Optimized procedure for high-throughput transcriptome profiling of small extracellular vesicles isolated from low volume serum samples.

OBJECTIVES: Small extracellular vesicles (EVs) contain various signaling molecules, thus playing a crucial role in cell-to-cell communication and emerging as a promising source of biomarkers. However, the lack of standardized procedures impedes their translation to clinical practice. Thus, we compared different approaches for high-throughput analysis of small EVs transcriptome. METHODS: Small EVs were isolated from 150 µL of serum. Quality and quantity were assessed by dynamic light scattering, transmission electron microscopy, and Western blot. Comparison of RNA extraction efficiency was performed, and expression of selected genes was analyzed by RT-qPCR. Whole transcriptome analysis was done using microarrays. RESULTS: Obtained data confirmed the suitability of size exclusion chromatography for isolation of small EVs. Analyses of gene expression showed the best results in case of samples isolated by Monarch Total RNA Miniprep Kit. Totally, 7,182 transcripts were identified to be deregulated between colorectal cancer patients and healthy controls. The majority of them were non-coding RNAs with more than 70 % being lncRNAs, while protein-coding genes represented the second most common gene biotype. CONCLUSIONS: We have optimized the protocol for isolation of small EVs and their RNA from low volume of sera and confirmed the suitability of Clariom D Pico Assays for transcriptome profiling.

An invasion front gene expression signature for higher-risk patient selection in stage IIA MSS colon cancer.

Stage II colon cancer (CC) encompasses a heterogeneous group of patients with diverse survival experiences: 87% to 58% 5-year relative survival rates for stages IIA and IIC, respectively. While stage IIA patients are usually spared the adjuvant chemotherapy, some of them relapse and may benefit from it; thus, their timely identification is crucial. Current gene expression signatures did not specifically target this group nor did they find their place in clinical practice. Since processes at invasion front have also been linked to tumor progression, we hypothesize that aside from bulk tumor features, focusing on the invasion front may provide additional clues for this stratification. A retrospective matched case-control collection of 39 stage IIA microsatellite-stable (MSS) untreated CCs was analyzed to identify prognostic gene expression-based signatures. The endpoint was defined as relapse within 5 years vs. no relapse for at least 6 years. From the same tumors, three different classifiers (bulk tumor, invasion front, and constrained baseline on bulk tumor) were developed and their performance estimated. The baseline classifier, while the weakest, was validated in two independent data sets. The best performing signature was based on invasion front profiles [area under the receiver operating curve (AUC) = 0.931 (0.815-1.0)] and contained genes associated with KRAS pathway activation, apical junction complex, and heme metabolism. Its combination with bulk tumor classifier further improved the accuracy of the predictions.

Investigation of long non-coding RNAs in extracellular vesicles from low-volume blood serum specimens of colorectal cancer patients.

Colorectal cancer (CRC) is the second most prevalent cancer type worldwide, which highlights the urgent need for non-invasive biomarkers for its early detection and improved prognosis. We aimed to investigate the patterns of long non-coding RNAs (lncRNAs) in small extracellular vesicles (sEVs) collected from low-volume blood serum specimens of CRC patients, focusing on their potential as diagnostic biomarkers. Our research comprised two phases: an initial exploratory phase involving RNA sequencing of sEVs from 76 CRC patients and 29 healthy controls, and a subsequent validation phase with a larger cohort of 159 CRC patients and 138 healthy controls. Techniques such as dynamic light scattering, transmission electron microscopy, and Western blotting were utilized for sEV characterization. Optimized protocol for sEV purification, RNA isolation and preamplification was applied to successfully sequence the RNA content of sEVs and validate the results by RT-qPCR. We successfully isolated sEVs from blood serum and prepared sequencing libraries from a low amount of RNA. High-throughput sequencing identified differential levels of 460 transcripts between CRC patients and healthy controls, including mRNAs, lncRNAs, and pseudogenes, with approximately 20% being lncRNAs, highlighting several tumor-specific lncRNAs that have not been associated with CRC development and progression. The validation phase confirmed the upregulation of three lncRNAs (NALT1, AL096828, and LINC01637) in blood serum of CRC patients. This study not only identified lncRNA profiles in a population of sEVs from low-volume blood serum specimens of CRC patients but also highlights the value of innovative techniques in biomolecular research, particularly for the detection and analysis of low-abundance biomolecules in clinical samples. The identification of specific lncRNAs associated with CRC provides a foundation for future research into their functional roles in cancer development and potential clinical applications.