TET-mediated 5hmC in breast cancer: mechanism and clinical potential

Breast cancer is the most common cancer among women, with differences in clinical features due to its distinct molecular subtypes. Current studies have demonstrated that epigenetic modifications play a crucial role in regulating the progression of breast cancer. Among these mechanisms, DNA demethylation and its reverse process have been studied extensively for their roles in activating or silencing cancer related gene expression. Specifically, Ten-Eleven Translocation (TET) enzymes are involved in the conversion process from 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), which results in a significant difference in the global level of 5hmC in breast cancer compared with normal tissues. In this review, we summarize the functions of TET proteins and the regulated 5hmC levels in the pathogenesis of breast cancer. Discussions on the clinical values of 5hmC in early diagnosis and the prediction of prognosis are also mentioned.

Unveiling the potential of tankyrase I inhibitors for the treatment of type 2 diabetes mellitus: A hybrid approach using network pharmacology, 2D structural similarity, molecular docking, MD simulation and in-vitro studies

AIMS

This study explores the association between the Wnt signaling pathway and T2DM, emphasizing the role of Tankyrase1 (TNKS1) in metabolic regulation. Using network pharmacology and computational approaches, it aims to identify potential FDA-approved drugs for repurposing as Wnt inhibitors to improve insulin sensitivity and reduce fat accumulation.

MATERIALS AND METHODS

Network pharmacology analysis was performed to explore the association between the Wnt pathway and T2DM, identifying Catenin Beta 1 (CTNBB1) as a key hub gene involved in disease progression. A 2D structural similarity search was conducted using reference tankyrase inhibitors (E7449 and XAV939). Potential drug candidates were subjected to molecular docking and 100 ns molecular dynamics (MD) simulations with the Tankyrase I (PDB ID: 4W6E) protein. The shortlisted compounds were further evaluated for Wnt inhibitory activity using the TCF/LEF reporter assay, while their anti-diabetic potential was assessed through a glucose uptake assay in L6 myoblast cells.

KEY FINDINGS

Niclosamide, Capmatinib, Esomeprazole, and Fenofibrate were identified as promising candidates with strong binding affinities and stable interactions with key amino acids (Gly1185, Ser1221, Tyr1224, Asp1198, Tyr1213, and His1201). Experimental validation through in-vitro Wnt inhibition and glucose uptake assays confirmed that drugs Fenofibrate and Conivaptan exhibited significant Wnt inhibitory activity, suggesting their potential role in modulating T2DM-related pathways.

SIGNIFICANCE

This study highlights the role of the Wnt signaling pathway in T2DM pathogenesis and identifies potential drug candidates for repurposing as Tankyrase1/Wnt inhibitors. The findings provide a foundation for further in-vivo investigations into the anti-diabetic potential of the identified drugs, paving the way for novel therapeutic strategies in T2DM management.

Unraveling the Role of the Wnt Pathway in Hepatocellular Carcinoma: From Molecular Mechanisms to Therapeutic Implications

Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors in the world, and its incidence and mortality have increased year by year. HCC research has increasingly focused on understanding its pathogenesis and developing treatments.The Wnt signaling pathway, a complex and evolutionarily conserved signal transduction system, has been extensively studied in the genesis and treatment of several malignant tumors. Recent investigations suggest that the pathogenesis of HCC may be significantly influenced by dysregulated Wnt/β-catenin signaling. This article aimed to examine the pathway that controls Wnt signaling in HCC and its mechanisms. In addition, we highlighted the role of this pathway in HCC etiology and targeted treatment.

Portable, Wireless Potentiostat Sensor for Ultra-Sensitive, Real-Time Detection of 5hmC in Genomic DNA Using Tree-Like Graphene

Aberrant alterations in genomic 5-hydroxymethylcytosine (5hmC), an oxidation product of 5-methylcytosine (5mC) by Ten-eleven translocation (TET) enzymes, are frequently associated with cancers. Quick and precise 5hmC quantification is vital since it is a key biomarker for diagnosis, pathophysiology, and therapy. Here, we present a portable, wireless potentiostat sensor for real-time, ultrasensitive 5hmC-DNA sensing based on a tree-like graphene (teG)-modified screen-printed microelectrode. One-pot electrochemical exfoliation of pencil graphite enabled the cost-effective, eco-friendly, and scalable synthesis of teG, which exhibited high electrical conductivity, excellent electrochemical conductivity, low surface roughness, and high 5hmC-DNA adsorption, surpassing those of pencil graphite (pG) and graphene oxide (GO). The teG-modified gold electrodes exhibited exceptional sensitivity (6.15 × 10-6 mM-1 cm-2), selectivity, and reproducibility, with an ultralow detection limit of 12.6 fM for 5hmC-DNA. The sensor's performance was validated by quantifying 5hmC levels in genomic DNA from various biological specimens, including primary mouse tissues with altered TET function, mouse hepatocellular carcinoma, and human prostate cancer cell lines. To enhance practicality, a flexible, screen-printed microelectrode on mulberry paper was developed and integrated with a portable, wireless potentiostat powered by the Arduino Nano 33 IoT. Open-circuit potential (OCP)-based detection enabled label-free, real-time monitoring with wireless data transmission to an Android mobile application, successfully differentiating 5hmC levels between cancerous and noncancerous cells. These findings highlight teG's high surface area, superior charge transport, and scalability, positioning it as a promising platform for next-generation biosensing. The developed sensor provides a rapid, cost-effective, and highly sensitive tool for 5hmC quantification, with significant implications for early cancer diagnostics and treatment.

Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing

We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we constructed a hybrid model with CNN and transformer layers, named HK model 2. We improve the area under the receiver operating characteristic curve (AUC) for 5mC detection from 0.91 for HK model 1 to 0.99 for HK model 2. We further demonstrate that HK model 2 can detect other types of base modifications, such as 5-hydroxymethylcytosine (5hmC) and N6-methyladenine (6mA). Using HK model 2 to analyze 5mC patterns of cell-free DNA (cfDNA) molecules, we demonstrate the enhanced detection of patients with hepatocellular carcinoma, with an AUC of 0.97. Moreover, HK model 2-based detection of 6mA enables the detection of jagged ends of cfDNA and the delineation of cellular chromatin structures. HK model 2 is thus a versatile tool expanding the applications of single molecule real-time sequencing in liquid biopsies.

Protocol for the analysis of cell-free DNA end characteristics for accurate cancer diagnosis

Fragmentation patterns of plasma cell-free DNA (cfDNA) are promising biomarkers in cancer diagnosis. Here, we present a protocol to enrich tumor-derived cfDNA molecules through end selection. We describe steps for installing software, aligning plasma cfDNA data to the reference genome, and performing end selection on cfDNA. We then detail procedures for building cancer diagnostic models with artificial intelligence. Overall, we provide commands to align cfDNA whole-genome sequencing data starting from raw reads, then extract fragmentomic features, and finally build diagnostic models with performance evaluations. For complete information on the generation and use of this protocol, please refer to Ju et al.1.

A multiomics dataset of paired CT image and plasma cell-free DNA end motif for patients with pulmonary nodules

Diagnosing lung cancer at a curable stage offers the opportunity for a favorable prognosis. The emerging epigenomics analysis on plasma cell-free DNA (cfDNA), including 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications, has acted as a promising approach facilitating the identification of lung cancer. And, integrating 5mC biomarker with chest computed tomography (CT) image features could optimize the diagnosis of lung cancer, exceeding the performance of models built on single feature. However, the clinical applicability of integrated markers might be limited by the potential risk of overfitting due to small sample size. Hence, we prospectively collected peripheral blood sample and the paired chest CT images of 2032 patients with indeterminate pulmonary nodules across 5 centers, and constructed a large-scale, multi-institutional, multiomics database that encompass CT imaging data and plasma cfDNA fragmentomic in 5mC-, 5hmC-enriched regions. To our best knowledge, this dataset is the first radio-epigenomic dataset with the largest sample size, and provides multi-dimensional insights for early diagnosis of lung cancer, facilitating the individuated management for lung cancer.

