Biology subjects

Firlej, V.

Publications and source records attributed to Firlej, V..

2 recordsLinked to original sources

bioRxiv2023

A novel lncRNA PROCA11 regulates cell plasticity in response to androgen deprivation of prostate cancer cells

Long non-coding RNAs (lncRNAs) represent vast and yet poorly characterized family of genes that can fine tune cellular plasticity, thereby allowing the emergence of aggressive therapy-resistant and metastatic cancers. Androgen deprivation therapies (ADT) are commonly used to treat prostate cancer by inactivating the Androgen Receptor (AR). However, castration-resistant prostate cancer (CRPC) with neuroendocrine subtypes (NEPC) often emerge. In this study, we explore the role of lncRNAs in response to androgen deprivation. Using a dynamic prostate cancer cell system mimicking the CRPC and NEPC onset, we identified 15 novel lncRNAs, with PROCA11 standing out as a first-choice candidate, being also highly abundant in high-risk prostate cancer tumors. This majorly nuclear lncRNA is expressed at low levels in androgen-dependent conditions of growth and strongly activated upon hormone withdrawal, preceding neuroendocrine genes and persisting at high levels in neuroendocrine cells. Extensive computational analysis of clinical data and functional studies in cells revealed PROCA11 association with basal-to-luminal transformation of the transcriptomic landscape and activation of metabolic and signaling pathways reminiscent of neurogenesis and of maintenance of AR signaling. We propose that PROCA11 is involved in the intricate circuits regulating cellular plasticity enabling cell survival and proliferation and emergence of the NE phenotype in response to ADT.

cancer biology↗

bioRxiv2019

Blind exploration of the unreferenced transcriptome reveals novel RNAs for prostate cancer diagnosis

The broad use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens is restricted by the limited power of conventional reference-based protocols relying on uniquely mapped reads and transcript annotations. Here, we implemented a blind reference-free computational protocol, DE-kupl, to directly infer RNA variations of any origin, including yet unreferenced RNAs, from high coverage total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into unannotated putative long noncoding (lnc)RNAs expressed in prostate cancer tissues. Through filtering and visual inspection of 1,179 candidates, we defined 21 lncRNA probes that were further validated for robust tumor-specific expression by NanoString single molecule-based RNA measurements in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling over 90% of true positive detection of cancer in an independent dataset from The Cancer Genome Atlas. Remarkably, this clinical signature made of only 9 unannotated lncRNAs largely outperformed PCA3, the only RNA biomarker approved by the Food and Drug Administration agency, specifically, in detection of high-risk prostate tumors. The proposed reference-free computational workflow is modular, highly sensitive and robust and can be applied to any pathology and any clinical application.

genomics↗