A systematic review of computational approaches to understand cancer biology for informed drug repurposing

De novo in silico screening of natural products for antidiabetic drug discovery: ADMET profiling, molecular docking, and molecular dynamics simulations

Epigenetic dysfunction which has implicated disease conditions such as diabetes highlights the urgency for the discovery of novel therapeutic alternatives. The rising global incidences of diabetes and the limitations of existing treatments further exacerbate the quest for novel antidiabetic agents' discovery. This study leverages computational approaches to screen selected bioactive natural product phytoconstituents for their potential anti-diabetic properties. Utilizing pharmaceutical profiling, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions, molecular docking, and molecular dynamics (MD) simulations, the drug-likeness and binding affinity of these natural compounds against human pancreatic amylase was investigated. Out of the total 24,316 ZINC compounds screened for their binding scores with amylase, ZINC85593620, ZINC85593668, and ZINC85490447 came top. The compounds had higher binding scores than the standards (acarbose and ranirestat) with ZINC85593620 having the highest docking score of - 12.162 kcal/mol and interacted with key amino acid residues such as TRP 59, ILE 148, and ASP 197. Further validation through MD simulations reveals that all the compounds showed minimal fluctuations relative to the standards indicating strong and stable binding interactions suggesting potential effective inhibition of the enzyme. ZINC85593620 and ZINC85593668 showed promising distribution and availability characteristics for amylase inhibition. Overall, the compounds displayed potential amylase inhibition which underscores their use as promising natural products in developing new antidiabetic drugs. Further experimental validations are recommended to offer a potential solution to the pressing need for safer and more effective antidiabetic therapies.

Single-cell atlas reveals multi-faced responses of losartan on tubular mitochondria in diabetic kidney disease

BACKGROUND AND OBJECTIVE

Mitochondria are crucial to the function of renal tubular cells, and their dynamic perturbation in many aspects is an important mechanism of diabetic kidney disease (DKD). Single-nucleus RNA sequencing (snRNA-seq) technology is a high-throughput sequencing analysis technique for RNA at the level of a single cell nucleus. Here, our DKD mouse kidney single-cell RNA sequencing conveys a more comprehensive mitochondrial profile, which helps us further understand the therapeutic response of this unique organelle family to drugs.

METHODS

After high fat diet (HFD), mice were intraperitoneally injected with streptozotocin (STZ) to induce DKD, and then divided into three subsets: CON (healthy) subset, DKD (vehicle) subset, and LST (losartan; 25 mg/kg/day) subset. Divide HK-2 cell into LG (low glucose; 5 mM) and HG (high glucose; 30 mM) and HG + LST (losartan; 1 µ M) subsets. snRNA-seq was performed on the renal tissues of LST and DKD subset mice. To reveal the effects of losartan on gene function and pathway changes in renal tubular mitochondria, Gene Ontology (GO) enrichment analysis and GSEA/GSVA scoring were performed to analyze the specific response of proximal tubular (PT) cell mitochondria to losartan treatment, including key events in mitochondrial homeostasis such as mitochondrial morphology, dynamics, mitophagy, autophagic flux, mitochondrial respiratory chain, apoptosis, and ROS generation. Preliminary validation through in vitro and in vivo experiments, including observation of changes in mitochondrial morphology and dynamics using probes such as Mitotracker Red, and evaluation of the effect of losartan on key events of mitochondrial homeostasis perturbation using electron microscopy, laser confocal microscopy, immunofluorescence, and Western blotting. Detection of autophagic flux in cells by transfecting Ad-mCherry-GFP-LC3B dual fluorescence labeled adenovirus. Various fluorescent probes and energy detector are used to detect mitochondrial apoptosis, ROS, and respiration of mitochondrion.

RESULTS

Through the single-cell atlas of DKD mouse kidneys, it was found that losartan treatment significantly increased the percentage of PT cells. Gene Ontology (GO) enrichment analysis of differentially expressed genes showed enrichment of autophagy of mitochondrion pathway. Further GSEA analysis and GSVA scoring revealed that mitophagy and other key mitochondrial perturbation events, such as ROS production, apoptosis, membrane potential, adenosine triphosphate (ATP) synthesis, and mitochondrial dynamics, were involved in the protective mechanism of losartan on PT cells, thereby improving mitochondrial homeostasis. Consistent results were also obtained in mice and cellular experiments. In addition, we highlighted a specific renal tubular subpopulation with mitophagy phenotype found in single-cell data, and preliminarily validated it with co-localization and increased expression of Pink1 and Gclc in kidney specimens of DKD patients treated with losartan.

CONCLUSIONS

Our research suggests that scRNA-seq can reflect the multifaceted mitochondrial landscape of DKD renal tubular cells after drug treatment, and these findings may provide new targets for DKD therapy at the organelle level.

Conjugated bile acids alleviate acute pancreatitis through inhibition of TGR5 and NLRP3 mediated inflammation

INTRODUCTION

Severe acute pancreatitis (SAP) is a crucial gastrointestinal disease characterized by systemic inflammatory responses and persistent multiple organ failure. The role of bile acids (BAs) in diverse inflammatory diseases is increasingly recognized as crucial, but the underlying role of BA conjugation remains elusive.

OBJECTIVES

Our study aim to investigate the potential role of conjugated bile acids in SAP and reveal the molecular mechanisms underlying its regulatory effects. We hypothesized that taurochenodeoxycholic acid (TCDCA) and glycochenodeoxycholic acid (GCDCA) could protect SAP through inhibiting the activation of NLRP3 inflammasomes via the TGR5 pathway in macrophages.

METHODS

To test our hypothesis, we used BA-CoA: amino acid N-acyltransferase knockout (Baat-/-) mice and established SAP mouse models using caerulein- and sodium taurocholate- induced. We utilized a range of methods, including pathology sections, qRT-PCR, immunofluorescence, Western blotting, and ELISA, to identify the mechanisms of regulation.

RESULTS

BA-CoA: Amino acid N-acyltransferase knockout (Baat-/-) mice significantly exacerbated pancreatitis by increasing pancreatic and systemic inflammatory responses and pancreatic damage in SAP mouse models. Moreover, the serum TCDCA levels in Baat-/- mice were lower than those in wild-type (WT) mice with or without SAP, and GCDCA and TCDCA showed stronger anti-inflammatory effects than chenodeoxycholic acid (CDCA) in vitro. TCDCA treatment alleviated SAP in a Takeda G protein-coupled receptor 5 and NOD-like receptor family, pyrin domain containing 3-dependent manner in vivo. Reinforcing our conclusions from the mouse study, clinical SAP patients exhibited decreased serum content of conjugated BAs, especially GCDCA, which was inversely correlated with the severity of systemic inflammatory responses.

CONCLUSION

Conjugated bile acids significantly inhibit NLRP3 inflammasome activation by activating TGR5 pathway, thereby alleviating pancreatic immunopathology. The results provide new insights into the variability of clinical outcomes and paves the way for developing more effective therapeutic interventions for AP.

Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

Repurposing metabolic regulators: antidiabetic drugs as anticancer agents

Drug repurposing in cancer taps into the capabilities of existing drugs, initially designed for other ailments, as potential cancer treatments. It offers several advantages over traditional drug discovery, including reduced costs, reduced development timelines, and a lower risk of adverse effects. However, not all drug classes align seamlessly with a patient's condition or long-term usage. Hence, repurposing of chronically used drugs presents a more attractive option. On the other hand, metabolic reprogramming being an important hallmark of cancer paves the metabolic regulators as possible cancer therapeutics. This review emphasizes the importance and offers current insights into the repurposing of antidiabetic drugs, including metformin, sulfonylureas, sodium-glucose cotransporter 2 (SGLT2) inhibitors, dipeptidyl peptidase 4 (DPP-4) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), thiazolidinediones (TZD), and α-glucosidase inhibitors, against various types of cancers. Antidiabetic drugs, regulating metabolic pathways have gained considerable attention in cancer research. The literature reveals a complex relationship between antidiabetic drugs and cancer risk. Among the antidiabetic drugs, metformin may possess anti-cancer properties, potentially reducing cancer cell proliferation, inducing apoptosis, and enhancing cancer cell sensitivity to chemotherapy. However, other antidiabetic drugs have revealed heterogeneous responses. Sulfonylureas and TZDs have not demonstrated consistent anti-cancer activity, while SGLT2 inhibitors and DPP-4 inhibitors have shown some potential benefits. GLP-1RAs have raised concerns due to possible associations with an increased risk of certain cancers. This review highlights that further research is warranted to elucidate the mechanisms underlying the potential anti-cancer effects of these drugs and to establish their efficacy and safety in clinical settings.

