👤 Ratna Medicherla

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. Show less

Superficial spreading melanoma (SSM) and nodular melanoma (NM) are believed to represent sequential phases of linear progression from radial to vertical growth. Several lines of clinical, pathologic, and epidemiologic evidence suggest, however, that SSM and NM might be the result of independent pathways of tumor development. We utilized an integrative genomic approach that combines single nucleotide polymorphism array (6.0; Affymetrix) with gene expression array (U133A 2.0; Affymetrix) to examine molecular differences between SSM and NM. Pathway analysis of the most differentially expressed genes between SSM and NM (N = 114) revealed significant differences related to metabolic processes. We identified 8 genes (DIS3, FGFR1OP, G3BP2, GALNT7, MTAP, SEC23IP, USO1, and ZNF668) in which NM/SSM-specific copy number alterations correlated with differential gene expression (P < 0.05; Spearman's rank). SSM-specific genomic deletions in G3BP2, MTAP, and SEC23IP were independently verified in two external data sets. Forced overexpression of metabolism-related gene MTAP (methylthioadenosine phosphorylase) in SSM resulted in reduced cell growth. The differential expression of another metabolic-related gene, aldehyde dehydrogenase 7A1 (ALDH7A1), was validated at the protein level by using tissue microarrays of human melanoma. In addition, we show that the decreased ALDH7A1 expression in SSM may be the result of epigenetic modifications. Our data reveal recurrent genomic deletions in SSM not present in NM, which challenge the linear model of melanoma progression. Furthermore, our data suggest a role for altered regulation of metabolism-related genes as a possible cause of the different clinical behavior of SSM and NM. Show less