Chemical Modification Coupled with Isothermal CRISPR-Based Assay for Sensitive Detection of DNA Hydroxymethylation

5-Hydroxymethylcytosine (5hmC) plays a key role in the DNA demethylation process and serves as a stable epigenetic marker in the human genome which is closely associated with disease progression, particularly in diabetes, colorectal cancer, and liver cancer. However, convenient and sensitive methods for detecting and quantifying 5hmC in the genome are scarce, especially in complex biological environments. Herein, a novel attempt at hypersensitive quantitative detection of 5hmC was presented. A multifunctional photosensitive probe was therefore introduced for specific labeling, enrichment, and elution of 5hmC-DNA. Combining with isothermal assay leveraging rolling circle amplification and Cas12a for accurate recognition, we achieved quantitative detection of 5hmC DNA in trace amounts at a level of 11 fM. Global 5hmC was measured in various biological samples using as little as 10 ng of input DNA by a real-time PCR instrument. The reported approach imposed no sequence restrictions, demonstrating promising potential for detecting modified bases in trace amounts of nucleic acids within complex environments, such as blood, urine, and saliva samples.

Implementation of SAMPL guidelines: Recommendations for improving statistical reporting in biomedical journals

The quality of statistical reporting in biomedical journals remains insufficient despite the introduction of SAMPL guidelines in 2015. These guidelines aim to improve clarity and accuracy but are underutilised by authors and editorial boards. Common deficiencies include unclear descriptions of statistical test purposes, inadequate reporting of effect sizes, poor analysis of assumptions, and limited consideration of outliers. Addressing these challenges requires broader adoption of SAMPL recommendations, improved statistical literacy among researchers and editors, and stronger editorial oversight. To enhance transparency and reliability in biomedical research, the SAMPL guidelines should become standard practice, supported by targeted training and clear guidance for authors.

Tankyrase 2 as a therapeutic target in non-small cell lung cancer: Implications for apoptosis and migration

This letter addresses Wang and Zhang's investigation into the role of tankyrase 2 (TNKS2) as a pivotal driver of malignancy in non-small cell lung cancer (NSCLC) through mechanisms including apoptosis inhibition, enhanced cellular migration, and β-catenin pathway activation. Their study in NSCLC cell lines demonstrates that TNKS2 overexpression stabilizes β-catenin, subsequently triggering oncogenic gene expression and facilitating cellular migration-key attributes of metastatic potential. These insights position TNKS2 as a compelling target for therapy and a potential prognostic marker in NSCLC. Nevertheless, translating these in vitro findings to clinical practice requires validation in in vivo models. Additionally, further research should investigate TNKS2 expression in patient samples and assess its implications in therapy resistance and combination treatment strategies.

cfDNA hydroxymethylcytosine profiling for detection metastasis and recurrence of Esophageal Squamous Cell Carcinoma

BACKGROUND

A blood-based approach to monitor metastasis and recurrence of esophageal squamous cell carcinoma (ESCC) remains undeveloped. This study aimed to establish a dependable model utilizing cfDNA 5-hydroxymethylcytosines (5hmC) signatures to detect these conditions in ESCC.

METHODS

The 5hmC-Seal technique was employed to generate comprehensive 5hmC profiles from the plasma cell-free DNA (cfDNA) of 122 ESCC patients, classified into 72 with metastasis, 50 without metastasis, 30 with recurrence, and 92 without recurrence. Initial steps involved identifying distinct hydroxymethylation signatures linked to metastasis and recurrence. Machine learning algorithms were then utilized to construct predictive models.

RESULTS

The study confirmed that 5hmC-based markers are predictive of metastasis and recurrence among ESCC patients. The analysis of 14 5hmC biomarkers revealed a sensitivity of 88.90% and a specificity of 84.00% (AUC = 0.922) in differentiating patients with ESCC metastasis from those without in the validation cohort. Similarly, 11 5hmC biomarkers showed a sensitivity of 93.30% and a specificity of 89.10% (AUC = 0.936) in identifying recurrent versus non-recurrent ESCC cases. Additionally, a wp-score for metastasis and recurrence, derived from the 5hmC marker, prognosticated patient outcomes.

CONCLUSIONS

The findings indicate that 5hmC markers from cfDNA serve as effective epigenetic indicators for the non-invasive detection of ESCC metastasis and recurrence.

Targeting Latent HIV Reservoirs: Effectiveness of Combination Therapy with HDAC and PARP Inhibitors

The "Kick and Kill" strategy, which aims to reactivate latent HIV reservoirs and facilitate the clearance of reactivated HIV-infected cells, has yet to achieve a functional cure due to the limited efficacy of current latency reversal agents. This study evaluates the combination efficacy of histone deacetylase (HDAC) inhibitor with poly(ADP-ribose) polymerase (PARP) inhibitor in latency reversal and immune-mediated clearance. Latently infected J-Lat cells and dual-fluorescent HIV-infected primary CD4 T cells were treated with the HDAC inhibitor (vorinostat) and one of four PARP inhibitors (olaparib, rucaparib, niraparib, or talazoparib). PARP inhibitors, when administered alone, showed no latency reversal activity. However, when combined with vorinostat, their efficacy increased threefold compared to vorinostat alone. This effect was mediated by the inhibition of tankyrase, a PARP superfamily member, which modulates the Hippo signaling pathway. In HIVGR670-infected primary cells, the combination reduced the reservoir size by 67%. In addition, talazoparib alone significantly reduced actively infected cells by 50%. Talazoparib-treated peripheral blood mononuclear cells co-cultured with K562 cells demonstrated enhanced NK-cell-mediated cytotoxicity, with a 10% reduction in K562 cell viability. These findings demonstrate that combining HDAC and PARP inhibitors augments latency reversal and reservoir reduction. With both the HDAC inhibitors and PARP inhibitors used in this study approved by the FDA for cancer treatment, this combination therapy holds strong potential for rapid clinical integration, contingent upon the confirmation of efficacy and safety in ongoing in vivo studies.

5-hydroxymethylcytosine sequencing of plasma cell-free DNA identifies epigenomic features in prostate cancer patients receiving androgen deprivation therapies

BACKGROUND

We evaluated whether 5hmC signatures in cell-free DNA (cfDNA) are associated with treatment failure to androgen-deprivation therapies (ADT) among men with hormone-naive prostate cancer.

METHODS

We collected a total of 139 serial plasma samples from 55 prostate cancer patients receiving ADT at 3 time points including baseline (before initiating ADT, n = 55); 3 months (after initiating ADT, n = 55); and disease progression (n = 15) within 24 months or 24 months if no progression was detected (n = 14). We used selective chemical labeling sequencing to quantify 5hmC abundance across the genome and Kaplan-Meier analysis to assess survival association.

RESULTS

Here we show a significant 5hmC difference in 1642 of 23433 genes between patients with and without progression (false discovery rate [FDR] < 0.1) in baseline plasma samples. Patients with progression demonstrate significant 5hmC enrichments in multiple hallmark gene sets, with androgen responses as the top enriched gene-set (FDR = 1.19E-13). We further show a significant association between high activity scores in these gene sets and poor progression-free survival (P < 0.05), even after adjusting for circulating tumor DNA fraction and prostate-specific antigen values. Additionally, our longitudinal analysis shows that the high activity score is significantly reduced after 3 months of initiating ADT (P = 0.0004) but returns to higher levels when the disease progresses (P = 0.0317).

CONCLUSIONS

5hmC-based activity scores from gene-sets involved in AR responses show great potential in assessing treatment resistance, monitoring disease progression, and identifying patients who would benefit from upfront treatment intensification. However, further studies are needed to validate these findings.

