Study of gut microbial homeostasis and targeted therapy for hepatocellular carcinoma based on eigenvalue-fitting superiority algorithm

Abstract

In this paper, firstly, we propose the eigenvalue-based goodness-of-fit algorithm to evaluate intestinal microorganisms and liver cancer, based on the basic principle of one-sided right-tailed fitting criterion, using the covariance matrix of the signal can capture the correlation between signal samples, and the maximum-minimum eigenvalue-based goodness-of-fit detection algorithm can be obtained. Secondly, based on the eigenvalue goodness-of-fit algorithm, a training set and a test set were tested using the full-dimensional clinical data set of enrolled cases to construct a prediction model for the risk of metastatic recurrence after HCC surgery and to select evaluation indexes for the prediction model. Then, to further clarify the diagnostic role of lgEr/lgFp on hepatocellular carcinoma, its intestinal microorganism lgEr/lgFp was analyzed with the diagnostic role of AFP on hepatocellular carcinoma. The results showed that for lgEr/lgFp ratio, the analysis was compared between the high AFP cirrhosis group and liver cancer group and the low AFP liver cancer group and cirrhosis group, and it was found that there was a highly significant difference P<0.01. This study emphasizes the influence of intestinal homeostasis on the development of liver cancer, and establishes the link between intestinal homeostasis and the development of liver cancer.

Keywords

• goodness-of-fit algorithm
• covariance matrix
• one-sided right-tailed fitting criterion
• risk prediction model
• intestinal microorganisms
• 97m80