Identification of Key Genes Related With Aspartic Acid Metabolism and Corresponding Protein Expression in Human Colon Cancer With Postoperative Prognosis and the Underlying Molecular Pathways Prediction

Objective: Colon cancer is one of the most frequent and lethal neoplasias. Altered metabolic activity is a well-known hallmark for cancer. The present study is aiming to screen key genes associated with tumor metabolism and construct a prognostic signature of colon cancer patients. Methods: Glutamine- and UC- metabolism related genes were downloaded from GSEA MsigDB. Three key genes were screened by Cox regression analysis with data samples downloaded from TCGA and GSE29623 database. Consistent clustering based on the prognostic genes identified was employed to divide the colon cancer samples into two clusters with significant OS differences. The mRNA and protein expression of the key genes in colon tissues and matched adjacent noncancerous tissues of 16 patients were detected by IHC, qPCR, and Western blot to validate the constructed clustering model. GO, GSVA, and IPA were used to predict the relevant metabolic pathways. Results: According to the three key genes identified, i.e., ASNS, CEBPA, and CAD, the cohort can be divided into two clusters with prognosis differences. Clinical specimen results confirmed that the risk model established was effective, and the different expression pattern of ASNS and CEBPA was correlated with TNM stage and lymph node metastasis, whilst that of CAD was correlated with post-operative tumor metastasis and recurrence. Molecular mechanism prediction indicated that CREB, insulin, and RNA Pol II were the key nodes affecting CEBPA and ASNS expression. Moreover, TIDE algorithm reflected the better immune response of the cluster with shorter OS. Further immune infiltration and checkpoints analyses provided important reference for clinicians to perform individualized immunotherapy. Conclusion: Differential expression profile of three aspartic acid metabolic-associated genes, ASNS, CEBPA, and CAD, can be considered as a risk model with a good evaluation effect on the prognosis of colon cancer patients.