Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

MDNNSyn: A Multi-Modal Deep Learning Framework for Drug Synergy Prediction

Synergistic drug combination prediction tasks based on the computational models have been widely studied and applied in the cancer field. However, most of models only consider the interactions between drug pairs and specific cell lines, without taking into account the multiple biological relationships of drug-drug and cell line-cell line that also largely affect synergistic mechanisms. To this end, here we propose a multi-modal deep learning framework, termed MDNNSyn, which adequately applies multi-source information and trains multi-modal features to infer potential synergistic drug combinations. MDNNSyn extracts topology modality features by implementing the multi-layer hypergraph neural network on drug synergy hypergraph and constructs semantic modality features through similarity strategy. A multi-modal fusion network layer with gated neural network is then employed for synergy score prediction. MDNNSyn is compared to five classic and state-of-the-art prediction methods on DrugCombDB and Oncology-Screen datasets. The model achieves area under the curve (AUC) scores of 0.8682 and 0.9013 on two datasets, an improvement of 3.70 % and 2.71 % over the second-best model. Case study indicates that MDNNSyn is capable of detecting potential synergistic drug combinations.

Unraveling the intricate physiological processes dysregulated in CHD-affected and Dan-Lou tablet-treated individuals

Coronary heart disease (CHD), a multifactorial cardiovascular condition, arises from the accumulation of atherosclerotic plaque in the coronary arteries, resulting in compromised blood flow to the heart and complications such as angina, myocardial infarction, or heart failure. Addressing global prevalence, risk factors, and genetics is crucial for effective management. The current study aims to identify molecular biomarkers for CHD by scrutinizing the expression patterns of differentially expressed genes (DEGs), utilizing various bioinformatic tools. In this investigation, a total of 24 samples underwent examination using the GEO2R tool. These included eight samples from individuals before treatment (GSM5434123-30), eight samples from patients after Dan-Lou tablet treatment (GSM5434131-38), and eight samples from healthy control subjects (GSM5434139-46). A suite of bioinformatics tools was used to detect enriched genes within the network, namely, Cytoscape (v3.10.1) and Molecular Complex Detection (MCODE). Functional analysis of the DEGs was conducted via clusterProfiler, a R-based package, and ClueGO. 182 and 174 DEGs corresponding to untreated and treated patient sample groups were functionally annotated for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. ARF6 gene dysregulation was implicated in the myeloid cell apoptotic process (GO:0033028), regulation of actin cytoskeleton (hsa:04810), and other vital cellular functions. The myeloid cell apoptotic process (GO:0033028) was also observed to be regulated by the differential expression of the STAT5B gene. Additionally, STAT5B was found to be associated with the regulation of erythrocyte differentiation (GO:0045646). Providing targeted therapy based on the patient's idiosyncratic gene expression profiles could lead to the curing of various disorders in the near future.

Pathogenic mechanisms, diagnostic, and therapeutic potential of microvesicles in diabetes and its complications

Extracellular vesicles (EVs), particularly microvesicles (MVs), have gained significant attention for their role as mediators of intercellular communication in both physiological and pathological contexts, including diabetes mellitus (DM) and its complications. This review provides a comprehensive analysis of the emerging roles of MVs in the pathogenesis of diabetes and associated complications such as nephropathy, retinopathy, cardiomyopathy, and neuropathy. MVs, through their cargo of proteins, lipids, mRNAs, and miRNAs, regulate critical processes like inflammation, oxidative stress, immune responses, and tissue remodeling, all of which contribute to the progression of diabetes and its complications. We examine the molecular mechanisms underlying MVs' involvement in these pathological processes and discuss their potential as biomarkers and therapeutic tools, particularly for drug delivery. Despite promising evidence, challenges remain in isolating and characterizing MVs, understanding their molecular mechanisms, and validating them for clinical use. Advanced techniques such as single-cell RNA sequencing and proteomics are required to gain deeper insights. Improved isolation and purification methods are essential for translating MVs into clinical applications, with potential to develop novel diagnostic and therapeutic strategies to improve patient outcomes in diabetes.

A comprehensive survey of drug-target interaction analysis in allopathy and siddha medicine

Effective drug delivery is the cornerstone of modern healthcare, ensuring therapeutic compounds reach their intended targets efficiently. This paper explores the potential of personalized and holistic healthcare, driven by the synergy between traditional and allopathic medicine systems, with a specific focus on the vast reservoir of medicinal compounds found in plants rooted in the historical legacy of traditional medicine. Motivated by the desire to unlock the therapeutic potential of medicinal plants and bridge the gap between traditional and allopathic medicine, this survey delves into in-silico computational approaches for studying Drug-Target Interactions (DTI) within the contexts of allopathy and siddha medicine. The contributions of this survey are multifaceted: it offers a comprehensive overview of in-silico methods for DTI analysis in both systems, identifies common challenges in DTI studies, provides insights into future directions to advance DTI analysis, and includes a comparative analysis of DTI in allopathy and siddha medicine. The findings of this survey highlight the pivotal role of in-silico computational approaches in advancing drug research and development in both allopathy and siddha medicine, emphasizing the importance of integrating these methods to drive the future of personalized healthcare.

Structural insights into CXCR4 modulation and oligomerization

Activation of the chemokine receptor CXCR4 by its chemokine ligand CXCL12 regulates diverse cellular processes. Previously reported crystal structures of CXCR4 revealed the architecture of an inactive, homodimeric receptor. However, many structural aspects of CXCR4 remain poorly understood. Here, we use cryo-electron microscopy to investigate various modes of human CXCR4 regulation. CXCL12 activates CXCR4 by inserting its N terminus deep into the CXCR4 orthosteric pocket. The binding of US Food and Drug Administration-approved antagonist AMD3100 is stabilized by electrostatic interactions with acidic residues in the seven-transmembrane-helix bundle. A potent antibody blocker, REGN7663, binds across the extracellular face of CXCR4 and inserts its complementarity-determining region H3 loop into the orthosteric pocket. Trimeric and tetrameric structures of CXCR4 reveal modes of G-protein-coupled receptor oligomerization. We show that CXCR4 adopts distinct subunit conformations in trimeric and tetrameric assemblies, highlighting how oligomerization could allosterically regulate chemokine receptor function.

Aryl hydrocarbon receptor dynamics in esophageal squamous cell carcinoma: From immune modulation to therapeutic opportunities

Esophageal squamous cell carcinoma (ESCC) is a substantial global health burden. Immune escape mechanisms are important in ESCC progression, enabling cancer cells to escape the surveillance of the host immune system. One key player in this process is the Aryl Hydrocarbon Receptor (AhR), which influences multiple cellular processes, including proliferation, differentiation, metabolism, and immune regulation. Dysregulated AhR signaling participates in ESCC development by stimulating carcinogenesis, epithelial-mesenchymal transition, and immune escape. Targeting AhR signaling is a potential therapeutic approach for ESCC, with AhR ligands showing efficacy in preclinical studies. Additionally, modification of AhR ligands and combination therapies present new opportunities for therapeutic intervention. This review aims to address the knowledge gap related to the role of AhR signaling in ESCC pathogenesis and immune escape.

