Dexrazoxane inhibits the growth of esophageal squamous cell carcinoma by attenuating SDCBP/MDA-9/syntenin-mediated EGFR-PI3K-Akt pathway activation

Syndecan-binding protein (SDCBP) was reported to stimulate the advancement of esophageal squamous cell carcinoma (ESCC) and could potentially be a target for ESCC treatment. There is a growing corpus of research on the anti-tumor effects of iron chelators; however, very few studies have addressed the involvement of dexrazoxane in cancer. In this study, structure-based virtual screening was employed to select drugs targeting SDCBP from the Food and Drug Administration (FDA)-approved drug databases. The sepharose 4B beads pull-down assay revealed that dexrazoxane targeted SDCBP by interacting with its PDZ1 domain. Additionally, dexrazoxane inhibited ESCC cell proliferation and anchorage-independent colony formation via SDCBP. ESCC cell apoptosis and G2 phase arrest were induced as measured by the flow cytometry assay. Subsequent research revealed that dexrazoxane attenuated the binding ability between SDCBP and EGFR in an immunoprecipitation assay. Furthermore, dexrazoxane impaired EGFR membrane localization and inactivated the EGFR/PI3K/Akt pathway. In vivo, xenograft mouse experiments indicated that dexrazoxane suppressed ESCC tumor growth. These data indicate that dexrazoxane might be established as a potential anti-cancer agent in ESCC by targeting SDCBP.

Doxorubicin loaded thermostable nanoarchaeosomes: a next-generation drug carrier for breast cancer therapeutics

Breast cancer has a poor prognosis due to the toxic side effects associated with high doses of chemotherapy. Liposomal drug encapsulation has resulted in clinical success in enhancing chemotherapy tolerability. However, the formulation faces severe limitations with a lack of colloidal stability, reduced drug efficiency, and difficulties in storage conditions. Nanoarchaeosomes (NA) are a new generation of highly stable nanovesicles composed of the natural ether lipids extracted from archaea. In our study, we synthesized and characterized the NA, evaluated their colloidal stability, drug release potential, and anticancer efficacy. Transmission electron microscopy images have shown that the NA prepared from the hyperthermophilic archaeon Aeropyrum pernix K1 was in the size range of 61 ± 3 nm. The dynamic light scattering result has confirmed that the NA were stable at acidic pH (pH 4) and high temperature (70 °C). The NA exhibited excellent colloidal stability for 50 days with storage conditions at room temperature. The cell viability results have shown that the pure NA did not induce cytotoxicity in NIH 3T3 fibroblast cells and are biocompatible. Then NA were loaded with doxorubicin (NAD), and FTIR and UV-vis spectroscopy results have confirmed high drug loading efficiency of 97 ± 1% with sustained drug release for 48 h. The in vitro cytotoxicity studies in MCF-7 breast cancer cell lines showed that NAD induced cytotoxicity at less than 10 nM concentration. Fluorescence-activated cell sorting (FACS) results confirmed that NAD induced late apoptosis in nearly 92% of MCF-7 cells and necrosis in the remaining cells with cell cycle arrest at the G0/G1 phase. Our results confirmed that the NA could be a potential next-generation carrier with excellent stability, high drug loading efficiency, sustained drug release ability, and increased therapeutic efficacy, thus reducing the side effects of conventional drugs.

Metabolomic Approach to Identify Potential Biomarkers in KRAS-Mutant Pancreatic Cancer Cells

Pancreatic cancer is characterized by its high mortality rate and limited treatment options, often driven by oncogenic RAS mutations. In this study, we investigated the metabolomic profiles of pancreatic cancer cells based on their KRAS genetic status. Utilizing both KRAS-wildtype BxPC3 and KRAS-mutant PANC1 cell lines, we identified 195 metabolites differentially altered by KRAS status through untargeted metabolomics. Principal component analysis and hierarchical condition trees revealed distinct separation between KRAS-wildtype and KRAS-mutant cells. Metabolite set enrichment analysis highlighted significant pathways such as homocysteine degradation and taurine and hypotaurine metabolism. Additionally, lipid enrichment analysis identified pathways including fatty acyl glycosides and sphingoid bases. Mapping of identified metabolites to KEGG pathways identified nine significant metabolic pathways associated with KRAS status, indicating diverse metabolic alterations in pancreatic cancer cells. Furthermore, we explored the impact of TRPML1 inhibition on the metabolomic profile of KRAS-mutant pancreatic cancer cells. TRPML1 inhibition using ML-SI1 significantly altered the metabolomic profile, leading to distinct separation between vehicle-treated and ML-SI1-treated PANC1 cells. Metabolite set enrichment analysis revealed enriched pathways such as arginine and proline metabolism, and mapping to KEGG pathways identified 17 significant metabolic pathways associated with TRPML1 inhibition. Interestingly, some metabolites identified in PANC1 compared to BxPC3 were oppositely regulated by TRPML1 inhibition, suggesting their potential as biomarkers for KRAS-mutant cancer cells. Overall, our findings shed light on the distinct metabolite changes induced by both KRAS status and TRPML1 inhibition in pancreatic cancer cells, providing insights into potential therapeutic targets and biomarkers for this deadly disease.

Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC

Background

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice.

Methods

We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments.

Results

We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment.

Conclusions

We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

Innovative target mining stratagems to navigate drug repurposing endeavours

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Informed by Cancer Stem Cells of Solid Tumors: Advances in Treatments Targeting Tumor-Promoting Factors and Pathways

Cancer stem cells (CSCs) represent a subpopulation within tumors that promote cancer progression, metastasis, and recurrence due to their self-renewal capacity and resistance to conventional therapies. CSC-specific markers and signaling pathways highly active in CSCs have emerged as a promising strategy for improving patient outcomes. This review provides a comprehensive overview of the therapeutic targets associated with CSCs of solid tumors across various cancer types, including key molecular markers aldehyde dehydrogenases, CD44, epithelial cellular adhesion molecule, and CD133 and signaling pathways such as Wnt/β-catenin, Notch, and Sonic Hedgehog. We discuss a wide array of therapeutic modalities ranging from targeted antibodies, small molecule inhibitors, and near-infrared photoimmunotherapy to advanced genetic approaches like RNA interference, CRISPR/Cas9 technology, aptamers, antisense oligonucleotides, chimeric antigen receptor (CAR) T cells, CAR natural killer cells, bispecific T cell engagers, immunotoxins, drug-antibody conjugates, therapeutic peptides, and dendritic cell vaccines. This review spans developments from preclinical investigations to ongoing clinical trials, highlighting the innovative targeting strategies that have been informed by CSC-associated pathways and molecules to overcome therapeutic resistance. We aim to provide insights into the potential of these therapies to revolutionize cancer treatment, underscoring the critical need for a multi-faceted approach in the battle against cancer. This comprehensive analysis demonstrates how advances made in the CSC field have informed significant developments in novel targeted therapeutic approaches, with the ultimate goal of achieving more effective and durable responses in cancer patients.

MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework

The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can leverage multiple sources of information, including sequence data that contains biological structural information, and interaction data. While useful, these methods cannot be effectively applied to each type of information individually (e.g., chemical structure and interaction network) and do not take into account the specificity of DTI data such as low- or zero-interaction biological entities. To overcome these limitations, we propose a method called MFA-DTI (Multi-feature Fusion Adopted framework for DTI). MFA-DTI consists of three modules: an interaction graph learning module that processes the interaction network to generate interaction vectors, a chemical structure learning module that extracts features from the chemical structure, and a fusion module that combines these features for the final prediction. To validate the performance of MFA-DTI, we conducted experiments on six public datasets under different settings. The results indicate that the proposed method is highly effective in various settings and outperforms state-of-the-art methods.

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network

Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Loss of MTA2-mediated downregulation of PTK7 inhibits hepatocellular carcinoma metastasis progression by modulating the FAK-MMP7 axis

The expression of metastasis tumor-associated protein 2 (MTA2) and protein tyrosine kinase 7 (PTK7) is associated with hepatocellular carcinoma (HCC) progression. However, the functional effect and mechanism through which MTA2 regulates PTK7-mediated HCC progression remains unclear. Here, we found that MTA2 knockdown significantly down-regulated PTK7 expression in HCC cells (SK-Hep-1 and PLC/PRF/5). Data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases show that the PTK7 expression level was higher in HCC tissues than in normal liver tissues. In HCC patients, the PTK7 expression level clearly correlated with tumor stage and grade, lower overall survival (OS) correlated positively with MTA2 level, and PTK7 expression acted as a downstream factor for MTA2 expression. In addition, matrix metalloproteinase 7 (MMP7) expression was closely regulated by PTK7, and the mRNA and protein expression levels of MTA2 and PTK7 correlated positively with lower OS. MMP7 downregulation by PTK7 knockdown clearly decreased the migration and invasion abilities of HCC cells. In HCC cells, recombinant human MMP7 reversed the PTK7 knockdown-induced suppression of migration and invasion. Furthermore, deactivation of FAK using siFAK or FAK inhibitor (PF-573228, PF) synergistically contributed to PTK7 knockdown-inhibited FAK activity, MMP7 expression, and the migration and invasion abilities of HCC cells. Collectively, our findings show that PTK7 mediates HCC progression by regulating the MTA2-FAK-MMP7 axis and may be a diagnostic value for HCC patients.

A probabilistic knowledge graph for target identification

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

The acute phase reactant orosomucoid-2 directly promotes rheumatoid inflammation

Acute phase proteins involved in chronic inflammatory diseases have not been systematically analyzed. Here, global proteome profiling of serum and urine revealed that orosomucoid-2 (ORM2), an acute phase reactant, was differentially expressed in rheumatoid arthritis (RA) patients and showed the highest fold change. Therefore, we questioned the extent to which ORM2, which is produced mainly in the liver, actively participates in rheumatoid inflammation. Surprisingly, ORM2 expression was upregulated in the synovial fluids and synovial membranes of RA patients. The major cell types producing ORM2 were synovial macrophages and fibroblast-like synoviocytes (FLSs) from RA patients. Recombinant ORM2 robustly increased IL-6, TNF-α, CXCL8 (IL-8), and CCL2 production by RA macrophages and FLSs via the NF-κB and p38 MAPK pathways. Interestingly, glycophorin C, a membrane protein for determining erythrocyte shape, was the receptor for ORM2. Intra-articular injection of ORM2 increased the severity of arthritis in mice and accelerated the infiltration of macrophages into the affected joints. Moreover, circulating ORM2 levels correlated with RA activity and radiographic progression. In conclusion, the acute phase protein ORM2 can directly increase the production of proinflammatory mediators and promote chronic arthritis in mice, suggesting that ORM2 could be a new therapeutic target for RA.

AurkA/TPX2 co-overexpression in nontransformed cells promotes genome instability through induction of chromosome mis-segregation and attenuation of the p53 signalling pathway

The Aurora-A kinase (AurkA) and its major regulator TPX2 (Targeting Protein for Xklp2) are key mitotic players frequently co-overexpressed in human cancers, and the link between deregulation of the AurkA/TPX2 complex and tumourigenesis is actively investigated. Chromosomal instability, one of the hallmarks of cancer related to the development of intra-tumour heterogeneity, metastasis and chemo-resistance, has been frequently associated with TPX2-overexpressing tumours. In this study we aimed to investigate the actual contribution to chromosomal instability of deregulating the AurkA/TPX2 complex, by overexpressing it in nontransformed hTERT RPE-1 cells. Our results show that overexpression of both AurkA and TPX2 results in increased AurkA activation and severe mitotic defects, compared to AurkA overexpression alone. We also show that AurkA/TPX2 co-overexpression yields increased aneuploidy in daughter cells and the generation of micronucleated cells. Interestingly, the p53/p21 axis response is impaired in AurkA/TPX2 overexpressing cells subjected to different stimuli; consistently, cells acquire increased ability to proliferate after independent induction of mitotic errors, i.e. following nocodazole treatment. Based on our observation that increased levels of the AurkA/TPX2 complex affect chromosome segregation fidelity and interfere with the activation of a pivotal surveillance mechanism in response to altered cell division, we propose that co-overexpression of AurkA and TPX2 per se represents a condition promoting the generation of a genetically unstable context in nontransformed human cells.

Case report: Efficacy of icotinib treatment in lung adenocarcinoma with esophageal squamous cell carcinoma: a rare case of double primary malignant tumors

Background

Lung adenocarcinoma with esophageal squamous cell carcinoma is rare and the prognosis is poor, therefore there is an urgent need to improve this situation. The objective of this study was to explore the effect of first-generation tyrosine kinase inhibitors (TKIs) in the patient of the double primary malignant tumors.

Case report

We report a case of lung adenocarcinoma with esophageal squamous cell carcinoma treated by icotininb after five-year follow-up. A 71-year-old Chinese woman complaining of swallowing obstruction, heartburn, regurgitation of gastric acid for more than 2 months. An esophageal lesion was found by chest CT scans in T7 vertebral level. The diagnosis by gastroscopic biopsy was squamous cell carcinoma (SCC) with EGFR over-expression. Simultaneously, chest CT showed a 2 cm x 1 cm solitary lesion in the right superior pulmonary. The histological diagnosis by percutaneous lung Biopsy was "adenocarcinoma." Epidermal growth factor receptor (EGFR) gene mutation status was evaluated by Sanger sequencing, and an exon 21 point mutation (L858R) was identified. When the double primary malignant tumors were diagnosed, the patient refused operation and received a tyrosine kinase inhibitor (TKI), icotinib, at the dose of 125 mg, three times per day. All serum tumor biomarkers such as CEA and cancer antigen 125 (CA125) were in the normal range during the treatment period. After five-year follow-up, the patient has no evidence of recurrence or metastasis. The lung cancer was stable, meanwhile the esophageal lesion was almost cured.

