Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data

Repurposing major metabolites of lamiaceae family as potential inhibitors of α-synuclein aggregation to alleviate neurodegenerative diseases: an in silico approach

Neurodegenerative disorders (NDs) are typically characterized by progressive loss of neuronal function and the deposition of misfolded proteins in the brain and peripheral organs. They are molecularly classified based on the specific proteins involved, underscoring the critical role of protein-processing systems in their pathogenesis. Alpha-synuclein (α-syn) is a neural protein that is crucial in initiating and progressing various NDs by directly or indirectly regulating other ND-associated proteins. Therefore, reducing the α-syn aggregation can be an excellent option for combating ND initiation and progression. This study presents an in silico phytochemical-based approach for discovering novel neuroprotective agents from bioactive compounds of the Lamiaceae family, highlighting the potential of computational methods such as functional networking, pathway enrichment analysis, molecular docking, and simulation in therapeutic discovery. Functional network and enrichment pathway analysis established the direct or indirect involvement of α-syn in various NDs. Furthermore, molecular docking interaction and simulation studies were conducted to screen 85 major bioactive compounds of the Lamiaceae family against the α-syn aggregation. The results showed that five compounds (α-copaene, γ-eudesmol, carnosol, cedryl acetate, and spathulenol) had a high binding affinity towards α-syn with potential inhibitory activity towards its aggregation. MD simulations validated the stability of the molecular interactions determined by molecular docking. In addition, in silico pharmacokinetic analysis underscores their potential as promising drug candidates, demonstrating excellent blood-brain barrier (BBB) permeability, bioactivity, and reduced toxicity. In summary, this study identifies the most suitable compounds for targeting the α-syn aggregation and recommends these compounds as potential therapeutic agents against various NDs, pending further in vitro and in vivo validation.

Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning

In recent years, many approved drugs have been discovered using phenotypic screening, which elaborates the exact mechanisms of action or molecular targets of drugs. Drug susceptibility prediction is an important type of phenotypic screening. Large-scale pharmacogenomics studies have provided us with large amounts of drug sensitivity data. By analyzing these data using computational methods, we can effectively build models to predict drug susceptibility. However, due to the differences in data distribution among databases, researchers cannot directly utilize data from multiple sources. In this study, we propose a deep transfer learning model. We integrate the genomic characterization of cancer cell lines with chemical information on compounds, combined with the Encyclopedia of Cancer Cell Lines (CCLE) and the Genomics of Cancer Drug Sensitivity (GDSC) datasets, through a domain-adapted approach and predict the half-maximal inhibitory concentrations (IC50 values). Afterward, the validity of the prediction results of our model is verified. This study effectively addresses the challenge of cross-database distribution discrepancies in drug sensitivity prediction by integrating multi-source heterogeneous data and constructing a deep transfer learning model. This model serves as a reliable computational tool for precision drug development. Its widespread application can facilitate the optimization of therapeutic strategies in personalized medicine while also providing technical support for high-throughput drug screening and the discovery of new drug targets.

scVAG: Unified single-cell clustering via variational-autoencoder integration with Graph Attention Autoencoder

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptional profiling of cell heterogeneity. However, analyzing this noisy, high-dimensional matrix remains challenging. We present scVAG, an integrated deep learning framework combining Variational-Autoencoder (VAE) and Graph Attention Autoencoder (GATE) for enhanced single-cell clustering. Building upon scGAC, our approach replaces its restrictive linear principal component analysis (PCA) with nonlinear dimensionality reduction better suited for scRNA-seq data. Specifically, we integrate VAE and GATE to enable more flexible latent space encoding. Extensive experiments on 20 datasets demonstrate scVAG's superior performance over previous state-of-the-art methods including scGAC, SCEA, SC3, Seurat, scGNN, scASGC, DESC, NIC, scLDS2, DRJCC, sLMIC, and jSRC. On average, scVAG improves clustering accuracy by 5 percent in ARI and 4 percent in NMI parameters. Visualizations highlight scVAG's capacity to recover interpretable biological structures. Our VAE-GATE pipeline extracts intricate expression patterns into compact representations that precisely delineate cell subpopulations consistent with ground truth labels. Overall, scVAG establishes a robust architecture for elucidating cell taxonomies from noisy transcriptomic inputs.