Circular RNAs: Novel Players in Cancer Mechanisms and Therapeutic Strategies

Circular RNAs (circRNAs) are a novel class of noncoding RNAs that have emerged as pivotal players in gene regulation. Our understanding of circRNAs has greatly expanded over the last decade, with studies elucidating their biology and exploring their therapeutic applications. In this review, we provide an overview of the current understanding of circRNA biogenesis, outline their mechanisms of action in cancer, and assess their clinical potential as biomarkers. Furthermore, we discuss circRNAs as a potential therapeutic strategy, including recent advances in circRNA production and translation, along with proof-of-concept preclinical studies of cancer vaccines.

Ligand discovery by activity-based protein profiling

Genomic technologies have led to massive gains in our understanding of human gene function and disease relevance. Chemical biologists are a primary beneficiary of this information, which can guide the prioritization of proteins for chemical probe and drug development. The vast functional and structural diversity of disease-relevant proteins, however, presents challenges for conventional small molecule screening libraries and assay development that in turn raise questions about the broader "druggability" of the human proteome. Here, we posit that activity-based protein profiling (ABPP), by generating global maps of small molecule-protein interactions in native biological systems, is well positioned to address major obstacles in human biology-guided chemical probe and drug discovery. We will support this viewpoint with case studies highlighting a range of small molecule mechanisms illuminated by ABPP that include the disruption and stabilization of biomolecular (protein-protein/nucleic acid) interactions and underscore allostery as a rich source of chemical tools for historically "undruggable" protein classes.

The role of cornulin (CRNN) in the progression of cutaneous squamous cell carcinoma involving AKT activation in SCL-1

Cutaneous squamous cell carcinoma (cSCC) is the second most common non-melanoma skin cancer that has been on the rise in recent times, particularly among older individuals. Cornulin (CRNN) is increasingly recognized as an oncogene involved in developing various types of tumors. However, the precise contribution to cSCC remains unclear. Our study observed a significant increase in CRNN expression in cSCC samples compared to healthy skin. CRNN expression in the SCL-1 cell line derived from cSCC was reduced, leading to a halt in cell growth during the transition from the G1 phase to the S phase. This reduction inhibits cell division, promotes cell death, and decreases cell invasion and migration. CRNN overexpression has been found to enhance cell growth and prevent cells from undergoing natural cell death, and the cancer-promoting effects of CRNN are linked to AKT activation. Using a mouse xenograft model, we demonstrated that the inhibition of CRNN led to a decline in cSCC tumor growth in a living organism, providing evidence of CRNN's involvement in cSCC occurrence and development. This study establishes a foundation for evaluating the effectiveness of CRNN in treating cSCC, enabling further investigation in this area.

A U-shaped association between selenium intake and cancer risk

While selenium is a cofactor of several antioxidant enzymes against cancer and is essential for human health, its excess intake may also be harmful. Though a safe intake of selenium has recently been recommended, it is not well understood in the Asian population. We aimed to determine the association between dietary intake of selenium and cancer risk in a case-control study of 3758 incident cancer cases (i.e., stomach, colon, rectum, lung cancers, and other sites) and 2929 control subjects in Vietnam. Daily intake of selenium was derived from a semiquantitative food frequency questionnaire. The unconditional logistic regression model was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between selenium intake and cancer risk. We observed a U-shaped association between selenium intake and cancer risk. A safe intake ranged from 110.8 to 124.4 µg/day (mean 117.8 µg/day). Compared to individuals with the safe intake of selenium, individuals with the lowest intake (i.e., 27.8-77.2 µg/day) were associated with an increased risk of cancer (OR = 3.78, 95% CI 2.89-4.95) and those with the highest intake (169.1-331.7 µg/day) also had an increased cancer risk (OR = 1.86, 95% CI 1.45-2.39). A U-shaped pattern of association between selenium intake and cancer risk was stronger among participants with body mass index (BMI) < 23 kg/m2 and never smokers than BMI ≥ 23 kg/m2 and ever smokers (P'sheterogeneity = 0.003 and 0.021, respectively) but found in both never and ever-drinkers of alcohol (Pheterogeneity = 0.001). A U-shaped association between selenium intake and cancer risk was seen in cancer sites of the stomach, colon, rectum, and lung cancers. In summary, we found a U-shaped association between selenium intake and cancer risk and a safe selenium intake (mean: 117.8 µg/day) in the Vietnamese population. Further mechanistic investigation is warranted to understand better a U-shaped association between selenium intake and cancer risk.

Exosomal transcript cargo and functional correlation with HNSCC patients' survival

BACKGROUND

HPV status in a subset of HNSCC is linked with distinct treatment outcomes. Present investigation aims to elucidate the distinct clinicopathological features of HPV-positive and HPV-negative HNSCC and investigate their association with the HNSCC patient survival.

MATERIALS AND METHODS

The total RNA of exosomes from HPV-positive (93VU147T) and HPV-negative (OCT-1) HNSCC cells was isolated, and the transcripts were estimated using Illumina HiSeq X. The expression of altered transcripts and their clinical relevance were further analyzed using publicly available cancer transcriptome data from The Cancer Genome Atlas (TCGA).

RESULTS

Transcriptomic analyses identified 3785 differentially exported transcripts (DETs) in HPV-positive exosomes compared to HPV-negative exosomes. DETs that regulate the protein machinery, cellular redox potential, and various neurological disorder-related pathways were over-represented in HPV-positive exosomes. TCGA database revealed the clinical relevance of altered transcripts. Among commonly exported abundant transcripts, SGK1 and MAD1L1 showed high expression, which has been correlated with poor survival in HNSCC patients. In the top 20 DETs of HPV-negative exosomes, high expression of FADS3, SGK3, and TESK2 correlated with poor survival of the HNSCC patients in the TCGA database.

CONCLUSION

Overall, our study demonstrates that HPV-positive and HPV-negative cells' exosomes carried differential transcripts cargo that may be related to pathways associated with neurological disorders. Additionally, the altered transcripts identified have clinical relevance, correlating with patient survival in HNSCC, thereby highlighting their potential as biomarkers and as therapeutic targets.

