Integration of Transcriptomics and Metabolomics Reveals the Antitumor Mechanism Underlying Tadalafil in Colorectal Cancer

Effect of Fumonisin B1 and Hydrolyzed FB1 Exposure on Intestinal and Hepatic Toxicity in BALB/c Mice

Fumonisins, a class of mycotoxins, pose significant health risks due to widespread contamination. The presence of masked mycotoxins complicates risk assessments because of insufficient regulation and potential toxicity as well as in vivo transformation. This study aims to compare the toxic effects of continuous exposure to fumonisin B1 (FB1) and hydrolyzed FB1 (HFB1) on the gut-liver axis in mice. After 21 d of exposure to FB1 and HFB1, the distributions of FB1 and its metabolites in mice were analyzed, and their effects on intestinal morphology, gut microbial diversity, short-chain fatty acids (SCFAs), inflammatory factors, and hippocampal metabolites were assessed. The results revealed that the highest concentrations of FB1 (61.87%) and HFB1 (53.56%) were detected in the cecum, followed by the colon. Exposure to FB1 and HFB1 resulted in compromised intestinal integrity, villi atrophy, elevated levels of inflammatory factors, and decreased total SCFAs. Both FB1 and HFB1 led to a significant reduction in the Firmicutes to Bacteroides ratio. Blood biochemical analysis and liver metabolomics indicated that FB1 and HFB1 also induced disturbances in the liver homeostasis. The complex correlations observed between the metabolomic and microbiota results underscore the involvement of the gut-liver axis in the disruption induced by these two mycotoxins. These findings highlight the systemic effects of FB1 and HFB1 on liver and gut health, providing valuable insights for further research into their mechanisms and health implications.

Joint metabolomics and transcriptomics analysis systematically reveal the impact of MYCN in neuroblastoma

The limited understanding of the molecular mechanism underlying MYCN-amplified (MNA) neuroblastoma (NB) has hindered the identification of effective therapeutic targets for MNA NB, contributing to its higher mortality rate compared to MYCN non-amplified (non-MNA) NB. Therefore, a comprehensive analysis integrating metabolomics and transcriptomics was conducted to systematically investigate the MNA NB. Metabolomics analysis utilized plasma samples from 28 MNA NB patients and 68 non-MNA NB patients, while transcriptomics analysis employed tissue samples from 15 MNA NB patients and 37 non-MNA NB patients. Notably, joint metabolomics and transcriptomics analysis was performed. A total of 46 metabolites exhibited alterations, with 21 displaying elevated levels and 25 demonstrating reduced levels in MNA NB. In addition, 884 mRNAs in MNA NB showed significant changes, among which 766 mRNAs were higher and 118 mRNAs were lower. Joint-pathway analysis revealed three aberrant pathways involving glycerolipid metabolism, purine metabolism, and lysine degradation. This study highlights the substantial differences in metabolomics and transcriptomics between MNA NB and non-MNA NB, identifying three abnormal metabolic pathways that may serve as potential targets for understanding the molecular mechanisms underlying MNA NB.

Colorectal Cancer Detection via Metabolites and Machine Learning

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

Metabolomics-transcriptomics joint analysis: unveiling the dysregulated cell death network and developing a diagnostic model for high-grade neuroblastoma

High-grade neuroblastoma (HG-NB) exhibits a significantly diminished survival rate in comparison to low-grade neuroblastoma (LG-NB), primarily attributed to the mechanism of HG-NB is unclear and the lacking effective therapeutic targets and diagnostic model. Therefore, the current investigation aims to study the dysregulated network between HG-NB and LG-NB based on transcriptomics and metabolomics joint analysis. Meanwhile, a risk diagnostic model to distinguish HG-NB and LG-NB was also developed. Metabolomics analysis was conducted using plasma samples obtained from 48 HG-NB patients and 36 LG-NB patients. A total of 39 metabolites exhibited alterations, with 20 showing an increase and 19 displaying a decrease in HG-NB. Additionally, transcriptomics analysis was performed on NB tissue samples collected from 31 HG-NB patients and 20 LG-NB patients. Results showed that a significant alteration was observed in a total of 1,199 mRNAs in HG-NB, among which 893 were upregulated while the remaining 306 were downregulated. In particular, the joint analysis of both omics data revealed three aberrant pathways, namely the cAMP signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway, which were found to be associated with cell death. Notably, a diagnostic model for HG-NB risk classification was developed based on the genes MGST1, SERPINE1, and ERBB3 with an area under the receiver operating characteristic curve of 0.915. In the validation set, the sensitivity and specificity were determined to be 75.0% and 80.0%, respectively.

