Molecular structure and mechanism of protein MSMB, TPPP3, SPI1: Construction of novel 4 pancreatic cancer-related protein signatures model based on machine learning

The high mortality rate of pancreatic cancer is closely related to its inconspicuous early symptoms and difficult diagnosis. In recent years, with the rapid development of proteomics and bioinformatics, the use of machine learning technology to analyze protein characteristics provides a new idea for the early diagnosis and treatment of pancreatic cancer. The main purpose of this study is to deeply analyze the molecular mechanism and action mechanism of MSMB, TPPP3 and SPI1, which are closely related to pancreatic cancer, by constructing a feature model based on machine learning. The study collected a large number of proteomic data from pancreatic cancer patients and screened out candidate proteins associated with pancreatic cancer. Then the molecular characteristics of MSMB, TPPP3 and SPI1 were analyzed by bioinformatics tools. On this basis, machine learning algorithms were used to model the expression patterns and functions of these proteins. The accuracy and generalization ability of the model were verified by cross-validation and independent test sets, and finally a feature model that effectively distinguished pancreatic cancer from normal tissue was determined. Through the construction and verification of the machine learning model, we found that the expression patterns of MSMB, TPPP3 and SPI1 proteins in pancreatic cancer tissues were significantly different. The expression of MSMB protein is down-regulated in pancreatic cancer tissue, while the expression of TPPP3 and SPI1 protein is up-regulated. Further functional analysis indicated that MSMB may be involved in the development of pancreatic cancer through regulation of cell cycle and apoptosis, TPPP3 may be related to cytoskeleton stability and cell migration ability, and SPI1 may play an important role in immune escape of pancreatic cancer. These findings provide new insights into the molecular mechanisms of pancreatic cancer.