Transcriptome Analysis in Vulvar Squamous Cell Cancer.
Katharina PrieskeMalik AlawiAnna JaegerMaximilian Christian WanknerKathrin EylmannSusanne ReuterPatrick LebokEike C BurandtNiclas C BlessinBarbara SchmalfeldtLeticia Oliveira-FerrerSimon A JoosseLinn WoelberPublished in: Cancers (2021)
To date, therapeutic strategies in vulvar squamous cell carcinoma (VSCC) are lacking molecular pathological information and targeted therapy hasn't been approved in the treatment of VSCC, yet. Two etiological pathways are widely accepted: HPV induced vs. HPV independent, associated with chronic skin disease, often harboring TP53 mutations (mut). The aim of this analysis was to analyze the RNA expression patterns for subtype stratification on VSCC samples that can be integrated into the previously performed whole exome sequencing data for the detection of prognostic markers and potential therapeutic targets. We performed multiplex gene expression analysis (NanoString) with 770 genes in 24 prior next generation sequenced samples. An integrative data analysis was performed. Here, 98 genes were differentially expressed in TP53mut vs. HPV+ VSCC, in the TP53mut cohort, where 56 genes were upregulated and 42 were downregulated in comparison to the HPV+ tumors. Aberrant expression was primarily observed in cell cycle regulation, especially in HPV+ disease. Within the TP53mut group, a distinct cluster was identified that was correlated to a significantly worse overall survival ( p = 0.017). The RNA expression profiles showed distinct patterns with regard to the known VSCC subtypes and could potentially enable further subclassification in the TP53mut groups.
Keyphrases
- high grade
- cell cycle
- genome wide identification
- squamous cell
- data analysis
- genome wide
- squamous cell carcinoma
- poor prognosis
- cervical cancer screening
- cell proliferation
- transcription factor
- dna methylation
- papillary thyroid
- sentinel lymph node
- healthcare
- long non coding rna
- drug induced
- electronic health record
- nucleic acid
- social media
- young adults
- high throughput
- high glucose
- binding protein
- risk assessment
- deep learning
- diabetic rats
- lymph node metastasis
- artificial intelligence
- lymph node
- wound healing
- rectal cancer