A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables

Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

Neutrophil extracellular traps impede cancer metastatic seeding via protease-activated receptor 2-mediated downregulation of phagocytic checkpoint CD24

BACKGROUND

Phagocytic clearance by macrophages represents a critical immune surveillance mechanism in cancer liver metastasis. Neutrophils, the most abundant immune cells encountered by cancer cells in circulation, play key roles in metastasis through neutrophil extracellular traps (NETs). Although NETs promote macrophage phagocytosis during infection, whether they regulate phagocytosis during cancer metastasis is unknown. The present study aimed to explore the roles of NETs in regulating macrophage phagocytosis during the seeding process of liver metastasis and the mechanisms underlying the roles.

METHODS

A lipopolysaccharide-induced NET model was applied to study the role of NETs on colorectal cancer (CRC) liver metastasis. The neutrophils isolated from human peripheral blood were stimulated with PMA to release NETs, which were collected and added to the cultures of different CRC cell lines for in vitro studies. Macrophage phagocytosis was assessed with flow cytometry in vitro and in vivo. RNA-seq and microRNA array analyses were performed to identify key pathways regulated by NETs and downstream key molecules. The macrophage phenotypes were evaluated using immunohistochemistry, flow cytometry, and cytokine and chemokine arrays.

RESULTS

NETs promote macrophage phagocytosis both in vitro and in vivo. Neutrophil elastase (NE), which was able to inactivate the canonical signal of protease-activated receptor 2 (PAR2), downregulated the phagocytotic checkpoint CD24. Notably, PAR2 deficiency imitated the effect of NETs on phagocytosis and CD24. Mechanistic studies indicated that inhibiting PAR2 expression upregulated miR-34a and miR-146a and downregulated CD24 in cancer cells. In addition, PAR2 depletion enhanced the recruitment and M1 polarization of macrophages by upregulating CSF-1 and CXCL1. The correlation of NETs/NE and CD24 was corroborated using human CRC specimens. Furthermore, PAR2 blockade combined with an anti-EGFR antibody (cetuximab (CTX)) synergistically enhanced the phagocytic ability of macrophages and suppressed liver metastasis in vivo.

CONCLUSIONS

NET-derived elastase inactivated PAR2 canonical signaling and promoted phagocytosis by downregulating CD24, which functions as a phagocytotic checkpoint in CRC liver metastasis. Thus, PAR2 inhibitors combined with CTX may serve as a novel therapeutic strategy against advanced CRC.

Current Approaches of Pancreatic Cancer Surveillance in High-Risk Individuals

Currently, those recommended to undergo pancreatic cancer (PC) surveillance include appropriately aged individuals at high risk of PC due to an identifiable genetic susceptibility or those without identifiable genetic susceptibility who nonetheless have a strong family history of PC. With increases in identification of individuals at high risk for PC and increased use of PC surveillance in clinical practice, there has been increasing debate about who should undergo surveillance as well as how surveillance should be performed including use of imaging and blood-based testing. Furthermore, there is increasing interest in the outcomes of PC surveillance in high-risk individuals with some studies demonstrating that surveillance leads to downstaging of PC and improvements in survival. In this review, we summarize the current state of PC surveillance in high-risk individuals, providing an overview of the risk factors associated with PC, selection of high-risk individuals for PC surveillance, and the current, but non-uniform, recommendations for performing PC surveillance. Additionally, we review approaches to apply various imaging and blood-based tests to surveillance and the outcomes of PC surveillance.

Pharmacophore-based identification and in Silico characterization of microbial metabolites as potential modulators of Wnt signaling pathway in colorectal cancer therapy

Aberrant activation of the Wnt/β-catenin signaling pathway, primarily driven by APC mutation and AXIN degradation via Tankyrase, contributes significantly to colorectal cancer (CRC) progression and metastasis. The accumulation of β-catenin, resulting from the dysregulated ubiquitination, underscores the need for alternative therapeutic strategies targeting Tankyrase and β-catenin. This present study explores microbial metabolites as a source of novel anti-cancer agents, leveraging their unique bioactivity and structural diversity, often exhibiting superior target specificity and lower toxicity than synthetic drugs. Through a computational drug discovery pipeline, a large library of 27641 microbial metabolites was initially screened based on multiple drug-likeliness criteria, resulting in the selection of 2527 compounds. Among the screened compounds, an integrated computational workflow comprising molecular docking, molecular dynamic simulations (MDS), MM/PBSA analysis, and Principal component analysis (PCA) identified Terreustoxin I (T1) as a potential Tankyrase inhibitor. In contrast, compound 10- phenyl-[12]-cytochalasin Z16 (B1) demonstrated a strong binding affinity within the β-catenin active site. Under physiological conditions, these lead compounds were evaluated for conformational stability, binding efficacy, and dynamic behavior. Additionally, ADMET profiling, physiochemical properties, and bioactivity score predictions confirmed the identified compounds' pharmacokinetic suitability and reduced toxicity profile. In silico, cytotoxicity predictions showed significant activity against SW480 and HCT90 colorectal cell lines, with additional anti-neoplastic and anti-leukemic properties, strengthening their candidacy as effective anti-cancer agents. These findings provide a foundation for further experimental validation and development of novel CRC therapies with improved safety and efficacy potential.

Hepatocellular carcinoma: signaling pathways and therapeutic advances

Liver cancer represents a major global health concern, with projections indicating that the number of new cases could surpass 1 million annually by 2025. Hepatocellular carcinoma (HCC) constitutes around 90% of liver cancer cases and is primarily linked to factors incluidng aflatoxin, hepatitis B (HBV) and C (HCV), and metabolic disorders. There are no obvious symptoms in the early stage of HCC, which often leads to delays in diagnosis. Therefore, HCC patients usually present with tumors in advanced and incurable stages. Several signaling pathways are dis-regulated in HCC and cause uncontrolled cell propagation, metastasis, and recurrence of HCC. Beyond the frequently altered and therapeutically targeted receptor tyrosine kinase (RTK) pathways in HCC, pathways involved in cell differentiation, telomere regulation, epigenetic modification and stress response also provide therapeutic potential. Investigating the key signaling pathways and their inhibitors is pivotal for achieving therapeutic advancements in the management of HCC. At present, the primary therapeutic approaches for advanced HCC are tyrosine kinase inhibitors (TKI), immune checkpoint inhibitors (ICI), and combination regimens. New trials are investigating combination therapies involving ICIs and TKIs or anti-VEGF (endothelial growth factor) therapies, as well as combinations of two immunotherapy regimens. The outcomes of these trials are expected to revolutionize HCC management across all stages. Here, we provide here a comprehensive review of cellular signaling pathways, their therapeutic potential, evidence derived from late-stage clinical trials in HCC and discuss the concepts underlying earlier clinical trials, biomarker identification, and the development of more effective therapeutics for HCC.

5-hydroxymethylcytosine features of portal venous blood predict metachronous liver metastases of colorectal cancer and reveal phosphodiesterase 4 as a therapeutic target

Metachronous liver metastases (MLM) are characterised by high incidence and high mortality in clinical colorectal cancer treatment. Currently traditional clinical methods cannot effectively predict and prevent the occurrence of metachronous liver metastasis in colorectal cancer. Based on 5hmC-Seal analysis of blood and tissue samples, this study found that portal venous blood was more relevant to tumour gDNA than peripheral blood. We performed a novel epigenetic liquid biopsy strategy using the 10 5hmC epigenetic alterations, to accurately distinguish MLM patients from patients without metastases. Among these epigenetic alterations, phosphodiesterase 4 (PDE4D) was highly increased in MLM patients and correlated with poor survival. Moreover, our studies demonstrated that PDE4D was a key metastasis-driven target for drug development. Interfering with the function of PDE4D significantly repressed liver metastases. Similarly, roflumilast, a PDE4 inhibitor for chronic obstructive pulmonary disease (COPD) therapy, also inhibits liver metastases. Further studies indicate that blocking the function of PDE4D can affect CRC invasion through the HIF-1α-CCN2 pathway. To develop a more efficient PDE4 inhibitor and reduce the occurrence of adverse events, we also designed several new compounds based on 2-arylbenzofurans and discovered lead L11 with potent affinity for PDE4D and significant suppression of liver metastases. In this work, our study provides a promising strategy for predicting metachronous liver metastasis and discovers L11 as a potential repurposed drug for inhibiting liver metastasis, which have the potential to benefit patients with CRC in the future. KEY POINTS: 5hmC epigenetic markers derived from portal venous blood could accurately predict metachronous metastasis of colorectal cancer. PDE4D was a key metastasis-driven target that promoted metachronous metastasis via the HIF-1α-CCN2 pathway. The newly synthesised compound L11 could specifically inhibit PDE4D and abolish metachronous metastasis of colorectal cancer without obvious toxic side effects.

Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks

Phenotype-driven approaches identify disease-counteracting compounds by analyzing the phenotypic signatures that distinguish diseased from healthy states. These approaches can guide the discovery of targeted perturbations, including small-molecule drugs and genetic interventions, that modulate disease phenotypes toward healthier states. Here, we introduce PDGrapher, a causally inspired graph neural network (GNN) designed to predict combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem of directly predicting the perturbagens needed to achieve a desired response. PDGrapher is a GNN that embeds disease cell states into gene regulatory or protein-protein interaction networks, learns a latent representation of these states, and identifies the optimal combinatorial perturbations that most effectively shift the diseased state toward the desired treated state within that latent space. In experiments in nine cell lines with chemical perturbations, PDGrapher identified effective per-turbagens in up to 13.33% more test samples than competing methods and achieved a normalized discounted cumulative gain of up to 0.12 higher to classify therapeutic targets. It also demonstrated competitive performance on ten genetic perturbation datasets. A key advantage of PDGrapher is its direct prediction paradigm, in contrast to the indirect and computationally intensive models traditionally employed in phenotype-driven research. This approach accelerates training by up to 25 times compared to existing methods. PDGrapher provides a fast approach for identifying therapeutic perturbations and advancing phenotype-driven drug discovery.

Storage and Query of Drug Knowledge Graphs Using Distributed Graph Databases: A Case Study

BACKGROUND

Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs' storage and query performance based on distributed graph databases. This study evaluates the feasibility and performance of distributed graph databases in managing large-scale drug knowledge graphs.

METHODS

First, a drug knowledge graph storage and query system is designed based on the Nebula Graph database. Second, the system's writing and query performance is evaluated. Finally, two drug repurposing benchmarks are used to provide a more extensive and reliable assessment.

RESULTS

The performance of distributed graph databases surpasses that of single-machine databases, including data writing, regular queries, constrained queries, and concurrent queries. Additionally, the advantages of distributed graph databases in writing performance become more pronounced as the data volume increases. The query performance benefits of distributed graph databases also improve with the complexity of query tasks. The drug repurposing evaluation results show that 78.54% of the pathways are consistent with currently approved drug treatments according to repoDB. Additionally, 12 potential pathways for new drug indications are found to have literature support according to DrugRepoBank.

CONCLUSIONS

The proposed system is able to construct, store, and query a large graph of multisource drug knowledge and provides reliable and explainable drug-disease paths for drug repurposing.

Global DNA methylomes reveal oncogenic-associated 5-hydroxylmethylated cytosine (5hmC) signatures in the cell-free DNA of cancer patients

Characterization of tumor epigenetic aberrations is integral to understanding the mechanisms of tumorigenesis and provide diagnostic, prognostic, and predictive information of high clinical relevance. Among the different tumor-associated epigenetic signatures, 5 methyl-cytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are the two most well-characterized DNA methylation alterations linked to cancer pathogenesis. 5hmC has a tissue-specific distribution and its abundance is subjected to changes in tumor DNA, making it a promising biomarker. Detecting tumor-related DNA methylation alterations in tissues is highly invasive, while the analysis of the cell-free DNA (cfDNA) is poised to supplement, if not replace, surgical biopsies. Despite many studies attempted to identify new epigenetic targets for liquid biopsy assays, little is known about the regulatory roles of 5hmC, its impacts on the molecular phenotypes in tumors. Most importantly, whether the oncogenic-associated 5hmC signatures found in tumor tissues can be recapitulated in patients' cfDNA. In this study, we performed the unbiased and simultaneous detection of 5mC and 5hmC whole-genome DNA modifications at base-resolution from two distinct cancer cohorts, from patients with bladder cancer or B-Cell lymphoma, their corresponding normal tissues, and cfDNAs from plasma. We analyzed tissue-specific methylation patters and searched for signatures in gene coding and regulatory regions linked to cancerous states. We then looked for methylation signatures in patients' cfDNA to determine if they were consistent with the tumor-specific patterns. We determined the functional significance of 5hmC in tissue specific transcription and uncovered hundreds of tumor-associated 5hmC signatures. These tumor-associated 5hmC changes, particularly in genes and enhancers, were functionally significant in tumorigenesis pathways and correlated with tumor specific gene expression. To investigate if cfDNA is a faithful surrogate for tumor-associated 5hmC, we devised a targeted capture strategy to examine the alterations of 5hmC in cfDNA from patients with bladder cancer and lymphoma with sufficient sensitivity and specificity and confirmed that they recapitulated the patterns we observed in tumor tissues. Our results provide analytic validation of 5hmC as a cancer-specific biomarker. The methods described here for systematic characterization of 5hmC at functional elements open new avenues to discover epigenetic markers for non-invasive diagnosis, monitoring, and stratifying cancer.

Reversal gene expression assessment for drug repurposing, a case study of glioblastoma

BACKGROUND

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement.

METHODS

This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database.

RESULTS

We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFC) that the drug candidates could induce. Among five prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells.

CONCLUSIONS

The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

HERB 2.0: an updated database integrating clinical and experimental evidence for traditional Chinese medicine

Clinical trials and meta-analyses are considered high-level medical evidence with solid credibility. However, such clinical evidence for traditional Chinese medicine (TCM) is scattered, requiring a unified entrance to navigate all available evaluations on TCM therapies under modern standards. Besides, novel experimental evidence has continuously accumulated for TCM since the publication of HERB 1.0. Therefore, we updated the HERB database to integrate four types of evidence for TCM: (i) we curated 8558 clinical trials and 8032 meta-analyses information for TCM and extracted clear clinical conclusions for 1941 clinical trials and 593 meta-analyses with companion supporting papers. (ii) we updated experimental evidence for TCM, increased the number of high-throughput experiments to 2231, and curated references to 6 644. We newly added high-throughput experiments for 376 diseases and evaluated all pairwise similarities among TCM herbs/ingredients/formulae, modern drugs and diseases. (iii) we provide an automatic analyzing interface for users to upload their gene expression profiles and map them to our curated datasets. (iv) we built knowledge graph representations of HERB entities and relationships to retrieve TCM knowledge better. In summary, HERB 2.0 represents rich data type, content, utilization, and visualization improvements to support TCM research and guide modern drug discovery. It is accessible through http://herb.ac.cn/v2 or http://47.92.70.12.

PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization

Single-cell perturbation (scPerturbation) sequencing techniques, represented by single-cell genetic perturbation (e.g. Perturb-seq) and single-cell chemical perturbation (e.g. sci-Plex), result from the integration of single-cell toolkits with conventional bulk screening methods. These innovative sequencing techniques empower researchers to dissect perturbation effects in biological systems at an unprecedented resolution. Despite these advancements, a notable gap exists in the availability of a dedicated database for exploring scPerturbation data. To address this gap, we present PerturBase, the most comprehensive database designed for the analysis and visualization of scPerturbation data (http://www.perturbase.cn/). PerturBase curates 122 datasets from 46 publicly available studies, covering 115 single-modal and 7 multi-modal datasets that include 24 254 genetic and 230 chemical perturbations from approximately 5 million cells. The database, comprising the 'Dataset' and 'Perturbation' modules, provides insights into various results, encompassing quality control, denoising, differential gene expression analysis, functional analysis of perturbation effects and characterization of relationships between perturbations. All the datasets and results are presented on user-friendly, easy-to-browse web pages and can be visualized through intuitive and interactive plot and table formats. In summary, PerturBase stands as a pioneering, high-content database intended for searching, visualizing and analyzing scPerturbation datasets, contributing to a deeper understanding of perturbation effects.

Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p < 0.050 for all). The superiority of the clinic-RadmC maintains well even after adjusting for clinic-radiological variables. Furthermore, the clinic-RadmC-guided strategy could reduce the unnecessary invasive procedures for benign IPLs by 10.9% ~ 35%, and avoid the delayed treatment for lung cancer by 3.1% ~ 38.8%. In summary, our study indicates that the clinic-RadmC provides a more effective and noninvasive tool for optimizing lung cancer diagnoses, thus facilitating the precision interventions.

"Aging Clocks" Based on Cell-Free DNA

Aging is associated with systemic changes in the physiological and molecular parameters of the body. These changes are referred to as biomarkers of aging. Statistical models that link changes in individual biomarkers to biological age are called aging clocks. These tools facilitate a comprehensive evaluation of bodily health and permit the quantitative determination of the rate of aging. A particularly promising area for the development of aging clocks is the analysis of cell-free DNA (cfDNA), which is present in the blood and contains numerous potential biomarkers. This review explores in detail the fragmentomics, topology, and epigenetic landscape of cfDNA as possible biomarkers of aging. The review further underscores the potential of leveraging single-molecule sequencing of cfDNA in conjunction with long-read technologies to simultaneously profile multiple biomarkers, a strategy that could lead to the development of more precise aging clocks.

Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq

Dynamic 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications to DNA regulate gene expression in a cell-type-specific manner and are associated with various biological processes, but the two modalities have not yet been measured simultaneously from the same genome at the single-cell level. Here we present SIMPLE-seq, a scalable, base resolution method for joint analysis of 5mC and 5hmC from thousands of single cells. Based on orthogonal labeling and recording of 'C-to-T' mutational signals from 5mC and 5hmC sites, SIMPLE-seq detects these two modifications from the same molecules in single cells and enables unbiased DNA methylation dynamics analysis of heterogeneous biological samples. We applied this method to mouse embryonic stem cells, human peripheral blood mononuclear cells and mouse brain to give joint epigenome maps at single-cell and single-molecule resolution. Integrated analysis of these two cytosine modifications reveals distinct epigenetic patterns associated with divergent regulatory programs in different cell types as well as cell states.

Wnt/β-Catenin Signaling in Liver Pathobiology

The liver has a critical role in regulating host metabolism, immunity, detoxification, and homeostasis. Proper liver function is essential for host health, and dysregulation of hepatic signaling pathways can lead to the onset of disease. The Wnt/β-catenin signaling pathway is an important regulator of liver homeostasis and function. Throughout life, hepatic Wnt/β-catenin signaling contributes to liver development and growth, metabolic zonation, and regeneration. Extensive research has demonstrated that aberrant Wnt/β-catenin signaling drives liver pathologies, including cancers, steatohepatitis, and cholestasis. In this review, we discuss the Wnt/β-catenin pathway as it pertains to liver function and how disruptions in this pathway contribute to the onset and progression of liver diseases. Further, we discuss ongoing research that targets the Wnt/β-catenin pathway for the treatment of liver pathologies.

Revolutionising Neurological Therapeutics: Investigating Drug Repurposing Strategies

Repurposing drugs (DR) has become a viable approach to hasten the search for cures for neurodegenerative diseases (NDs). This review examines different off-target and on-target drug discovery techniques and how they might be used to find possible treatments for non-diagnostic depressions. Off-target strategies look at the known or unknown side effects of currently approved drugs for repositioning, whereas on-target strategies connect disease pathways to targets that can be treated with drugs. The review highlights the potential of experimental and computational methodologies, such as machine learning, proteomic techniques, network and genomics-based approaches, and in silico screening, in uncovering new drug-disease correlations. It also looks at difficulties and failed attempts at drug repurposing for NDs, highlighting the necessity of exact and standardised procedures to increase success rates. This review's objectives are to address the purpose of drug repurposing in human disorders, particularly neurological diseases, and to provide an overview of repurposing candidates that are presently undergoing clinical trials for neurological conditions, along with any possible causes and early findings. We then include a list of drug repurposing strategies, restrictions, and difficulties for upcoming research.

Advances in the diagnosis of colorectal cancer: the application of molecular biomarkers and imaging techniques: a literature review

Background

Following neoplasms of the lung and breast, colorectal cancer (CRC) is the third most frequent malignancy globally. Screening for CRC at the age of 50 years is strongly encouraged for prompt earlier diagnosis owing to prognoses being greatly correlated with time of detection and cancer staging.

Aim

This review aimed to elucidate the most recent advancements in the detection of CRC, with an emphasis on the latest innovations in diagnostic molecular biomarkers in conjunction with radiological imaging alongside stool-based tests for CRC screening.

Methods

A comprehensive review of the literature was performed, focusing on specific terms in different electronic databases, including that of PubMed/MEDLINE. Keywords pertaining to "colorectal cancer," "diagnosis," "screening," "imaging," and "biomarkers," among others, were employed in the search strategy. Articles screened and evaluated were deemed relevant to the study aim and were presented in the medium of the English language.

Results

There have been several innovations in the diagnostics and identification of CRC. These generally comprise molecular biomarkers, currently being studied for suitability in disease detection. Examples of these include genetic, epigenetic, and protein biomarkers. Concurrently, recent developments in CRC diagnostics highlight the advancements made in radiological imaging that offer precise insights on tumor biology in addition to morphological information. Combining these with statistical methodologies will increase the sensitivity and specificity of CRC diagnostics. However, putting these strategies into reality is hampered by several issues.

Conclusion

Progress in diagnostic technology alongside the identification of a few prognostic predictive molecular biomarkers suggested great promise for prompt detection and management of CRC. This clearly necessitates further efforts to learn more in this specific sector.

Repurposing an epithelial sodium channel inhibitor as a therapy for murine and human skin inflammation

Inflammatory skin disease is characterized by a pathologic interplay between skin cells and immunocytes and can result in disfiguring cutaneous lesions and systemic inflammation. Immunosuppression is commonly used to target the inflammatory component; however, these drugs are often expensive and associated with side effects. To identify previously unidentified targets, we carried out a nonbiased informatics screen to identify drug compounds with an inverse transcriptional signature to keratinocyte inflammatory signals. Using psoriasis, a prototypic inflammatory skin disease, as a model, we used pharmacologic, transcriptomic, and proteomic characterization to find that benzamil, the benzyl derivative of the US Food and Drug Administration-approved diuretic amiloride, effectively reversed keratinocyte-driven inflammatory signaling. Through three independent mouse models of skin inflammation (Rac1G12V transgenic mice, topical imiquimod, and human skin xenografts from patients with psoriasis), we found that benzamil disrupted pathogenic interactions between the small GTPase Rac1 and its adaptor NCK1. This reduced STAT3 and NF-κB signaling and downstream cytokine production in keratinocytes. Genetic knockdown of sodium channels or pharmacological inhibition by benzamil prevented excess Rac1-NCK1 binding and limited proinflammatory signaling pathway activation in patient-derived keratinocytes without systemic immunosuppression. Both systemic and topical applications of benzamil were efficacious, suggesting that it may be a potential therapeutic avenue for treating skin inflammation.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Wnt Signaling in Hepatocellular Carcinoma: Biological Mechanisms and Therapeutic Opportunities

Hepatocellular carcinoma (HCC), characterized by significant morbidity and mortality rates, poses a substantial threat to human health. The expression of ligands and receptors within the classical and non-classical Wnt signaling pathways plays an important role in HCC. The Wnt signaling pathway is essential for regulating multiple biological processes in HCC, including proliferation, invasion, migration, tumor microenvironment modulation, epithelial-mesenchymal transition (EMT), stem cell characteristics, and autophagy. Molecular agents that specifically target the Wnt signaling pathway have demonstrated significant potential for the treatment of HCC. However, the precise mechanism by which the Wnt signaling pathway interacts with HCC remains unclear. In this paper, we review the alteration of the Wnt signaling pathway in HCC, the mechanism of Wnt pathway action in HCC, and molecular agents targeting the Wnt pathway. This paper provides a theoretical foundation for identifying molecular agents targeting the Wnt pathway in hepatocellular carcinoma.