Conclusion

Icotininb is an effective treatment in the patients of the double primary malignant tumors of lung adenocarcinoma with EGFR gene mutation and esophageal squamous cell carcinoma with EGFR over-expression.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

MicroRNAs: A novel signature in the metastasis of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a malignant epithelial tumor, characterized by squamous cell differentiation, it is the sixth leading cause of cancer-related deaths globally. The increased mortality rate of ESCC patients is predominantly due to the advanced stage of the disease when discovered, coupled with higher risk of metastasis, which is an exceedingly malignant characteristic of cancer, frequently leading to a high mortality rate. Unfortunately, there is currently no specific and effective marker to predict and treat metastasis in ESCC. MicroRNAs (miRNAs) are a class of small non-coding RNA molecules, approximately 22 nucleotides in length. miRNAs are vital in modulating gene expression and serve pivotal regulatory roles in the occurrence, progression, and prognosis of cancer. Here, we have examined the literature to highlight the intimate correlations between miRNAs and ESCC metastasis, and show that ESCC metastasis is predominantly regulated or regulated by genetic and epigenetic factors. This review proposes a potential role for miRNAs as diagnostic and therapeutic biomarkers for metastasis in ESCC metastasis, with the ultimate aim of reducing the mortality rate among patients with ESCC.

Transcriptome profiling and analysis of patients with esophageal squamous cell carcinoma from Kazakhstan

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer in Central Asia, often diagnosed at advanced stages. Understanding population-specific patterns of ESCC is crucial for tailored treatments. This study aimed to unravel ESCC's genetic basis in Kazakhstani patients and identify potential biomarkers for early diagnosis and targeted therapies. ESCC patients from Kazakhstan were studied. We analyzed histological subtypes and conducted in-depth transcriptome sequencing. Differential gene expression analysis was performed, and significantly dysregulated pathways were identified using KEGG pathway analysis (p-value < 0.05). Protein-protein interaction networks were constructed to elucidate key modules and their functions. Among Kazakhstani patients, ESCC with moderate dysplasia was the most prevalent subtype. We identified 42 significantly upregulated and two significantly downregulated KEGG pathways, highlighting molecular mechanisms driving ESCC pathogenesis. Immune-related pathways, such as viral protein interaction with cytokines, rheumatoid arthritis, and oxidative phosphorylation, were elevated, suggesting immune system involvement. Conversely, downregulated pathways were associated with extracellular matrix degradation, crucial in cancer invasion and metastasis. Protein-protein interaction network analysis revealed four distinct modules with specific functions, implicating pathways in esophageal cancer development. High-throughput transcriptome sequencing elucidated critical molecular pathways underlying esophageal carcinogenesis in Kazakhstani patients. Insights into dysregulated pathways offer potential for early diagnosis and precision treatment strategies for ESCC. Understanding population-specific patterns is essential for personalized approaches to ESCC management.

Cholesterol metabolism in tumor microenvironment: cancer hallmarks and therapeutic opportunities

Cholesterol is crucial for cell survival and growth, and dysregulation of cholesterol homeostasis has been linked to the development of cancer. The tumor microenvironment (TME) facilitates tumor cell survival and growth, and crosstalk between cholesterol metabolism and the TME contributes to tumorigenesis and tumor progression. Targeting cholesterol metabolism has demonstrated significant antitumor effects in preclinical and clinical studies. In this review, we discuss the regulatory mechanisms of cholesterol homeostasis and the impact of its dysregulation on the hallmarks of cancer. We also describe how cholesterol metabolism reprograms the TME across seven specialized microenvironments. Furthermore, we discuss the potential of targeting cholesterol metabolism as a therapeutic strategy for tumors. This approach not only exerts antitumor effects in monotherapy and combination therapy but also mitigates the adverse effects associated with conventional tumor therapy. Finally, we outline the unresolved questions and suggest potential avenues for future investigations on cholesterol metabolism in relation to cancer.

Discovery and optimization of (2-naphthylthio)acetic acid derivative as selective Bfl-1 inhibitor

Bcl-2 anti-apoptotic protein family suppresses cell death by deploying a surface groove to capture the critical BH3 α-helix of pro-apoptotic members. Bfl-1 is a relatively understudied member of this family, though it has been implicated in the pathogenesis and chemoresistance of a variety of human cancers. Reported small molecular Bfl-1 inhibitors encountered the issue of either lack in potency or poor selectivity against its most homologous member Mcl-1. In order to tackle this issue, compound library was screened and a hit compound UMI-77 was identified. We modified its chemical structure to remove the characteristic of PAINS (pan-assay interference compounds), demonstrated the real binding affinity and achieved selectivity against Mcl-1 under the guidance of computational modeling. After optimization 15 was obtained as leading compound to block Bfl-1/BIM interaction in vitro with more than 10-fold selectivity over Mcl-1. We believe 15 is of great value for the exploration of Bfl-1 biological function and its potential as therapeutic target.

The role of kinesin family members in hepatobiliary carcinomas: from bench to bedside

As a major component of the digestive system malignancies, tumors originating from the hepatic and biliary ducts seriously endanger public health. The kinesins (KIFs) are molecular motors that enable the microtubule-dependent intracellular trafficking necessary for mitosis and meiosis. Normally, the stability of KIFs is essential to maintain cell proliferation and genetic homeostasis. However, aberrant KIFs activity may destroy this dynamic stability, leading to uncontrolled cell division and tumor initiation. In this work, we have made an integral summarization of the specific roles of KIFs in hepatocellular and biliary duct carcinogenesis, referring to aberrant signal transduction and the potential for prognostic evaluation. Additionally, current clinical applications of KIFs-targeted inhibitors have also been discussed, including their efficacy advantages, relationship with drug sensitivity or resistance, the feasibility of combination chemotherapy or other targeted agents, as well as the corresponding clinical trials. In conclusion, the abnormally activated KIFs participate in the regulation of tumor progression via a diverse range of mechanisms and are closely associated with tumor prognosis. Meanwhile, KIFs-aimed inhibitors also carry out a promising tumor-targeted therapeutic strategy that deserves to be further investigated in hepatobiliary carcinoma (HBC).

Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions

Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.

GSL-DTI: Graph structure learning network for Drug-Target interaction prediction

MOTIVATION

Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins.

RESULTS

We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.

SADR: Self-Supervised Graph Learning With Adaptive Denoising for Drug Repositioning

Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications.

A novel network pharmacology strategy to decode mechanism of Wuling Powder in treating liver cirrhosis

BACKGROUND

Liver cirrhosis is a chronic liver disease with hepatocyte necrosis and lesion. As one of the TCM formulas Wuling Powder (WLP) is widely used in the treatment of liver cirrhosis. However, it's key functional components and action mechanism still remain unclear. We attempted to explore the Key Group of Effective Components (KGEC) of WLP in the treatment of Liver cirrhosis through integrative pharmacology combined with experiments.

METHODS

The components and potential target genes of WLP were extracted from published databases. A novel node importance calculation model considering both node control force and node bridging force is designed to construct the Function Response Space (FRS) and obtain key effector proteins. The genetic knapsack algorithm was employed to select KGEC. The effectiveness and reliability of KGEC were evaluated at the functional level by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the effectiveness and potential mechanism of KGEC were confirmed by CCK-8, qPCR and Western blot.

RESULTS

940 effective proteins were obtained in FRS. KEGG pathways and GO terms enrichments analysis suggested that effective proteins well reflect liver cirrhosis characteristics at the functional level. 29 components of WLP were defined as KGEC, which covered 100% of the targets of the effective proteins. Additionally, the pathways enriched for the KGEC targets accounted for 83.33% of the shared genes between the targets and the pathogenic genes enrichment pathways. Three components scopoletin, caryophyllene oxide, and hydroxyzinamic acid from KGEC were selected for in vivo verification. The qPCR results demonstrated that all three components significantly reduced the mRNA levels of COL1A1 in TGF-β1-induced liver cirrhosis model. Furthermore, the Western blot assay indicated that these components acted synergistically to target the NF-κB, AMPK/p38, cAMP, and PI3K/AKT pathways, thus inhibiting the progression of liver cirrhosis.

CONCLUSION

In summary, we have developed a new model that reveals the key components and potential mechanisms of WLP for the treatment of liver cirrhosis. This model provides a reference for the secondary development of WLP and offers a methodological strategy for studying TCM formulas.

The phenotypic reversion of cancer: Experimental evidences on cancer reversibility through epigenetic mechanisms (Review)

Different experimental models reveal that malignant cancer cells can be induced to change their phenotype into a benign one. This phenotypic transformation, confirmed both in vitro and in vivo, currently is known as 'tumor reversion'. This evidence raises a radical question among current cancer models: Is cancer reversible? How do genetic and epigenetic alterations hierarchically relate? Understanding the mechanisms of 'tumor reversion' represents a key point in order to evolve the actual cancer models and develop new heuristic models that can possibly lead to drugs that target epigenetic mechanisms, for example epigenetic drugs. Even though evidence of tumor reversion dates back to the 1950s, this remains a completely new field of research recently re‑discovered thanks to the interest in cell reprogramming research, developmental biology and the increasing understanding of epigenetic mechanisms. In the current review, a comprehensive review of all the main experimental models on tumor reversion was presented.

Targeting neutrophils: Mechanism and advances in cancer therapy

BACKGROUND

Cancer is a thorny problem which cannot be conquered by mankind at present and recent researchers have put their focus on tumor microenviroment. Neutrophils, the prominent leukocytes in peripheral blood that accumulate in tumours, serves as frontline cells in response to tumour progression owing to the rapid development of micro biotechnology. Hence, targeted therapy with these neutrophils has made targeting treatment a promising field in cancer therapy.

MAIN BODY

We broadly summarise some studies on the phenotypes and functions of tumour-associated neutrophils as well as the unique web-like products of neutrophils that play a role in cancer progression-neutrophil extracellular traps-and the interactions between neutrophils and the tumour microenvironment. Moreover, several targeted neutrophils therapeutic studies have made some progress and provided potential strategies for the treatment of cancer.

CONCLUSION

This review aims to offer a holistic perspective on therapeutic interventions targeting neutrophils to further inspire more researches on cancer therapies.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Intercellular crosstalk between cancer cells and cancer-associated fibroblasts via exosomes in gastrointestinal tumors

Gastrointestinal (GI) tumors are a significant global health threat, with high rates of morbidity and mortality. Exosomes contain various biologically active molecules like nucleic acids, proteins, and lipids and can serve as messengers for intercellular communication. They play critical roles in the exchange of information between tumor cells and the tumor microenvironment (TME). The TME consists of mesenchymal cells and components of the extracellular matrix (ECM), with fibroblasts being the most abundant cell type in the tumor mesenchyme. Cancer-associated fibroblasts (CAFs) are derived from normal fibroblasts and mesenchymal stem cells that are activated in the TME. CAFs can secrete exosomes to modulate cell proliferation, invasion, migration, drug resistance, and other biological processes in tumors. Additionally, tumor cells can manipulate the function and behavior of fibroblasts through direct cell-cell interactions. This review provides a summary of the intercellular crosstalk between GI tumor cells and CAFs through exosomes, along with potential underlying mechanisms.

The Nitro Group Reshapes the Effects of Pyrido[3,4-g]quinazoline Derivatives on DYRK/CLK Activity and RNA Splicing in Glioblastoma Cells

Serine-threonine protein kinases of the DYRK and CLK families regulate a variety of vital cellular functions. In particular, these enzymes phosphorylate proteins involved in pre-mRNA splicing. Targeting splicing with pharmacological DYRK/CLK inhibitors emerged as a promising anticancer strategy. Investigation of the pyrido[3,4-g]quinazoline scaffold led to the discovery of DYRK/CLK binders with differential potency against individual enzyme isoforms. Exploring the structure-activity relationship within this chemotype, we demonstrated that two structurally close compounds, pyrido[3,4-g]quinazoline-2,10-diamine 1 and 10-nitro pyrido[3,4-g]quinazoline-2-amine 2, differentially inhibited DYRK1-4 and CLK1-3 protein kinases in vitro. Unlike compound 1, compound 2 efficiently inhibited DYRK3 and CLK4 isoenzymes at nanomolar concentrations. Quantum chemical calculations, docking and molecular dynamic simulations of complexes of 1 and 2 with DYRK3 and CLK4 identified a dramatic difference in electron donor-acceptor properties critical for preferential interaction of 2 with these targets. Subsequent transcriptome and proteome analyses of patient-derived glioblastoma (GBM) neurospheres treated with 2 revealed that this compound impaired CLK4 interactions with spliceosomal proteins, thereby altering RNA splicing. Importantly, 2 affected the genes that perform critical functions for cancer cells including DNA damage response, p53 signaling and transcription. Altogether, these results provide a mechanistic basis for the therapeutic efficacy of 2 previously demonstrated in in vivo GBM models.