A weighted Bayesian integration method for predicting drug combination using heterogeneous data

BACKGROUND

In the management of complex diseases, the strategic adoption of combination therapy has gained considerable prominence. Combination therapy not only holds the potential to enhance treatment efficacy but also to alleviate the side effects caused by excessive use of a single drug. Presently, the exploration of combination therapy encounters significant challenges due to the vast spectrum of potential drug combinations, necessitating the development of efficient screening strategies.

METHODS

In this study, we propose a prediction scoring method that integrates heterogeneous data using a weighted Bayesian method for drug combination prediction. Heterogeneous data refers to different types of data related to drugs, such as chemical, pharmacological, and target profiles. By constructing a multiplex drug similarity network, we formulate new features for drug pairs and propose a novel Bayesian-based integration scheme with the introduction of weights to integrate information from various sources. This method yields support strength scores for drug combinations to assess their potential effectiveness.

RESULTS

Upon comprehensive comparison with other methods, our method shows superior performance across multiple metrics, including the Area Under the Receiver Operating Characteristic Curve, accuracy, precision, and recall. Furthermore, literature validation shows that many top-ranked drug combinations based on the support strength score, such as goserelin and letrozole, have been experimentally or clinically validated for their effectiveness.

CONCLUSIONS

Our findings have significant clinical and practical implications. This new method enhances the performance of drug combination predictions, enabling effective pre-screening for trials and, thereby, benefiting clinical treatments. Future research should focus on developing new methods for application in various scenarios and for integrating diverse data sources.

Data Management in Biobanking: Strategies, Challenges, and Future Directions

Biobanking plays a pivotal role in biomedical research by providing standardized processing, precise storing, and management of biological sample collections along with the associated data. Effective data management is a prerequisite to ensure the integrity, quality, and accessibility of these resources. This review provides a current landscape of data management in biobanking, discussing key challenges, existing strategies, and potential future directions. We explore multiple aspects of data management, including data collection, storage, curation, sharing, and ethical considerations. By examining the evolving technologies and methodologies in biobanking, we aim to provide insights into addressing the complexities and maximizing the utility of biobank data for research and clinical applications.

Integrated edge information and pathway topology for drug-disease associations

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

Integrated ML-Based Strategy Identifies Drug Repurposing for Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) affects an estimated global population of around 3 million individuals. IPF is a medical condition with an unknown cause characterized by the formation of scar tissue in the lungs, leading to progressive respiratory disease. Currently, there are only two FDA-approved small molecule drugs specifically for the treatment of IPF and this has created a demand for the rapid development of drugs for IPF treatment. Moreover, denovo drug development is time and cost-intensive with less than a 10% success rate. Drug repurposing currently is the most feasible option for rapidly making the drugs to market for a rare and sporadic disease. Normally, the repurposing of drugs begins with a screening of FDA-approved drugs using computational tools, which results in a low hit rate. Here, an integrated machine learning-based drug repurposing strategy is developed to significantly reduce the false positive outcomes by introducing the predock machine-learning-based predictions followed by literature and GSEA-assisted validation and drug pathway prediction. The developed strategy is deployed to 1480 FDA-approved drugs and to drugs currently in a clinical trial for IPF to screen them against "TGFB1", "TGFB2", "PDGFR-a", "SMAD-2/3", "FGF-2", and more proteins resulting in 247 total and 27 potentially repurposable drugs. The literature and GSEA validation suggested that 72 of 247 (29.14%) drugs have been tried for IPF, 13 of 247 (5.2%) drugs have already been used for lung fibrosis, and 20 of 247 (8%) drugs have been tested for other fibrotic conditions such as cystic fibrosis and renal fibrosis. Pathway prediction of the remaining 142 drugs was carried out resulting in 118 distinct pathways. Furthermore, the analysis revealed that 29 of 118 pathways were directly or indirectly involved in IPF and 11 of 29 pathways were directly involved. Moreover, 15 potential drug combinations are suggested for showing a strong synergistic effect in IPF. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating IPF and other related conditions.