Targeting Hepatic Cancer Stem Cells (CSCs) and Related Drug Resistance by Small Interfering RNA (siRNA)

Tumor recurrence after curative therapy and hepatocellular carcinoma (HCC) cells' resistance to conventional therapies is the reasons for the worse clinical results of HCC patients. A tiny population of cancer cells with a strong potential for self-renewal, differentiation, and tumorigenesis has been identified as cancer stem cells (CSCs). The discovery of CSC surface markers and the separation of CSC subpopulations from HCC cells have been made possible by recent developments in the study of hepatic (liver) CSCs. Hepatic CSC surface markers include epithelial cell adhesion molecules (EpCAM), CD133, CD90, CD13, CD44, OV-6, ALDH, and K19. CSCs have a significant influence on the development of cancer, invasiveness, self-renewal, metastasis, and drug resistance in HCC, and thus provide a therapeutic chance to treat HCC and avoid its recurrence. Therefore, it is essential to develop treatment approaches that specifically and effectively target hepatic stem cells. Given this, one potential treatment approach is to use particular small interfering RNA (siRNA) to target CSC, disrupting their behavior and microenvironment as well as changing their epigenetic state. The characteristics of CSCs in HCC are outlined in this study, along with new treatment approaches based on siRNA that may be used to target hepatic CSCs and overcome HCC resistance to traditional therapies.

OrthologAL: A Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data

Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, pre-clinical research often requires in vivo experiments in non-human species, which makes capitalizing on such valuable resources difficult. To facilitate the application of pharmacotranscriptomic databases to pre-clinical research models and to facilitate human orthologous conversion of non-human transcriptomes, we introduce OrthologAL. OrthologAL leverages the BioMart database to access different gene sets from Ensembl, facilitating the interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional gene expression data from any species, and OrthologAL will output a human ortholog-converted dataset for download and use. To demonstrate the utility of this application, we characterized orthologous conversion in single-cell, single-nuclei, and spatial transcriptomic data derived from common pre-clinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. We show that OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for applying pre-clinical, high-dimensional transcriptomics data in functional small molecule predictions using existing human-annotated databases.

CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases

In the context of the global damage caused by coronavirus disease 2019 (COVID-19) and the emergence of the monkeypox virus (MPXV) outbreak as a public health emergency of international concern, research into methods that can rapidly test potential therapeutics during an outbreak of a new infectious disease is urgently needed. Computational drug discovery is an effective way to solve such problems. The existence of various large open databases has mitigated the time and resource consumption of traditional drug development and improved the speed of drug discovery. However, the diversity of cell lines used in various databases remains limited, and previous drug discovery methods are ineffective for cross-cell prediction. In this study, we propose a correlation-dependent connectivity map (CDCM) to achieve cross-cell predictions of drug similarity. The CDCM mainly identifies drug-drug or disease-drug relationships from the perspective of gene networks by exploring the correlation changes between genes and identifying similarities in the effects of drugs or diseases on gene expression. We validated the CDCM on multiple datasets and found that it performed well for drug identification across cell lines. A comparison with the Connectivity Map revealed that our method was more stable and performed better across different cell lines. In the application of the CDCM to COVID-19 and MPXV data, the predictions of potential therapeutic compounds for COVID-19 were consistent with several previous studies, and most of the predicted drugs were found to be experimentally effective against MPXV. This result confirms the practical value of the CDCM. With the ability to predict across cell lines, the CDCM outperforms the Connectivity Map, and it has wider application prospects and a reduced cost of use.

Targeting ErbB and tankyrase1/2 prevent the emergence of drug-tolerant persister cells in ALK-positive lung cancer

Targeting the drug tolerant persister (DTP) state in cancer cells should prevent further development of resistance mechanisms. This study explored combination therapies to inhibit alectinib-induced DTP cell formation from anaplastic lymphoma kinase-positive non-small cell lung cancer (ALK + NSCLC) patient-derived cells. After drug-screening 3114 compounds, pan-HER inhibitors (ErbB pathway) and tankyrase1/2 inhibitors (Wnt/β-catenin signaling) emerged as top candidates to inhibit alectinib-induced DTP cells growth. We confirmed knockdown of both TNKS1/2 in DTP cells recovered the sensitivity to alectinib. Further, our study suggested knockdown of TNKS1/2 increased stability of Axin1/2, which induced β-catenin degradation and decreased its nuclear translocation, thereby suppressing transcription of antiapoptotic and proliferation-related genes (survivin, c-MYC). Targeting both pathways with alectinib+pan-HER inhibitor and alectinib+TNKS1/2 inhibitor suppressed alectinib-induced DTP cells, and the triple combination almost completely prevented the appearance of DTP cells. In conclusion, combination with ALK-TKI, pan-HER and TNKS1/2 inhibitors has the potential to prevent the emergence of DTP in ALK + NSCLC.

Exploring the impact of mitochondrial-targeting anthelmintic agents with GLUT1 inhibitor BAY-876 on breast cancer cell metabolism

BACKGROUND

Cancer cells alter their metabolic phenotypes with nutritional change. Single agent approaches targeting mitochondrial metabolism in cancer have failed due to either dose limiting off target toxicities, or lack of significant efficacy in vivo. To mitigate these clinical challenges, we investigated the potential utility of repurposing FDA approved mitochondrial targeting anthelmintic agents, niclosamide, IMD-0354 and pyrvinium pamoate, to be combined with GLUT1 inhibitor BAY-876 to enhance the inhibitory capacity of the major metabolic phenotypes exhibited by tumors.

METHODS

To test this, we used breast cancer cell lines MDA-MB-231 and 4T1 which exhibit differing basal metabolic rates of glycolysis and mitochondrial respiration, respectively. Metabolic characterization was carried out using Seahorse XFe96 Bioanalyzer and statistical analysis was carried out via ANOVA.

RESULTS

Here, we found that specific responses to mitochondrial and glycolysis targeting agents elicit responses that correlate with tested cell lines basal metabolic rates and fuel preference, highlighting the potential to cater metabolism targeting treatment regimens based on specific tumor nutrient handling. Inhibition of GLUT1 with BAY-876 potently inhibited glycolysis in both MDA-MB-231 and 4T1 cells, and niclosamide and pyrvinium pamoate perturbed mitochondrial respiration that resulted in potent compensatory glycolysis in the cell lines tested.

CONCLUSION

In this regard, combination of BAY-876 with both mitochondrial targeting agents resulted in inhibition of compensatory glycolysis and subsequent metabolic crisis. These studies highlight targeting tumor metabolism as a combination treatment regimen that can be tailored by basal and compensatory metabolic phenotypes.

