Prognosis and diagnosis of prostate cancer based on hypergraph regularization sparse least partial squares regression algorithm.

ABSTRACT:

Background

Prostate cancer (PCa) is a malignant tumor of the male reproductive system, and its incidence has increased significantly in recent years. This study aimed to further identify candidate biomarkers with prognostic and diagnostic significance by integrating gene expression and DNA methylation data from PCa patients through association analysis.

Material and methods

To this end, this paper proposes a sparse partial least squares regression algorithm based on hypergraph regularization (HR-SPLS) by integrating and clustering two kinds of data. Next, module 2, with the most significant weight, was selected for further analysis according to the weight of each module related to DNA methylation and mRNAs. Based on the DNA methylation sites in module 2, this paper uses mult

SUBMITTER: Huang RH 

PROVIDER: S-EPMC11210239 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature