Histone methyltransferase NSD2 mediates the survival and invasion of triple-negative breast cancer cells via stimulating ADAM9-EGFR-AKT signaling.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with a poor prognosis due to the lack of an effective targeted therapy. Histone lysine methyltransferases (KMTs) have emerged as attractive drug targets for cancer therapy. However, the function of the majority of KMTs in TNBC has remained largely unknown. In the current study, we found that KMT nuclear receptor binding SET domain protein 2 (NSD2) is overexpressed in TNBC tumors and that its overexpression is associated with poor survival of TNBC patients. NSD2 regulates TNBC cell survival and invasion and is required for tumorigenesis and tumor growth. Mechanistically, NSD2 directly controls the expression of EGFR and ADAM9, a member of the ADAM (a disintegrin and metalloproteinase) family that mediates the release of growth factors, such as HB-EGF. Through its methylase activity, NSD2 overexpression stimulates EGFR-AKT signaling and promotes TNBC cell resistance to the EGFR inhibitor gefitinib. Together, our results identify NSD2 as a major epigenetic regulator in TNBC and provide a rationale for targeting NSD2 alone or in combination with EGFR inhibitors as a targeted therapy for TNBC.

Long Read Single-Molecule Real-Time Sequencing Elucidates Transcriptome-Wide Heterogeneity and Complexity in Esophageal Squamous Cells.

Esophageal squamous cell carcinoma is a leading cause of cancer death. Mapping the transcriptional landscapes such as isoforms, fusion transcripts, as well as long noncoding RNAs have played a central role to understand the regulating mechanism during malignant processes. However, canonical methods such as short-read RNA-seq are difficult to define the entire polyadenylated RNA molecules. Here, we combined single-molecule real-time sequencing with RNA-seq to generate high-quality long reads and to survey the transcriptional program in esophageal squamous cells. Compared with the recent annotations of human transcriptome (Ensembl 38 release 91), single-molecule real-time data identified many unannotated transcripts, novel isoforms of known genes and an expanding repository of long intergenic noncoding RNAs (lincRNAs). By integrating with annotation of lincRNA catalog, 1,521 esophageal-cancer-specific lincRNAs were defined from single-molecule real-time reads. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these lincRNAs and their target genes are involved in a variety of cancer signaling pathways. Isoform usage analysis revealed the shifted alternative splicing patterns, which can be recaptured from clinical samples or supported by previous studies. Utilizing vigorous searching criteria, we also detected multiple transcript fusions, which are not documented in current gene fusion database or readily identified from RNA-seq reads. Two novel fusion transcripts were verified based on real-time PCR and Sanger sequencing. Overall, our long-read single-molecule sequencing largely expands current understanding of full-length transcriptome in esophageal cells and provides novel insights on the transcriptional diversity during oncogenic transformation.

circTADA2As suppress breast cancer progression and metastasis via targeting miR-203a-3p/SOCS3 axis.

More and more evidence indicates that circular RNAs (circRNAs) have important roles in several diseases, especially in cancers. However, their involvement remains to be investigated in breast cancer. Through screening circRNA profile, we identified 235 differentially expressed circRNAs in breast cancer. Subsequently, we explored the clinical significance of two circTADA2As in a large cohort of triple-negative breast cancer (TNBC), and performed functional analysis of circTADA2A-E6 in vitro and in vivo to support clinical findings. Finally, we evaluated the effect of circTADA2A-E6 on miR-203a-3p and its target gene SOCS3. We detected two circRNAs, circTADA2A-E6 and circTADA2A-E5/E6, which were among the top five differentially expressed circRNAs in breast cancer. They were consistently and significantly decreased in a large cohort of breast cancer patients, and their downregulation was associated with poor patient survival for TNBC. Especially, circTADA2A-E6 suppressed in vitro cell proliferation, migration, invasion, and clonogenicity and possessed tumor-suppressor capability. circTADA2A-E6 preferentially acted as a miR-203a-3p sponge to restore the expression of miRNA target gene SOCS3, resulting in a less aggressive oncogenic phenotype. circTADA2As as promising prognostic biomarkers in TNBC patients, and therapeutic targeting of circTADA2As/miRNA/mRNA network may be a potential strategy for the treatment of breast cancer.

