An approach to forecast human cancer by profiling microRNA expressions from NGS data.

BACKGROUND: microRNAs are single-stranded non-coding RNA sequences of 18 - 24 nucleotides in length. They play an important role in post-transcriptional regulation of gene expression. Evidences of microRNA acting as promoter/suppressor of several diseases including cancer are being unveiled. Recent studies have shown that microRNAs are differentially expressed in disease states when compared with that of normal states. Profiling of microRNA is a good measure to estimate the differences in expression levels, which can be further utilized to understand the progression of any associated disease. METHODS: Machine learning techniques, when applied to microRNA expression values obtained from NGS data, could be utilized for the development of effective disease prediction system. This paper discusses an approach for microRNA expression profiling, its normalization and a Support Vector based machine learning technique to develop a Cancer Prediction System. Presently, the system has been trained with data samples of hepatocellular carcinoma, carcinomas of the bladder and lung cancer. microRNAs related to specific types of cancer were used to build the classifier. RESULTS: When the system is trained and tested with 10 fold cross validation, the prediction accuracy obtained is 97.56% for lung cancer, 97.82% for hepatocellular carcinoma and 95.0% for carcinomas of the bladder. The system is further validated with separate test sets, which show accuracies higher than 90%. A ranking based on differential expression marks the relative significance of each microRNA in the prediction process. CONCLUSIONS: Results from experiments proved that microRNA expression profiling is an effective mechanism for disease identification, provided sufficiently large database is available.

Diversity of Hepatitis C virus in Southern India Based on 5'UTR Sequence.

Hepatitis C virus (HCV) exhibits genotype-specific variations in geographical distribution as a consequence of drug and immune induced evolution. Present study was aimed at discerning the distribution and prevalence of the various genotypes and subtypes of HCV in southern India. The HCV positive patient's serum was collected from different hospitals and blood banks from the states of Kerala, Tamil Nadu, Andhra Pradesh and Karnataka. Among 114 HCV positive samples, we could find only 44 isolates that are found both positive in ELISA and RT-PCR. From these samples 5' untranslated region (5'UTR) were amplified, sequenced and sub typed. Analysis of 5'UTR region of the 44 isolates shows that, genotypes 1, 3, 4 and 6 are present with genotype 3 being the most frequent. The present study shows that HCV genotype 3 subtype B was the most prevalent, forming 47.7 % among the population in southern India. The present study urges for discovering novel therapeutic agents that should be specific to genotype 3 subtype B, for the management of HCV in southern India.