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

The protective role of phosphodiesterase inhibitors in preventing colorectal cancer and advanced colorectal polyps: a systematic review and meta-analysis

AIM

Inflammatory cells within the tumour microenvironment are the driving forces behind colorectal cancer (CRC) tumourigenesis. Understanding the different pathways involved in CRC carcinogenesis paves the way for effective repurposing of drugs for cancer prevention. Emerging data from preclinical and clinical studies suggest that, due to their antiproliferative and anti-inflammatory properties, phosphodiesterase-5 inhibitors (PDE5i) might have an anticancer effect. The aim of this study was to clarify through systematic review and meta-analysis of published peer-reviewed studies whether an association exists between PDE5i use and CRC risk.

METHOD

This study was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Prospective registration was performed on PROSPERO (CRD42022372925). A systematic review was performed for studies reporting CRC and advanced colorectal polyp incidence in PDE5i 'ever-users' and PDE5i 'never-users'. Meta-analysis was performed using RevMan version 5.

RESULTS

Four observational cohort studies and two case-control studies, comprising 995 242 patients were included in the final analysis, of whom 347 126 were PDE5i ever-users. Patients who were PDE5i ever-users had a significantly lower incidence of CRC or advanced colorectal polyps than never-users (OR 0.88, CI 0.79-0.98, p = 0.02). To examine the primary preventative role of PDE5i, subgroup analysis of four studies including patients without a previous history of CRC found that use of PDE5i was associated with a lower incidence of CRC (OR 0.85, CI 0.75-0.95, p = 0.005, I2  = 64%). There was no significant temporal relationship found between PDE5i use and CRC risk as both current users and previous users had a significantly lower incidence of CRC than never-users.

CONCLUSION

Our study found a significant anticancer effect of PDE5i, as shown by a reduced risk of CRC in the context of both primary and secondary CRC prevention.

Schlafen4+-MDSC in Helicobacter-induced gastric metaplasia reveals role for GTPases

Introduction

MDSCs express SCHLAFEN 4 (SLFN4) in Helicobacter-infected stomachs coincident with spasmolytic polypeptide-expressing metaplasia (SPEM), a precursor of gastric cancer. We aimed to characterize SLFN4+ cell identity and the role of Slfn4 in these cells.

Methods

Single-cell RNA sequencing was performed on immune cells sorted from PBMCs and stomachs prepared from uninfected and 6-month H. felis-infected mice. Knockdown of Slfn4 by siRNA or PDE5/6 inhibition by sildenafil were performed in vitro. Intracellular ATP/GTP levels and GTPase activity of immunoprecipitated Slfn4 complexes were measured using the GTPase-Glo assay kit. The intracellular level of ROS was quantified by the DCF-DA fluorescent staining, and apoptosis was determined by cleaved Caspase-3 and Annexin V expression. Gli1CreERT2 x Slfn4 fl/fl mice were generated and infected with H. felis. Sildenafil was administered twice over 2 weeks by gavaging H. felis infected mice ~4 months after inoculation once SPEM had developed.

Results

Slfn4 was highly induced in both monocytic and granulocytic MDSCs from infected stomachs. Both Slfn4 +-MDSC populations exhibited strong transcriptional signatures for type-I interferon responsive GTPases and exhibited T cell suppressor function. SLFN4-containing protein complexes immunoprecipitated from myeloid cell cultures treated with IFNa exhibited GTPase activity. Knocking down Slfn4 or PDE5/6 inhibition with sildenafil blocked IFNa induction of GTP, SLFN4 and NOS2. Moreover, IFNa induction of Slfn +-MDSC function was inhibited by inducing their reactive oxygen species (ROS) production and apoptosis through protein kinase G activation. Accordingly, in vivo disruption of Slfn4 in Gli1CreERT2 x Slfn4 fl/fl mice or pharmacologic inhibition by sildenafil after Helicobacter infection also suppressed SLFN4 and NOS2, reversed T cell suppression and mitigated SPEM development.

Conclusion

Taken together, SLFN4 regulates the activity of the GTPase pathway in MDSCs and precludes these cells from succumbing to the massive ROS generation when they acquire MDSC function.