Gene network analysis using SWIM reveals interplay between the transcription factor‐encoding genes HMGA1, FOXM1, and MYBL2 in triple‐negative breast cancer

Abstract

Among breast cancer subtypes, triple‐negative breast cancer (TNBC) is the most aggressive with the worst prognosis and the highest rates of metastatic disease. To identify TNBC gene signatures, we applied the network‐based methodology implemented by the SWIM software to gene expression data of TNBC patients in The Cancer Genome Atlas (TCGA) database. SWIM enables to predict key (switch) genes within the co‐expression network, whose perturbations in expression pattern and abundance may contribute to the (patho)biological phenotype. Here, SWIM analysis revealed an interesting interplay between the genes encoding the transcription factors HMGA1, FOXM1, and MYBL2, suggesting a potential cooperation among these three switch genes in TNBC development. The correlative nature of this interplay in TNBC was assessed by in vitro experiments, demonstrating how they may actually modulate the expression of each other.

Identification of fibronectin 1 (FN1) and complement component 3 (C3) as immune infiltration-related biomarkers for diabetic nephropathy using integrated bioinformatic analysis

“…As a rapidly developing new field, network medicine combines molecular biology and network science and is expected to reveal the causes of human diseases and radically change their diagnosis and treatment [ 18 ]. Network medicine-based algorithms, such as protein-protein interaction (PPI) [ 19 ], switch genes miner (SWIM) [ 20 ] and weighted correlation network analysis (WGCNA) [ 21 ], have also been successfully used to investigate the mechanisms of chronic obstructive pulmonary disease [ 22 ], cancer, and other diseases [ 23–26 ]. In addition, network medicine-related algorithms, such as the connectivity map (CMap) and the search for off-label drugs and networks (SAveRUNNER), can be used to predict the link between diseases and drugs, significantly shortening the development cycle of new drugs [ 27 ].…”

Differential Regulation of Lacto-/Neolacto- Glycosphingolipid Biosynthesis Pathway Reveals Transcription Factors as Potential Candidates in Triple-Negative Breast Cancer

“…ZNF622 (zinc finger protein 622), also known as ZPR9 (zinc finger protein 9), enhances the transcriptional activity of the MYBL2 (also known as B-MYB) transcription factor to regulate cellular growth of neuroblastoma cells [69], but may also have cytoplasmic roles in the regulation of apoptosis [70]. Recent gene expression analyses studies suggest a role for MYBL2 in BC [71] and TNBC [72]. While ZFN622 has been identified as a candidate gene for colorectal cancer metastasis [73], there have not been any reports directly linking ZFN622 to BC or TNBC.…”

Are Transcription Factors Plausible Oncotargets for Triple Negative Breast Cancers?

“…Thus, it is suggested that blocking the interaction between HMGA1 and FOXM1 could be an attractive therapeutic approach for TNBC. A recent study performing gene network analysis using the software SWIM has confirmed the cooperation between HMGA1 and FOXM1 together with MYBL2 at contributing to TNBC pathogenesis [ 111 ].…”

“…HMGA1 expression positively correlated with tumour aggressiveness in TNBC [ 111 ]. Moreover, high HMGA1 expression was significantly associated with shorter OS, relapse free survival, distant metastasis free survival and post-progression survival in BC.…”