Deficiency of TET2-mediated KMT2D self-transcription confers a targetable vulnerability in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has become a leading cause of cancer-related mortality worldwide. Conventional therapies tend to exacerbate comorbidities, liver dysfunction, and relapse, rendering an urgent demand for novel strategy for management of HCC. Here, we reported that DNA dioxygenase TET2 collaborates with histone methyltransferase KMT2D to enable transcription of KMT2D and ARID1A in HCC. Mechanistically, KMT2D and ARID1A are the major epigenetic targets of TET2 through RNA-seq analysis. Moreover, KMT2D recruits TET2 to facilitate self-transcription via oxidation of 5-methylcytosine in promoter, thereby maintaining expression of ARID1A. Physiologically, KMT2D was identified as a tumor suppressor and mediates the antitumor effect of vitamin C in HCC. Tumors with depleted KMT2D present growth advantage over control group. Vitamin C is able to impair tumor growth, which is compromised by deficiency of KMT2D. Furthermore, loss of KMT2D sensitizes HCC tumors to cisplatin with reduced tumor weight and high level of DNA damage. Ultimately, TET2-KMT2D axis correlates with prognosis of patients with HCC. Patients with high amounts of TET2 and KMT2D present better outcome. Our findings not only put forth a heretofore unrecognized mechanism underlying cross-talk between TET2 and KMT2D in mediating self-transcription of KMT2D, but also propose a targetable vulnerability for HCC therapy on the basis of TET2-KMT2D axis.

Novel Expression of Apical Bile Acid Transport (ASBT) More Proximally Than Distal Ileum Contributing to Enhanced Intestinal Bile Acid Absorption in Obesity

Dietary lipid absorption is facilitated by bile acids. In the Zucker rat (ZR) model of obesity, bile acid absorption, mediated by the apical sodium bile acid transporter (ASBT), was increased in villus cells from the distal ileum. However, whether ASBT may be de novo expressed more proximally in the small intestine during obesity to facilitate additional bile acid absorption is not known. For this, starting from the end of the ileum to the mid jejunum, caudal-orally, five intestinal segments of equal length (S1-S5) were separated from lean and obese ZRs (LZR and OZR). Intestinal mucosa obtained from these segments were used for total RNA extraction, RT-qPCR and 3H-TCA uptake. The results showed that bile acid absorption along with the mRNA expression of ASBT and FXR progressively decreased caudal-orally in both LZRs and OZRs but was significantly higher in all small intestinal segments in OZRs. The expression of GATA4 was absent in the distal ileum (S1) in both LZRs and OZRs, but steadily increased along the proximal length in both. However, this steady increase was significantly reduced in the comparative obese proximal intestinal segments S2, S3, S4 and S5. The expressions of bile acid-activated G-protein-coupled bile acid receptor TGR5 and S1PR2 were unaltered in segments S1-S4 but were significantly increased in OZR S5. The paradigm changing observation of this study is that ASBT is expressed more proximally in the small intestine in obesity. This likely increases overall bile acid absorption and thereby lipid absorption in the proximal small intestine in obesity.

Methods for Detection and Mapping of Methylated and Hydroxymethylated Cytosine in DNA

The chemical modifications of DNA are of pivotal importance in the epigenetic regulation of cellular processes. Although the function of 5-methylcytosine (5mC) has been extensively investigated, the significance of 5-hydroxymethylcytosine (5hmC) has only recently been acknowledged. Conventional methods for the detection of DNA methylation frequently lack the capacity to distinguish between 5mC and 5hmC, resulting in the combined reporting of both. The growing importance of 5hmC has prompted the development of a multitude of methods for the qualitative and quantitative analysis of 5hmC in recent years, thereby facilitating researchers' understanding of the mechanisms underlying the onset and progression of numerous diseases. This review covers both established and novel methods for the detection of cytosine modifications, including 5mC, 5hmC, 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), with a particular focus on those that allow for accurate mapping and detection, particularly with third-generation sequencing. The review aims to help researchers choose the most appropriate methods based on their specific research goals and budget.

Cell-free DNA end characteristics enable accurate and sensitive cancer diagnosis

The fragmentation patterns of cell-free DNA (cfDNA) in plasma can potentially be utilized as diagnostic biomarkers in liquid biopsy. However, our knowledge of this biological process and the information encoded in fragmentation patterns remains preliminary. Here, we investigated the cfDNA fragmentomic characteristics against nucleosome positioning patterns in hematopoietic cells. cfDNA molecules with ends located within nucleosomes were relatively shorter with altered end motif patterns, demonstrating the feasibility of enriching tumor-derived cfDNA in patients with cancer through the selection of molecules possessing such ends. We then developed three cfDNA fragmentomic metrics after end selection, which showed significant alterations in patients with cancer and enabled cancer diagnosis. By incorporating machine learning, we further built high-performance diagnostic models, which achieved an overall area under the curve of 0.95 and 85.1% sensitivity at 95% specificity. Hence, our investigations explored the end characteristics of cfDNA fragmentomics and their merits in building accurate and sensitive cancer diagnostic models.

Optimizing Niclosamide for Cancer Therapy: Improving Bioavailability via Structural Modification and Nanotechnology

Inhibition of multiple cancer-related pathways has made niclosamide a promising candidate for the treatment of various cancers. However, its clinical application has been significantly limited by poor bioavailability. This review will discuss current findings on improving niclosamide bioavailability through modification of its chemical structure and utilization of novel nanotechnologies, like electrospraying and supercritical fluids, to improve drug delivery. For example, niclosamide derivatives, such as o-alkylamino-tethered niclosamide derivates, niclosamide ethanolamine salt, and niclosamide piperazine salt, have demonstrated increased water solubility without compromising anticancer activity in vitro. Additionally, this review briefly discusses recent findings on the first pass metabolism of niclosamide in vivo, the role of cytochrome P450-mediated hydroxylation, UDP-glucuronosyltransferase mediated glucuronidation, and how enzymatic inhibition could enhance niclosamide bioavailability. Ultimately, there is a need for researchers to synthesize, evaluate, and improve upon niclosamide derivatives while experimenting with the employment of nanotechnologies, such as targeted delivery and nanoparticle modification, as a way to improve drug administration. Researchers should strive to improve drug-target accuracy, its therapeutic index, and increase the drug's efficacy as an anti-neoplastic agent.

Implementation of SAMPL Guidelines in 100 clinical medicine articles: enhancing statistical reporting and recommendations for biomedical journals

BACKGROUND

Contemporary observations indicate insufficient quality in the reporting of statistical data. Despite the publication of the SAMPL guidelines in 2015, they have not been widely adopted. The aim of this article is to highlight the incorporation of SAMPL Guidelines in the statistical reviews of articles related to clinical medicine, as well as the changes implemented by authors in revised manuscripts as a result of such guidance. An additional objective is to provide recommendations for biomedical journals regarding the necessity of integrating SAMPL Guidelines into their daily practices.

METHODS

The study incorporated 100 selected statistical reviews of original clinical medicine articles from 8 biomedical journals, conducted between 2016 and 2023. Each of these reviews suggested specific SAMPL Guidelines to be implemented in the revised manuscript. It was evaluated which specific SAMPL Guidelines were most frequently enforced and what changes resulted from their implementation.

RESULTS

Seventy-five percent of the manuscripts in question garnered acceptance following a solitary round of statistical evaluation. Among the most frequently recommended and subsequently implemented SAMPL Guidelines by the authors are a more thorough description of the purpose of the applied statistical tests (65%), indication of the practical significance of the obtained results, including calculation of relevant effect size measures (64%), analysis of assumptions necessary for the application of specific statistical tests (58%), and consideration of the impact of outlier values on the obtained results (34%).

CONCLUSION

To improve the quality of statistical reporting in biomedical journals, greater emphasis should be placed on implementing SAMPL Guidelines.

5-hydroxymethylcytosine sequencing in plasma cell-free DNA identifies unique epigenomic features in prostate cancer patients resistant to androgen deprivation therapies

Background

Currently, no biomarkers are available to identify resistance to androgen-deprivation therapies (ADT) in men with hormone-naive prostate cancer. Since 5-hydroxymethylcytosines (5hmC) in gene body are associated with gene activation, in this study, we evaluated whether 5hmC signatures in cell-free DNA (cfDNA) predicts early resistance to ADT.

