Gene Regulatory Network Analysis for Triple-Negative Breast Neoplasms by Using Gene Expression Data.

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Triple-negative breast neoplasm (TNBN) presents unique physiological challenges.
• Understanding the gene regulatory network (GRN) is crucial for identifying therapeutic targets.

Purpose of the Study:

• To elucidate the physiology of TNBN by analyzing its gene regulatory network.
• To identify key regulatory genes within the TNBN GRN.

Main Methods:

• Constructed a TNBN GRN using gene expression data from The Cancer Genome Atlas.
• Employed least absolute shrinkage and selection operator (LASSO) regression for network construction.
• Performed comparative network analysis with triple-positive breast neoplasm (TPBN) data, including differential gene expression (DEG) and survival analyses.

Main Results:

• The TNBN GRN exhibited a scale-free, power-law distribution with 10,237 vertices and 17,773 edges.
• Centrality analysis identified ZDHHC20 and RAPGEF6 as critical network vertices (hubs).
• Multivariate survival analysis indicated significant hazard ratios for ZDHHC20 (1.677) and RAPGEF6 (1.676), suggesting their oncogenic role.

Conclusions:

• The TNBN GRN is scale-free, highlighting the vulnerability of hub vertices.
• ZDHHC20 and RAPGEF6 were identified as potential oncogenes within the TNBN network.
• Further investigation into ZDHHC20 and RAPGEF6 may lead to novel therapeutic strategies for TNBN.