Gut microbiome influences efficacy of Endostatin combined with PD-1 blockade against colorectal cancer

The combination of anti-angiogenic drugs and immune checkpoint inhibitors (ICIs) in the treatment of tumors is emerging as a way to improve ICIs-resistant tumor therapy. In addition, gut microbes (GMs) are involved in angiogenesis in the tumor microenvironment and are also associated with the antitumor function of immune checkpoint inhibitors. However, it is unclear whether gut microbes have a role in anti-tumor function in the combination of anti-angiogenic drugs and immune checkpoint inhibitors for cancer treatment. Endostatin, an angiogenesis inhibitor, has been widely used as an antiangiogenic therapy for cancer. We showed that combined therapy with an adenovirus encoding human endostatin, named Ad-E, and PD-1 blockade dramatically abrogated MC38 tumor growth. The structure of intestinal microbes in mice was changed after combination treatment. We found that the antitumor function of combination therapy was inhibited after the elimination of intestinal microbes. In mice with depleted microbiota, oral gavage of Bacteroides fragilis salvaged the antitumor effects of combination Ad-E and αPD-1 monoclonal antibody (mAb) to a certain extent. Further, Bacteroides fragilis could improve CD3+T cells, NK cells, and IFNγ+CD8+ T cells in the tumor microenvironment to inhibit tumor growth. Besides, Bacteroides fragilis might restore antitumor function by down-regulating isobutyric acid (IBA). Our results suggested that GMs may be involved in the combination of Ad-E and αPD-1 mAb for cancer treatment, which has oncological implications for tumor growth dynamics and cancer immune surveillance.

Ensemble decision of local similarity indices on the biological network for disease related gene prediction

Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.

microRNA 21 and long non-coding RNAs interplays underlie cancer pathophysiology: A narrative review

Non-coding RNAs (ncRNAs) are a diverse group of functional RNA molecules that lack the ability to code for proteins. Despite missing this traditional role, ncRNAs have emerged as crucial regulators of various biological processes and have been implicated in the development and progression of many diseases, including cancer. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two prominent classes of ncRNAs that have emerged as key players in cancer pathophysiology. In particular, miR-21 has been reported to exhibit oncogenic roles in various forms of human cancer, including prostate, breast, lung, and colorectal cancer. In this context, miR-21 overexpression is closely associated with tumor proliferation, growth, invasion, angiogenesis, and chemoresistance, whereas miR-21 inactivation is linked to the regression of most tumor-related processes. Accordingly, miR-21 is a crucial modulator of various canonical oncogenic pathways such as PTEN/PI3K/Akt, Wnt/β-catenin, STAT, p53, MMP2, and MMP9. Moreover, interplays between lncRNA and miRNA further complicate the regulatory mechanisms underlying tumor development and progression. In this regard, several lncRNAs have been found to interact with miR-21 and, by functioning as competitive endogenous RNAs (ceRNAs) or miRNA sponges, can modulate cancer tumorigenesis. This work presents and discusses recent findings highlighting the roles and pathophysiological implications of the miR-21-lncRNA regulatory axis in cancer occurrence, development, and progression. The data collected indicate that specific lncRNAs, such as MEG3, CASC2, and GAS5, are strongly associated with miR-21 in various types of cancer, including gastric, cervical, lung, and glioma. Indeed, these lncRNAs are well-known tumor suppressors and are commonly downregulated in different types of tumors. Conversely, by modulating various mechanisms and oncogenic signaling pathways, their overexpression has been linked with preventing tumor formation and development. This review highlights the significance of these regulatory pathways in cancer and their potential for use in cancer therapy as diagnostic and prognostic markers.

Down-regulation of RTEL1 Improves M1/M2 Macrophage Polarization by Promoting SFRP2 in Fibroblasts-derived Exosomes to Alleviate COPD

Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease worldwide. Macrophage polarization plays a substantial role in the pathogenesis of COPD. This study is aimed to explore the regulatory mechanism of regulator of telomere elongation 1 (RTEL1) in COPD. COPD model mouse was conducted by cigarette smoke (CS). The pathological features of lung in mice were observed by histological staining. After extracting exosomes, macrophages were co-cultured with fibroblasts-derived exosomes. Then, the effects of RTEL1 and exosomal secreted frizzled-related protein 2 (SFRP2) on macrophage proliferation, inflammation, apoptosis, and M1, M2 macrophage polarization (iNOS and CD206) were evaluated by cell counting kit-8, EdU assay, enzyme-linked immuno sorbent assay, and western blotting, respectively. CS-induced COPD model mouse was successfully constructed. Through in vitro experiments, knockdown of RTEL1 inhibited macrophage proliferation, inflammation (MMP9, IL-1β and TNF-α), and promoted apoptosis (Bax, cleaved-caspase3, Bcl-2) in CS extract-induced lung fibroblasts. Meanwhile, RTEL1 knockdown promoted M1 and suppressed M2 macrophage polarization in COPD. Additionally, silencing SFRP2 in fibroblasts-derived exosomes reversed the effects of RTEL1 knockdown on proliferation, inflammation, apoptosis, and M1, M2 macrophage polarization. Collectively, down-regulation of RTEL1 improved M1/M2 macrophage polarization by promoting SFRP2 in fibroblasts-derived exosomes to alleviate CS-induced COPD.

Investigational CXCR4 inhibitors in early phase development for the treatment of hematological malignancies

INTRODUCTION

CXCR4/CXCL12 axis regulates cell proliferation, survival, and differentiation, as well as the homing and mobilization of hematopoietic stem cells (HSCs) from bone marrow niches to the peripheral blood. Furthermore, CXCR4 and CXCL12 are key mediators of cross-talk between hematological malignancies and their microenvironments. CXCR4 overexpression drives disease progression, boosts tumor cell survival, and promotes chemoresistance, leading to poor prognosis.

AREAS COVERED

In light of these discoveries, scientific investigations, and clinical trials have underscored the therapeutic promise found in small-molecule antagonists like plerixafor, peptides/peptidomimetics, such as BKT140, monoclonal antibodies like PF-06747143 and ulocuplumab, as well as microRNAs. Their efficacy is evident in reducing tumor burden, inducing apoptosis and sensitizing malignant cells to conventional chemotherapies. This overview delves into the pathogenic role of the CXC4/CXCL12 axis in hematological neoplasms and examines the clinical application of key CXCR4 antagonists.

EXPERT OPINION

The information collectively emphasizes the potential of CXCR4 antagonists as a therapeutic strategy for hematologic malignancies, showcasing advancements in preclinical and clinical studies. As these therapeutic strategies progress through clinical trials, their potential to reshape the prognosis of hematologic malignancies will become increasingly apparent.

