A novel prognostic biomarker for muscle invasive bladder urothelial carcinoma based on 11 DNA methylation signature

Contemporary Molecular Markers for Predicting Systemic Treatment Response in Urothelial Bladder Cancer: A Narrative Review

The search for dependable molecular biomarkers to enhance routine clinical practice is a compelling challenge across all oncology fields. Urothelial bladder carcinoma, known for its significant heterogeneity, presents difficulties in predicting responses to systemic therapies and outcomes post-radical cystectomy. Recent advancements in molecular cancer biology offer promising avenues to understand the disease's biology and identify emerging predictive biomarkers. Stratifying patients based on their recurrence risk post-curative treatment or predicting the efficacy of conventional and targeted therapies could catalyze personalized treatment selection and disease surveillance. Despite progress, reliable molecular biomarkers to forecast responses to systemic agents, in neoadjuvant, adjuvant, or palliative treatment settings, are still lacking, underscoring an urgent unmet need. This review aims to delve into the utilization of current and emerging molecular signatures across various stages of urothelial bladder carcinoma to predict responses to systemic therapy.

Stage-differentiated ensemble modeling of DNA methylation landscapes uncovers salient biomarkers and prognostic signatures in colorectal cancer progression

Background

Aberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, contributing to cancer etiology. A gap in our understanding concerns the epigenomics of stagewise cancer progression. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC).

Methods

The methylation β-matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using an ensemble of approaches involving stage-differentiated modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2), and then filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by matching with clinical data and performing Kaplan–Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis.

Results

We found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. The stage-differentiated models yielded the following consensus salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were significantly hypermethylated in the promoter regions, indicating down-regulation of expression and implying a putative CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1 survived all the analytical filters, and represents a novel early-stage epigenetic biomarker / target.

Conclusions

We have designed and executed a workflow for stage-differentiated epigenomic analysis of colorectal cancer progression, and identified several stage-salient diagnostic biomarkers, and an early-stage prognostic biomarker panel. The study has led to the discovery of an alternative CIMP-like signature in colorectal cancer, reinforcing the role of CIMP drivers in tumor pathophysiology.