Repurposing lipid-lowering drugs on asthma and lung function: evidence from a genetic association analysis

OBJECTIVE

To explore the correlation between asthma risk and genetic variants affecting the expression or function of lipid-lowering drug targets.

METHODS

We conducted Mendelian randomization (MR) analyses using variants in several genes associated with lipid-lowering medication targets: HMGCR (statin target), PCSK9 (alirocumab target), NPC1L1 (ezetimibe target), APOB (mipomersen target), ANGPTL3 (evinacumab target), PPARA (fenofibrate target), and APOC3 (volanesorsen target), as well as LDLR and LPL. Our objective was to investigate the relationship between lipid-lowering drugs and asthma through MR. Finally, we assessed the efficacy and stability of the MR analysis using the MR Egger and inverse variance weighted (IVW) methods.

RESULTS

The elevated triglyceride (TG) levels associated with the APOC3, and LPL targets were found to increase asthma risk. Conversely, higher LDL-C levels driven by LDLR were found to decrease asthma risk. Additionally, LDL-C levels (driven by APOB, NPC1L1 and HMGCR targets) and TG levels (driven by the LPL target) were associated with improved lung function (FEV1/FVC). LDL-C levels driven by PCSK9 were associated with decreased lung function (FEV1/FVC).

CONCLUSION

In conclusion, our findings suggest a likely causal relationship between asthma and lipid-lowering drugs. Moreover, there is compelling evidence indicating that lipid-lowering therapies could play a crucial role in the future management of asthma.

A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis

BACKGROUND

Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis.

METHODS

To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based).

RESULTS

Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580).

CONCLUSIONS

By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention

BACKGROUND

Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level.

METHODS

In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules.

RESULTS

To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI.

CONCLUSIONS

AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.

Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions

Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

A pan-cancer multi-omics analysis of lactylation genes associated with tumor microenvironment and cancer development

Background

Lactylation is a significant post-translational modification bridging the gap between cancer epigenetics and metabolic reprogramming. However, the association between lactylation and prognosis, tumor microenvironment (TME), and response to drug therapy in various cancers remains unclear.

Methods

First, the expression, prognostic value, and genetic and epigenetic alterations of lactylation genes were systematically explored in a pan-cancer manner. Lactylation scores were derived for each tumor using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The correlation of lactylation scores with clinical features, prognosis, and TME was assessed by integrating multiple computational methods. In addition, GSE135222 data was used to assess the efficacy of lactylation scores in predicting immunotherapy outcomes. The expression of lactylation genes in breast cancers and gliomas were verified by RNA-sequencing.

Results

Lactylation genes were significantly upregulated in most cancer types. CREBBP and EP300 exhibited high mutation rates in pan-cancer analysis. The prognostic impact of the lactylation score varied by tumor type, and lactylation score was a protective factor for KIRC, ACC, READ, LGG, and UVM, and a risk factor for CHOL, DLBC, LAML, and OV. In addition, a high lactylation score was associated with cold TME. The infiltration levels of CD8+ T, γδT, natural killer T cell (NKT), and NK cells were lower in tumors with higher lactylation scores. Finally, immunotherapy efficacy was worse in patients with high lactylation scores than other types.

Conclusion

Lactylation genes are involved in malignancy formation. Lactylation score serves as a promising biomarker for predicting patient prognosis and immunotherapy efficacy.

