Multiple myeloma (MM) is an incurable malignancy characterized by altered expression of coding and noncoding genes promoting tumor growth and drug resistance. Although the crucial role of long noncoding RNAs (lncRNAs) in MM is clearly established, the function of the noncoding RNAome, which might allow the design of novel therapeutics, is largely unknown. We performed an unbiased CRISPR-Cas9 loss-of-function screen of 671 lncRNAs in MM cells and their bortezomib (BZB)–resistant derivative. To rank functionally and clinically relevant candidates, we designed and used a bioinformatic prioritization pipeline combining functional data from cellular screens with prognostic and transcriptional data from patients with MM. With this approach, we unveiled and prioritized 8 onco-lncRNAs essential for MM cell fitness, associated with high expression and poor prognosis in patients with MM. The previously uncharacterized RP11-350G8.5 emerged as the most promising target, irrespective of BZB resistance. We (1) demonstrated the anti-tumoral effect obtained by RP11-350G8.5 inhibition in vitro and in vivo; (2) highlighted a modulation of the unfolded protein response and the induction of immunogenic cell death triggered by the RP11-350G8.5 knockout, via RNA sequencing and molecular studies; (3) characterized its cytoplasmic homing through RNA fluorescence in situ hybridization; and (4) predicted its 2-dimensional structure and identified 2 G-quadruplex and 3 hairpin-forming regions by biophysical assays, including thioflavin T, 1H nuclear magnetic resonance, and circular dichroism, to pave the way to the development of novel targeted therapeutics. Overall, we provided innovative insights about unexplored lncRNAs in MM and identified RP11-350G8.5 as an oncogenic target for treatment-naïve and BZB-resistant patients with MM.

Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disorder with marked clinical heterogeneity and increased tumor risk, complicating disease surveillance, risk stratification and treatment decisions. This review summarizes current evidence on transcriptomic approaches relevant to NF1, with particular emphasis on extracellular vesicles (EVs), EV-associated RNA and bioinformatic strategies. NF1-specific evidence on EVs remains limited and has mainly addressed EV-associated proteins, biological effects and experimental models, whereas comprehensive transcriptomic profiling of circulating EVs in well-characterized NF1 cohorts remains largely unexplored. Transcriptomic and multi-omics studies of NF1-associated tumors provide complementary information on molecular heterogeneity, tumor progression and the tumor microenvironment, while studies of EV-associated RNA in other diseases provide a broader biological and methodological framework. Bioinformatic approaches may facilitate the processing and integration of EV-associated RNA datasets, the identification of candidate molecular signatures and their integration with clinical and genomic information. However, the diagnostic, prognostic and predictive value of EV-associated RNAs in NF1 remains investigational and requires independent clinical validation. Future studies should focus on well-characterized longitudinal cohorts, standardized EV isolation and characterization, reproducible analytical workflows and integration of molecular profiles with clinical and genomic data. These approaches may determine whether EV-associated transcriptomic signatures can contribute to disease characterization, risk stratification and treatment monitoring in NF1.