Nanotechnology for controlling mango malformation: a promising approach

Mango (Mangifera indica L.) is one of the most important fruit crops in the world with yields of approximately 40 million tons annually and its production continues to decrease every year as a result of the attack of certain pathogens i.e. Colletotrichum gloeosporioides, Erythricium salmonicolor, Amritodus atkinsoni, Idioscopus clypealis, Idioscopus nitidulus, Bactrocera obliqua, Bactrocera frauenfeldi, Xanthomonas campestris, and Fusarium mangiferae. So F. mangiferae is the most harmful pathogen that causes mango malformation disease in mango which decreases its 90% yield. Nanotechnology is an eco-friendly and has a promising effect over traditional methods to cure fungal diseases. Different nanoparticles possess antifungal potential in terms of controlling the fungal diseases in plants but applications of nanotechnology in plant disease managements is minimal. The main focus of this review is to highlight the previous and current strategies to control mango malformation and highlights the promising applications of nanomaterials in combating mango malformation. Hence, the present review aims to provide brief information on the disease and effective management strategies.Communicated by Ramaswamy H. Sarma.

Biological role and regulation of circular RNA as an emerging biomarker and potential therapeutic target for cancer

Circular RNAs (circRNAs) are a unique family of endogenous RNAs devoid of 3' poly-A tails and 5' end caps. These single-stranded circRNAs, found in the cytoplasm, are synthesized via back-splicing mechanisms, merging introns, exons, or both, resulting in covalently closed circular loops. They are profusely expressed across the eukaryotic transcriptome and offer heightened stability against exonuclease RNase R compared to linear RNA counterparts. This review endeavors to provide a comprehensive overview of circRNAs' characteristics, biogenesis, and mechanisms of action. Furthermore, aimed to shed light on the potential of circRNAs as significant biomarkers in various cancer types. It has been performed an exhaustive literature review, drawing on recent studies and findings related to circRNA characteristics, synthesis, function, evaluation techniques, and their associations with oncogenesis. CircRNAs are intricately associated with tumor progression and development. Their multifaceted roles encompass gene regulation through the sponging of proteins and microRNAs, controlling transcription and splicing, interacting with RNA binding proteins (RBPs), and facilitating gene translation. Due to these varied roles, circRNAs have become a focal point in tumor pathology investigations, given their promising potential as both biomarkers and therapeutic agents. CircRNAs, due to their unique biogenesis and multifunctionality, hold immense promise in the realm of oncology. Their stability, widespread expression, and intricate involvement in gene regulation underscore their prospective utility as reliable biomarkers and therapeutic targets in cancer. As our understanding of circRNAs deepens, advanced techniques for their detection, evaluation, and manipulation will likely emerge. These advancements might catalyze the translation of circRNA-based diagnostics and therapeutics into clinical practice, potentially revolutionizing cancer care and prognosis.

Aurora kinase B disruption suppresses pathological retinal angiogenesis by affecting cell cycle progression

PURPOSE

The detrimental effects of pathological angiogenesis on the visual function are indisputable. Within a prominent role in chromosome segregation and tumor progression, aurora kinase B (AURKB) assumes a prominent role. However, its role in pathological retinal angiogenesis remains unclear. This study explores this latent mechanism.

METHODS

To inhibit AURKB expression, we designed specific small interfering RNAs targeting AURKB and transfected them into vascular endothelial cells. Barasertib was selected as the AURKB inhibitor. The anti-angiogenic effects of both AURKB siRNA and barasertib were assessed in vitro by cell proliferation, transwell migration, and tube formation. To evaluate the angiogentic effects of AURKB in vivo, neonatal mice were exposed to 75% oxygen followed by normoxic repositioning to establish an oxygen-induced retinopathy (OIR) model. Subsequently, phosphate-buffered saline and barasertib were administered into OIR mice via intravitreal injection. The effects of AURKB on cell cycle proteins were determined by western blot analysis.

RESULTS

We found that AURKB was overexpressed during pathological angiogenesis. AURKB siRNA and barasertib significantly inhibited endothelial cell proliferation, migration, and tube formation in vitro. Furthermore, AURKB inhibition attenuated retinal angiogenesis in the OIR model. A possible mechanism is the disruption of cell cycle by AURKB inhibition.

CONCLUSION

In conclusion, AURKB significantly influenced pathological retinal angiogenesis, thereby presenting a promising therapeutic target in ocular neovascular diseases.

Cx4Mab-1: A Novel Anti-Mouse CXCR4 Monoclonal Antibody for Flow Cytometry

The CXC chemokine receptor 4 (CXCR4, CD184) is a member of the G protein-coupled receptor family that is expressed in most leukocytes. Overexpression of CXCR4 is associated with poor prognosis in not only hematopoietic malignancy but also solid tumors. Because CXCR4 is an attractive target for tumor therapy, reliable preclinical murine models using anti-CXCR4 monoclonal antibodies (mAbs) have been warranted. This study established a novel anti-mouse CXCR4 (mCXCR4) mAb using the Cell-Based Immunization and Screening method. Flow cytometric analysis showed that an anti-mCXCR4 mAb, Cx4Mab-1 (rat IgG2a, kappa), recognized mCXCR4-overexpressed Chinese hamster ovary-K1 (CHO/mCXCR4) cells and endogenously mCXCR4-expressing mouse myeloma P3X63Ag8U.1 (P3U1) cells. Furthermore, Cx4Mab-1 did not recognize mCXCR4-knockout P3U1 cells. The dissociation constants of Cx4Mab-1 for CHO/mCXCR4 and P3U1 were determined as 6.4 × 10-9 M and 2.3 × 10-9 M, respectively, indicating that Cx4Mab-1 possesses a high affinity to both endogenous and exogenous mCXCR4-expressing cells. These results indicate that Cx4Mab-1 could be a useful tool for preclinical mouse models.

HPV-related diseases in male patients: an underestimated conundrum

PURPOSE

Human papillomavirus (HPV) infection is the most common sexually transmitted disease, in males and females worldwide. While the role of HPV in female diseases is well known and largely studied, males have negligibly been included in these programs, also because the proportion of women suffering and dying from HPV-related diseases is much larger than men. The aim of this review is to focus on HPV-related diseases in male patients.

