1.92 e − 05 ) based on this risk model. Combined with the clinical data in TCGA database, there is a correlation between the risk model and the patient’s N, T, gender, and fustat. The results of multivariate Cox regression show that the risk score of this risk model can be used as an independent risk factor for BRCA patients. In particular, we draw a nomogram that can predict the 5-, 7-, and 10-year survival rates of BRCA patients. Subsequently, we conducted a series of pancancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to investigate drug sensitivity. Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. This study provides valuable clues for future research on the PPAR signaling pathway in BRCA.

Prognostic Signature and Therapeutic Value Based on Membrane Lipid Biosynthesis-Related Genes in Breast Cancer

“…To fully explore the prognostic value of membrane lipid biosynthesis-related genes in BRCA, we used LASSO regression curve analysis to establish a prognostic risk model in BRCA, which included GPAA1, PIGF, ST3GAL1, ST6GALNAC4, PLPP2, ELOVL1, HACD1, SGPP1, PRKD2, VAPB, CERS2, SGMS2, ALDH3B2, and HACD3 (Figures 2(a) and 2(b) ). LASSO regression curve analysis is often used in medical modeling [ 28 , 29 ]. Based on the calculation formula of the risk model related to membrane lipid biosynthesis, we can divide breast cancer patients into high-risk and low-risk groups.…”

“…Statistics and machine learning use LASSO regression to identify the most critical factors and improve the accuracy of statistical models. LASSO is a popular machine learning algorithm widely used in medical research [ 25 , 28 , 29 ]. In our study, risk scores were obtained for all patients and then divided into high- or low-risk subgroups based on the median risk score of the TCGA cohort.…”

Cell Division Cycle-Associated Protein 3 (CDCA3) Is a Potential Biomarker for Clinical Prognosis and Immunotherapy in Pan-Cancer

“…The association of CDCA3 expression with patient survival was analyzed using the Kaplan-Meier survival analysis. In addition, we have used a similar process in our multiple previous studies [ 30 , 31 ].…”

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

“…In recent years, the relationship between the prognosis of cancer and genes associated with the PPAR signaling pathway have extensively explored. PPAR pathway-related genes have been used to develop predictive models for uterine cervical cancer 24 , renal clear cell carcinoma 25 and breast cancer 26 , 27 . The liver is one of the organs with the highest content of PPARα, which is related to the process of energy metabolism and immune regulation.…”