Computational Identification of microRNA-17-3p in Breast Cancer Cells.

BACKGROUND: MicroRNAs (miRNAs) are small, highly conserved non-coding RNA molecules involved in the RNA silencing and post-transcriptional regulation of gene expression. miRNAs are well conserved in both plants and animals, and are thought to be a vital and evolutionarily ancient component of gene regulation and also act as oncogenes or tumor suppressors. It is known that Express Sequence Tags (EST) are a short sub-sequence of cDNA sequence, which contain information of condition or tissue specific transcripts (coding and non-coding) of an organism. METHODS: In the present study, we have applied the bioinformatics tools to identify miRNA from breast cancer using EST resource. Through bioinformatics approach, the presence of an EST encoding hsa-miR-17- 3p of breast cancer was identified. RESULTS: Further studies reveal that hsa-miR-17 is confirmed in the breast cancer specific EST sequence among the predicted miRNAs secondary structure. Moreover, miR-17-3p could be responsible for a tumor suppression, which plays a major role in human breast cancer. CONCLUSION: Further studies are required to investigate the molecular mechanisms behind miR-17-3p involves in the suppression of breast cancer cells. Interestingly, our target analysis suggesting that all the targets involved in multiple signaling pathways in different cell regulations moreover, we need to have more number of in vitro and in vivo studies that prove miR-17-3p as candidate microRNA for breast cancer cells.

Land-use and land-cover change in Western Ghats of India.

The Western Ghats (WG) of India, one of the hottest biodiversity hotspots in the world, has witnessed major land-use and land-cover (LULC) change in recent times. The present research was aimed at studying the patterns of LULC change in WG during 1985-1995-2005, understanding the major drivers that caused such change, and projecting the future (2025) spatial distribution of forest using coupled logistic regression and Markov model. The International Geosphere Biosphere Program (IGBP) classification scheme was mainly followed in LULC characterization and change analysis. The single-step Markov model was used to project the forest demand. The spatial allocation of such forest demand was based on the predicted probabilities derived through logistic regression model. The R statistical package was used to set the allocation rules. The projection model was selected based on Akaike information criterion (AIC) and area under receiver operating characteristic (ROC) curve. The actual and projected areas of forest in 2005 were compared before making projection for 2025. It was observed that forest degradation has reduced from 1985-1995 to 1995-2005. The study obtained important insights about the drivers and their impacts on LULC simulations. To the best of our knowledge, this is the first attempt where projection of future state of forest in entire WG is made based on decadal LULC and socio-economic datasets at the Taluka (sub-district) level.