Cornulin as a Key Diagnostic and Prognostic Biomarker in Cancers of the Squamous Epithelium

The prevalence of squamous cell carcinoma is increasing, and efforts that aid in an early and accurate diagnosis are crucial to improve clinical outcomes for patients. Cornulin, a squamous epithelium-specific protein, has recently garnered attention due to its implications in the progression of squamous cell carcinoma developed in several tissues. As an epidermal differentiation marker, it is involved in skin anchoring, regulating cellular proliferation, and is a putative tumor suppressor. The physiologically healthy squamous epithelium displays a considerable level of Cornulin, whereas squamous cell carcinomas have marked downregulation, suggesting that Cornulin expression levels can be utilized for the early detection and follow-up on the progression of these types of cancer. Cornulin's expression patterns in cervical cancer have been examined, and findings support the stepwise downregulation of Cornulin levels that accompanies the progression to neoplasia in the cervix. Additional studies documented a similar trend in expression in other types of cancer, such as cutaneous, esophageal, and oropharyngeal squamous cell carcinomas. The consistent and predictable pattern of Cornulin expression across several squamous cell carcinomas and its correlation with key clinicopathological parameters make it a reliable biomarker for assessing the transformation and progression events in the squamous epithelium, thus potentially contributing to the early detection, definitive diagnosis, and more favorable prognosis for these cancer patients.

Plectin: Dual Participation in Tumor Progression

The plectin gene can encode a cytoskeletal linking protein, plectin, known for its interaction with three critical components of the cellular cytoskeleton: intermediate filaments, microtubules, and actin filaments. In recent years, more and more studies have reported that plectin is closely related to tumorigenesis and development, exhibiting both tumor-suppressive and tumor-promoting functions. Here, we first introduce the molecular structure and function of plectin, and then we summarize the current understanding of the crucial role of plectin in cancer progression. Finally, we also discuss the possible reasons for the different roles of plectin expression in various types of cancer and highlight the double-edged sword role of plectin in tumor progression. The review aims to deepen the comprehensive understanding of plectin's role in cancer and further help to develop novel therapeutic strategies and drug targets.

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

BACKGROUND

The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interaction (DTI). However, existing DTI prediction models typically rely on a limited amount of data to extract drug and target features, potentially affecting the comprehensiveness and robustness of features. In addition, although multiple networks are used for DTI prediction, the integration of heterogeneous information often involves simplistic aggregation and attention mechanisms, which may impose certain limitations.

RESULTS

MSH-DTI, a deep learning model for predicting drug-target interactions, is proposed in this paper. The model uses self-supervised learning methods to obtain drug and target structure features. A Heterogeneous Interaction-enhanced Feature Fusion Module is designed for multi-graph construction, and the graph convolutional networks are used to extract node features. With the help of an attention mechanism, the model focuses on the important parts of different features for prediction. Experimental results show that the AUROC and AUPR of MSH-DTI are 0.9620 and 0.9605 respectively, outperforming other models on the DTINet dataset.

CONCLUSION

The proposed MSH-DTI is a helpful tool to discover drug-target interactions, which is also validated through case studies in predicting new DTIs.

Synthesis and characterization of new electrospun medical scaffold-based modified cellulose nanofiber and bioactive natural propolis for potential wound dressing applications

Recently, the design of polymer nanofibers using the electrospinning process has attracted much interest. Particularly the use of natural polymers has promoted many advantages in their biomedical applications. However, the combination of multiple natural polymers remains a great challenge in terms of electrospun production and applied performance. From this perspective, the current investigation highlights the study of the preparation of electrospun nanomaterial scaffolds based on combined natural polymers for improved wound healing performance. First, we have synthesized a crosslinked polymer by reacting microcrystalline cellulose (MC) and chitosan (CS) biopolymer via the intermediate of citric acid as a crosslinking agent. Then a natural propolis biomolecule was incorporated into the polymer network. Different MC/CS blend ratios of 90/10 and 70/30 were then used and various machine parameters were optimized to obtain nanofiber scaffolds with excellent strength and structures. SEM, IR, physicochemical, mechanical, and morpho-logical characterization were then performed. SEM evaluation revealed homogeneous and bead-free nanofibrous structures, with well-defined morphology and a random deposition that could accurately mimic the extracellular matrix of native skin. The calculated average nanofiber diameters for the MC/CS blend ratios at 90/10 and 70/30 were 431.4 and 441.2 nm, respectively. The results showed that when the chitosan amount increased, larger nanofibers with narrow diameter distribution appeared. The prepared nanomaterials had a significant and close water vapor permeability of about 1735.12 and 1698.52 g per m per day for the two blend ratios of 90/10 and 70/30, respectively. The examination of swelling behavior revealed a noteworthy enhancement in hydrophilicity, a necessary attribute for improved healing efficacy. FT-IR analysis confirmed the success and the stability of the chemical crosslinking reaction between the two biopolymers before nanofiber conception. Excellent mechanical properties were acquired, based on the chitosan content. Both developed nanofiber scaffolds exhibited high tensile strength and Young's modulus values. The incorporation of 30% chitosan versus 10% results in an increase in tensile strength of 11% and 14% in Young's modulus. Therefore, we could adjust the different mechanical properties simply by varying the mixing rate of the electrospun polymers. Using epithelial HepG2 cells, viability and kinetic cell adhesion assays were assessed to obtain biological evaluation. No cytotoxicity was observed and good cytocompatibility was confirmed. Functionalized nanofiber biomaterials with different MC/CS ratios substantiated significant bactericidal effectiveness against Gram-positive and Gram-negative bacterial culture strains. The novel functional electrospun wound dressing scaffold demonstrated effective and promising biomedical performance, healing both acute and chronic wounds.

Green mediated synthesis of cerium oxide nanoparticles by using Oroxylum indicum for evaluation of catalytic and biomedical activity

The present perspective emphasizes the green synthesis of CeO2-NPs using Oroxylum indicum fruit extract. The synthesized NPs were characterized utilizing analytical techniques, including FT-IR, UV-vis, XRD, SEM-EDX, and VSM. Of them, XRD analysis ratifies the cubic fluorite crystal structure along with a particle size of 23.58 nm. EDX results support the presence of cerium and oxygen in a proper ratio. The surface morphology of NPs, however, was scrutinized using SEM. The lower IC50 value (20.8 μg mL-1) of NPs compared to the reference substance, ascorbic acid (33.2 μg mL-1), demonstrates CeO2-NPs to be a compatible antioxidant. Moreover, the drug-releasing capability of CeO2-NPs was a buffer pH-dependent parameter. The acidic pH solution was 20.5%, while the basic pH solution was 16.9%. The drug-releasing capability was analyzed using the Higuchi model and Korsmeyer-Peppas kinetics. The values of the determination coefficient (R 2) were found to be 0.9944 and 0.9834, respectively. The photocatalytic activity of CeO2-NPs was evaluated, considering methylene blue as a model dye. The degradation percentage was attained up to 56.77% after it had been exposed for 150 min. Apart from this, the synthesized NPs were screened against two fungus species, Bipolaris sorokiniana and Fusarium. The percentage of growth was measured at 56% and 49%, respectively.