Results

We collected a total of 139 serial plasma samples from 55 prostate cancer patients receiving ADT at three time points including baseline (prior to initiating ADT, N=55), 3-month (after initiating ADT, N=55), and disease progression (N=15) within 24 months or 24-month if no progression was detected (N=14). To quantify 5hmC abundance across the genome, we used selective chemical labeling sequencing and mapped sequence reads to individual genes. Differential methylation analysis in baseline samples identified significant 5hmC difference in 1,642 of 23,433 genes between patients with and without progression (false discovery rate, FDR<0.1). Patients with disease progression showed significant 5hmC enrichments in multiple hallmark gene sets with androgen responses as top enriched gene set (FDR=1.19E-13). Interestingly, this enrichment was driven by a subgroup of patients featuring a significant 5hmC hypermethylation in the gene sets involving AR , FOXA1 and GRHL2 . To quantify overall activities of these gene sets, we developed a gene set activity scoring algorithm and observed significant association of high activity scores with poor progression-free survival (P<0.05). Longitudinal analysis showed that the high activity scores were significantly reduced after 3-months of initiating ADT (P<0.0001) but returned to higher levels when the disease was progressed (P<0.05).

Conclusions

This study demonstrates that 5hmC-based activity scores from gene sets involved in AR , FOXA1 and GRHL2 may be used as biomarkers to determine early treatment resistance, monitor disease progression, and potentially identify patients who would benefit from upfront treatment intensification.

5-Hydroxymethylcytosine in circulating cell-free DNA as a potential diagnostic biomarker for SLE

BACKGROUND

SLE is a complex autoimmune disease with heterogeneous manifestations and unpredictable outcomes. Early diagnosis is challenging due to non-specific symptoms, and current treatments only manage symptoms. Epigenetic alternations, including 5-Hydroxymethylome (5hmC) modifications, are important contributors to SLE pathogenesis. However, the 5hmC modification status in circulating cell-free DNA (cfDNA) of patients with SLE remains largely unexplored. We investigated the distribution of 5hmC in cfDNA of patients with SLE and healthy controls (HCs), and explored its potential as an SLE diagnosis marker.

METHODS

We used 5hmC-Seal to generate genome-wide 5hmC profiles of plasma cfDNA and bioinformatics analysis to screen differentially hydroxymethylated regions (DhMRs). In vitro mechanistic exploration was conducted to investigate the regulatory effect of CCCTC-binding factor (CTCF) in 5hmC candidate biomarkers.

RESULTS

We found distinct differences in genomic regions and 5hmC modification motif patterns between patients with SLE and HCs, varying with disease progression. Increased 5hmC modification enrichment was detected in SLE. Additionally, we screened 151 genes with hyper-5hmC, which are significantly involved in SLE-related processes, and 5hmC-modified BCL2, CD83, ETS1 and GZMB as SLE biomarkers. Our findings suggest that CTCF regulates 5hmC modification of these genes by recruiting TET (ten-eleven translocation) protein, and CTCF knockdown affected the protein expression of these genes in vitro.

CONCLUSIONS

Our findings demonstrate the increased 5hmC distribution in plasma cfDNA in different disease activity in patients with SLE compared with HCs and relating DhMRs involved in SLE-associated pathways. Furthermore, we identified a panel of SLE relevant biomarkers, and these viewpoints could provide insight into the pathogenesis of SLE.

Analytical Validation of an Early Detection Pancreatic Cancer Test Using 5-Hydroxymethylation Signatures

Early detection of pancreatic cancer has been shown to improve patient survival rates. However, effective early detection tools to detect pancreatic cancer do not currently exist. The Avantect Pancreatic Cancer Test, leveraging the 5-hydroxymethylation [5-hydroxymethylcytosine (5hmC)] signatures in cell-free DNA, was developed and analytically validated to address this unmet need. We report a comprehensive analytical validation study encompassing precision, sample stability, limit of detection, interfering substance studies, and a comparison with an alternative method. The assay performance on an independent case-control patient cohort was previously reported with a sensitivity for early-stage (stage I/II) pancreatic cancer of 68.3% (95% CI, 51.9%-81.9%) and an overall specificity of 96.9% (95% CI, 96.1%-97.7%). Precision studies showed a cancer classification of 100% concordance in biological replicates. The sample stability studies revealed stable assay performance for up to 7 days after blood collection. The limit of detection studies revealed equal results between early- and late-stage cancer samples, emphasizing strong early-stage performance characteristics. Comparisons of concordance of the Avantect assay with the enzymatic methyl sequencing (EM-Seq) method, which measures both methylation (5-methylcytosine) and 5hmC, were >95% for all samples tested. The Avantect Pancreatic Cancer Test showed strong analytical validation in multiple validation studies required for laboratory-developed test accreditation. The comparison of 5hmC versus EM-Seq further validated the 5hmC approach as a robust and reproducible assay.

5-Hydroxymethylated Biomarkers in Cell-Free DNA Predict Occult Colorectal Cancer up to 36 Months Before Diagnosis in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

PURPOSE

Using the prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial samples, we identified cell-free DNA (cfDNA) candidate biomarkers bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that detected occult colorectal cancer (CRC) up to 36 months before clinical diagnosis.

MATERIALS AND METHODS

We performed the 5hmC-seal assay and sequencing on ≤8 ng cfDNA extracted from PLCO study participant plasma samples, including n = 201 cases (diagnosed with CRC within 36 months of blood collection) and n = 401 controls (no cancer diagnosis on follow-up). We conducted association studies and machine learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio.

RESULTS

We successfully obtained 5hmC profiles from these decades-old samples. A weighted Cox model of 32 5hmC-modified gene bodies showed a predictive detection value for CRC as early as 36 months before overt tumor diagnosis (training set AUC, 77.1% [95% CI, 72.2 to 81.9] and validation set AUC, 72.8% [95% CI, 65.8 to 79.7]). Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and race/ethnicity, and significantly outperformed risk factors such as age and obesity (assessed as BMI). Finally, when splitting cases at median weighted prediction scores, Kaplan-Meier analyses showed significant risk stratification for CRC occurrence in both the training set (hazard ratio, [HR], 3.3 [95% CI, 2.6 to 5.8]) and validation set (HR, 3.1 [95% CI, 1.8 to 5.8]).

CONCLUSION

Candidate 5hmC biomarkers and a scoring algorithm have the potential to predict CRC occurrence despite the absence of clinical symptoms and effective predictors. Developing a minimally invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient outcomes.

Identification of Key Immune and Cell Cycle Modules and Prognostic Genes for Glioma Patients through Transcriptome Analysis

BACKGROUND

Gliomas, the most prevalent type of primary brain tumor, stand out as one of the most aggressive and lethal types of human cancer.

METHODS & RESULTS

To uncover potential prognostic markers, we employed the weighted correlation network analysis (WGCNA) on the Chinese Glioma Genome Atlas (CGGA) 693 dataset to reveal four modules significantly associated with glioma clinical traits, primarily involved in immune function, cell cycle regulation, and ribosome biogenesis. Using the least absolute shrinkage and selection operator (LASSO) regression algorithm, we identified 11 key genes and developed a prognostic risk score model, which exhibits precise prognostic prediction in the CGGA 325 dataset. More importantly, we also validated the model in 12 glioma patients with overall survival (OS) ranging from 4 to 132 months using mRNA sequencing and immunohistochemical analysis. The analysis of immune infiltration revealed that patients with high-risk scores exhibit a heightened immune infiltration, particularly immune suppression cells, along with increased expression of immune checkpoints. Furthermore, we explored potentially effective drugs targeting 11 key genes for gliomas using the library of integrated network-based cellular signatures (LINCS) L1000 database, identifying that in vitro, both torin-1 and clofarabine exhibit promising anti-glioma activity and inhibitory effect on the cell cycle, a significant pathway enriched in the identified glioma modules.

CONCLUSIONS

In conclusion, our study provides valuable insights into molecular mechanisms and identifying potential therapeutic targets for gliomas.