METHODS

We performed a literature analysis on the electronic database PubMed. We considered randomized trials, observational and retrospective studies, original articles having as topic the relationship between HPV male infection and the following items: oral, anal penile cancers, warts, condylomas, male infertility, altered sperm parameters, anti-sperm antibodies (ASA). We also included experimental in vitro studies focused on the effects of HPV infection on oocyte fertilization, blastocyst development, and trophoblastic cell invasiveness. In addition, studies describing the adjuvant administration of the HPV vaccination as a possible strategy to promote HPV clearance from semen in infected males were included.

RESULTS

Regarding head and neck HPV-related diseases, the most important non-neoplastic disease is recurrent respiratory papillomatosis (RRP). Regarding neoplastic diseases, the proportion of head and neck cancers attributable to HPV has increased dramatically worldwide. In addition, nowadays, it is thought that half of head and neck squamous cell carcinomas (HNSCCs) cases in the United States are caused by infection with high-risk HPV. HPV is noteworthy in andrological practice too. It was described as having a high HPV prevalence, ranging between 50 and 70%, in male penile shaft, glans penis/coronal sulcus, semen as well as in scrotal, perianal, and anal regions. Moreover, in male patients, HPV infection has been associated, among other diseases, with penile cancers. HPV semen infection has been reported in about 10% in men from the general population and about 16% in men with unexplained infertility, although these data seem widely underestimated according to clinical experience. In particular, HPV semen infection seems to be most related to asthenozoospermia and to anti-sperm antibodies (ASAs).

CONCLUSIONS

HPV infection represents a health problem with a detrimental social and public impact. Despite this evidence, little has been done to date to widely promote vaccination among young males.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

CCL-DTI: contributing the contrastive loss in drug-target interaction prediction

BACKGROUND

The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module.

RESULTS

In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets.

CONCLUSIONS

Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.

Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19

A novel virus emerged from Wuhan, China, at the end of 2019 and quickly evolved into a pandemic, significantly impacting various industries, especially healthcare. One critical lesson from COVID-19 is the importance of understanding and predicting underlying comorbidities to better prioritize care and pharmacological therapies. Factors like age, race, and comorbidity history are crucial in determining disease mortality. While clinical data from hospitals and cohorts have led to the identification of these comorbidities, traditional approaches often lack a mechanistic understanding of the connections between them. In response, we utilized a deep learning approach to integrate COVID-19 data with data from other diseases, aiming to detect comorbidities with mechanistic insights. Our modified algorithm in the mpDisNet package, based on word-embedding deep learning techniques, incorporates miRNA expression profiles from SARS-CoV-2 infected cell lines and their target transcription factors. This approach is aligned with the emerging field of network medicine, which seeks to define diseases based on distinct pathomechanisms rather than just phenotypes. The main aim is discovery of possible unknown comorbidities by connecting the diseases by their miRNA mediated regulatory interactions. The algorithm can predict the majority of COVID-19's known comorbidities, as well as several diseases that have yet to be discovered to be comorbid with COVID-19. These potentially comorbid diseases should be investigated further to raise awareness and prevention, as well as informing the comorbidity research for the next possible outbreak.

DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge

The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.

The Causal Relationship between PCSK9 Inhibitors and Malignant Tumors: A Mendelian Randomization Study Based on Drug Targeting

Objective: This study explores the potential causal association between proprotein convertase subtilisin/kexin 9 (PCSK9) inhibitors and tumor development using Mendelian randomization (MR) based on drug targets. Methods: Instrumental variables within ±100 kb of the PCSK9 gene locus, impacting low-density lipoprotein cholesterol (LDL-C), were utilized for MR analysis. Coronary heart disease (CHD) served as a positive control to validate the causal relationship between PCSK9 inhibitors and various cancers. We employed reverse MR to address the reverse causation concerns. Data from positive controls and tumors were sourced from OpenGWAS. Results: MR analysis suggested a negative causal relationship between PCSK9 inhibitors and both breast and lung cancers (95%CIBreast cancer 0.81~0.99, p = 2.25 × 10-2; 95%CILung cancer 0.65~0.94, p = 2.55 × 10-3). In contrast, a positive causal link was observed with gastric, hepatic, and oral pharyngeal cancers and cervical intraepithelial neoplasia (95%CIGastric cancer 1.14~1.75, p = 1.88 × 10-2; 95%CIHepatic cancer 1.46~2.53, p = 1.16 × 10-2; 95%CIOral cavity and pharyngeal cancer 4.49~6.33, p = 3.36 × 10-4; 95%CICarcinoma in situ of cervix uteri 4.56~7.12, p = 6.91 × 10-3), without heterogeneity or pleiotropy (p > 0.05). Sensitivity analyses confirmed these findings. The results of MR of drug targets suggested no causal relationship between PCSK9 inhibitors and bladder cancer, thyroid cancer, pancreatic cancer, colorectal cancer, malignant neoplasms of the kidney (except for renal pelvis tumors), malignant neoplasms of the brain, and malignant neoplasms of the esophagus (p > 0.05). Reverse MR helped mitigate reverse causation effects. Conclusions: The study indicates a divergent causal relationship of PCSK9 inhibitors with certain cancers. While negatively associated with breast and lung cancers, a positive causal association was observed with gastric, hepatic, oral cavity, and pharyngeal cancers and cervical carcinoma in situ. No causal links were found with bladder, thyroid, pancreatic, colorectal, certain kidney, brain, and esophageal cancers.

The Role of SOX2 and SOX9 in Radioresistance and Tumor Recurrence

Head and neck squamous cell carcinoma (HNSCC) exhibits considerable variability in patient outcome. It has been reported that SOX2 plays a role in proliferation, tumor growth, drug resistance, and metastasis in a variety of cancer types. Additionally, SOX9 has been implicated in immune tolerance and treatment failures. SOX2 and SOX9 induce treatment failure by a molecular mechanism that has not yet been elucidated. This study explores the inverse association of SOX2/SOX9 and their distinct expression in tumors, influencing the tumor microenvironment and radiotherapy responses. Through public RNA sequencing data, human biopsy samples, and knockdown cellular models, we explored the effects of inverted SOX2 and SOX9 expression. We found that patients expressing SOX2LowSOX9High showed decreased survival compared to SOX2HighSOX9Low. A survival analysis of patients stratified by radiotherapy and human papillomavirus brings additional clinical relevance. We identified a gene set signature comprising newly discovered candidate genes resulting from inverted SOX2/SOX9 expression. Moreover, the TGF-β pathway emerges as a significant predicted contributor to the overexpression of these candidate genes. In vitro findings reveal that silencing SOX2 enhances tumor radioresistance, while SOX9 silencing enhances radiosensitivity. These discoveries lay the groundwork for further studies on the therapeutic potential of transcription factors in optimizing HNSCC treatment.

Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer

Combination therapy can greatly improve the efficacy of cancer treatment, so identifying the most effective drug combination and interaction can accelerate the development of combination therapy. Here we developed a computational network biological approach to identify the effective drug which inhibition risk pathway crosstalk of cancer, and then filtrated and optimized the drug combination for cancer treatment. We integrated high-throughput data concerning pan-cancer and drugs to construct miRNA-mediated crosstalk networks among cancer pathways and further construct networks for therapeutic drug. Screening by drug combination method, we obtained 687 optimized drug combinations of 83 first-line anticancer drugs in pan-cancer. Next, we analyzed drug combination mechanism, and confirmed that the targets of cancer-specific crosstalk network in drug combination were closely related to cancer prognosis by survival analysis. Finally, we save all the results to a webpage for query ( http://bio-bigdata.hrbmu.edu.cn/oDrugCP/ ). In conclusion, our study provided an effective method for screening precise drug combinations for various cancer treatments, which may have important scientific significance and clinical application value for tumor treatment.

Targeting proprotein convertase subtilisin/kexin type 9 (PCSK9): from bench to bedside

Proprotein convertase subtilisin/kexin type 9 (PCSK9) has evolved as a pivotal enzyme in lipid metabolism and a revolutionary therapeutic target for hypercholesterolemia and its related cardiovascular diseases (CVD). This comprehensive review delineates the intricate roles and wide-ranging implications of PCSK9, extending beyond CVD to emphasize its significance in diverse physiological and pathological states, including liver diseases, infectious diseases, autoimmune disorders, and notably, cancer. Our exploration offers insights into the interaction between PCSK9 and low-density lipoprotein receptors (LDLRs), elucidating its substantial impact on cholesterol homeostasis and cardiovascular health. It also details the evolution of PCSK9-targeted therapies, translating foundational bench discoveries into bedside applications for optimized patient care. The advent and clinical approval of innovative PCSK9 inhibitory therapies (PCSK9-iTs), including three monoclonal antibodies (Evolocumab, Alirocumab, and Tafolecimab) and one small interfering RNA (siRNA, Inclisiran), have marked a significant breakthrough in cardiovascular medicine. These therapies have demonstrated unparalleled efficacy in mitigating hypercholesterolemia, reducing cardiovascular risks, and have showcased profound value in clinical applications, offering novel therapeutic avenues and a promising future in personalized medicine for cardiovascular disorders. Furthermore, emerging research, inclusive of our findings, unveils PCSK9's potential role as a pivotal indicator for cancer prognosis and its prospective application as a transformative target for cancer treatment. This review also highlights PCSK9's aberrant expression in various cancer forms, its association with cancer prognosis, and its crucial roles in carcinogenesis and cancer immunity. In conclusion, this synthesized review integrates existing knowledge and novel insights on PCSK9, providing a holistic perspective on its transformative impact in reshaping therapeutic paradigms across various disorders. It emphasizes the clinical value and effect of PCSK9-iT, underscoring its potential in advancing the landscape of biomedical research and its capabilities in heralding new eras in personalized medicine.

Dynamic network curvature analysis of gene expression reveals novel potential therapeutic targets in sarcoma

Network properties account for the complex relationship between genes, making it easier to identify complex patterns in their interactions. In this work, we leveraged these network properties for dual purposes. First, we clustered pediatric sarcoma tumors using network information flow as a similarity metric, computed by the Wasserstein distance. We demonstrate that this approach yields the best concordance with histological subtypes, validated against three state-of-the-art methods. Second, to identify molecular targets that would be missed by more conventional methods of analysis, we applied a novel unsupervised method to cluster gene interactomes represented as networks in pediatric sarcoma. RNA-Seq data were mapped to protein-level interactomes to construct weighted networks that were then subjected to a non-Euclidean, multi-scale geometric approach centered on a discrete notion of curvature. This provides a measure of the functional association among genes in the context of their connectivity. In confirmation of the validity of this method, hierarchical clustering revealed the characteristic EWSR1-FLI1 fusion in Ewing sarcoma. Furthermore, assessing the effects of in silico edge perturbations and simulated gene knockouts as quantified by changes in curvature, we found non-trivial gene associations not previously identified.

Protein disulfide isomerase family member 4 promotes triple-negative breast cancer tumorigenesis and radiotherapy resistance through JNK pathway

BACKGROUND

Despite radiotherapy ability to significantly improve treatment outcomes and survival in triple-negative breast cancer (TNBC) patients, acquired resistance to radiotherapy poses a serious clinical challenge. Protein disulfide isomerase exists in endoplasmic reticulum and plays an important role in promoting protein folding and post-translational modification. However, little is known about the role of protein disulfide isomerase family member 4 (PDIA4) in TNBC, especially in the context of radiotherapy resistance.

METHODS

We detected the presence of PDIA4 in TNBC tissues and paracancerous tissues, then examined the proliferation and apoptosis of TNBC cells with/without radiotherapy. As part of the validation process, xenograft tumor mouse model was used. Mass spectrometry and western blot analysis were used to identify PDIA4-mediated molecular signaling pathway.

RESULTS

Based on paired clinical specimens of TNBC patients, we found that PDIA4 expression was significantly higher in tumor tissues compared to adjacent normal tissues. In vitro, PDIA4 knockdown not only increased apoptosis of tumor cells with/without radiotherapy, but also decreased the ability of proliferation. In contrast, overexpression of PDIA4 induced the opposite effects on apoptosis and proliferation. According to Co-IP/MS results, PDIA4 prevented Tax1 binding protein 1 (TAX1BP1) degradation by binding to TAX1BP1, which inhibited c-Jun N-terminal kinase (JNK) activation. Moreover, PDIA4 knockdown suppressed tumor growth xenograft model in vivo, which was accompanied by an increase in apoptosis and promoted tumor growth inhibition after radiotherapy.

CONCLUSIONS

The results of this study indicate that PDIA4 is an oncoprotein that promotes TNBC progression, and targeted therapy may represent a new and effective anti-tumor strategy, especially for patients with radiotherapy resistance.

Acrizanib as a Novel Therapeutic Agent for Fundus Neovascularization via Inhibitory Phosphorylation of VEGFR2

Purpose

The present study aimed to evaluate the effect of acrizanib, a small molecule inhibitor targeting vascular endothelial growth factor receptor 2 (VEGFR2), on physiological angiogenesis and pathological neovascularization in the eye and to explore the underlying molecular mechanisms.

Methods

We investigated the potential role of acrizanib in physiological angiogenesis using C57BL/6J newborn mice, and pathological angiogenesis using the mouse oxygen-induced retinopathy (OIR) and laser-induced choroidal neovascularization (CNV) models. Moreover, vascular endothelial growth factor (VEGF)-treated human umbilical vein endothelial cells (HUVECs) were used as an in vitro model for studying the molecular mechanism underlying acrizanib's antiangiogenic effects.