Multi-omics approaches for drug-response characterization in primary biliary cholangitis and autoimmune hepatitis variant syndrome

BACKGROUND

Primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH) variant syndrome (VS) exhibit a complex overlap of AIH features with PBC, leading to poorer prognoses than those with PBC or AIH alone. The biomarkers associated with drug response and potential molecular mechanisms in this syndrome have not been fully elucidated.

METHODS

Whole-transcriptome sequencing was employed to discern differentially expressed (DE) RNAs within good responders (GR) and poor responders (PR) among patients with PBC/AIH VS. Subsequent gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted for the identified DE RNAs. Plasma metabolomics was employed to delineate the metabolic profiles distinguishing PR and GR groups. The quantification of immune cell profiles and associated cytokines was achieved through flow cytometry and immunoassay technology. Uni- and multivariable logistic regression analyses were conducted to construct a predictive model for insufficient biochemical response. The performance of the model was assessed by computing the area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity.

FINDINGS

The analysis identified 224 differentially expressed (DE) mRNAs, 189 DE long non-coding RNAs, 39 DE circular RNAs, and 63 DE microRNAs. Functional pathway analysis revealed enrichment in lipid metabolic pathways and immune response. Metabolomics disclosed dysregulated lipid metabolism and identified PC (18:2/18:2) and PC (16:0/20:3) as predictors. CD4+ T helper (Th) cells, including Th2 cells and regulatory T cells (Tregs), were upregulated in the GR group. Pro-inflammatory cytokines (IFN-γ, TNF-α, IL-9, and IL-17) were downregulated in the GR group, while anti-inflammatory cytokines (IL-10, IL-4, IL-5, and IL-22) were elevated. Regulatory networks were constructed, identifying CACNA1H and ACAA1 as target genes. A predictive model based on these indicators demonstrated an AUC of 0.986 in the primary cohort and an AUC of 0.940 in the validation cohort for predicting complete biochemical response.

CONCLUSION

A combined model integrating genomic, metabolic, and cytokinomic features demonstrated high accuracy in predicting insufficient biochemical response in patients with PBC/AIH VS. Early recognition of individuals at elevated risk for insufficient response allows for the prompt initiation of additional treatments.

Erzhi pills reverse PD-L1-mediated immunosuppression in melanoma microenvironment

Background

Cancer immunotherapies aimed at activating immune system, especially by blocking immune checkpoints, have become a successful modality for treating patients with advanced cancers. However, its clinical practice is frequently conceded by high outcomes, low initial response rates and severe side effects. New strategies are necessary to complement and advance this biological therapy. Erzhi Pills (EZP) have diverse pharmaceutical effects including immune regulation, anti-tumor and anti-senescence. We hypothesized that EZP could exert its antitumor effect through immunomodulation.

Purpose

The aim of this study was to investigate the effects of EZP on anti-tumor activities, and define its molecular mechanisms.

Methods

By applying melanoma model with high immune infiltrates, we determined the anti-melanoma effect of EZP. To identify whether this effect was mediated by direct targeting tumor cells, cell viability and apoptosis were examined in vitro. Network pharmacology analysis was used to predict the potential mechanisms of EZP for melanoma via immune response. Flow cytometry, immunohistochemistry (IHC), enzyme-linked immunosorbent assay (ELISA) and crystal violet (CV) experiments were performed to detect T cell infiltrations and functions mediated by EZP. The mechanism of EZP was further investigated by western blotting both in vivo and in vitro.

Results

The administration of EZP significantly inhibited tumor weight and volume. EZP extract could only slightly reduce cell viability and induce melanoma apoptosis. Network pharmacology analysis predicted that JAK-STAT signaling pathway and T cell receptor signaling pathway might be involved during EZP treatment. Flow cytometry and IHC analyses showed that EZP increased the number of CD4+ T cells and enhanced the function of CD8+ T cells. In co-culture experiments, EZP elevated killing ability of T cells. Western blotting showed that EZP treatment reduced PD-L1 signaling pathway.

