Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer.

Background: Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and progression of pancreatic cancer as essential building blocks for protein synthesis and key regulators of cellular metabolism. Understanding the interplay between pancreatic cancer and amino acid metabolism offers potential avenues for improving patient clinical outcomes.

Crosstalk between metabolic remodeling and epigenetic reprogramming: A new perspective on pancreatic cancer.

Pancreatic cancer is a highly malignant solid tumor with a poor prognosis and a high mortality rate. Thus, exploring the mechanisms underlying the development and progression of pancreatic cancer is critical for identifying targets for diagnosis and treatment. Two important hallmarks of cancer-metabolic remodeling and epigenetic reprogramming-are interconnected and closely linked to regulate one another, creating a complex interaction landscape that is implicated in tumorigenesis, invasive metastasis, and immune escape.