Background: Bioinformatics pipelines for variant calling have undergone significant advancements due to the decreasing costs of next-generation sequencing. Accurate mutation detection is crucial for personalised medicine in cancer, particularly in assignment of therapy. Somatic variant calling, however, remains challenging due to diverse cancer types, heterogeneity, complex mutational profiles, and unpredictable sequencing errors. A dataset of fully characterised tumoral genomes and sequencing reads, large enough to represent the variability inherent in different cancer types, is still lacking, even considering synthetic data. The lack of such datasets hampers rigorous evaluation, benchmarking and optimization of variant callers for specific cancer types. Results: The contribution of this work is twofold. First, we conducted a comprehensive analysis of nine somatic sample simulators (Synggen, BAMSurgeon, SVEngine, VarSim, Xome-Blender, tHapMix, Pysim-sv, SCNVSim, HeteroGenesis) assessing their ability to control biological parameters , including variants characteristics (type, number, position, length, content, zygosity), and sample characteristics (clonality, contamination); and technical parameters , including reads characteristics (sequencing errors, coverage, base qualities). No single simulator provided complete control over both biological and technical parameters, nor guidance on tuning biological parameters for cancer-specific simulations. Consequently, we developed MOV&RSim, a novel simulator that leverages data-driven information to set variants and reads characteristics, producing realistic tumoral samples, and providing full control on biological and technical parameters. Additionally, we leveraged well-annotated variant databases to create cancer-specific presets that inform the simulator’s parameters for 21 cancer types. Conclusion: This new simulator, containerised with Docker and freely available for academic use, empowers users to define each biological parameter of a tumoral genome and faithfully replicates the variability of technical noise observed in real sequencing reads. The proposed simulator and presets represent the most adaptable and comprehensive framework currently available for generating tumor samples, enabling comprehensive benchmarking and, ultimately, the optimization of somatic variant callers across diverse cancer types.

Background: Angiopoietin-like 3 (ANGPTL3) and proprotein convertase subtilisin/kexin type 9 (PCSK9) are key regulators of lipid homeostasis. We have previously shown that gene silencing of ANGPTL3 significantly induces PCSK9 expression in the human hepatoma cell line Huh7. Here, we investigated the biological significance of this regulation in the cultured human hepatoma cell line Huh7. Methods: We performed an RNA-seq analysis in Huh7 cells transfected with siRNA-ANGPTL3, siRNA-PCSK9, and double siRNA-ANGPTL3/PCSK9. Selected findings were assessed by RT-qPCR, Western blotting, and ELISA. Results: Among 13,945 detected transcripts, 192 genes were differentially expressed after ANGPTL3 silencing, 88 after PCSK9 silencing, and 219 after combined ANGPTL3/PCSK9 silencing, compared with scramble-siRNA controls. When ANGPTL3 gene expression was silenced, we observed a compensatory induction in PCSK9 mRNA and protein expression. Bioinformatic analysis revealed that gene silencing of ANGPTL3 or both ANGPTL3/PCSK9 suppresses serpin family A member 1 (SERPINA1), which encodes α1-antitrypsin, and lectin mannose-binding 1 (LMAN1). These data were confirmed by Western blot and RT-PCR analysis. In addition, ANGPTL3-siRNA, alone or combined with PCSK9-siRNA, significantly increased FV and FVIII mRNA expression and secretion in conditioned medium. Conclusions: Our data identified SERPINA1 and LMAN1 as genes downregulated in response to ANGPTL3 silencing in Huh7 hepatoma cells, which was also associated with increased expression of FV and FVIII. Our study suggests a potential link between ANGPTL3 silencing and coagulation-related processes, extending the biological relevance of ANGPTL3 beyond lipid metabolism.