Circulating exosomal mRNA profiling identifies novel signatures for the detection of prostate cancer

Abstract

The landscape and characteristics of circulating exosomal messenger RNAs (emRNAs) are poorly understood, which hampered the accurate detection of circulating emRNAs. Through comparing RNA sequencing data of circulating exosomes with the corresponding data in tissues, we illustrated the different characteristics of emRNAs compared to tissue mRNAs. We then developed an improved strategy for emRNA detection based on the features of circulating emRNAs. Using the optimized detection strategy, we further validated prostate cancer (PCa) associated emRNAs discovered by emRNA-seq in a large cohort of patients and identified emRNA signatures for PCa screening and diagnosis using logistic regression analysis. The receiver operating characteristic curve (ROC) analysis showed that the circulating emRNA-based screening signature yielded an area under the ROC curve (AUC) of 0.948 in distinguishing PCa patients from healthy controls. The circulating emRNA-based diagnostic signature also showed a great performance in predicting prostate biopsy results (AUC: 0.851). In conclusion, our study developed an optimized emRNA detection strategy and identified novel emRNA signatures for the detection of PCa.

Keywords: Diagnosis; Exosome; Prostate cancer; RNA-sequencing.

Fig. 1

Characterization of circulating exosomal mRNAs (emRNAs). a, Workflow of the study, including sample processing, emRNA sequencing, demonstrating the landscape and characteristics of emRNA, optimizing the detection strategy, and identifying tumor-specific emRNA signatures. b, The type and distribution of RNAs in circulating exosomes. Raw reads are the sequences detected by RNA sequencing. Query reads are those after trimming. Mappable reads are those mapped to known human RNA or genomes. Circos plots showing all mRNAs (c), and oncogene mRNAs (d), from PCa tissues and circulating exosomes of the same cohort of patients. e, Scatter plot illustrating the correlation between tissue mRNA and emRNA levels. f, Venn diagrams showing the distinctive expression patterns between emRNAs and tissue mRNAs (based on the threshold of p value< 0.05 and fold change > 2 for upregulated and fold change < 0.5 for downregulated)

Fig. 2

Validation of circulating exosomal mRNAs (emRNAs) as novel biomarkers for PCa diagnosis. a, Heatmap demonstrates the significantly dysregulated emRNAs in PCa patients. Each column represents an individual sample, and each row represents an emRNA. b, Workflow of the validation of potential circulating emRNAs. c, The scatter plot shows that the expression levels of circulating emRNAs, including CDC42, IL32, MAX, NCF2, PDGFA and SRSF2, are upregulated in PCa patients (n = 141) compared to healthy controls (n = 30). d, The scatter plot shows that the expression levels of circulating emRNAs, including CDC42, IL32, MAX, NCF2, PDGFA and SRSF2, are upregulated in PCa patients (n = 141) compared to patients with BPH (negative prostate biopsy, n = 170). e and g, ROC analysis shows the diagnostic performance of 6 mRNAs and the emRNA-based screening model (CDC42, IL32, MAX, NCF2, PDGFA and SRSF2; AUC: 0.948; P < 0.0001). f and h, ROC analysis shows the diagnostic performance of 6 emRNAs and the emRNA-based diagnostic model (CDC42, IL32, MAX, NCF2, PDGFA and SRSF2) (AUC: 0.851; P < 0.0001)