Circulating small non-coding RNA signature in head and neck squamous cell carcinoma

Abstract

The Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common human cancer, causing 350,000 individuals die worldwide each year. The overall prognosis in HNSCC patients has not significantly changed for the last decade. Complete understanding of the molecular mechanisms in HNSCC carcinogenesis could allow an earlier diagnosis and the use of more specific and effective therapies. In the present study we used deep sequencing to characterize small non-coding RNAs (sncRNAs) in serum from HNSCC patients and healthy donors. We identified, for the first time, a multi-marker signature of 3 major classes of circulating sncRNAs in HNSCC, revealing the presence of circulating novel and known miRNAs, and tRNA- and YRNA-derived small RNAs that were significantly deregulated in the sera of HNSCC patients compared to healthy controls. By implementing a triple-filtering approach we identified a subset of highly biologically relevant miRNA-mRNA interactions and we demonstrated that the same genes/pathways affected by somatic mutations in cancer are affected by changes in the abundance of miRNAs. Therefore, one important conclusion from our work is that during cancer development, there seems to be a convergence of oncogenic processes driven by somatic mutations and/or miRNA regulation affecting key cellular pathways.

Keywords: circulating small RNAs; head and neck cancer; microRNAs; next-generation sequencing; tRNA halves.

Figure 1. Length distribution and annotation of sequencing reads from serum small RNAs

Sequencing reads from both normal and cancer samples were pooled to analyze the quality of reads, i.e., length distribution, and the types and proportions of small RNAs from which the reads are derived from. Pooling is used only to examine the general characteristics of the reads, and not to measure the differential expression of small RNAs between control and cancer groups. A. Plot of length against abundance of pooled mapped reads according to their annotation as miRNAs, YRNAs, tRNAs, rRNAs, or other sRNAs (snRNAs and snoRNAs). B. Pie chart showing the percent of reads mapping to the indicated types of small RNAs in pooled datasets obtained by sequencing of small RNAs in the sera of normal and cancer cases.

Figure 2. Multi-dimensional scaling (MDS) plot of circulating small RNAs. The plotMDS function of edgeR was used to examine relationship between samples of circulating miRNA

A., tRNA-derived small RNA B., and YRNA-derived small RNA C.. This function clusters the small RNA samples according to two automatically determined dimensions (dim 1 and 2). Dimension 1 represents the cancer effect, while dimension 2 represents the homogeneity between biological replicates. The analyzed small RNA samples are from normal controls (N1-7), and cancer patients (T1-7). “logFC” is log fold change.

Figure 3. Association between differentially abundant circulating miR-103a-3p/miR-107 and HNSCC T stage

Circulating miRNA levels were tested for normal distribution using the Shapiro-Wilk Normality Test and subjected to correlation analysis (r: Pearson correlation) in GraphPad Prism 6.05.

Figure 4. Interaction networks of COSMIC cancer consensus genes overtargeted by upregulated circulating HNSCC miRNAs

Subnetworks represent functional categories significantly enriched in sets of genes overtargeted by upregulated miRNAs. A. lymphoid organ development & lymphocyte activation, B. blood vessel morphogenesis C. regulation of cell matrix adhesion, D. positive regulation of apoptosis, and E. negative regulation of transcription. Thick grey edges highlight validated miRNA-mRNA interactions, thin edges represent predicted interactions, ovals represent COSMIC genes overtargeted by upregulated HNSCC miRNAs (rectangles); color-coded red indicates upregulation, blue indicates downregulation. Circled miRNAs putatively target three or more genes in the specific subnetwork.

Figure 5. Interaction networks of COSMIC cancer consensus genes overtargeted by downregulated circulating HNSCC miRNAs

Subnetworks represent functional categories significantly enriched in sets of genes overtargeted by downregulated miRNAs. A. regulation of phosphorylation, B. bromodomain, C. negative regulation of apoptosis, D. positive regulation of cyclin-dependent kinase activity. Thick grey edges highlight validated miRNA-mRNA interactions, thin edges represent predicted interactions, ovals represent COSMIC genes overtargeted by downregulated HNSCC miRNAs (rectangles); color-coded red indicates upregulation, blue indicates downregulation. Circled miRNAs putatively target three or more genes in the specific subnetwork.