The -553 T/A polymorphism in the promoter region of the FGF2 gene is associated with increased breast cancer risk in Polish women

Inferring cancer common and specific gene networks via multi-layer joint graphical model

Cancer is a complex disease caused primarily by genetic variants. Reconstructing gene networks within tumors is essential for understanding the functional regulatory mechanisms of carcinogenesis. Advances in high-throughput sequencing technologies have provided tremendous opportunities for inferring gene networks via computational approaches. However, due to the heterogeneity of the same cancer type and the similarities between different cancer types, it remains a challenge to systematically investigate the commonalities and specificities between gene networks of different cancer types, which is a crucial step towards precision cancer diagnosis and treatment. In this study, we propose a new sparse regularized multi-layer decomposition graphical model to jointly estimate the gene networks of multiple cancer types. Our model can handle various types of gene expression data and decomposes each cancer-type-specific network into three components, i.e., globally shared, partially shared and cancer-type-unique components. By identifying the globally and partially shared gene network components, our model can explore the heterogeneous similarities between different cancer types, and our identified cancer-type-unique components can help to reveal the regulatory mechanisms unique to each cancer type. Extensive experiments on synthetic data illustrate the effectiveness of our model in joint estimation of multiple gene networks. We also apply our model to two real data sets to infer the gene networks of multiple cancer subtypes or cell lines. By analyzing our estimated globally shared, partially shared, and cancer-type-unique components, we identified a number of important genes associated with common and specific regulatory mechanisms across different cancer types.

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology

Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.

KIF26B promotes cell proliferation and migration through the FGF2/ERK signaling pathway in breast cancer

BACKGROUND

Many studies have suggested that high KIF26B expression is directly linked to poor prognostic outcomes in breast cancer. However, the exact role of KIF26B in breast cancer progression is not fully understood. In this study, we aimed to explore the function and mechanism of KIF26B in breast cancer progression.

METHODS

Quantitative real-time PCR and immunohistochemistry analysis were used to detect KIF26B expression in breast cancer cell lines and patient samples. Cell proliferation was assessed by CCK-8 assay, and cell migration and invasion were evaluated by wound healing assay and transwell assay. Western blot analysis was carried out to assess the underlying molecular mechanisms. Tumor formation and metastasis were determined by in vivo mouse experiments.

RESULTS

KIF26B levels were significantly increased in breast cancer cells and patient samples. KIF26B level correlated with tumor size, TNM grade, and differentiation in patients with breast cancer. Overexpressing KIF26B in vitro promoted breast cancer cell proliferation and migration by activating FGF2/ERK signaling, while silencing KIF26B had the opposite effects. Similarly, KIF26B knockdown repressed tumor formation and metastasis in nude mice.

CONCLUSION

KIF26B promoted the development and progression of breast cancer and might act as a potential therapeutic target for treating breast cancer.

A role for FGF2 in visceral adiposity-associated mammary epithelial transformation

Obesity is a leading risk factor for post-menopausal breast cancer, and this is concerning as 40% of cancer diagnoses in 2014 were associated with overweight/obesity. Despite this epidemiological link, the underlying mechanism responsible is unknown. We recently published that visceral adipose tissue (VAT) releases FGF2 and stimulates the transformation of skin epithelial cells. Furthermore, obesity is differentially associated with many epithelial cancers, and this mechanistic link could be translational. As FGF2 and FGFR1 are implicated in breast cancer progression, we hypothesize that VAT-derived FGF2 plays a translational role in promoting adiposity-associated mammary epithelial cell transformation. In this brief report, data suggest that FGF2/FGFR1 signaling is a potential mechanistic link in VAT-stimulated transformation of breast epithelial cells.