A phylogenetic model for understanding the effect of gene duplication on cancer progression

Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers

Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.

MicroRNA-1275 inhibits cell migration and invasion in gastric cancer by regulating vimentin and E-cadherin via JAZF1

Background

Emerging evidence has shown that miR-1275 plays a critical role in tumour metastasis and the progression of various types of cancer. In this study, we analysed the role and mechanism of miR-1275 in the progression and prognosis of gastric cancer (GC).

Methods

Target genes of miR-1275 were identified and verified by luciferase assay and Western blotting. The function of miR-1275 in invasion and metastasis was analysed in vitro and in vivo in nude mice. The signal pathway regulated by miR-1275 was examined by qRT-PCR, Western blotting and chromatin immunoprecipitation analyses. The expression of miR-1275and JAZF1 were measured in specimens of GC and adjacent non cancerous tissues.

Results

We identified JAZF1 as a direct miR-1275 target. miR-1275 supresses migration and invasion of GC cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin. Knocking-down miR-1275 or overexpressing JAZF1 resulted in upregulation of Vimentin but downregulation of E-cadherin. Meanwhile, we validated in 120 GC patients specimens that low miR-1275expression and high JAZF1 mRNA expression levels were closely associated with lymph node metastasis and poor prognosis. The expression of JAZF1 in protein level displayed the correlations with Vimentin but inversely with E-cadherin.

Conclusions

Increased miR-1275 expression inhibited GC metastasis by regulating vimentin/E-cadherin via direct suppression of JAZF1expression, suggesting that miR-1275 is a tumour-suppressor miRNA with the potential as a prognostic biomarker or therapeutic target in GC.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5929-1) contains supplementary material, which is available to authorized users.

KLF5 and MYC modulated LINC00346 contributes to gastric cancer progression through acting as a competing endogeous RNA and indicates poor outcome

It was found in this study that long intergenic non-protein coding RNA 346 (LINC00346) was an lncRNA aberrantly expressed in gastric cancer (GC) based on multiple Gene Expression Omnibus (GEO) databases of GC cohorts. The LINC00346 gene was recurrently amplified and upregulated in GC, and its expression was positively correlated with poor pathologic stage, large tumor size, and poor prognosis. In addition, the oncogenic transcription factors KLF5 and MYC could bind to the LINC00346 promoter and enhance its expression. Gene Set Enrichment Analysis (GSEA) in the GEO datasets revealed that cell cycle and focal adhesion genes were enriched in patients with high LINC00346 expression. In vitro and in vivo assays of LINC00346 alterations revealed a complex integrated phenotype affecting cell growth, migration and invasion. Strikingly, high-throughput sequencing analysis after LINC00346 alterations highlighted alterations in cell cycle and focal adhesion pathways in GC cells. Mechanistically, argonaute 2 (Ago2) was recruited by LINC00346, which functioned as a molecular sponge for miR-34a-5p by antagonizing its ability to repress CD44, NOTCH1, and AXL protein translation. Taken together, our findings support a model in which the KLF5, MYC/LINC00346/miR-34a-5p cross-talk served as critical effectors in GC tumorigenesis and progression, suggesting a new therapeutic direction in the treatment of GC.

Long noncoding RNA GAPLINC promotes gastric cancer cell proliferation by acting as a molecular sponge of miR-378 to modulate MAPK1 expression

Background

Dysregulated long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) play key roles in the development of human cancers. LncRNA GAPLINC has been reported to be increased in gastric cancer (GC) tissues.

Methods

Real-time PCR assays were used to measure expressions of GAPLINC, miR-378, and MAPK1 mRNA. Western blot assays were employed to examine MAPK1 protein expression. Cell proliferation and cell cycle were measured by CCK-8 and propidium iodide-detection assays, respectively. The interaction between GAPLINC and miR-378 was confirmed by site-directed mutagenesis and luciferase assays. Luciferase assays were also used to study whether GAPLINC was able to act as a molecular sponge of miR-378 to modulate MAPK1 expression.

Results

The lncRNA GAPLINC expression was upregulated and positively correlated with MAPK1 expression in gastric cancer tissues and cells. Additionally, lncRNA GAPLINC promoted the expression of MAPK1 and the enhancement of GC cell proliferation and cell cycle progression by LncRNA GAPLINC was dependent on MAPK1 in vitro and in vivo. Consequently, we found that miR-378 expression was inversely correlated with GAPLINC expression in GC tissues and cells. miR-378 could directly bind to GAPLINC and decreased GAPLINC expression, thus reducing MAPK1 expression. Furthermore, overexpression of miR-378 inhibited MAPK1 expression, cell proliferation, and cell cycle progression of gastric cancer cells, while these effects were abrogated by upregulating lncRNA GAPLINC expression.

Conclusion

Taken together, lncRNA GAPLINC promotes gastric cancer cell proliferation by acting as a molecular sponge of miR-378 to modulate MAPK1 expression.

Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry

Background

Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data – ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin’s theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics.

Results

Natural selection drives function conservation and adaptation to emerging pathogens or new environments; selection plays key role in immune and resistance systems. Here I focus on computational methods for evaluating selection in molecular sequences, and argue that they have a high potential for applications. Pharma and biotech industries can successfully use this potential, and should take the initiative to enhance their research and development with state of the art bioinformatics approaches.

Conclusions

This review provides a quick guide to the current computational approaches that apply the evolutionary principles of natural selection to real life problems – from drug target validation, vaccine design and protein engineering to applications in agriculture, ecology and conservation.