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. 2020 Dec 4;10(1):21290.
doi: 10.1038/s41598-020-77774-9.

Detection of urinary miRNAs for diagnosis of clear cell renal cell carcinoma

Affiliations

Detection of urinary miRNAs for diagnosis of clear cell renal cell carcinoma

Giovanni Cochetti et al. Sci Rep. .

Abstract

The lack of symptoms at the early stages of clear cell renal cell carcinoma (ccRCC) allows the tumour to metastasize, leading to a dramatic reduction in patient survival. Therefore, we studied and set up a method based on urinary microRNAs (miRNAs) for the diagnosis of ccRCC. First, miRNA expression in ccRCC specimens and kidney tissues from healthy subjects (HSs) was investigated through analysis of data banks and validated by comparing expression of miRNAs in ccRCC and adjacent non-cancerous kidney tissue specimens by RT-qPCR. Subsequently, we developed an algorithm to establish which miRNAs are more likely to be found in the urine of ccRCC patients that indicated miR-122, miR-1271, and miR-15b as potential interesting markers. The evaluation of their levels and three internal controls in the urine of 13 patients and 14 HSs resulted in the development of a score (7p-urinary score) to evaluate the presence of ccRCC in patients. The resulting area under the Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity were equal to 0.96, 100% (95% CI 75-100%), and 86% (95% CI 57-98%), respectively. In conclusion, our study provides a proof of concept that combining the expression values of some urinary miRNAs might be useful in the diagnosis of ccRCC.

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Conflict of interest statement

The authors declare no competing interests .

Figures

Figure 1
Figure 1
The best urinary miRNA markers as derived by data bank analysis and algorithm. (A) Top 10 miRNAs overexpressed in ccRCC samples compared with their expression in kidney samples from healthy subjects (HSs), based on data bank analysis. (B) Algorithm used to calculate the probability for a miRNA to represent a urinary marker of ccRCC. Overexpression in ccRCC, expression in the kidney, bladder, and prostate (the latter used to calculate the score in men only *), and standard deviation (SD) of miRNA expression in normal tissue and ccRCC were calculated using the data bank values expressed as Log2. C, The top 10 miRNAs and their scores potentially representing the best urinary markers of ccRCC, as suggested by the algorithm.
Figure 2
Figure 2
Expression levels of urinary miRNAs and ROC curves. (A) Ct value of miR-122, miR-1271, and miR-15b in the urine of HSs (green dots) and patients with ccRCC (red dots); in the Tables, the percentage of HSs and ccRCC patients with Ct value lower and higher than the median Ct is reported. Median Cts were equal to 31.46, 28.31, and 29.48 for miR-122, miR-1271, and miR-15b, respectively. (B) ROC curves of miR-122, miR-1271, and miR-15b.
Figure 3
Figure 3
Predictive power derived from parameter combination (7p-urinary score). ROC curve based on the sum of parameters #2, #4, #5, #7, #8, #11, and #12 as shown in Table 4.
Figure 4
Figure 4
7p-urinary score of HSs and patients with ccRCC. (A) The 7p-urinary score mean of HSs is significantly different (unpaired t-test) from that of patients with ccRCC. (B) The 7p-urinary score mean of patients with ccRCC with a size smaller than 5 cm is significantly different (Mann–Whitney test) from that of patients with ccRCC size greater than 5 cm (tumour size is considered to be the largest diameter of the tumour as evaluated by CT scan). (C) The 7p-urinary score mean of patients with grade 1 (G1) ccRCC is not significantly different (ordinary one-way ANOVA—Tukey) from that of patients with ccRCC with grade 2/3 (G2/G3, same group) and grade 4 (G4). (D) The 7p-urinary score mean of patients with grade 1 ccRCC is significantly different (unpaired t-test) from that of patients with ccRCC with grade 2/3/4 (G2/G3/G4, same group).
Figure 5
Figure 5
Selection of miRNA targets. The Venn diagrams represent the number of possible miRNA target genes as predicted by miRDB (red), TargetScan (yellow), and miRTarBase (green) tools. In particular, the number of target genes in each intersection and unique to a tool is represented. The genes selected by all the tools are considered to be the most likely miRNA target genes. The Venn diagram was drawn using the online software found at the following link http://bioinformatics.psb.ugent.be/webtools/Venn/.

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