ABTB2 Regulatory Variant as Predictor of Epirubicin-Based Neoadjuvant Chemotherapy in Luminal A Breast Cancer

Advanced Hydrogels in Breast Cancer Therapy

Breast cancer is the most common malignancy among women and is the second leading cause of cancer-related death for women. Depending on the tumor grade and stage, breast cancer is primarily treated with surgery and antineoplastic therapy. Direct or indirect side effects, emotional trauma, and unpredictable outcomes accompany these traditional therapies, calling for therapies that could improve the overall treatment and recovery experiences of patients. Hydrogels, biomimetic materials with 3D network structures, have shown great promise for augmenting breast cancer therapy. Hydrogel implants can be made with adipogenic and angiogenic properties for tissue integration. 3D organoids of malignant breast tumors grown in hydrogels retain the physical and genetic characteristics of the native tumors, allowing for post-surgery recapitulation of the diseased tissues for precision medicine assessment of the responsiveness of patient-specific cancers to antineoplastic treatment. Hydrogels can also be used as carrier matrices for delivering chemotherapeutics and immunotherapeutics or as post-surgery prosthetic scaffolds. The hydrogel delivery systems could achieve localized and controlled medication release targeting the tumor site, enhancing efficacy and minimizing the adverse effects of therapeutic agents delivered by traditional procedures. This review aims to summarize the most recent advancements in hydrogel utilization for breast cancer post-surgery tissue reconstruction, tumor modeling, and therapy and discuss their limitations in clinical translation.

Pharmacogenomic markers of metoprolol and α-OH-metoprolol concentrations: a genome-wide association study

Aim: Few genome-wide association studies (GWASs) have been conducted to identify predictors of drug concentrations. The authors therefore sought to discover the pharmacogenomic markers involved in metoprolol pharmacokinetics. Patients & methods: The authors performed a GWAS of a cross-sectional study of 993 patients from the Montreal Heart Institute Biobank taking metoprolol. Results: A total of 391 and 444 SNPs reached the significance threshold of 5 × 10-8 for metoprolol and α-OH-metoprolol concentrations, respectively. All were located on chromosome 22 at or near the CYP2D6 gene, encoding CYP450 2D6, metoprolol's main metabolizing enzyme. Conclusion: The results reinforce previous findings of the importance of the CYP2D6 locus for metoprolol concentrations and confirm that large biobanks can be used to identify genetic determinants of drug pharmacokinetics at a GWAS significance level.

A model to predict the prognosis of diffuse large B-cell lymphoma based on ultrasound images

The purpose of this paper was to assess the value of ultrasonography in the prognosis of diffuse large b-cell lymphoma (DLBCL) by developing a new prognostic model. One hundred and eleven DLBCL patients with complete clinical information and ultrasound findings were enrolled in our study. Univariate and multivariate regression analyses were used to identify independent risk factors for progression-free survival (PFS) and overall survival (OS). Receiver operator characteristic (ROC) curves were plotted and the corresponding area under the curve (AUC) was calculated to assess the accuracy of the international prognostic index (IPI) and new model in DLBCL risk stratification. The results suggested that hilum loss and ineffective treatment were independent risk variables for both PFS and OS in DLBCL patients. Additionally, the new model that added hilum loss and ineffective treatment to IPI had a better AUC for PFS and OS than IPI alone (AUC: 0.90, 0.88, and 0.82 vs. 0.71, 0.74, and 0.68 for 1-, 3-, and 5-year PFS, respectively; AUC: 0.92, 0.85 and 0.86 vs. 0.71, 0.75 and 0.76, for 1-, 3-, and 5-year OS, respectively). The model based on ultrasound images could better suggest PFS and OS of DLBCL, allowing for better risk stratification.

Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.

Preliminary Study on the Sequencing of Whole Genomic Methylation and Transcriptome-Related Genes in Thyroid Carcinoma

Simple Summary

Epigenetic alterations are critical for tumor onset and development. DNA methylation is one of the most studied pathways concerning various types of cancer. A promising and exciting avenue of research is the discovery of biomarkers of early-stage malignancies for disease prevention and prognostic indicators following cancer treatment by examining the DNA methylation modification of relevant genes implicated in cancer development. We have made significant advances in the study of DNA methylation and thyroid cancer. This study is novel in that it distinguished methylation changes that occurred primarily in the gene body region of the aforementioned hypermethylated or hypomethylated thyroid cancer genes. Our findings imply that exposing whole-genome DNA methylation patterns and gene expression profiles in thyroid cancer provides new insight into the carcinogenesis of thyroid cancer, demonstrating that gene expression mediated by DNA methylation modifications may play a significant role in tumor growth.

Abstract

Thyroid carcinoma is the most prevalent endocrine cancer globally and the primary cause of cancer-related mortality. Epigenetic modifications are progressively being linked to metastasis. This study aimed to examine whole-genome DNA methylation patterns and the gene expression profiles in thyroid cancer tissue samples using a MethylationEPIC BeadChip (850K), RNA sequencing, and a targeted bisulfite sequencing assay. The results of the Illumina Infinium human methylation kit (850K) analyses identified differentially methylated CpG locations (DMPs) and differentially methylated CpG regions (DMRs) encompassing nearly the entire genome with high resolution and depth. Gene ontology and KEGG pathway analyses revealed that the genes associated with DMRs belonged to various domain-specific ontologies, including cell adhesion, molecule binding, and proliferation. The RNA-Seq study found 1627 differentially expressed genes, 1174 of which that were up-regulated and 453 of which that were down-regulated. The targeted bisulfite sequencing assay revealed that CHST2, DPP4, DUSP6, ITGA2, SLC1A5, TIAM1, TNIK, and ABTB2 methylation levels were dramatically lowered in thyroid cancer patients when compared to the controls, but GALNTL6, HTR7, SPOCD1, and GRM5 methylation levels were significantly raised. Our study revealed that the whole-genome DNA methylation patterns and gene expression profiles in thyroid cancer shed new light on the tumorigenesis of thyroid cancer.

Mechanisms of Resistance to Chemotherapy in Breast Cancer and Possible Targets in Drug Delivery Systems

Breast cancer (BC) is one of the most important cancers worldwide, and usually, chemotherapy can be used in an integrative approach. Usually, chemotherapy treatment is performed in association with surgery, radiation or hormone therapy, providing an increased outcome to patients. However, tumors can develop resistance to different drugs, progressing for a more aggressive phenotype. In this scenario, the use of nanocarriers could help to defeat tumor cell resistance, providing a new therapeutic perspective for patients. Thus, this systematic review aims to bring the molecular mechanisms involved in BC chemoresistance and extract from the previous literature information regarding the use of nanoparticles as potential treatment for chemoresistant breast cancer.