Conclusion

These findings indicated that EZP exerted anti-melanoma effects by inducing apoptosis and blocking PD-L1 to activate T cells. EZP might represent a promising candidate drug for cancer immunotherapies.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Integrating Transcriptomic and Structural Insights: Revealing Drug Repurposing Opportunities for Sporadic ALS

Amyotrophic lateral sclerosis (ALS) is a progressive and devastating neurodegenerative disorder characterized by the loss of upper and lower motor neurons, resulting in debilitating muscle weakness and atrophy. Currently, there are no effective treatments available for ALS, posing significant challenges in managing the disease that affects approximately two individuals per 100,000 people annually. To address the urgent need for effective ALS treatments, we conducted a drug repurposing study using a combination of bioinformatics tools and molecular docking techniques. We analyzed sporadic ALS-related genes from the GEO database and identified key signaling pathways involved in sporadic ALS pathogenesis through pathway analysis using DAVID. Subsequently, we utilized the Clue Connectivity Map to identify potential drug candidates and performed molecular docking using AutoDock Vina to evaluate the binding affinity of short-listed drugs to key sporadic ALS-related genes. Our study identified Cefaclor, Diphenidol, Flubendazole, Fluticasone, Lestaurtinib, Nadolol, Phenamil, Temozolomide, and Tolterodine as potential drug candidates for repurposing in sporadic ALS treatment. Notably, Lestaurtinib demonstrated high binding affinity toward multiple proteins, suggesting its potential as a broad-spectrum therapeutic agent for sporadic ALS. Additionally, docking analysis revealed NOS3 as the gene that interacts with all the short-listed drugs, suggesting its possible involvement in the mechanisms underlying the therapeutic potential of these drugs in sporadic ALS. Overall, our study provides a systematic framework for identifying potential drug candidates for sporadic ALS therapy and highlights the potential of drug repurposing as a promising strategy for discovering new therapies for neurodegenerative diseases.

Single-cell transcriptome sequencing reveals aberrantly activated inter-tumor cell signaling pathways in the development of clear cell renal cell carcinoma

BACKGROUND

Aberrant intracellular or intercellular signaling pathways are important mechanisms that contribute to the development and progression of cancer. However, the intercellular communication associated with the development of ccRCC is currently unknown. The purpose of this study was to examine the aberrant tumor cell-to-cell communication signals during the development of ccRCC.

METHODS

We conducted an analysis on the scRNA-seq data of 6 ccRCC and 6 normal kidney tissues. This analysis included sub clustering, CNV analysis, single-cell trajectory analysis, cell-cell communication analysis, and transcription factor analysis. Moreover, we performed validation tests on clinical samples using multiplex immunofluorescence.

RESULTS

This study identified eleven aberrantly activated intercellular signaling pathways in tumor clusters from ccRCC samples. Among these, two of the majors signaling molecules, MIF and SPP1, were mainly secreted by a subpopulation of cancer stem cells. This subpopulation demonstrated high expression levels of the cancer stem cell markers POU5F1 and CD44 (POU5F1hiCD44hiE.T), with the transcription factor POU5F1 regulating the expression of SPP1. Further research demonstrated that SPP1 binds to integrin receptors on the surface of target cells and promotes ccRCC development and progression by activating potential signaling mechanisms such as ILK and JAK/STAT.

CONCLUSION

Aberrantly activated tumor intercellular signaling pathways promote the development and progression of ccRCC. The cancer stem cell subpopulation (POU5F1hiCD44hiE.T) promotes malignant transformation and the development of a malignant phenotype by releasing aberrant signaling molecules and interacting with other tumor cells.