A defective splicing machinery promotes senescence through MDM4 alternative splicing

Defects in the splicing machinery are implicated in various diseases, including cancer. We observed a general reduction in the expression of spliceosome components and splicing regulators in human cell lines undergoing replicative, stress-induced, and telomere uncapping-induced senescence. Supporting the view that defective splicing contributes to senescence, splicing inhibitors herboxidiene, and pladienolide B induced senescence in normal and cancer cell lines. Furthermore, depleting individual spliceosome components also promoted senescence. All senescence types were associated with an alternative splicing transition from the MDM4-FL variant to MDM4-S. The MDM4 splicing shift was reproduced when splicing was inhibited, and spliceosome components were depleted. While decreasing the level of endogenous MDM4 promoted senescence and cell survival independently of the MDM4-S expression status, cell survival was also improved by increasing MDM4-S. Overall, our work establishes that splicing defects modulate the alternative splicing of MDM4 to promote senescence and cell survival.

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications of GD1. Although imaging studies are the gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, are needed for the rapid management of skeletal complications. This study aimed to identify potential protein biomarkers capable of predicting the early diagnosis of bone skeletal complications in GD1 patients using artificial intelligence. An in silico study was performed using the novel Therapeutic Performance Mapping System methodology to construct mathematical models of GD1-associated complications at the protein level. Pathophysiological characterization was performed before modeling, and a data science strategy was applied to the predicted protein activity for each protein in the models to identify classifiers. Statistical criteria were used to prioritize the most promising candidates, and 18 candidates were identified. Among them, PDGFB, IL1R2, PTH and CCL3 (MIP-1α) were highlighted due to their ease of measurement in blood. This study proposes a validated novel tool to discover new protein biomarkers to support clinician decision-making in an area where medical needs have not yet been met. However, confirming the results using in vitro and/or in vivo studies is necessary.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl β-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 μg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

Thrombosis and Aging: Fibrin Clot Properties and Oxidative Stress

Significance: Aging is a complex process associated with an increased risk of many diseases, including thrombosis. This review summarizes age-related prothrombotic mechanisms in clinical settings of thromboembolism, focusing on the role of fibrin structure and function modified by oxidative stress. Recent Advances: Aging affects blood coagulation and fibrinolysis via multiple mechanisms, including enhanced oxidative stress, with an imbalance in the oxidant/antioxidant mechanisms, leading to loss of function and accumulation of oxidized proteins, including fibrinogen. Age-related prothrombotic alterations are multifactorial involving enhanced platelet activation, endothelial dysfunction, and changes in coagulation factors and inhibitors. Formation of more compact fibrin clot networks displaying impaired susceptibility to fibrinolysis represents a novel mechanism, which might contribute to atherothrombosis and venous thrombosis. Alterations to fibrin clot structure/function are at least in part modulated by post-translational modifications of fibrinogen and other proteins involved in thrombus formation, with a major impact of carbonylation. Fibrin clot properties are also involved in the efficacy and safety of therapy with oral anticoagulants, statins, and/or aspirin. Critical Issues: Since a prothrombotic state is observed in very elderly individuals free of diseases associated with thromboembolism, the actual role of activated blood coagulation in health remains elusive. It is unclear to what extent oxidative modifications of coagulation and fibrinolytic proteins, in particular fibrinogen, contribute to a prothrombotic state in healthy aging. Future Directions: Ongoing studies will show whether novel therapies that may alter oxidative stress and fibrin characteristics are beneficial to prevent atherosclerosis and thromboembolic events associated with aging.

The Role of Cilia and the Complex Genetics of Congenital Heart Disease

Congenital heart disease (CHD) can affect up to 1% of live births, and despite abundant evidence of a genetic etiology, the genetic landscape of CHD is still not well understood. A large-scale mouse chemical mutagenesis screen for mutations causing CHD yielded a preponderance of cilia-related genes, pointing to a central role for cilia in CHD pathogenesis. The genes uncovered by the screen included genes that regulate ciliogenesis and cilia-transduced cell signaling as well as many that mediate endocytic trafficking, a cell process critical for both ciliogenesis and cell signaling. The clinical relevance of these findings is supported by whole-exome sequencing analysis of CHD patients that showed enrichment for pathogenic variants in ciliome genes. Surprisingly, among the ciliome CHD genes recovered were many that encoded direct protein-protein interactors. Assembly of the CHD genes into a protein-protein interaction network yielded a tight interactome that suggested this protein-protein interaction may have functional importance and that its disruption could contribute to the pathogenesis of CHD. In light of these and other findings, we propose that an interactome enriched for ciliome genes may provide the genomic context for the complex genetics of CHD and its often-observed incomplete penetrance and variable expressivity.

Circular RNAs and the JAK/STAT pathway: New frontiers in cancer therapeutics

Circular RNAs, known as circRNAs, have drawn more attention to cancer biology in the last few years. Novel functions of circRNAs in cancer therapy open promising prospects for personalized medicine. This review focuses on the molecular properties and potential of circRNAs as biomarkers or therapeutic targets in cancer treatment. Unique properties of circular RNAs associated with a circular form provide stability and resilience to RNA exonuclease degradation. Circular RNAs' most important characteristic is that they are involved in the JAK/STAT pathway associated with oncogenesis. Notably, their deregulation has been reported in multiple carcinomas due to involvement in JAK/STAT signaling cascade modulation. Increased knowledge about circRNAs' interaction with the JAK/STAT pathway leads to the emergence of new possibilities for targeted cancer therapy. In addition, since circRNAs demonstrate tissue-relatedness of expression, they may be a reliable biomarker for predicting and diagnosing cancer. With the development of new technologies for targeting circRNAs, novel therapeutics can be produced that offer more personalized cancer treatment options based on the nature of the patient. The present review explores the exciting prospects of circRNAs for transforming cancer treatment into personalized medicine. It describes the current understanding of circRNA biology, its relationship to tumorigenesis, and possible targeting methods.

Unraveling the role of physicochemical differences in predicting protein-protein interactions

The ability to accurately predict protein-protein interactions is critically important for understanding major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes the concept of physicochemical similarity to determine which interactions will most likely occur. In our approach, the physicochemical features of proteins are extracted using bioinformatics tools for different organisms. Then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It was found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid composition (the frequency of specific amino acid pairs in a protein sequence). While current approaches often overlook the specificity of protein-protein interactions with different organisms, our method yields context-specific features that determine protein-protein interactions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators, as well as to the barnase-barstar complex, demonstrating the method's versatility across different biological systems. Our approach can be applied to predict protein-protein interactions in any biological system, providing an important tool for investigating complex biological processes' mechanisms.

Association between CYP1A2 gene variants -163 C/A (rs762551) and -3860 G/A (rs2069514) and bladder cancer susceptibility

BACKGROUND

Bladder cancer (BLCA) poses a significant global health challenge due to its high incidence, poor prognosis, and limited treatment options.

AIMS AND OBJECTIVES

This study aims to investigate the association between two specific polymorphisms, CYP1A2-163 C/A and CYP1A2-3860G/A, within the Cytochrome P450 1A2 (CYP1A2) gene and susceptibility to BLCA.