Results

The intravitreal injection of acrizanib did not show a considerable impact on physiological angiogenesis and retinal thickness, indicating a potentially favorable safety profile. In the mouse models of OIR and CNV, acrizanib showed promising results in reducing pathological neovascularization, inflammation, and vascular leakage, indicating its potential efficacy against pathological angiogenesis. Consistent with in vivo results, acrizanib blunted angiogenic events in VEGF-treated HUVECs such as proliferation, migration, and tube formation. Furthermore, acrizanib inhibited the multisite phosphorylation of VEGFR2 to varying degrees and the activation of its downstream signal pathways in VEGF-treated HUVECs.

Conclusions

This study suggested the potential efficacy and safety of acrizanib in suppressing fundus neovascularization. Acrizanib functioned through inhibiting multiple phosphorylation sites of VEGFR2 in endothelial cells to different degrees.

Translational Relevance

These results indicated that acrizanib might hold promise as a potential candidate for the treatment of ocular vascular diseases.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Proteomics analysis of serum and urine identifies VCP and CTSA as potential biomarkers associated with multiple myeloma

AIMS

We analyzed the differentially expressed proteins (DEPs) in serum and urine in order to provide new potential biomarkers for MM.

METHODS

Data-Independent Acquisition-based proteomics of serum and urine was performed to identify potential biomarkers for MM patients. Then we performed Western Blotting (WB), ELISA along with their ROC curve analysis to confirm DEPs.

RESULTS

A total of 1653 proteins in serum and 4519 proteins in urine were identified using Data-Dependent Acquisition method. VCP was the only protein that showed significant differences in different comparison groups in both serum and urine. Pathway analysis revealed that protein processing in the endoplasmic reticulum was the most relevant pathway associated with MM. Furthermore, the increased expression of HSP90B1, VCP, CTSA, HYOU1, PDIA4, and RAB7A was detected by WB. The results of ELISA indicated that a combination of VCP and CTSA provided a high area under curve (AUC) value of 0.883 (95 % CI, 0.769-0.997, p < 0.001) to diagnose NDMM. The combination of VCP, CTSA, ALB, and HGB exhibited better performance (AUC = 0.981), with 100 % specificity and 86.7 % sensitivity.

CONCLUSION

These findings suggest VCP and CTSA exhibit potential as biomarkers for MM, which may be helpful in the molecular mechanisms and pathogenesis upon further investigation.

Identification of prognostic coagulation-related signatures in clear cell renal cell carcinoma through integrated multi-omics analysis and machine learning

Clear cell renal cell carcinoma is a threat to public health with high morbidity and mortality. Clinical evidence has shown that cancer-associated thrombosis poses significant challenges to treatments, including drug resistance and difficulties in surgical decision-making in ccRCC. However, the coagulation pathway, one of the core mechanisms of cancer-associated thrombosis, recently found closely related to the tumor microenvironment and immune-related pathway, is rarely researched in ccRCC. Therefore, we integrated bulk RNA-seq data, DNA mutation and methylation data, single-cell data, and proteomic data to perform a comprehensive analysis of coagulation-related genes in ccRCC. First, we demonstrated the importance of the coagulation-related gene set by consensus clustering. Based on machine learning, we identified 5 coagulation signature genes and verified their clinical value in TCGA, ICGC, and E-MTAB-1980 databases. It's also demonstrated that the specific expression patterns of coagulation signature genes driven by CNV and methylation were closely correlated with pathways including apoptosis, immune infiltration, angiogenesis, and the construction of extracellular matrix. Moreover, we identified two types of tumor cells in single-cell data by machine learning, and the coagulation signature genes were differentially expressed in two types of tumor cells. Besides, the signature genes were proven to influence immune cells especially the differentiation of T cells. And their protein level was also validated.

Recent Progress in the Application of Exosome Analysis in Ovarian Cancer Management

Exosomes are very small (nano-sized) vesicles participating in tumor development by involvement in intercellular communication mediated by transferring biocomponents. Exosomes appear to play vital roles in various cancer development, such as ovarian cancer, a common malignancy in women. Several hallmarks of ovarian cancer are reported to be affected by the exosomemediated cellular cross-talk, including modulating peritoneal dissemination and chemoresistance. Since the expression of some biomolecules, such as miRNAs and mRNA, is changed in ovarian cancer, these exo-biomolecules can be applied as prognostic, diagnostic, and therapeutic biomarkers. Also, the selective loading of specific chemotherapeutic agents into exosomes highlights these biocarries as potential delivery devices. Exosomes could be artificially provided and engineered to better target the site of interest in ovarian cancer. In the present review, we summarize the notable achievement of exosome application in ovarian cancer management to gain applicable transitional insight against this cancer.

Immunohistochemical expression of cytochrome P4A11 (CYP4A11), carbonic anhydrase 9 (CAIX) and Ki67 in renal cell carcinoma; diagnostic relevance and relations to clinicopathological parameters

BACKGROUND

Cytochrome P4A11 (CYP4A11) is a member of cytochrome p450 family, which is involved in arachidonic acid metabolism that participates in promoting malignant cell proliferation, progression, and angiogenetic capacity. Carbonic Anhydrase 9 (CAIX) is a transmembrane protein that plays an integral part in regulating hypoxia which affects cancer cell metabolism, proliferation and promotes metastasis. The aim of this study was to evaluate the immunohistochemical expression of CYP4A11, CAIX and ki67 in RCC subtypes in relation to clinicopathological parameters and to evaluate the diagnostic significance of CYP4A11 and CAIX in differentiating renal cell carcinoma (RCC) subtypes.

MATERIALS AND METHODS

one hundred primary RCC cases, collected from Pathology Department, Faculty of Medicine, Tanta University and from private laboratories, were evaluated for immunohistochemical expression of CYP4A11, CAIX and ki67.

RESULTS

CYP4A11 was expressed in 59% of RCC; with 91.7% sensitivity and 90% specificity in differentiating clear cell and non-clear cell subtypes. CAIX was expressed in 50% of RCC; with 95% sensitivity, 80% specificity. High expression of CYP4A11 was statistically positively associated with higher tumor grade, high expression of CAIX was statistically positively associated with lower tumor grade and absence of necrosis and high ki67 labeling index was significantly associated with clear cell subtype, larger tumor sizes, higher tumor grade, advanced tumor stage, fat invasion and vascular invasion.

CONCLUSIONS

CYP4A11 and CAIX can be used as diagnostic markers to differentiate clear cell RCC from other subtypes. CYP4A11 is more diagnostically accurate and specific than CAIX. High expression of CYP4A11, low CAIX expression and high ki67 labeling index were related to features of aggressive tumor behavior.