RESUMO
Type 2 diabetes mellitus and breast cancer are complex, chronic, heterogeneous, and multi-factorial diseases; with common risk factors including but not limited to diet, obesity, and age. They also share mutually inclusive phenotypic features such as the metabolic deregulations resulting from hyperglycemia, hypoxic conditions and hormonal imbalances. Although, the association between diabetes and cancer has long been speculated; however, the exact molecular nature of this link remains to be fully elucidated. Both the diseases are leading causes of death worldwide and a causal relationship between the two if not addressed, may translate into a major global health concern. Previous studies have hypothesized hyperglycemia, hyperinsulinemia, hormonal imbalances and chronic inflammation, as some of the possible grounds for explaining how diabetes may lead to cancer initiation, yet further research still needs to be done to validate these proposed mechanisms. At the crux of this dilemma, hyperglycemia and hypoxia are two intimately related states involving an intricate level of crosstalk and hypoxia inducible factor 1, at the center of this, plays a key role in mediating an aggressive disease state, particularly in solid tumors such as breast cancer. Subsequently, elucidating the role of HIF1 in establishing the diabetes-breast cancer link on hypoxia-hyperglycemia axis may not only provide an insight into the molecular mechanisms underlying the association but also, illuminate on the prognostic outcome of the therapeutic targeting of HIF1 signaling in diabetic patients with breast cancer or vice versa. Hence, this review highlights the critical role of HIF1 signaling in patients with both T2DM and breast cancer, potentiates its significance as a prognostic marker in comorbid patients, and further discusses the potential prognostic outcome of targeting HIF1, subsequently establishing the pressing need for HIF1 molecular profiling-based patient selection leading to more effective therapeutic strategies emerging from personalized medicine.
RESUMO
The increasing prevalence of multi-morbidities, particularly the incidence of breast cancer in diabetic/osteoarthritic patients emphasize on the need for exploring the underlying molecular mechanisms resulting in carcinogenesis. To address this, present study employed a systems biology approach to identify switch genes pivotal to the crosstalk between diseased states resulting in multi-morbid conditions. Hub genes previously reported for type 2 diabetes mellitus (T2DM), osteoarthritis (OA), and triple negative breast cancer (TNBC), were extracted from published literature and fed into an integrated bioinformatics analyses pipeline. Thirty-one hub genes common to all three diseases were identified. Functional enrichment analyses showed these were mainly enriched for immune and metabolism associated terms including advanced glycation end products (AGE) pathways, cancer pathways, particularly breast neoplasm, immune system signalling and adipose tissue. The T2DM-OA-TNBC interactome was subjected to protein-protein interaction network analyses to identify meta hub/clustered genes. These were prioritized and wired into a three disease signalling map presenting the enriched molecular crosstalk on T2DM-OA-TNBC axes to gain insight into the molecular mechanisms underlying disease-disease interactions. Deciphering the molecular bases for the intertwined metabolic and immune states may potentiate the discovery of biomarkers critical for identifying and targeting the immuno-metabolic origin of disease.
RESUMO
The bidirectional causal relationship between type 2 diabetes mellitus (T2DM) and breast cancer (BC) has been established by numerous epidemiological studies. However, the underlying molecular mechanisms are not yet fully understood. Identification of hub genes implicated in T2DM-BC molecular crosstalk may help elucidate on the causative mechanisms. For this, expression series GSE29231 (T2DM-adipose tissue), GSE70905 (BC- breast adenocarcinoma biopsies) and GSE150586 (diabetes and BC breast biopsies) were extracted from Gene Expression Omnibus (GEO) database, and analyzed to obtain differentially expressed genes (DEGs). The overlapping DEGs were determined using FunRich. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Transcription Factor (TF) analyses were performed on EnrichR software and a protein-protein interaction (PPI) network was constructed using STRING software. The network was analyzed on Cytoscape to determine hub genes and Kaplan-Meier plots were obtained. A total of 94 overlapping DEGs were identified between T2DM and BC samples. These DEGs were mainly enriched for GO terms RNA polymerase II core promoter proximal region sequence and its DNA binding, and cAMP response element binding protein, and KEGG pathways including bladder cancer, thyroid cancer and PI3K-AKT signaling. Eight hub genes were identified: interleukin 6 (IL6), tumor protein 53 (TP53), interleukin 8 (CXCL8), MYC, matrix metalloproteinase 9 (MMP9), beta-catenin 1 (CTNNB1), nitric oxide synthase 3 (NOS3) and interleukin 1 beta (IL1ß). MMP9 and MYC associated unfavorably with overall survival (OS) in breast cancer patients, IL6, TP53, IL1ß and CTNNB1 associated favorably, whereas NOS3 did not show any correlation with OS. Salt inducible kinase 1 (SIK1) was identified as a significant key DEG for comorbid samples when compared with BC, also dysregulated in T2DM and BC samples (adjusted p <0.05). Furthermore, four of the significant hub genes identified, including IL6, CXCL8, IL1B and MYC were also differentially expressed for comorbid samples, however at p < 0.05. Our study identifies key genes including SIK1, for comorbid state and 8 hub genes that may be implicated in T2DM-BC crosstalk. However, limitations associated with the insilico nature of this study necessitates for subsequent validation in wet lab. Hence, further investigation is crucial to study the molecular mechanisms of action underlying these genes to fully explore their potential as diagnostic and prognostic biomarkers and therapeutic targets for T2DM-BC association.