Background: Exosomes, endosome-derived membrane microvesicles, contain specific RNA transcripts that are thought to be involved in cell-cell communication. These RNA transcripts have great potential as disease biomarkers. To characterize exosomal RNA profiles systemically, we performed RNA sequencing analysis using three human plasma samples and evaluated the efficacies of small RNA library preparation protocols from three manufacturers. In all we evaluated 14 libraries (7 replicates). Results: From the 14 size-selected sequencing libraries, we obtained a total of 101.8 million raw single-end reads, an average of about 7.27 million reads per library. Sequence analysis showed that there was a diverse collection of the exosomal RNA species among which microRNAs (miRNAs) were the most abundant, making up over 42.32% of all raw reads and 76.20% of all mappable reads. At the current read depth, 593 miRNAs were detectable. The five most common miRNAs (miR-99a-5p, miR-128, miR-124-3p, miR-22-3p, and miR-99b-5p) collectively accounted for 48.99% of all mappable miRNA sequences. MiRNA target gene enrichment analysis suggested that the highly abundant miRNAs may play an important role in biological functions such as protein phosphorylation, RNA splicing, chromosomal abnormality, and angiogenesis. From the unknown RNA sequences, we predicted 185 potential miRNA candidates. Furthermore, we detected significant fractions of other RNA species including ribosomal RNA (9.16% of all mappable counts), long non-coding RNA (3.36%), piwi-interacting RNA (1.31%), transfer RNA (1.24%), small nuclear RNA (0.18%), and small nucleolar RNA (0.01%); fragments of coding sequence (1.36%), 5^′ untranslated region (0.21%), and 3^′ untranslated region (0.54%) were also present. In addition to the RNA composition of the libraries, we found that the three tested commercial kits generated a sufficient number of DNA fragments for sequencing but each had significant bias toward capturing specific RNAs. Conclusions: This study demonstrated that a wide variety of RNA species are embedded in the circulating vesicles. To our knowledge, this is the first report that applied deep sequencing to discover and characterize profiles of plasma-derived exosomal RNAs. Further characterization of these extracellular RNAs in diverse human populations will provide reference profiles and open new doors for the development of blood-based biomarkers for human diseases.

Colon cancer arises from the accumulation of multiple genetic and epigenetic alterations to normal colonic tissue. microRNAs (miRNAs) are small, non-coding regulatory RNAs that post-transcriptionally regulate gene expression. Differential miRNA expression in cancer versus normal tissue is a common event and may be pivotal for tumor onset and progression.

Colorectal cancer (CRC) is one of the leading causes of cancer related mortality worldwide. Although young-onset CRC raises the possibility of a hereditary component, hereditary CRC syndromes only explain a minority of young-onset CRC cases. There is evidence to suggest that young-onset CRC have a different molecular profile than late-onset CRC. While the pathogenesis of young-onset CRC is well characterized in individuals with an inherited CRC syndrome, knowledge regarding the molecular features of sporadic young-onset CRC is limited. Understanding the molecular mechanisms of young-onset CRC can help us tailor specific screening and management strategies. While the incidence of late-onset CRC has been decreasing, mainly attributed to an increase in CRC screening, the incidence of young-onset CRC is increasing. Differences in the molecular biology of these tumors and low suspicion of CRC in young symptomatic individuals, may be possible explanations. Currently there is no evidence that supports that screening of average risk individuals less than 50 years of age will translate into early detection or increased survival. However, increasing understanding of the underlying molecular mechanisms of young-onset CRC could help us tailor specific screening and management strategies. The purpose of this review is to evaluate the current knowledge about young-onset CRC, its clinicopathologic features, and the newly recognized molecular alterations involved in tumor progression.

Cancer risk and Lhermitte-Duclos disease (LDD) risk estimates for Cowden syndrome (CS) are broad and based on a small number of patients. Risk estimates are vital to the development of diagnostic criteria, genetic counseling, and cancer surveillance. To further elaborate and estimate the risks associated with CS, a large cohort of patients was evaluated.

Background: Both shorter and longer telomeres in peripheral blood leukocyte (PBL) DNA have been associated with cancer risk. However, associations remain inconsistent across studies of the same cancer type. This study compares DNA preparation methods to determine telomere length from patients with colorectal cancer. Methods: We examined PBL relative telomere length (RTL) measured by quantitative PCR (qPCR) in 1,033 patients with colorectal cancer and 2,952 healthy controls. DNA was extracted with phenol/chloroform, PureGene, or QIAamp. Results: We observed differences in RTL depending on DNA extraction method (P < 0.001). Phenol/chloroform-extracted DNA had a mean RTL (T/S ratio) of 0.78 (range 0.01–6.54) compared with PureGene-extracted DNA (mean RTL of 0.75; range 0.00–12.33). DNA extracted by QIAamp yielded a mean RTL of 0.38 (range 0.02–3.69). We subsequently compared RTL measured by qPCR from an independent set of 20 colorectal cancer cases and 24 normal controls in PBL DNA extracted by each of the three extraction methods. The range of RTL measured by qPCR from QIAamp-extracted DNA (0.17–0.58) was less than from either PureGene or phenol/chloroform (ranges, 0.04–2.67 and 0.32–2.81, respectively). Conclusions: RTL measured by qPCR from QIAamp-extracted DNA was less than from either PureGene or phenol/chloroform (P < 0.001). Impact: Differences in DNA extraction method may contribute to the discrepancies between studies seeking to find an association between the risk of cancer or other diseases and RTL. Cancer Epidemiol Biomarkers Prev; 22(11); 2047–54. ©2013 AACR.