Roger Chammas

Functional studies have shown that long noncoding RNAs (lncRNAs) play different roles in gene expression regulation, acting as epigenetic factors. Accumulating evidence indicates that lncRNAs modulate diverse biological processes, and their altered expression is associated with the development and progression of cancer, including BC. Here, aiming to identify lncRNAs associated with breast cancer, we performed a re-analysis of the expression data from 1071 tumors of eligible patients in the LACRN-MPBC Study. Using normalized data, we compared the expression profiles of triple-negative breast cancer (TNBC; 170 cases) and a heterogeneous group of non-TNBC tumors (782 cases). A total of 59 differentially expressed lncRNAs (DELncRNAs) could be identified based on the criteria of |log2FC| ≥ 1 and FDR < 0.05, using the DESeq2 R package. In the Kaplan–Meier survival analysis, we identified DELncRNAs that affect disease-free survival and/or overall survival, with eight of them in TNBC and 27 in non-TNBC patients. Multivariate Cox proportional-hazards models were used to evaluate independent predictors of survival. Two DELncRNAs, VLDLR-AS1 and PART1, were identified as independent prognostic markers for TNBC patients, while seven (LINC00239, FAM30A, LINC00842, LINC01315, EGOT, LY6E-DT, and SLC25A21-AS1) were independent prognostic markers for non-TNBC patients. Our computational study identifies several candidate lncRNAs associated with clinical outcomes in breast cancer. However, the DELncRNAs identified here should be interpreted as preliminary candidates, which require future validation and functional studies to determine their biological roles and evaluate their potential as prognostic biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide, largely because it is diagnosed late. Diagnosis typically relies on imaging and biopsy, but only after tumors have advanced, which reduces treatment effectiveness. Current biochemical biomarkers are limited to CA19-9, which is more useful for prognosis and monitoring than for diagnosis. Circulating miRNAs may offer minimally invasive complementary biomarkers for PDAC diagnosis, potentially accounting for ethnic and treatment-related variables. We performed small RNA sequencing on plasma from five non-metastatic-stage and five metastatic-stage PDAC patients and 10 healthy controls to generate an exploratory miRNA profile. Candidate miRNAs were then validated by RT-qPCR in an independent cohort of 70 PDAC patients and 106 healthy controls, and nine miRNAs showed promising differential plasma expression. Notably, miRNA484 and miRNA335-5p were associated with PDAC in CA19-9-negative patients, with ROC AUCs of 0.984 and 0.88, respectively. Overall, these results indicate that miRNA484 and miRNA335-5p may have clinical value as complementary biomarkers for PDAC patients who are CA19-9-negative.

Accurate prediction of breast cancer recurrence remains difficult because prognosis varies significantly across molecular subtypes. This underscores the need for scalable, affordable, and interpretable prognostic tools. We developed machine learning models integrating hematological indices with clinicopathologic data to predict 2- and 10-year recurrence or all-case death. We retrospectively analyzed 4277 women with primary breast cancer (2008-2022). The cohort included hormone receptor-positive (HR + ), HER2-positive, and triple-negative (TNBC) subtypes. We trained multiple classifiers and integrated them into a stacked ensemble using logistic regression as the final learner. Class imbalance was addressed with SMOTE applied to training sets. The ensemble achieved strong discrimination: general cohort AUC 0.859 (2-year) and 0.811 (10-year), with specificity 84-82% and sensitivity 68-61%. Subtype-specific performance remained robust across time horizons: HR + AUC 0.862/0.804, HER2 + AUC 0.877/0.831, and TNBC AUC 0.826/0.826 (2-year/10-year respectively). SHAP analysis identified advanced tumor stage, elevated inflammatory ratios (NLR, PLR, MLR), elevated red cell distribution width, and age as key adverse predictors with stronger effects on early recurrence. This interpretable tool uses routine blood tests and clinico-pathological data at diagnosis. It flaggs high-risk patients for intensified therapy, useful where genomic testing is unavailable.

Aberrant O-glycosylation and miRNA dysregulation are established features of tumor aggressiveness, particularly in triple-negative breast cancer (TNBC). Using a Cosmc-silenced 4 T1 murine model (Tn+), characterized by truncated O-glycans and enhanced metastatic potential, tissue and serum miRNA profiling identified miR-21-5p as a circulating marker associated with the Tn+ phenotype. Transcriptomic analysis revealed that miR-21-5p targets multiple tumor suppressor genes, including Btg2, Spry1, Tbx2, Rhob, and Dusp8, suggesting its involvement in epithelial–mesenchymal transition and immune modulation. Integrative single-cell RNA sequencing (scRNA-seq) of the 4 T1 tumor microenvironment revealed distinct cellular clusters with inverse expression patterns of GALNT enzymes (involved in Tn synthesis) and miR-21-5p target genes, defining Tn+-like and Tn—like subpopulations. Translating these findings to human breast cancer (TCGA-BRCA), a prognostic model combining clinical variables (age, metastasis, PAM50 subtype) with three-gene expression (OLR1, PCSK6, and GALNT6) significantly improved patient risk stratification (P = 0.015). By integrating multi-omics analyses (scRNA-seq, TCGA) with an aggressive Tn+ TNBC model, this study defines a novel three-gene prognostic signature that links the glyco-miRNA axis to tumor aggressiveness, offering a promising tool for advanced patient stratification and the development of precision glyco-therapeutics.

Ganglioside GM3, a fundamental glycosphingolipid on the mammalian cell surface, is a key regulator of transmembrane signaling and cellular recognition. In oncology, GM3 acts as a tumor suppressor by modulating the activity of various receptor tyrosine kinases (RTKs) and their downstream pathways. Recent studies highlight its function in the tumor microenvironment (TME), specifically its ability to impede pathological angiogenesis. This review summarizes the molecular mechanisms by which GM3 interferes with pro-angiogenic signaling, such as the VEGF/VEGFR axis, and discusses how this inhibition can be used for therapy. We explore the clinical potential of GM3-based strategies, including monoclonal antibodies and cancer vaccines, discussing the potential of targeting GM3 to reshape the TME and suppress tumor-associated vascularization.

Rapid advances in biomedical research and nanomedicine have driven the development and implementation of New Approach Methodologies (NAMs) for predictive evaluations of novel formulations.

RESUMO O conceito da “bala mágica” de Paul Ehrlich inspirou o desenvolvimento de terapias seletivas contra o câncer. A descoberta de que o câncer resulta do acúmulo de alterações genéticas e epigenéticas levou à identificação de alvos moleculares específicos e ao surgimento das terapias alvo-dirigidas. O sucesso do imatinibe na leucemia mieloide crônica marcou o início da oncologia de precisão, baseada na caracterização molecular dos tumores. Mais recentemente, imunoterapias, terapias celulares CAR-T, vacinas terapêuticas e abordagens teranósticas ampliaram as opções de tratamento personalizado. Entretanto, a heterogeneidade tumoral, a evolução clonal e os mecanismos de resistência limitam a eficácia dessas estratégias em todos os pacientes. No Brasil, a incorporação dessas tecnologias ao SUS depende de avaliação pela Conitec, considerando benefício clínico, custo-efetividade e impacto orçamentário. O desafio atual é expandir o acesso à medicina de precisão de forma sustentável, apoiada por diagnóstico molecular, pesquisa clínica e geração de evidências em condições reais de uso.