Computational protocol: Serum miR-122-5p and miR-206 expression: non-invasive prognostic biomarkers for renal cell carcinoma

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Protocol publication

[…] In order to obtain a small RNA expression profile in serum of ccRCC patients, we performed small RNA sequencing experiments with serum samples from patients with ccRCC (n = 18) and benign renal tumors (BRT; n = 8). The BRT group consisted of four oncocytoma and four complicated kidney cysts; these patients underwent surgery for the suspicious of malignancy. The experiments were carried out by Biogazelle (Zwijnaarde, Belgium) as a contract service. In brief, serum samples were shipped on dry ice to Biogazelle. The RNA was isolated with the Qiagen miRNeasy Serum/Plasma kit (Hilden, Germany). The NEBNext Small RNA Library Prep Set kit (New England Biolabs, Ipswich, USA) was used for library preparation and the small RNA library pools were then sequenced on an Illumina NextSeq 500 sequencer (San Diego, USA). Sequencing reads were mapped to the reference genome (GRCh38) using the short-read-aligner Bowtie []. Genome annotation data from miRBase (release21), Ensemble (release 78), and USCS (assembly hg38), and other small RNA types (e.g., piRNA, sn(o)RNA, rRNA and tRNA fragments) was used to annotate the mapped reads to the mature miRNAs. The miRNA expression data were filtered using a cut-off of four reads and normalized based on the total read count per sample. Each miRNA read count was divided by the total read count in that sample and multiplied by the median of total read count across all samples. After normalization all data were log2-transformed. The raw data of the small RNA sequencing are provided at the Gene Expression Omnibus (GEO) database (record: GSE85699). [...] The statistical analysis was performed using IBM SPSS Statistics v24 and R v3.3.3. Mann–Whitney–Wilcoxon test was used to compare the expression in subgroups. ROC-analysis and area under curve (AUC) calculations were used to compare the discrimination between control and ccRCC samples for single miRNA expression variables (pROC-package for R). Logistic regression-based model was used to merge the expression of miR-206 and miR-122-5p into one variable to identify the additional discriminative value of simultaneous expression analysis during ROC/AUC-analysis. Optimized cut-off selection during survival analyses was carried out using cutp-function of survMisc package for R (principle: univariate Cox regression-based consecutive analysis of all available cut-offs in the cohort; cut-off selection is based on the best p level < 0.05). A p value < 0.05 was considered as statistically significant, and all analyses with p values between 0.05 and 0.1 were considered to be a trend to statistical significance. […]

Pipeline specifications

Software tools Bowtie, SPSS
Databases GEO miRBase
Applications Miscellaneous, sRNA-seq analysis
Organisms Homo sapiens
Diseases Carcinoma, Renal Cell, Kidney Neoplasms, Neoplasms