Konozy E, Abu Zeid IM, Ahmad A, Altayb HN. Prediction of anticancer peptides derived from the true lectins of Phoenix dactylifera and their synergetic effect with mitotane

Background and aims: Cancer continues to be a significant source of both illness and death on a global scale, traditional medicinal plants continue to serve as a fundamental resource of natural bioactive compounds as an alternative source of remedies. Although there have been numerous studies on the therapeutic role of Phoenix dactylifera, the study of the role of peptides has not been thoroughly investigated. This study aimed to investigate the anticancer activity of lectin peptides from P. dactylifera using in silico and in vivo analysis. Methods: Different computational tools were used to extract and predict anticancer peptides from the true lectins of P. dactylifera. Nine peptides that are bioactive substances have been investigated for their anticancer activity against MCF-7 and T47D (two forms of breast cancer). To counteract the unfavorable effects of mitotane, the most potent peptides (U3 and U7) were combined with it and assessed for anticancer activity against MCF-7 and HepG2. Results: In silico analysis revealed that nine peptides were predicted with anticancer activity. In cell lines, the lowest IC50 values were measured in U3 and U7 against MCF-7 and T47D cells. U3 or U7 in combination with mitotane demonstrated the lowest IC50 against MCF-7 and HepG2. The maximum level of cell proliferation inhibition was 22% when U3 (500 µg/mL) and 25 µg/mL mitotane were combined, compared to 41% when 25 µg/mL mitotane was used alone. When mitotane and U3 or U7 were combined, it was shown that these bioactive substances worked synergistically with mitotane to lessen its negative effects. The combination of peptides and mitotane could be regarded as an efficient chemotherapeutic medication having these bioactive properties for treating a variety of tumors while enhancing the reduction of side effects.

Organ-Specificity of Breast Cancer Metastasis

Breast cancer (BC) remains one of the most common malignancies among women worldwide. Breast cancer shows metastatic heterogeneity with priority to different organs, which leads to differences in prognosis and response to therapy among patients. The main targets for metastasis in BC are the bone, lung, liver and brain. The molecular mechanism of BC organ-specificity is still under investigation. In recent years, the appearance of new genomic approaches has led to unprecedented changes in the understanding of breast cancer metastasis organ-specificity and has provided a new platform for the development of more effective therapeutic agents. This review summarises recent data on molecular organ-specific markers of metastasis as the basis of a possible therapeutic approach in order to improve the diagnosis and prognosis of patients with metastatically heterogeneous breast cancer.

RAD21 is the core subunit of the cohesin complex involved in directing genome organization

BACKGROUND

The ring-shaped cohesin complex is an important factor for the formation of chromatin loops and topologically associating domains (TADs) by loop extrusion. However, the regulation of association between cohesin and chromatin is poorly understood. In this study, we use super-resolution imaging to reveal the unique role of cohesin subunit RAD21 in cohesin loading and chromatin structure regulation.

RESULTS

We directly visualize that up-regulation of RAD21 leads to excessive chromatin loop extrusion into a vermicelli-like morphology with RAD21 clustered into foci and excessively loaded cohesin bow-tying a TAD to form a beads-on-a-string-type pattern. In contrast, up-regulation of the other four cohesin subunits results in even distributions. Mechanistically, we identify that the essential role of RAD21 is attributed to the RAD21-loader interaction, which facilitates the cohesin loading process rather than increasing the abundance of cohesin complex upon up-regulation of RAD21. Furthermore, Hi-C and genomic analysis reveal how RAD21 up-regulation affects genome-wide higher-order chromatin structure. Accumulated contacts are shown at TAD corners while inter-TAD interactions increase after vermicelli formation. Importantly, we find that in breast cancer cells, the expression of RAD21 is aberrantly high with poor patient survival and RAD21 forms beads in the nucleus. Up-regulated RAD21 in HeLa cells leads to compartment switching and up-regulation of cancer-related genes.