METHODS

The study employed a case-control design, genotyping 340 individuals using Polymerase Chain Reaction-High-Resolution Melting Curve (PCR-HRM). Various genetic models were applied to evaluate allele and genotype frequencies. Genetic linkage analysis was facilitated using R packages.

RESULTS

The study reveals a significant association with the - 163 C/A allele, particularly in the additive model. Odds ratio (OR) analysis links CYP1A2-163 C/A (rs762551) and CYP1A2-3860G/A(rs2069514) polymorphisms to BLCA susceptibility. The rs762551 C/A genotype is prevalent in 55% of BLCA cases and exhibits an OR of 2.21. The A/A genotype has an OR of 1.54. Regarding CYP1A2-3860G/A, the G/A genotype has an OR of 1.54, and the A/A genotype has an OR of 2.08. Haplotype analysis shows a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%. This study underscores associations between CYP1A2 gene variants, particularly rs762551 (CYP1A2-163 C/A), and an increased susceptibility to BLCA. Haplotype analysis of 340 individuals reveals a predominant C-C haplotype at 38.2%, followed by a C-A haplotype at 54.7%, and a less frequent A-A haplotype at 7.1%.

CONCLUSION

In conclusion, the - 163 C/A allele, C/A genotype of rs762551, and G/A genotype of rs2069514 emerge as potential genetic markers associated with elevated BLCA risk.

Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

Advances on Delivery System of Active Ingredients of Dried Toad Skin and Toad Venom

Dried toad skin (TS) and toad venom (TV) are the dried skin of the Bufo bufo gargarizans Cantor and the Bufo melanostictus Schneider, which remove the internal organs and the white secretions of the skin and retroauricular glands. Since 2005, cinobufacini preparations have been approved by the State Food and Drug Administration for use as adjuvant therapies in the treatment of various advanced cancers. Meanwhile, bufalenolides has been identified as the main component of TS/TV, exhibiting antitumor activity, inducing apoptosis of cancer cells and inhibiting cancer cell proliferation or metastasis through a variety of signaling pathways. However, clinical agents frequently face limitations such as inherent toxicity at high concentrations and insufficient tumor targeting. Additionally, the development and utilization of these active ingredients are hindered by poor water solubility, low bioavailability, and rapid clearance from the bloodstream. To address these challenges, the design of a targeted drug delivery system (TDDS) aims to enhance drug bioavailability, improve targeting within the body, increase drug efficacy, and reduce adverse reactions. This article reviews the TDDS for TS/TV, and their active components, including passive, active, and stimuli-responsive TDDS, to provide a reference for advancing their clinical development and use.

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph

BACKGROUND

Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery.

RESULTS

We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs.

CONCLUSIONS

GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

CD38/NAD+ glycohydrolase and associated antigens in chronic lymphocytic leukaemia: From interconnected signalling pathways to therapeutic strategies

Chronic lymphocytic leukaemia (CLL) is a heterogenous disease characterized by the accumulation of neoplastic CD5+/CD19+ B lymphocytes. The spreading of the leukaemia relies on the CLL cell's ability to survive in the blood and migrate to and proliferate within the bone marrow and lymphoid tissues. Some patients with CLL are either refractory to the currently available therapies or relapse after treatment; this emphasizes the need for novel therapeutic strategies that improving clinical responses and overcome drug resistance. CD38 is a marker of a poor prognosis and governs a set of survival, proliferation and migration signals that contribute to the pathophysiology of CLL. The literature data evidence a spatiotemporal association between the cell surface expression of CD38 and that of other CLL antigens, such as the B-cell receptor (BCR), CD19, CD26, CD44, the integrin very late antigen 4 (VLA4), the chemokine receptor CXCR4, the vascular endothelial growth factor receptor-2 (VEGF-R2), and the neutrophil gelatinase-associated lipocalin receptor (NGAL-R). Most of these proteins contribute to CLL cell survival, proliferation and trafficking, and cooperate with CD38 in multilayered signal transduction processes. In general, these antigens have already been validated as therapeutic targets in cancer, and a broad repertoire of specific monoclonal antibodies and derivatives are available. Here, we review the state of the art in this field and examine the therapeutic opportunities for cotargeting CD38 and its partners in CLL, e.g. by designing novel bi-/trispecific antibodies.

Discovery of a Covalent Inhibitor That Overcame Resistance to Venetoclax in AML Cells Overexpressing BFL-1

Clinical and biological studies have shown that overexpression of BFL-1 is one contributing factor to venetoclax resistance. The resistance might be overcome by a potent BFL-1 inhibitor, but such an inhibitor is rare. In this study, we show that 56, featuring an acrylamide moiety, inhibited the BFL-1/BID interaction with a Ki value of 105 nM. More interestingly, 56 formed an irreversible conjugation adduct at the C55 residue of BFL-1. 56 was a selective BFL-1 inhibitor, and its MCL-1 binding affinity was 10-fold weaker, while it did not bind BCL-2 and BCL-xL. Mechanistic studies showed that 56 overcame venetoclax resistance in isogenic AML cell lines MOLM-13-OE and MV4-11-OE, which both overexpressed BFL-1. More importantly, 56 and venetoclax combination promoted stronger apoptosis induction than either single agent. Collectively, our data show that 56 overcame resistance to venetoclax in AML cells overexpressing BFL-1. These attributes make 56 a promising candidate for future optimization.

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment

AIM

To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).

MATERIALS AND METHODS

GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.

RESULTS

In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.

CONCLUSIONS

New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

Mapping the influence of hydrocarbons mixture on molecular mechanisms, involved in breast and lung neoplasms: in silico toxicogenomic data-mining

BACKGROUND

Exposure to chemical mixtures inherent in air pollution, has been shown to be associated with the risk of breast and lung cancers. However, studies on the molecular mechanisms of exposure to a mixture of these pollutants, such as hydrocarbons, in the development of breast and lung cancers are scarce. We utilized in silico toxicogenomic analysis to elucidate the molecular pathways linked to both cancers that are influenced by exposure to a mixture of selected hydrocarbons. The Comparative Toxicogenomics Database and Cytoscape software were used for data mining and visualization.

RESULTS

Twenty-five hydrocarbons, common in air pollution with carcinogenicity classification of 1 A/B or 2 (known/presumed or suspected human carcinogen), were divided into three groups: alkanes and alkenes, halogenated hydrocarbons, and polyaromatic hydrocarbons. The in silico data-mining revealed 87 and 44 genes commonly interacted with most of the investigated hydrocarbons are linked to breast and lung cancer, respectively. The dominant interactions among the common genes are co-expression, physical interaction, genetic interaction, co-localization, and interaction in shared protein domains. Among these genes, only 16 are common in the development of both cancers. Benzo(a)pyrene and tetrachlorodibenzodioxin interacted with all 16 genes. The molecular pathways potentially affected by the investigated hydrocarbons include aryl hydrocarbon receptor, chemical carcinogenesis, ferroptosis, fluid shear stress and atherosclerosis, interleukin 17 signaling pathway, lipid and atherosclerosis, NRF2 pathway, and oxidative stress response.

