The postoperative survival time and quality of life of patients with colon adenocarcinoma (COAD) varies widely. In order to make accurate decisions after surgery, clinicians need to distinguish patients with different prognostic trends. However, we still lack effective methods to predict the prognosis of COAD patients. Accumulated evidences indicated that the inhibition of peroxisome proliferator-activated receptors (PPARs) and a portion of their target genes were associated with the development of COAD. Our study found that the expression of several PPAR pathway-related genes were linked to the prognosis of COAD patients. Therefore, we developed a scoring system (named PPAR-Riskscore) that can predict patients' outcomes. PPAR-Riskscore was constructed by univariate Cox regression based on the expression of 4 genes (NR1D1, ILK, TNFRSF1A, and REN) in tumor tissues. Compared to typical TNM grading systems, PPAR-Riskscore has better predictive accuracy and sensitivity. The reliability of the system was tested on six external validation datasets. Furthermore, PPAR-Riskscore was able to evaluate the immune cell infiltration and chemotherapy sensitivity of each tumor sample. We also combined PPAR-Riskscore and clinical features to create a nomogram with greater clinical utility. The nomogram can help clinicians make precise treatment decisions regarding the possible long-term survival of patients after surgery.
Copyright © 2022 Xiao-Yu Zhou et al.