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.

Potential roles of hsa_circ_000839 and hsa_circ_0005986 in breast cancer

Background

Breast cancer (BC) is one of the leading causes of death among women around the world. Circular RNAs (circRNAs) are a newly discovered group of non‐coding RNAs that their roles are being investigated in BC and other cancer types. In this study, we evaluated the association of hsa_circ_0005986 and hsa_circ_000839 in tumor and adjacent normal tissues of BC patients with their clinicopathological characteristics.

Materials and methods

Total RNA was extracted from tumors and adjacent non‐tumor tissues by the Trizol isolation reagent, and cDNA was synthesized using First Strand cDNA Synthesis Kit (Thermo Scientific). The expression level of hsa_circ_0005986 and hsa_circ_000839 was quantified using RT‐qPCR. Online in silico tools were used for identifying potentially important competing endogenous RNA (ceRNA) networks of these two circRNAs.

Results

The expression level of hsa_circ_0005986 and hsa_circ_000839 was lower in the tumor as compared to adjacent tissues. The expression level of hsa_circ_0005986 in the patients who had used hair dye in the last 5 years was significantly lower. Moreover, a statistically significant negative correlation between body mass index (BMI) and hsa_circ_000839 expression was observed. In silico analysis of the ceRNA network of these circRNAs revealed mRNAs and miRNAs with crucial roles in BC.

Conclusion

Downregulation of hsa_circ_000839 and hsa_circ_0005986 in BC tumors suggests a tumor‐suppressive role for these circRNAs in BC, meriting the need for more experimentations to delineate the exact mechanism of their involvement in BC pathogenesis.

Circular RNA-associated ceRNA network involved in HIF-1 signalling in triple-negative breast cancer: circ_0047303 as a potential key regulator

The aggressive and highly metastatic nature of triple‐negative breast cancer (TNBC) causes patients to suffer from the poor outcome. HIF‐1 signalling pathway is a prominent pathway that contributes to angiogenesis and metastasis progression in tumours. On the contrary, the undeniable importance of circular RNAs (circRNAs) as multifunctional non‐coding RNAs (ncRNAs) has been identified in breast cancer. These ncRNAs owing to their high stability and specificity have been becoming a hotspot in cancer researches. circRNAs act as competing endogenous RNAs (ceRNAs) and compete with mRNAs for shared miRNAs, thus modulate gene expression. Since the most dysregulated biological functions in TNBC are associated with cellular invasion, understanding the molecular pathogenesis of these processes is a crucial step towards the development of new treatment approaches. The purpose of this study is to undermine the circRNA‐associated ceRNA network involved in HIF‐1 signalling in TNBC using an integrative bioinformatics approach. In the next step, the novel circ_0047303‐mediated ceRNA regulatory axes have been extracted and validated across TNBC samples. We show that circ_0047303 has the highest degree in the circRNA‐associated ceRNA network and shows a significant up‐expression in TNBC. Moreover, our results suggest that circ_0047303 could mediate the upregulation of key angiogenesis‐related genes, including HIF‐1, EIF4E2 and VEGFA in TNBC through sponging the tumour‐suppressive miRNAs. The circ_0047303 could be a promising molecular biomarker and/or therapeutic target for TNBC.

Integrative analyses of triple negative dysregulated transcripts compared with non-triple negative tumors and their functional and molecular interactions

Triple-negative (TN) tumors are a subtype of breast cancer with aggressive behaviors and limited targeted therapies. Microarray studies were not concerned with interactions and functional relations of dysregulated transcripts. Here, we aimed to conduct integrative strategy to analyze gene and miRNA available microarray data as well as bioinformatic analyses to catch a more inclusive picture of pivotal dysregulated transcripts and their interactions in TN tumors. Several online datasets and offline bioinformatic tools were used to detect differentially expressed (DE) transcripts, both protein and nonprotein coding, in TN compared with non-TN tumors and their functional and molecular interactions. Sixteen upregulated and 58 downregulated genes with a log fold change higher or equal to | 2 | were identified, including nine transcription factors. Coexpression network revealed EN1 as a hub gene, moreover Kaplan-Meier plotter survival analysis indicated that it was an appropriate prognostic marker for TN patients with breast cancer. Functional annotation analysis of protein-protein interaction network showed FOXM1 as an upexpressed and ESR1 as a downexpressed hub genes are suitable targets as far as antitumor protein therapy is concerned in TN breast cancers. The consensus analysis of two microRNA datasets revealed seven DE miRNAs. The gene-transcriptional factor (TF)-miRNA network revealed mir-135b and mir-29b are the hub nodes and involved in feedback loops with GATA3. This study suggests that dysregulated TFs and miRNAs have pivotal roles in regulation of TN oncotranscriptomic profile and might become both biomarkers and therapeutic targets.