An integrative framework to identify cell death-related microRNAs in esophageal squamous cell carcinoma.

Cell death is a critical biological process involved in many important functions, and defects in this system are usually linked with numerous human diseases including cancers. Esophageal squamous cell carcinoma is one of the most chemo- and biological therapy resistant cancers. Based on knowledge repository and four miRNAs profiling data, we proposed a general framework to hunt for cell death miRNAs in a context dependent manner. We predicted 12 candidate miRNAs from hundreds of others. Follow-up experimental verification of 7 miRNAs indicated at least 3 miRNAs (MIR20b, MIR498 and MIR196) were involved in both apoptosis and autophagy processes. These results indicated miRNAs intimately connected the two cell death modules in esophageal squamous cell carcinoma. This integrative framework can also be easily extended to identify miRNAs in other key cellular signaling pathways or may find conditional specific miRNAs in other cancer types.

[Analysis of codon usage in potato and its application in the modification of t-PA gene].

Bioperl-1.0 was used under Hongqi LINUX system to program the codon analysis software. According to the analysis of 98 codon DNA sequences with this software, the codon usage in potato was calculated and 4 codons have been inferred to the optimal codons. The codons of tissue plasminogen activator (t-PA) gene sequence have been reconstructed according to the results. The t-PA gene sequence containing the optimal codons of potato will be used for t-PA production by potato bioreactor.

Hunting for robust gene signature from cancer profiling data: sources of variability, different interpretations, and recent methodological developments.

Gene microarray is a powerful platform to investigate the expression patterns of thousands of genes simultaneously. One central objective of such analysis is to select sets of genes (i.e., gene signatures) which correlate with clinical characteristics, such as disease subtype diagnosis, response to drug treatment and prognosis. However, previous studies have found that mRNA signatures are highly unstable and strongly depend on the selection of patient samples. Based on five large microRNA profiling datasets, we empirically found that microRNA signatures are also generally unstable. Therefore, concerns arise regarding the reproducibility and clinical applicability of these derived gene signatures. Here, we first provide a brief review on the sources of variability and different interpretations of multiple distinct gene signatures. We then focus on those recent methodological progresses aimed at developing more stable gene signatures.

Peeling off the hidden genetic heterogeneities of cancers based on disease-relevant functional modules.

Discovering molecular heterogeneities in phenotypically defined disease is of critical importance both for understanding pathogenic mechanisms of complex diseases and for finding efficient treatments. Recently, it has been recognized that cellular phenotypes are determined by the concerted actions of many functionally related genes in modular fashions. The underlying modular mechanisms should help the understanding of hidden genetic heterogeneities of complex diseases. We defined a putative disease module to be the functional gene groups in terms of both biological process and cellular localization, which are significantly enriched with genes highly variably expressed across the disease samples. As a validation, we used two large cancer datasets to evaluate the ability of the modules for correctly partitioning samples. Then, we sought the subtypes of complex diffuse large B-cell lymphoma (DLBCL) using a public dataset. Finally, the clinical significance of the identified subtypes was verified by survival analysis. In two validation datasets, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Then, for the notoriously heterogeneous DLBCL, we demonstrated that two partitioned subtypes using an identified module ("cellular response to stress") had very different 5-year overall rates (65% vs. 14%) and were highly significantly (P < 0.007) correlated with the clinical survival rate. Finally, we built a multivariate Cox proportional-hazard prediction model that included 4 genes as risk predictors for survival over DLBCL. The proposed modular approach is a promising computational strategy for peeling off genetic heterogeneities and understanding the modular mechanisms of human diseases such as cancers.