Abstract
Renal cell carcinoma (RCC) incidence has increased over the past two decades. Recent studies reported microRNAs as promising biomarkers for early cancer detection, accurate prognosis, and molecular targets for future treatment. This study aimed to evaluate the expression levels of miR-34a and 11 of its bioinformatically selected target genes and proteins to test their potential dysregulation in RCC. Quantitative real-time PCR for miR-34a and its targets; MET oncogene; gene-regulating apoptosis (TP53INP2 and DFFA); cell proliferation (E2F3); and cell differentiation (SOX2 and TGFB3) as well as immunohistochemical assay for VEGFA, TP53, Bcl2, TGFB1, and Ki67 protein expression have been performed in 85 FFPE RCC tumor specimens. Clinicopathological parameter correlation and in silico network analysis have also implicated. We found RCC tissues displayed significantly higher miR-34a expression level than their corresponding noncancerous tissues, particularly in chromophobic subtype. MET and E2F3 were significantly upregulated, while TP53INP2 and SOX2 were downregulated. ROC analysis showed high diagnostic performance of miR-34a (AUC = 0.854), MET (AUC = 0.765), and E2F3 (AUC = 0.761). The advanced pathological grade was associated with strong TGFB1, VEGFA, and Ki67 protein expression and absent Tp53 staining. These findings indicate miR-34a along with its putative target genes could play a role in RCC tumorigenesis and progression.
Highlights
Renal cell carcinoma (RCC) accounts for approximately 3% of human malignancies, and its incidence appears to be increasing globally [1]
As angiogenesis is of central importance in the growth and metastasis of tumors [171], we investigated the expression of the angiogenesis-mediated protein, VEGFA in RCC compared to noncancer tissues
The current study does confirm the association of miR-34a overexpression with RCC in our population, suggesting its potential role in pathogenesis and progression of this type of cancer
Summary
Renal cell carcinoma (RCC) accounts for approximately 3% of human malignancies, and its incidence appears to be increasing globally [1]. RCC is not a single disease; it is derived from cells of the renal tubular epithelium, it has several histological subtypes which differ in their clinical outcome and biological features. Over the past few years, emerging numerous bioinformatic tools have been developed to identify candidate disease-causing genes [4], including microRNA (miRNA) genes. This class of noncoding RNAs is small, single stranded, and 19–25 nucleotide long that act as negative regulators involved in posttranscriptional silencing of the gene expression [5].
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