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Ferreira Cunha, A.

Publications and source records attributed to Ferreira Cunha, A..

2 recordsLinked to original sources

Multi-scale Transcriptional Characterization of HPV16 and HPV18 in Cervical Cancer: Host Pathway Alterations and Links With Viral Gene Expression Programs

High-risk HPV is central to cervical carcinogenesis, but whether individual genotypes drive distinct tumor biology or converge on a shared oncogenic state remains unclear. Profiling viral and host gene expression across two independent cohorts (n = 93; 76 HPV16, 17 HPV18), we show that HPV16- and HPV18-associated cervical cancers differ in host transcriptional programs. A 55-gene signature discriminated the two genotypes, and 293 genes were concordantly differentially expressed across cohorts. Pathway and transcription-factor activity inference organized these differences along genotype-specific axes: HPV16 tumors were enriched for epithelial polarity and RTK-RAS-MAPK signatures, with elevated inferred CDX2 regulon activity, a transcription factor not normally active in cervical epithelium, suggesting an aberrant lineage-identity program not previously linked to this genotype. HPV18 tumors instead showed metabolic reprogramming and inflammatory signaling (STAT3, HIF1A), alongside reduced CASP8 and increased FDX1 expression, a pattern consistent with sensitization to immunogenic cell death. Both genotypes also organized along a continuum of early-to-late viral gene expression correlating with distinct host pathways: extracellular-matrix and translation-related in HPV16, interferon/antiviral and cell-cycle/mitochondrial in HPV18, indicating that viral transcriptional state associates with host biology beyond genotype alone. These results point to candidate genotype-specific vulnerabilities that merit functional validation.

cancer biology↗

ABaCo: Addressing Heterogeneity Challenges in Metagenomic Data Integration with Adversarial Generative Models

The rapid advancement of high-throughput metagenomics has produced extensive and heterogeneous datasets with significant implications for environmental and human health. Integrating these datasets is crucial for understanding the functional roles of microbiomes and the interactions within microbial communities. However, this integration remains challenging due to technical heterogeneity and the inherent complexity of these biological systems. To address these challenges, we introduce ABaCo, a generative model that combines a Variational Autoencoder (VAE) with an adversarial discriminator specifically designed to handle the unique characteristics of metagenomic data. Our results demonstrate that ABaCo effectively integrates metagenomic data from multiple studies, corrects technical heterogeneity, outperforms existing methods, and preserves taxonomic-level biological signals. We have developed ABaCo as an open-source, fully documented Python library to facilitate, support and enhance metagenomics research in the scientific community.

bioinformatics↗