Identification and validation of NFIA as a novel prognostic marker in renal cell carcinoma.
Roger de AlwisSarah SchochMazharul IslamChristina MöllerBörje LjungbergHåkan AxelsonPublished in: The journal of pathology. Clinical research (2023)
Prognostic tools are an essential component of the clinical management of patients with renal cell carcinoma (RCC). Although tumour stage and grade can provide important information, they fail to consider patient- and tumour-specific biology. In this study, we set out to find a novel molecular marker of RCC by using hepatocyte nuclear factor 4A (HNF4A), a transcription factor implicated in RCC progression and malignancy, as a blueprint. Through transcriptomic analyses, we show that the nuclear factor I A (NFIA)-driven transcription network is active in primary RCC and that higher levels of NFIA confer a survival benefit. We validate our findings using immunohistochemical staining and analysis of a 363-patient tissue microarray (TMA), showing for the first time that NFIA can independently predict poor cancer-specific survival in clear cell RCC (ccRCC) patients (hazard ratio = 0.46, 95% CI = 0.24-0.85, p value = 0.014). Furthermore, we confirm the association of HNF4A with higher grades and stages in ccRCC in our TMA cohort. We present novel data that show HNF4A protein expression does not confer favourable prognosis in papillary RCC, confirming our survival analysis with publicly available HNF4A RNA expression data. Further work is required to elucidate the functional role of NFIA in RCC as well as the testing of these markers on patient material from diverse multi-centre cohorts, to establish their value for the prognostication of RCC.
Keyphrases
- renal cell carcinoma
- nuclear factor
- toll like receptor
- transcription factor
- case report
- end stage renal disease
- clear cell
- ejection fraction
- newly diagnosed
- electronic health record
- chronic kidney disease
- single cell
- poor prognosis
- free survival
- rna seq
- immune response
- big data
- peritoneal dialysis
- machine learning
- patient reported outcomes
- artificial intelligence
- binding protein
- nucleic acid