Biology subjects

Fernandes, M. A. C.

Publications and source records attributed to Fernandes, M. A. C..

5 recordsLinked to original sources

bioRxiv2026

Subgroup-Specific Associations of GRIA Genes Encoding AMPA Glutamate Receptor Subunits with Patient Survival in Medulloblastoma

Brain cancers hijack biological systems involved in neural development and synaptic plasticity. Medulloblastoma (MB), the most common malignant brain tumor in children, is thought to arise from disruptions in neurodevelopmental programs. Glutamatergic transmission mediated by -amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors (AMPARs) has been implicated in synaptic communication between adult brain tumors and surrounding neurons; however, the possible role of AMPARs in MB remains largely unexplored. Here, we analyzed the expression of genes encoding AMPAR subunits, GRIA1-4, in datasets of MB tumor and cell lines, revealing distinct expression patterns and associations with overall survival (OS) across molecular subgroups and histological variants. Expression levels differed among MB molecular subgroups. Analysis using single-cell RNA sequencing (scRNA-seq) was consistent with enrichment of GRIA1 in Group 3 and GRIA4 in SHH MB. Higher GRIA1, GRIA2, and GRIA4 transcription was associated with more favorable patient outcomes in specific MB subgroups. In contrast, high expression of GRIA3 in SHH, or of either GRIA3 or GRIA4 in Group 3 MB, was associated with worse prognosis. Particularly robust but opposing associations with patient survival were found for GRIA3 and GRIA4 in SHH MB. Analysis of GRIA mRNA levels in MB cell lines using both quantitative reverse transcription polymerase chain reaction (qRT-PCR) and data from The Human Protein Atlas, supported some of the gene expression patterns observed in tumors. Together, these findings suggest that GRIA genes and their corresponding AMPAR subunits may have subgroup-specific prognostic relevance in MB.

cancer biology↗

bioRxiv2025

Delayed growth of Ewing sarcoma tumors associated with reduced expression and activity of Trk receptors, PI3K, and IGF1R

Ewing sarcoma (ES) is an aggressive childhood tumor. We have previously shown that tropomyosin receptor kinase (Trk) neurotrophin receptors regulate viability, and the pan-Trk and kinase inhibitor K252a restores sensitivity to chemotherapy, in ES cells. Here, we show that administration of K252a in mice delays ES tumor growth and reduces expression and activity of multiple targets. BALB/c nu/nu nude mice inoculated with SK-ES-1 ES cells were given daily intraperitoneal (i.p.) injections of K252a for 18 days. Immunohistochemical analysis of tumors was performed to quantify the expression and phosphorylation of Trk receptors, phosphoinositide 3-kinase (PI3K), and insulin-like growth factor 1 receptor (IGF1R). Associations between genes encoding Trks, PI3G, and IGF1R and overall survival (OS) in patients with ES was examined. Treatment with K252a led to a transient delay in ES tumor growth and reduced the expression and phosphorylation of TrkA, TrkB, PI3K, and IGF1R. Combined treatment with K252a and the IGF1R inhibitor NVP-ADW742 was more effective in reducing ES cell viability than each compound alone. Significant associations between expression of NTRK genes and patient OS were found, indicating that NTRK genes should be further evaluated as biomarkers for prognosis in patients with ES.

cancer biology↗

bioRxiv2025

Opposite patterns of association of TWIST1 expression with patient survival in SHH and Group 4 medulloblastoma

PurposeTwist1 is a transcription factor that regulates embryonic development, stemness, and differentiation, and can also stimulate initiation of tumorigenesis in peripheral solid cancers. However, its role in central nervous system tumors, including medulloblastoma (MB), the main type of malignant brain cancer that afflicts children, remains poorly understood. Here, we examined expression of Twist1, and its potential significance in prognosis, in different histological variants and molecular subgroups of MB. MethodsGene expression data for TWIST1 and corresponding overall survival (OS) of patients was analyzed in 612 MB samples using a previously described dataset. A cross-sectional analysis of Twist1 protein content in 24 MB tumor samples from patients was carried out by immunohistochemistry. ResultsTWIST1 transcript levels were higher in classic MB compared to desmoplastic tumors. Within samples with classic histology, higher TWIST1 expression was associated with a longer OS. Tumors in the SHH subgroup had lower TWIST1 expression compared to all other subgroups, and Group 4 showed lower expression than WNT tumors. In Group 4 MB, higher TWIST1 levels were associated with shorter OS, whereas patients with SHH tumors and higher TWIST1 levels showed longer OS. Twist1 protein was detectable in part of classic and LCA MB tumors belonging to the SHH, WNT or Group 3/4 subgroups. ConclusionWe found opposite patterns of association between TWST1 and patient survival in Group 4 and SHH MB subgroups. Our results highlight the importance of stratifying tumors by molecular subgroup and histological classification when exploring novel potential biomarkers and therapeutic targets in MB.

cancer biology↗

bioRxiv2024

Modulation of Stemness and Differentiation Regulators by Valproic Acid in Medulloblastoma

Changes in epigenetic processes such as histone acetylation are proposed as key events influencing cancer cell function and the initiation and progression of pediatric brain tumors. Valproic acid (VPA) is an antiepileptic drug that acts partially by inhibiting histone deacetylases (HDACs) and could be repurposed as an epigenetic anticancer therapy. Here, we show that VPA reduced medulloblastoma (MB) cell viability and led to cell cycle arrest. These effects were accompanied by enhanced H3K9 histone acetylation (H3K9ac) and decreased expression of the MYC oncogene. VPA impaired the expansion of MB neurospheres enriched in stemness markers and reduced MYC while increasing TP53 expression in these neurospheres. In addition, VPA induced morphological changes consistent with neuronal differentiation and the increased expression of differentiation marker genes TUBB3 and ENO2. The expression of stemness genes SOX2, NES, and PRTG was differentially affected by VPA in MB cells with different TP53 status. VPA increased H3K9 occupancy of the promoter region of TP53. Among the genes regulated by VPA, the stemness regulators MYC and NES showed an association with patient survival in specific MB subgroups. Our results indicate that VPA may exert antitumor effects in MB by influencing histone acetylation, which may result in the modulation of stemness, neuronal differentiation, and the expression of genes associated with patient prognosis in specific molecular subgroups. Importantly, the actions of VPA in MB cells and neurospheres include a reduction in the expression of MYC and an increase in TP53.

cancer biology↗

bioRxiv2021

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k = 6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

bioinformatics↗