IntroductionProstate cancer (PCa) is a leading malignancy in men, with global incidence showing geographical variations. In India, PCa burden is increasing, especially in urban areas and for men over 50 years of age. Our collaborative effort with the Cancer Prostate Consortium of India (CAPCI) aims to map the genetic landscape of PCa in India, investigate somatic mosaicism, and assess the utility of low-pass whole-genome sequencing (LP-WGS) in determining PCa pathogenesis.Materials and methodsWe performed variant calling using the CONVEX pipeline on whole-exome sequencing (WES) and LP-WGS samples, categorized by tumor grades. Results and discussionClinVar validation of WES variants identified 12 consensus somatic variants mapped to gnomAD, with 11 being pathogenic, likely pathogenic, or variants of uncertain significance (VUS). We also screened 956 germline variants common to at least two of the 49 samples, identifying 88 common variants, including missense and small InDels. Common and unique somatic variants were found in APC, BRAF, ATM, TP53, SMAD4 and CHEK2 genes. Four of these (rs62619935, rs202160435, rs121913343, and rs141568342) are extremely rare (MAF <= 0.001), seven single nucleotide variants (SNVs) are novel, with four being VUS. Pathogenic somatic variants in higher Gleason score samples (4+4 and 4+3) were homozygous, while benign prostatic hyperplasia (BPH) samples with lower Gleason scores were heterozygous, aligning with population stratification. Whereas the rs1042522 variant in the TP53 gene, with a gnomAD MAF of nearly 38%, was a common somatic SNV, we also identified distinct CAG repeat mosaicism associated with PCa risk.

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.

Background:Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people. Objectives:In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making. Methods:An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots. Results:Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM. Conclusion:A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Prostate cancer (PCa) is one of the burgeoning cancers worldwide. Of late, the number of cases in the Indian subcontinent has been doubling and the incidence as per the population-based cancer registries (PBCR) has increased at an alarming rate. In assessing the trends of PCa, there needs a statistical framework on incidence, comparing PBCRs vis-a-vis with hospital-based cancer registries not only to that of cohorts from India but also to that of West and other consortia. Our erstwhile pilot study on inferring whole-exome sequencing variants has yielded distinct polymorphisms in the Indian phenotype of PCa. There are impending challenges and gray areas that we discuss in this review in lieu of PCa pathogenesis and therapeutics.

Prostate cancer (PCa) is the third most common cancer among men in India, and no next-generation sequencing (NGS) studies have been attempted earlier. Recent advances in NGS have heralded the discovery of biomarkers from Caucasian/European and Chinese ancestry, but not much is known about the Indian phenotype/variant of PCa. In a pilot study using the whole exome sequencing of benign/PCa patients, we identified characteristic mutations specific to the Indian sub-population. We observed a large number of mutations in DNA repair genes,viz.helicases, TP53, and BRCA besides the variants of unknown significance with a possibly damaging rare variant (rs730881069/chr19:55154172C/TR136Q) in the TNNI3 gene that has been previously reported as a semi-conservative amino acid substitution. Our pilot study attempts to bring an understanding of PCa prognosis and recurrence for the Indian phenotype.

In the recent past, there has been a rise in Prostate Cancer (PCa) in Asia, particularly India. Although systematic reviews on PCa have dealt on the genetics, genomics and the environmental influence in causal of PCa, no predictive analytics in comparing the PCa from Caucasian, American to Asian population was attempted. In this review article, we have attempted to elaborate this aspect of PCa and deliberated on challenges related to next generation sequencing methods of PCa’s manifestation when compared to the west.