Gene Expression Monotonicity across Bladder Cancer Stages Informs on the Molecular Pathogenesis and Identifies a Prognostic Eight-Gene Signature.
Rafael StroggilosMaria FrantziJerome ZoidakisMarika MokouNapoleon MoulavasilisEmmanouil MavrogeorgisAnna MelidiManousos MakridakisKonstantinos StravodimosMaria G RoubelakisHarald MischakAntonia VlahouPublished in: Cancers (2022)
Despite advancements in molecular classification, tumor stage and grade still remain the most relevant prognosticators used by clinicians to decide on patient management. Here, we leverage publicly available data to characterize bladder cancer (BLCA)'s stage biology based on increased sample sizes, identify potential therapeutic targets, and extract putative biomarkers. A total of 1135 primary BLCA transcriptomes from 12 microarray studies were compiled in a meta-cohort and analyzed for monotonal alterations in pathway activities, gene expression, and co-expression patterns with increasing stage (Ta-T1-T2-T3-T4), starting from the non-malignant tumor-adjacent urothelium. The TCGA-2017 and IMvigor-210 RNA-Seq data were used to validate our findings. Wnt, MTORC1 signaling, and MYC activity were monotonically increased with increasing stage, while an opposite trend was detected for the catabolism of fatty acids, circadian clock genes, and the metabolism of heme. Co-expression network analysis highlighted stage- and cell-type-specific genes of potentially synergistic therapeutic value. An eight-gene signature, consisting of the genes AKAP7 , ANLN , CBX7 , CDC14B , ENO1 , GTPBP4 , MED19 , and ZFP2 , had independent prognostic value in both the discovery and validation sets. This novel eight-gene signature may increase the granularity of current risk-to-progression estimators.
Keyphrases
- genome wide
- gene expression
- genome wide identification
- dna methylation
- rna seq
- single cell
- poor prognosis
- network analysis
- copy number
- genome wide analysis
- transcription factor
- fatty acid
- bioinformatics analysis
- stem cells
- small molecule
- machine learning
- cell proliferation
- oxidative stress
- big data
- single molecule
- anti inflammatory
- long non coding rna
- deep learning
- climate change
- palliative care
- risk assessment
- case control