Non-coding transcriptome profiles in clear-cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is a common urological malignancy with an increasing incidence. The development of molecular biomarkers that can predict the response to treatment and guide personalized therapy selection would substantially improve patient outcomes. Dysregulation of non-coding RNA (ncRNA) has been shown to have a role in the pathogenesis of ccRCC. Thus, an increasing number of studies are being carried out with a focus on the identification of ncRNA biomarkers in ccRCC tissue samples and the connection of these markers with patients' prognosis, pathological stage and grade (including metastatic potential), and therapy outcome. RNA sequencing analysis led to the identification of several ncRNA biomarkers that are dysregulated in ccRCC and might have a role in ccRCC development. These ncRNAs have the potential to be prognostic and predictive biomarkers for ccRCC, with prospective applications in personalized treatment selection. Research on ncRNA biomarkers in ccRCC is advancing, but clinical implementation remains preliminary owing to challenges in validation, standardization and reproducibility. Comprehensive studies and integration of ncRNAs into clinical trials are essential to accelerate the clinical use of these biomarkers.

Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking

Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.