RESUMO
Prostate cancer (PCa) is one of the most prevalent cancers among men in India. Although studies on PCa have dealt with genetics, genomics, and the environmental influence in the causality of PCa, not many studies employing the Next Generation Sequencing (NGS) approaches of PCa have been carried out. In our previous study, we identified some causal genes and mutations specific to Indian PCa using Whole Exome Sequencing (WES). In the recent past, with the help of different cancer consortiums such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), along with differentially expressed genes (DEGs), many cancer-associated novel non-coding RNAs have been identified as biomarkers. In this work, we attempt to identify differentially expressed genes (DEGs) including long non-coding RNAs (lncRNAs) associated with signature pathways from an Indian PCa cohort using the RNA-sequencing (RNA-seq) approach. From a cohort of 60, we screened six patients who underwent prostatectomy; we performed whole transcriptome shotgun sequencing (WTSS)/RNA-sequencing to decipher the DEGs. We further normalized the read counts using fragments per kilobase of transcript per million mapped reads (FPKM) and analyzed the DEGs using a cohort of downstream regulatory tools, viz., GeneMANIA, Stringdb, Cytoscape-Cytohubba, and cbioportal, to map the inherent signatures associated with PCa. By comparing the RNA-seq data obtained from the pairs of normal and PCa tissue samples using our benchmarked in-house cuffdiff pipeline, we observed some important genes specific to PCa, such as STEAP2, APP, PMEPA1, PABPC1, NFE2L2, and HN1L, and some other important genes known to be involved in different cancer pathways, such as COL6A1, DOK5, STX6, BCAS1, BACE1, BACE2, LMOD1, SNX9, CTNND1, etc. We also identified a few novel lncRNAs such as LINC01440, SOX2OT, ENSG00000232855, ENSG00000287903, and ENST00000647843.1 that need to be characterized further. In comparison with publicly available datasets, we have identified characteristic DEGs and novel lncRNAs implicated in signature PCa pathways in an Indian PCa cohort which perhaps have not been reported. This has set a precedent for us to validate candidates further experimentally, and we firmly believe this will pave a way toward the discovery of biomarkers and the development of novel therapies.
RESUMO
PURPOSE: To evaluate the diagnostic accuracy of shear wave elastography (SWE) and transient elastography (TE) in the evaluation of liver fibrosis in chronic hepatitis B (CHB) and C (CHC) patients taking liver biopsy as gold standard. METHODS: Ethics committee approved this prospective cross-sectional study. Between October 2012 and December 2014, consecutive CHB/CHC patients fulfilling the inclusion criteria were included-age more than 18 years, informed written consent, willing and suitable for liver biopsy. SWE, TE, and biopsy were performed the same day. Liver stiffness measurement (LSM) cut-offs for various stages of fibrosis were generated for SWE and TE. AUC, sensitivity, specificity, and positive/negative predictive values were estimated individually or in combination. RESULTS: CH patients (n = 240, CHB 172, CHC 68), 176 males, 64 females, mean age 32.6 ± 11.6 years were enrolled. Mean LSM of patients with no histological fibrosis (F0) was 5.0 ± 0.7 and 5.1+1.4 kPa on SWE and TE, respectively. For differentiating F2 and F3-4 fibrosis on SWE, at 7.0 kPa cut-off, the sensitivity was 81.3% and specificity 77.6%. For TE, at 8.3 kPa cut-off, sensitivity was 81.8% and specificity 83.1%. For F3 vs. F4, SWE sensitivity was 83.3% and specificity 90.7%. At 14.8 kPa cut-off, TE showed similar sensitivity (83.3%) but specificity increased to 96.5%. Significant correlation between SWE and TE was observed (r = 0.33, p < 0.001). On combining SWE and TE, a drop in sensitivity with increased specificity for all stages of liver fibrosis occured. CONCLUSION: SWE is an accurate technique for evaluating liver fibrosis. SWE compares favorably with TE especially for predicting advanced fibrosis/cirrhosis. Combining SWE and TE further improves specificity.
