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1.
Biol Direct ; 19(1): 38, 2024 May 13.
Article in English | MEDLINE | ID: mdl-38741178

ABSTRACT

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC with high rates of metastasis. Targeted therapies such as tyrosine kinase and checkpoint inhibitors have improved treatment success, but therapy-related side effects and tumor recurrence remain a challenge. As a result, ccRCC still have a high mortality rate. Early detection before metastasis has great potential to improve outcomes, but no suitable biomarker specific for ccRCC is available so far. Therefore, molecular biomarkers derived from body fluids have been investigated over the past decade. Among them, RNAs from urine-derived extracellular vesicles (EVs) are very promising. METHODS: RNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models. RESULTS: An initial cluster analysis of RNA-seq expression data showed separation by the subjects' gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091). CONCLUSIONS: Our study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.


Subject(s)
Biomarkers, Tumor , Carcinoma, Renal Cell , Extracellular Vesicles , Kidney Neoplasms , RNA, Small Nucleolar , Humans , Carcinoma, Renal Cell/urine , Carcinoma, Renal Cell/genetics , Extracellular Vesicles/genetics , Extracellular Vesicles/metabolism , Biomarkers, Tumor/urine , Biomarkers, Tumor/genetics , Female , Male , Middle Aged , Kidney Neoplasms/urine , Kidney Neoplasms/genetics , Aged , RNA, Small Nucleolar/genetics , Cohort Studies , Adult
2.
Int J Mol Sci ; 25(6)2024 Mar 16.
Article in English | MEDLINE | ID: mdl-38542353

ABSTRACT

A toxicogenomic approach was used for toxicity evaluation of arsenic in the aquatic environment, and differential gene expression was investigated from 24 h and 96 h water-only acute toxicity tests with the aquatic oligochaete, Tubifex tubifex (Annelida, Clitellata). Several toxicological endpoints (survival and autotomy) of the oligochaete and tissue residues were measured, and dose-response modelling of gene expression data was studied. A reference transcriptome of the aquatic oligochaete, T. tubifex, was reconstructed for the first time, and genes related to cell stress response (Hsc70, Hsp10, Hsp60, and Hsp83), energy metabolism (COX1), oxidative stress (Cat, GSR, and MnSOD), and the genes involved in the homeostasis of organisms (CaM, RpS13, and UBE2) were identified and characterised. The potential use of the genes identified for risk assessment in freshwater ecosystems as early biomarkers of arsenic toxicity is discussed.


Subject(s)
Arsenic , Oligochaeta , Water Pollutants, Chemical , Animals , Arsenic/toxicity , Arsenic/metabolism , Ecosystem , Water/metabolism , Toxicogenetics , Water Pollutants, Chemical/toxicity , Water Pollutants, Chemical/metabolism , Oligochaeta/genetics , Oligochaeta/metabolism , Fresh Water
3.
J Cell Mol Med ; 28(3): e18093, 2024 02.
Article in English | MEDLINE | ID: mdl-38149798

ABSTRACT

Antisense Noncoding RNA in the INK4 Locus (ANRIL) is the prime candidate gene at Chr9p21, the well-defined genetic risk locus associated with coronary artery disease (CAD). ANRIL and its transcript variants were investigated for the susceptibility to CAD in adipose tissues (AT) and peripheral blood mononuclear cells (PBMCs) of the study group and the impact of 9p21.3 locus mutations was further analysed. Expressions of ANRIL, circANRIL (hsa_circ_0008574), NR003529, EU741058 and DQ485454 were detected in epicardial AT (EAT) mediastinal AT (MAT), subcutaneous AT (SAT) and PBMCs of CAD patients undergoing coronary artery bypass grafting and non-CAD patients undergoing heart valve surgery. ANRIL expression was significantly upregulated, while the expression of circANRIL was significantly downregulated in CAD patients. Decreased circANRIL levels were significantly associated with the severity of CAD and correlated with aggressive clinical characteristics. rs10757278 and rs10811656 were significantly associated with ANRIL and circANRIL expressions in AT and PBMCs. The ROC-curve analysis suggested that circANRIL has high diagnostic accuracy (AUC: 0.9808, cut-off: 0.33, sensitivity: 1.0, specificity: 0.88). circANRIL has high diagnostic accuracy (AUC: 0.9808, cut-off: 0.33, sensitivity: 1.0, specificity: 0.88). We report the first data demonstrating the presence of ANRIL and its transcript variants expressions in the AT and PBMCs of CAD patients. circANRIL having a synergetic effect with ANRIL plays a protective role in CAD pathogenesis. Therefore, altered circANRIL expression may become a potential diagnostic transcriptional biomarker for early CAD diagnosis.


Subject(s)
Coronary Artery Disease , RNA, Long Noncoding , Humans , Coronary Artery Disease/genetics , Leukocytes, Mononuclear/pathology , Biomarkers , Risk Factors , Coronary Artery Bypass , RNA, Long Noncoding/genetics
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