CONCLUSIONS

Within the inherent limitations of in silico toxicogenomics tools, we elucidated the molecular pathways associated with breast and lung cancer development potentially affected by hydrocarbons mixture. Our findings indicate adaptive responses to oxidative stress and inflammatory damages are instrumental in the development of both cancers. Additionally, ferroptosis-a non-apoptotic programmed cell death driven by lipid peroxidation and iron homeostasis-was identified as a new player in these responses. Finally, AHR potential involvement in modulating IL-8, a critical gene that mediates breast cancer invasion and metastasis to the lungs, was also highlighted. A deeper understanding of the interplay between genes associated with these pathways, and other survival signaling pathways identified in this study, will provide invaluable knowledge in assessing the risk of inhalation exposure to hydrocarbons mixture. The findings offer insights into future in vivo and in vitro laboratory investigations that focus on inhalation exposure to the hydrocarbons mixture.

PhosNetVis: a web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/.

Methods to Investigate the Secretome of Senescent Cells

The word "secretome" was first used to describe the proteins that cells secrete under different circumstances; however, recent studies have proven the existence of other molecules such as RNA and chemical compounds in the secretome. The study of secretome has significance for the diagnosis and treatment of disease as it provides insight into cellular functions, including immune responses, development, and homeostasis. By halting cell division, cellular senescence plays a role in both cancer defense and aging by secreting substances known as senescence-associated secretory phenotypes (SASP). A variety of techniques could be used to analyze the secretome: protein-based approaches like mass spectrometry and protein microarrays, nucleic acid-based methods like RNA sequencing, microarrays, and in silico prediction. Each method offers unique advantages and limitations in characterizing secreted molecules. Top-down and bottom-up strategies for thorough secretome analysis are became possible by mass spectrometry. Understanding cellular function, disease causes, and proper treatment targets is aided by these methodologies. Their approaches, benefits, and drawbacks will all be discussed in this review.

Exosomal NAT10 from esophageal squamous cell carcinoma cells modulates macrophage lipid metabolism and polarization through ac4C modification of FASN

N-acetyltransferase 10 (NAT10) is acknowledged as a tumor promoter in various cancers due to its role as a regulator of acetylation modification. Tumor-associated macrophages (TAMs) play a pivotal role in the tumor microenvironment (TME). However, the intercellular communication between esophageal squamous cell carcinoma (ESCC) cells and TAMs involving NAT10 remains poorly understood. This study aimed to elucidate the regulatory mechanism of NAT10 in modulating macrophage lipid metabolism and polarization. Experimental evidence was derived from in vitro and in vivo analyses. We explored the association between upregulated NAT10 in ESCC tissues, macrophage polarization, and the therapeutic efficacy of PD-1. Furthermore, we investigated the impact of methyltransferase 3 (METTL3)-induced m6A modification on the increased expression of NAT10 in ESCC cells. Additionally, we examined the role of exosomal NAT10 in stabilizing the expression of fatty acid synthase (FASN) and promoting macrophage M2 polarization through mediating the ac4C modification of FASN. Results indicated that NAT10, packaged by exosomes derived from ESCC cells, promotes macrophage M2 polarization by facilitating lipid metabolism. In vivo animal studies demonstrated that targeting NAT10 could enhance the therapeutic effect of PD-1 on ESCC by mediating macrophage reprogramming. Our findings offer novel insights into improving ESCC treatment through NAT10 targeting.

Mass spectrometry for the study of adipocyte cell secretome in cardiovascular diseases

Adipose tissue is classically considered the primary site of lipid storage, but in recent years has garnered appreciation for its broad role as an endocrine organ, capable of remotely signaling to other tissues to alter their metabolic program. The adipose tissue is now recognized as a crucial regulator of cardiovascular health, mediated by the secretion of several bioactive products, with a wide range of endocrine and paracrine effects on the cardiovascular system. Thanks to the development and improvement of high-throughput mass spectrometry, the size and components of the human secretome have been characterized. In this review, we summarized the recent advances in mass spectrometry-based studies of the cell and tissue secretome for the understanding of adipose tissue biology, which may help to decipher the complex molecular mechanisms controlling the crosstalk between the adipose tissue and the cardiovascular system, and their possible clinical translation.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

A promising strategy of surface-modified nanoparticles targeting CXCR4 for precision cancer therapy

Nanoparticle (NP) functionalization with specific ligands enhances targeted cancer therapy and imaging by promoting receptor recognition and improving cellular uptake. This review focuses on recent research exploring the interaction between cancer cell-expressed chemokine receptor 4 (CXCR4) and ligand-conjugated NPs, utilising small molecules, peptides, and antibodies. Active NP targeting has shown improved tumour targeting and reduced toxicity, enabling precision therapy and diagnosis. However, challenges persist in the clinical translation of targeted NPs due to issues with biological response, tumour accumulation, and maintaining NP quality at an industrial scale. Biological and intratumoral barriers further hinder efficient NP accumulation in tumours, hampering translatability. To address these challenges, the academic community is refocusing efforts on understanding NP biological fate and establishing robust preclinical models. Future studies should investigate NP-body interactions, develop computational models, and identify optimal preclinical models. Establishing central NP research databases and fostering collaboration across disciplines is crucial to expediting clinical translation. Overcoming these hurdles will unlock the transformative potential of CXCR4-ligand-NP conjugates in revolutionising cancer treatment.

PTEN deficiency induces an extrahepatic cholangitis-cholangiocarcinoma continuum via aurora kinase A in mice

BACKGROUND & AIMS

The PTEN-AKT pathway is frequently altered in extrahepatic cholangiocarcinoma (eCCA). We aimed to evaluate the role of PTEN in the pathogenesis of eCCA and identify novel therapeutic targets for this disease.

METHODS

The Pten gene was genetically deleted using the Cre-loxp system in biliary epithelial cells. The pathologies were evaluated both macroscopically and histologically. The characteristics were further analyzed by immunohistochemistry, reverse-transcription PCR, cell culture, and RNA sequencing. Some features were compared to those in human eCCA samples. Further mechanistic studies utilized the conditional knockout of Trp53 and Aurora kinase A (Aurka) genes. We also tested the effectiveness of an Aurka inhibitor.