Repurposing Therapeutic Drugs Complexed to Vanadium in Cancer

Repurposing drugs by uncovering new indications for approved drugs accelerates the process of establishing new treatments and reduces the high costs of drug discovery and development. Metal complexes with clinically approved drugs allow further opportunities in cancer therapy-many vanadium compounds have previously shown antitumor effects, which makes vanadium a suitable metal to complex with therapeutic drugs, potentially improving their efficacy in cancer treatment. In this review, covering the last 25 years of research in the field, we identified non-oncology-approved drugs suitable as ligands to obtain different vanadium complexes. Metformin-decavanadate, vanadium-bisphosphonates, vanadyl(IV) complexes with non-steroidal anti-inflammatory drugs, and cetirizine and imidazole-based oxidovanadium(IV) complexes, each has a parent drug known to have different medicinal properties and therapeutic indications, and all showed potential as novel anticancer treatments. Nevertheless, the precise mechanisms of action for these vanadium compounds against cancer are still not fully understood.

Network-based drug repurposing for HPV-associated cervical cancer

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

Spotlights on ubiquitin-specific protease 12 (USP12) in diseases: from multifaceted roles to pathophysiological mechanisms

Ubiquitination is one of the most significant post-translational modifications that regulate almost all physiological processes like cell proliferation, autophagy, apoptosis, and cell cycle progression. Contrary to ubiquitination, deubiquitination removes ubiquitin from targeted protein to maintain its stability and thus regulate cellular homeostasis. Ubiquitin-Specific Protease 12 (USP12) belongs to the biggest family of deubiquitinases named ubiquitin-specific proteases and has been reported to be correlated with various pathophysiological processes. In this review, we initially introduce the structure and biological functions of USP12 briefly and summarize multiple substrates of USP12 as well as the underlying mechanisms. Moreover, we discuss the influence of USP12 on tumorigenesis, tumor immune microenvironment (TME), disease, and related signaling pathways. This study also provides updated information on the roles and functions of USP12 in different types of cancers and other diseases, including prostate cancer, breast cancer, lung cancer, liver cancer, cardiac hypertrophy, multiple myeloma, and Huntington's disease. Generally, this review sums up the research advances of USP12 and discusses its potential clinical application value which deserves more exploration in the future.

The Vital Role Played by Deferiprone in the Transition of Thalassaemia from a Fatal to a Chronic Disease and Challenges in Its Repurposing for Use in Non-Iron-Loaded Diseases

The iron chelating orphan drug deferiprone (L1), discovered over 40 years ago, has been used daily by patients across the world at high doses (75-100 mg/kg) for more than 30 years with no serious toxicity. The level of safety and the simple, inexpensive synthesis are some of the many unique properties of L1, which played a major role in the contribution of the drug in the transition of thalassaemia from a fatal to a chronic disease. Other unique and valuable clinical properties of L1 in relation to pharmacology and metabolism include: oral effectiveness, which improved compliance compared to the prototype therapy with subcutaneous deferoxamine; highly effective iron removal from all iron-loaded organs, particularly the heart, which is the major target organ of iron toxicity and the cause of mortality in thalassaemic patients; an ability to achieve negative iron balance, completely remove all excess iron, and maintain normal iron stores in thalassaemic patients; rapid absorption from the stomach and rapid clearance from the body, allowing a greater frequency of repeated administration and overall increased efficacy of iron excretion, which is dependent on the dose used and also the concentration achieved at the site of drug action; and its ability to cross the blood-brain barrier and treat malignant, neurological, and microbial diseases affecting the brain. Some differential pharmacological activity by L1 among patients has been generally shown in relation to the absorption, distribution, metabolism, elimination, and toxicity (ADMET) of the drug. Unique properties exhibited by L1 in comparison to other drugs include specific protein interactions and antioxidant effects, such as iron removal from transferrin and lactoferrin; inhibition of iron and copper catalytic production of free radicals, ferroptosis, and cuproptosis; and inhibition of iron-containing proteins associated with different pathological conditions. The unique properties of L1 have attracted the interest of many investigators for drug repurposing and use in many pathological conditions, including cancer, neurodegenerative conditions, microbial conditions, renal conditions, free radical pathology, metal intoxication in relation to Fe, Cu, Al, Zn, Ga, In, U, and Pu, and other diseases. Similarly, the properties of L1 increase the prospects of its wider use in optimizing therapeutic efforts in many other fields of medicine, including synergies with other drugs.