Integrative network analysis identifies potential targets and drugs for ovarian cancer

Isocitrate dehydrogenase 1 mutation drives leukemogenesis by PDGFRA activation due to insulator disruption in acute myeloid leukemia (AML)

Acute myeloid leukemia (AML) is characterized by complex molecular alterations and driver mutations. Elderly patients show increased frequencies of IDH mutations with high chemoresistance and relapse rates despite recent therapeutic advances. Besides being associated with global promoter hypermethylation, IDH1 mutation facilitated changes in 3D DNA-conformation by CTCF-anchor methylation and upregulated oncogene expression in glioma, correlating with poor prognosis. Here, we investigated the role of IDH1 p.R132H mutation in altering 3D DNA-architecture and subsequent oncogene activation in AML. Using public RNA-Seq data, we identified upregulation of tyrosine kinase PDGFRA in IDH1-mutant patients, correlating with poor prognosis. DNA methylation analysis identified CpG hypermethylation within a CTCF-anchor upstream of PDGFRA in IDH1-mutant patients. Increased PDGFRA expression, PDGFRA-CTCF methylation and decreased CTCF binding were confirmed in AML CRISPR cells with heterozygous IDH1 p.R132H mutation and upon exogenous 2-HG treatment. IDH1-mutant cells showed higher sensitivity to tyrosine kinase inhibitor dasatinib, which was supported by reduced blast count in a patient with refractory IDH1-mutant AML after dasatinib treatment. Our data illustrate that IDH1 p.R132H mutation leads to CTCF hypermethylation, disrupting DNA-looping and insulation of PDGFRA, resulting in PDGFRA upregulation in IDH1-mutant AML. Treatment with dasatinib may offer a novel treatment strategy for IDH1-mutant AML.

Exosomal lncRNA ATB Derived from Ovarian Cancer Cells Promotes Angiogenesis via Regulating miR-204-3p/TGFβR2 Axis

Background

Ovarian cancer is a life-threatening disease with a high mortality rate in women. Our previous work presented that long non-coding RNA (lncRNA) activated by transforming growth factor beta (TGF-β) (lncRNA ATB) played a role of oncogene in ovarian cancer. However, whether exosomal lncRNA ATB from ovarian cancer cells could regulate the tumorigenesis of ovarian cancer remains unclear.

Methods

RT-qPCR assay was performed to evaluate the level of lncRNA ATB in cancer cells (SKOV3 and A2780). In addition, ovarian cancer cells-secreted exosomes were collected with ultracentrifugation. CCK8 assay was performed to detect the viability of ovarian cells and HUVECs. Meanwhile, Western blot was performed to detect the expression of mechanism related protein and tube formation assay was used to observe the angiogenesis of HUVECs. Finally, xenograft mice model was used to verify the role of ovarian cancer cell-derived exosomes in vivo.

Results

Ovarian cancer cells-derived exosomes promoted the viability, angiogenesis and migration of HUVECs; however, knockdown of lncRNA ATB in HUVECs reversed these phenomena. In addition, exosomal lncRNA ATB promoted the tumorigenesis of ovarian cancer via regulating miR-204-3p/TGFβR2 axis. Furthermore, ovarian cancer cells-secreted exosomal lncRNA ATB increased tumor growth in vivo.

Conclusion

Exosomal lncRNA ATB derived from ovarian cancer cells could improve tumor microenvironment via regulating miR-204-3p/TGFβR2 axis. Thus, this study might provide new knowledge for the treatment of ovarian cancer.

Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment.

METHODS

In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells.

RESULTS

We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support.

CONCLUSIONS

The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics

In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this supplement issue, covering four major areas, namely computational method development, genomics analysis, network-based analysis and biomarker prediction. The selected papers perform cutting edge computational research applied to a broad range of human diseases such as cancer, neural degenerative and chronic inflammatory disease. They also proposed solutions for fundamental medical genomics problems range from basic data processing and quality control to functional interpretation, biomarker and drug prediction, and database releasing.