RESULTS

We observed that genetic deletion of the Pten gene in the extrahepatic biliary epithelium and peri-ductal glands initiated sclerosing cholangitis-like lesions in mice, resulting in enlarged and distorted extrahepatic bile ducts in mice as early as 1 month after birth. Histologically, these lesions exhibited increased epithelial proliferation, inflammatory cell infiltration, and fibrosis. With aging, the lesions progressed from low-grade dysplasia to invasive carcinoma. Trp53 inactivation further accelerated disease progression, potentially by downregulating senescence. Further mechanistic studies showed that both human and mouse eCCA showed high expression of AURKA. Notably, the genetic deletion of Aurka completely eliminated Pten deficiency-induced extrahepatic bile duct lesions. Furthermore, pharmacological inhibition of Aurka alleviated disease progression.

CONCLUSIONS

Pten deficiency in extrahepatic cholangiocytes and peribiliary glands led to a cholangitis-to-cholangiocarcinoma continuum that was dependent on Aurka. These findings offer new insights into preventive and therapeutic interventions for extrahepatic CCA.

IMPACT AND IMPLICATIONS

The aberrant PTEN-PI3K-AKT signaling pathway is commonly observed in human extrahepatic cholangiocarcinoma (eCCA), a disease with a poor prognosis. In our study, we developed a mouse model mimicking cholangitis to eCCA progression by conditionally deleting the Pten gene via Pdx1-Cre in epithelial cells and peribiliary glands of the extrahepatic biliary duct. The conditional Pten deletion in these cells led to cholangitis, which gradually advanced to dysplasia, ultimately resulting in eCCA. The loss of Pten heightened Akt signaling, cell proliferation, inflammation, fibrosis, DNA damage, epigenetic signaling, epithelial-mesenchymal transition, cell dysplasia, and cellular senescence. Genetic deletion or pharmacological inhibition of Aurka successfully halted disease progression. This model will be valuable for testing novel therapies and unraveling the mechanisms of eCCA tumorigenesis.

PTPN23[Thr] variant reduces susceptibility and tumorigenesis in esophageal squamous cell carcinoma through dephosphorylation of EGFR

Post-translational modifications (PTMs) have emerged as pivotal regulators of the development of cancers, including esophageal squamous cell carcinoma (ESCC). Here, we conducted a comprehensive analysis of PTM-related genetic variants associated with ESCC risk using large-scale genome-wide and exome-wide association datasets. We observed significant enrichment of PTM-related variants in the ESCC risk loci and identified five variants that were significantly associated with ESCC risk. Among them, rs6780013 in PTPN23 exhibited the highest level of significance in ESCC susceptibility in 9,728 ESCC cases and 10,977 controls (odds ratio [OR] = 0.85, 95 % confidence interval [CI] = 0.81- 0.89, P = 9.77 × 10-14). Further functional investigations revealed that PTPN23[Thr] variant binds to EGFR and modulates its phosphorylation at Thr699. PTPN23[Thr] variant substantially inhibited ESCC cell proliferation both in vitro and in vivo. Our findings underscore the critical role of PTPN23[Thr]-EGFR interaction in ESCC development, providing more insights into the pathogenesis of this cancer.

Clinical efficacy and identification of factors confer resistance to afatinib (tyrosine kinase inhibitor) in EGFR-overexpressing esophageal squamous cell carcinoma

Epidermal growth factor receptor (EGFR) is reportedly overexpressed in most esophageal squamous cell carcinoma (ESCC) patients, but anti-EGFR treatments offer limited survival benefits. Our preclinical data showed the promising antitumor activity of afatinib in EGFR-overexpressing ESCC. This proof-of-concept, phase II trial assessed the efficacy and safety of afatinib in pretreated metastatic ESCC patients (n = 41) with EGFR overexpression (NCT03940976). The study met its primary endpoint, with a confirmed objective response rate (ORR) of 39% in 38 efficacy-evaluable patients and a median overall survival of 7.8 months, with a manageable toxicity profile. Transcriptome analysis of pretreatment tumors revealed that neurotrophic receptor tyrosine kinase 2 (NTRK2) was negatively associated with afatinib sensitivity and might serve as a predictive biomarker, irrespective of EGFR expression. Notably, knocking down or inhibiting NTRK2 sensitized ESCC cells to afatinib treatment. Our study provides novel findings on the molecular factors underlying afatinib resistance and indicates that afatinib has the potential to become an important treatment for metastatic ESCC patients.

Predictive and Experimental Motif Interaction Analysis Identifies Functions of the WNK-OSR1/SPAK Pathway

The WNK-OSR1/SPAK protein kinase signaling pathway regulates ion homeostasis and cell volume, but its other functions are poorly understood. To uncover undefined signaling functions of the pathway we analyzed the binding specificity of the conserved C-terminal (CCT) domains of OSR1 and SPAK to find all possible interaction motifs in human proteins. These kinases bind the core consensus sequences R-F-x-V/I and R-x-F-x-V/I. Motifs were ranked based on sequence, conservation, cellular localization, and solvent accessibility. Out of nearly 3,700 motifs identified, 90% of previously published motifs were within the top 2% of those predicted. Selected candidates (TSC22D1, CAVIN1, ATG9A, NOS3, ARHGEF5) were tested. Upstream kinases WNKs 1-4 and their close relatives, the pseudokinases NRBP1/2, contain CCT-like domains as well. We identified additional distinct motif variants lacking the conserved arginine previously thought to be required, and found that the NRBP1 CCT-like domain binds TSC22D1 via the same motif as OSR1 and SPAK. Our results further highlight the rich and diverse functionality of CCT and CCT-like domains in connecting WNK signaling to cellular processes.

Aberrant spliceosome activity via elevated intron retention and upregulation and phosphorylation of SF3B1 in chronic lymphocytic leukemia

Splicing factor 3b subunit 1 (SF3B1) is the largest subunit and core component of the spliceosome. Inhibition of SF3B1 was associated with an increase in broad intron retention (IR) on most transcripts, suggesting that IR can be used as a marker of spliceosome inhibition in chronic lymphocytic leukemia (CLL) cells. Furthermore, we separately analyzed exonic and intronic mapped reads on annotated RNA-sequencing transcripts obtained from B cells (n = 98 CLL patients) and healthy volunteers (n = 9). We measured intron/exon ratio to use that as a surrogate for alternative RNA splicing (ARS) and found that 66% of CLL-B cell transcripts had significant IR elevation compared with normal B cells (NBCs) and that correlated with mRNA downregulation and low expression levels. Transcripts with the highest IR levels belonged to biological pathways associated with gene expression and RNA splicing. A >2-fold increase of active pSF3B1 was observed in CLL-B cells compared with NBCs. Additionally, when the CLL-B cells were treated with macrolides (pladienolide-B), a significant decrease in pSF3B1, but not total SF3B1 protein, was observed. These findings suggest that IR/ARS is increased in CLL, which is associated with SF3B1 phosphorylation and susceptibility to SF3B1 inhibitors. These data provide additional support to the relevance of ARS in carcinogenesis and evidence of pSF3B1 participation in this process.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.