Comparative analysis of protein-protein interaction networks in metastatic breast cancer

Developing theragnostics for Alzheimer's disease: Insights from cancer treatment

The prevalence of Alzheimer's disease (AD) and its associated economic and societal burdens are on the rise, but there are no curative treatments for AD. Interestingly, this neurodegenerative disease shares several biological and pathophysiological features with cancer, including cell-cycle dysregulation, angiogenesis, mitochondrial dysfunction, protein misfolding, and DNA damage. However, the genetic factors contributing to the overlap in biological processes between cancer and AD have not been actively studied. In this review, we discuss the shared biological features of cancer and AD, the molecular targets of anticancer drugs, and therapeutic approaches. First, we outline the common biological features of cancer and AD. Second, we describe several anticancer drugs, their molecular targets, and their effects on AD pathology. Finally, we discuss how protein-protein interactions (PPIs), receptor inhibition, immunotherapy, and gene therapy can be exploited for the cure and management of both cancer and AD. Collectively, this review provides insights for the development of AD theragnostics based on cancer drugs and molecular targets.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Copper Chaperone for Superoxide Dismutase Subtypes as a Prognostic Marker in Luminal B Breast Cancer

Background

Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS.

Methods

The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform.

Results

We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression.

Conclusions

Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer.

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

Current trends of host-pathogen relationship in shrimp infectious disease via computational protein-protein interaction: A bibliometric analysis

Protein-protein interactions (PPIs) are essential for understanding cell physiology in normal and pathological conditions, as they might involve in all cellular processes. PPIs have been widely used to elucidate the pathobiology of human and plant diseases. Therefore, they can also be used to unveil the pathobiology of infectious diseases in shrimp, which is one of the high-risk factors influencing the success or failure of shrimp production. PPI network analysis, specifically host-pathogen PPI (HP-PPI), provides insights into the molecular interactions between the shrimp and pathogens. This review quantitatively analyzed the research trends within this field through bibliometric analysis using specific keywords, countries, authors, organizations, journals, and documents. This analysis has screened 206 records from the Scopus database for determining eligibility, resulting in 179 papers that were retrieved for bibliometric analysis. The analysis revealed that China and Thailand were the driving forces behind this specific field of research and frequently collaborated with the United States. Aquaculture and Diseases of Aquatic Organisms were the prominent sources for publications in this field. The main keywords identified included "white spot syndrome virus," "WSSV," and "shrimp." We discovered that studies on HP-PPI are currently quite scarce. As a result, we further discussed the significance of HP-PPI by highlighting various approaches that have been previously adopted. These findings not only emphasize the importance of HP-PPI but also pave the way for future researchers to explore the pathogenesis of infectious diseases in shrimp. By doing so, preventative measures and enhanced treatment strategies can be identified.

Orphan G Protein-Coupled Receptor GPR37 as an Emerging Therapeutic Target

G protein-coupled receptors (GPCRs) are successful druggable targets, making up around 35% of all FDA-approved medications. However, a large number of receptors remain orphaned, with no known endogenous ligand, representing a challenging but untapped area to discover new therapeutic targets. Among orphan GPCRs (oGPCRs) of interest, G protein-coupled receptor 37 (GPR37) is highly expressed in the central nervous system (CNS), particularly in the spinal cord and oligodendrocytes. While its cellular signaling mechanisms and endogenous receptor ligands remain elusive, GPR37 has been implicated in several important neurological conditions, including Parkinson's disease (PD), inflammation, pain, autism, and brain tumors. GPR37 structure, signaling, emerging physiology, and pharmacology are reviewed while integrating a discussion on potential therapeutic indications and opportunities.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Cytokine Landscape in Central Nervous System Metastases

The central nervous system is the location of metastases in more than 40% of patients with lung cancer, breast cancer and melanoma. These metastases are associated with one of the poorest prognoses in advanced cancer patients, mainly due to the lack of effective treatments. In this review, we explore the involvement of cytokines, including interleukins and chemokines, during the development of brain and leptomeningeal metastases from the epithelial-to-mesenchymal cell transition and blood–brain barrier extravasation to the interaction between cancer cells and cells from the brain microenvironment, including astrocytes and microglia. Furthermore, the role of the gut–brain axis on cytokine release during this process will also be addressed.

Protein–Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow

Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.