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Identification of Pathogen Signatures in Prostate Cancer Using RNA-seq.

ABSTRACT: Infections of the prostate by bacteria, human papillomaviruses, polyomaviruses, xenotropic murine leukemia virus (MLV)-related gammaretroviruses, human cytomegaloviruses and other members of the herpesvirus family have been widely researched. However, many studies have yielded conflicting and controversial results. In this study, we systematically investigated the transcriptomes of human prostate samples for the unique genomic signatures of these pathogens using RNA-seq data from both western and Chinese patients. Human and nonhuman RNA-seq reads were mapped onto human and pathogen reference genomes respectively using alignment tools Bowtie and BLAT. Pathogen infections and integrations were analyzed in adherence with the standards from published studies. Among the nine pathogens (Propionibacterium acnes, HPV, HCMV, XMRV, BKV, JCV, SV40, EBV, and HBV) we analyzed, Propionibacterium acnes genes were detected in all prostate tumor samples and all adjacent samples, but not in prostate samples from healthy individuals. SV40, HCMV, EBV and low-risk HPVs transcripts were detected in one tumor sample and two adjacent samples from Chinese prostate cancer patients, but not in any samples of western prostate cancer patients; XMRV, BKV and JCV sequences were not identified in our work; HBV, as a negative control, was absent from any samples. Moreover, no pathogen integration was identified in our study. While further validation is required, our analysis provides evidence of Propionibacterium acnes infections in human prostate tumors. Noted differences in viral infections across ethnicity remain to be confirmed with other large prostate cancer data sets. The effects of bacterial and viral infections and their contributions to prostate cancer pathogenesis will require continuous research on associated pathogens.

SUBMITTER: Chen Y

PROVIDER: S-EPMC4460021 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Project description:BackgroundGene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures.MethodsIn this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. We first performed pre-filtering steps and normalization, then utilized the empirical Bayes test in Limma package to identify the differentially expressed genes (DEGs). Next, we used a multi-objective optimization technique, "Multi-objective optimization for collecting cluster alternatives" (MOCCA in R package) on these DEGs to find Pareto-optimal cluster size, and then applied k-means clustering to the RNA-seq data based on the optimal cluster size. The best cluster was obtained through computing the average Spearman's Correlation Score among all the genes in pair-wise manner belonging to the module. The best cluster is treated as the signature for the respective disease or cellular condition.ResultsWe applied our framework to a cervical cancer RNA-seq dataset, which included 253 squamous cell carcinoma (SCC) samples and 22 adenocarcinoma (ADENO) samples. We identified a total of 582 DEGs by Limma analysis of SCC versus ADENO samples. Among them, 260 are up-regulated genes and 322 are down-regulated genes. Using MOCCA, we obtained seven Pareto-optimal clusters. The best cluster has a total of 35 DEGs consisting of all-upregulated genes. For validation, we ran PAMR (prediction analysis for microarrays) classifier on the selected best cluster, and assessed the classification performance. Our evaluation, measured by sensitivity, specificity, precision, and accuracy, showed high confidence.ConclusionsOur framework identified a multi-objective based cluster that is treated as a signature that can classify the disease and control group of samples with higher classification performance (accuracy 0.935) for the corresponding disease. Our method is useful to find signature for any RNA-seq or microarray data.

Project description:BackgroundCanine prostate adenocarcinoma (PAC) and transitional cell carcinoma (TCC) are typically characterized by metastasis and chemoresistance. Cell lines are important model systems for developing new therapeutic strategies. However, as they adapt to culturing conditions and undergo clonal selection, they can diverge from the tissue from which they were originally derived. Therefore, a comprehensive characterization of cell lines and their original tissues is paramount.MethodsThis study compared the transcriptomes of nine canine cell lines derived from PAC, PAC metastasis and TCC to their respective original primary tumor or metastasis tissues. Special interests were laid on cell culture-related differences, epithelial to mesenchymal transition (EMT), the prostate and bladder cancer pathways, therapeutic targets in the PI3K-AKT signaling pathway and genes correlated with chemoresistance towards doxorubicin and carboplatin.ResultsIndependent analyses for PAC, PAC metastasis and TCC revealed 1743, 3941 and 463 genes, respectively, differentially expressed in the cell lines relative to their original tissues (DEGs). While genes associated with tumor microenvironment were mostly downregulated in the cell lines, patient-specific EMT features were conserved. Furthermore, examination of the prostate and bladder cancer pathways revealed extensive concordance between cell lines and tissues. Interestingly, all cell lines preserved downstream PI3K-AKT signaling, but each featured a unique therapeutic target signature. Additionally, resistance towards doxorubicin was associated with G2/M cell cycle transition and cell membrane biosynthesis, while carboplatin resistance correlated with histone, m- and tRNA processing.ConclusionComparative whole-transcriptome profiling of cell lines and their original tissues identifies models with conserved therapeutic target expression. Moreover, it is useful for selecting suitable negative controls, i.e., cell lines lacking therapeutic target expression, increasing the transfer efficiency from in vitro to primary neoplasias for new therapeutic protocols. In summary, the dataset presented here constitutes a rich resource for canine prostate and bladder cancer research.

Project description:BackgroundCell lines form the cornerstone of cell-based experimentation studies into understanding the underlying mechanisms of normal and disease biology including cancer. However, it is commonly acknowledged that contamination of cell lines is a prevalent problem affecting biomedical science and available methods for cell line authentication suffer from limited access as well as being too daunting and time-consuming for many researchers. Therefore, a new and cost effective approach for authentication and quality control of cell lines is needed.ResultsWe have developed a new RNA-seq based approach named CeL-ID for cell line authentication. CeL-ID uses RNA-seq data to identify variants and compare with variant profiles of other cell lines. RNA-seq data for 934 CCLE cell lines downloaded from NCI GDC were used to generate cell line specific variant profiles and pair-wise correlations were calculated using frequencies and depth of coverage values of all the variants. Comparative analysis of variant profiles revealed that variant profiles differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and are highly correlated (ρ > 0.9). Our benchmarking studies revealed that CeL-ID method can identify a cell line with high accuracy and can be a valuable tool of cell line authentication in biomedical science. Finally, CeL-ID estimates the possible cross contamination using linear mixture model if no perfect match was detected.ConclusionsIn this study, we show the utility of an RNA-seq based approach for cell line authentication. Our comparative analysis of variant profiles derived from RNA-seq data revealed that variant profiles of each cell line are distinct and overall share low variant identity with other cell lines whereas identical or synonymous cell lines show significantly high variant identity and hence variant profiles can be used as a discriminatory/identifying feature in cell authentication model.

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Identification of Pathogen Signatures in Prostate Cancer Using RNA-seq.

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