An integrated approach to understand the regulatory role of miR-27 family in breast cancer metastasis

One of the prime reasons of increasing breast cancer mortality is metastasizing cancer cells. Owing to the side effects of clinically available drugs to treat breast cancer metastasis, it is of utmost importance to understand the underlying biogenesis of breast cancer tumorigenesis. In-silico identification of potential RNAs might help in utilizing the miR-27 family as a therapeutic target in breast cancer. The experimentally verified common interacting mRNAs for miR27 family are retrieved from three publicly available databases- TargetScan, miRDB and miRTarBase. Finally on comparing the common genes with HCMDB and GEPIA data, four breast cancer-associated differentially expressed metastatic mRNAs (GATA3, ENAH, ITGA2 and SEMA4D) are obtained. Corresponding to the miR27 family and associated mRNAs, interacting drugs are retrieved from Sm2mir and CTDbase, respectively. The interaction network-based approach was utilized to obtain the hub RNAs and triad modules by employing the 'Cytohubba' and 'MClique' plugins, respectively in Cytoscape. Further, sample-, subclass- and promoter methylation-based expression analyses reveals GATA3 and ENAH to be the most significant mRNAs in breast cancer metastasis having >10% genetic alteration in both METABRIC Vs TCGA datasets as per their oncoprint analysis via cBioPortal. Additionally, survival analysis in Oncolnc reveals SEMA4D as survival biomarker. Interactions among the miR27 family, their target mRNAs and drugs interacting with miRNAs and mRNAs can be extensively explored in both in-vivo and in-vitro setups to assess their therapeutic potential in the diminution of breast cancer.

Differential gene expression analysis combined with molecular dynamics simulation study to elucidate the novel potential biomarker involved in pulmonary TB

Tuberculosis (TB) is a lethal multisystem disease that attacks the lungs' first line of defense. A substantial threat to public health and a primary cause of death is pulmonary TB. This study aimed to identify and investigate the probable differentially expressed genes (DEGs) primarily involved in Pulmonary TB. Accordingly, three independent gene expression data sets, numbered GSE139825, GSE139871, and GSE54992, were utilized for this purpose. The identified DEGs were used for bioinformatics-based analysis, including physical gene interaction, Gene Ontology (GO), network analysis and pathway studies using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG). The computational analysis predicted that TNFAIP6 is the significant DEG in the gene expression profiling of TB datasets. According to gene ontology analysis, TNFAIP6 is also essential in injury and inflammation. Further, TNFA1P6 is strongly linked to arsenic poisoning, evident from the results of NetworkAnalyst, a comprehensive and interactive platform for gene expression profiling via network visual analytics. As a result, the TNFAIP6 gene was ultimately chosen as a candidate DEG and subsequently employed for in silico structural characterization studies. The tertiary structure of TNFAIP6 was modelled using the ROBETTA server, followed by validation with SAVES and ProSA webserver. Additionally, structural dynamic studies, including molecular dynamics simulation (MDS) and essential dynamics analysis, including principal component (PC) based free energy landscape (FEL) analysis, was used for checking the stability of TNFAIP6 models. The dynamics result established the structural rigidity of modelled TNFAIP6 through RMSD, RMSF and RoG results. The FEL analysis revealed the restricted conformational flexibility of TNFAIP6 by displaying a single minimum energy basin in the contour plot. The comprehensive computational analysis established that TNFAIP6 could serve as a viable biomarker to assess the severity of pulmonary TB.

Dlg4 and Vamp2 are involved in comorbid epilepsy and attention-deficit hyperactivity disorder: A microarray data study

OBJECTIVE

Children with epilepsy exhibit a significantly higher risk for attention-deficit hyperactivity disorder (ADHD), which is often associated with lower quality of life. In this study, we aimed to identify molecular mechanisms associated with both epilepsy and ADHD.

MATERIALS AND METHODS

Gene expression profiles of GSE12457 and GSE47752 were downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were separately screened in epilepsy and ADHD samples and compared with controls. Weighted gene coexpression network analysis (WGCNA) was used to identify candidate modules associated with the two disorders. Functional annotation and analysis of hub genes and molecular complex detection (MCODE) was also performed.

RESULTS

Three modules closely related to epilepsy and ADHD were screened using WGCNA; DEGs in this module were involved in the synaptic vesicle cycle, axon and neuron regeneration, and neurotransmission. The Dlg4 and Vamp2 genes were selected as common candidate factors in epilepsy and ADHD pathogenesis.

CONCLUSION

Dlg4 and Vamp2 could play essential roles in comorbidity between epilepsy and ADHD.

Luteolin inhibits melanoma growth in vitro and in vivo via regulating ECM and oncogenic pathways but not ROS

Luteolin inhibited growth of several cancer cells in vitro in previous studies, with limited in vivo studies, and no comprehensive understanding of molecular mechanisms at genomics level. This study identified luteolin as an effective agent to inhibit melanoma cell growth in vitro and in vivo. Molecular studies and genomic profiling were used to identify the mechanism of action of luteolin in melanoma cells. As a ROS (reactive oxygen species) scavenger, luteolin unexpectedly induced ROS; but co-treatment with antioxidants NAC or mito-TEMPO did not rescue cell growth inhibition, although the levels of ROS levels were reduced. Next, we profiled luteolin-induced differentially expressed genes (DEGs) in 4 melanoma cell lines using RNA-Seq, and performed pathway analysis using a combination of bioinformatics software including PharmetRx which was especially effective in discovering pharmacological pathways for potential drugs. Our results show that luteolin induces changes in three main aspects: the cell-cell interacting pathway (extracellular matrix, ECM), the oncogenic pathway and the immune response signaling pathway. Based on these results, we further validated that luteolin was especially effective in inhibiting cell proliferation when cells were seeded at low density, concomitantly with down-regulation of fibronectin accumulation. In conclusion, through extensive DEG profiling in a total of 4 melanoma cell lines, we found that luteolin-mediated growth inhibition in melanoma cells was perhaps not through ROS induction, but likely through simultaneously acting on multiple pathways including the ECM (extracellular matrix) pathway, the oncogenic signaling and the immune response pathways. Further investigations on the mechanisms of this promising compound are warranted and likely result in application to cancer patients as its safety pharmacology has been validated in autism patients.