CONCLUSIONS

Our results provide key insights into the molecular mechanism by which RAD21 facilitates the cohesin loading process and provide an explanation to how cohesin and loader work cooperatively to promote chromatin extrusion, which has important implications in construction of three-dimensional genome organization.

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.

Breast cancer liver metastasis: current and future treatment approaches

Nearly all fatalities arising from breast tumors are attributable to distant metastases. Breast cancer liver metastasis (BCLM) is associated with poor prognoses, with the median survival time being 2 to 3 years. Tumor intrinsic subtype directs preferential metastasis to specific organs, with HER2-enriched tumors demonstrating the highest rates of metastasis to the liver, though all subtypes can grow in the liver. There is no singular established standard-of-care for BCLM; therapeutic selection is driven by histologic and molecular hallmarks of the primary tumor or biopsied metastasis samples. Given the poor prognosis of patients with hepatic spread, pre-clinical studies are necessary to identify and evaluate promising new treatment strategies. It is critical that these laboratory studies accurately recapitulate the BCLM disease process, standard progression, and histological attributes. In this review, we summarize the histologic and molecular characteristics of BCLM, evaluate the efficacy of existing surgical and medical treatment strategies, and discuss future approaches to preclinical study of BCLM.

Transcription factor E2F4 is an indicator of poor prognosis and is related to immune infiltration in hepatocellular carcinoma

Background: Recent studies have shown that the transcription factor E2F4 is involved in the progression of various tumors, but its expression and influence on immune cell infiltration and biological functions are largely unknown in hepatocellular carcinoma (HCC).

Methods: The Cancer Genome Atlas (TCGA) database, the Tumor Immune Estimation Resource (TIMER) and related online tools as well as a tissue microarray (TMA) were used for analyses in our study.

Results: E2F4 expression was elevated in HCC tumor tissue compared with adjacent normal tissue at both the mRNA and protein levels. Overexpression of E2F4 was markedly related to a poor prognosis in HCC patients. In addition, positively and negatively correlated significant genes of E2F4 were identified in HCC. Pathway enrichment analyses revealed that the top 100 positively correlated significant genes of E2F4 were closely related to nuclear splicing and degradation-related pathways. Furthermore, nine hub genes correlated with E2F4 expression were validated based on a protein-protein interaction (PPI) network. It was also demonstrated that E2F4 expression was negatively correlated to immune purity and positively correlated to immune cell infiltration.

Conclusion: E2F4 could serve as a novel biomarker for HCC diagnosis and prognosis prediction.

Experimental Models as Refined Translational Tools for Breast Cancer Research

Breast cancer is one of the most common cancers worldwide, which makes it a very impactful malignancy in the society. Breast cancers can be classified through different systems based on the main tumor features and gene, protein, and cell receptors expression, which will determine the most advisable therapeutic course and expected outcomes. Multiple therapeutic options have already been proposed and implemented for breast cancer treatment. Nonetheless, their use and efficacy still greatly depend on the tumor classification, and treatments are commonly associated with invasiveness, pain, discomfort, severe side effects, and poor specificity. This has demanded an investment in the research of the mechanisms behind the disease progression, evolution, and associated risk factors, and on novel diagnostic and therapeutic techniques. However, advances in the understanding and assessment of breast cancer are dependent on the ability to mimic the properties and microenvironment of tumors in vivo, which can be achieved through experimentation on animal models. This review covers an overview of the main animal models used in breast cancer research, namely in vitro models, in vivo models, in silico models, and other models. For each model, the main characteristics, advantages, and challenges associated to their use are highlighted.

Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.