Combined expression of JHDM1D/KDM7A gene and long non-coding RNA RP11-363E7.4 as a biomarker for urothelial cancer prognosis.
Glenda Nicioli da SilvaIsadora Oliveira Ansaloni PereiraAna Paula Braga LimaTamires Cunha AlmeidaAndré Luiz Ventura SávioRenato Prado CostaKátia Ramos Moreira LeiteDaisy Maria Fávero SalvadoriPublished in: Genetics and molecular biology (2024)
Bladder cancer is the tenth most frequently diagnosed cancer globally. Classification of high- or low-grade tumors is based on cytological differentiation and is an important prognostic factor. LncRNAs regulate gene expression and play critical roles in the occurrence and development of cancer, however, there are few reports on their diagnostic value and co-expression levels with genes, which may be useful as specific biomarkers for prognosis and therapy in bladder cancer. Thus, we performed a marker lesion study to investigate whether gene/lncRNA expression in urothelial carcinoma tissues may be useful in differentiating low-grade and high-grade tumors. RT-qPCR was used to evaluate the expression of the JHDM1D gene and the lncRNAs CTD-2132N18.2, SBF2-AS1, RP11-977B10.2, CTD-2510F5.4, and RP11-363E7.4 in 20 histologically diagnosed high-grade and 10 low-grade tumors. A protein-to-protein interaction network between genes associated with JHDM1D gene was constructed using STRING website. The results showed a moderate (positive) correlation between CTD-2510F5.4 and CTD2132N18.2. ROC curve analyses showed that combined JHDM1D and RP11-363E7.4 predicted tumor grade with an AUC of 0.826, showing excellent accuracy. In conclusion, the results indicated that the combined expression of JHDM1D and RP11-363E7.4 may be a prognostic biomarker and a promising target for urothelial tumor therapy.
Keyphrases
- high grade
- low grade
- poor prognosis
- long non coding rna
- gene expression
- genome wide identification
- genome wide
- binding protein
- papillary thyroid
- copy number
- prognostic factors
- machine learning
- risk assessment
- dna methylation
- squamous cell
- stem cells
- mesenchymal stem cells
- deep learning
- magnetic resonance imaging
- computed tomography
- protein protein
- childhood cancer
- high intensity