Associations between genetically predicted levels of blood metabolites and pancreatic cancer risk

Plasma metabolites as mediators in immune cell-pancreatic cancer risk: insights from Mendelian randomization

Background

Immune cells play a crucial role in the development and progression of pancreatic cancer, yet the causal relationship remains uncertain due to complex immune microenvironments and conflicting research findings. Mendelian randomization (MR), this study aims to delineate the causal relationships between immune cells and pancreatic cancer while identifying intermediary factors.

Methods

The genome-wide association study (GWAS) data on immune cells, pancreatic cancer, and plasma metabolites are derived from public databases. In this investigation, inverse variance weighting (IVW) as the primary analytical approach to investigate the causal relationship between exposure and outcome. Furthermore, this study incorporates MR-Egger, simple mode, weighted median, and weighted mode as supplementary analytical approaches. To ensure the reliability of our findings, we further assessed horizontal pleiotropy and heterogeneity and evaluated the stability of MR results using the Leave-one-out method. In conclusion, this study employed mediation analysis to elucidate the potential mediating effects of plasma metabolites.

Results

Our investigation revealed a causal relationship between immune cells and pancreatic cancer, highlighting the pivotal roles of CD11c+ monocytes (odds ratio, ORIVW=1.105; 95% confidence interval, 95%CI: 1.002-1.218; P=0.045), HLA DR+ CD4+ antigen-presenting cells (ORIVW=0.920; 95%CI: 0.873-0.968; P=0.001), and HLA DR+ CD8br T cells (ORIVW=1.058; 95%CI: 1.002-1.117; P=0.041) in pancreatic cancer progression. Further mediation analysis indicated that oxalate (proportion of mediation effect in total effect: -11.6%, 95% CI: -89.7%, 66.6%) and the mannose to trans-4-hydroxyproline ratio (-19.4, 95% CI: -136%, 96.8%) partially mediate the relationship between HLA DR+ CD8br T cells and pancreatic cancer in nature. In addition, our analysis indicates that adrenate (-8.39%, 95% CI: -18.3%, 1.54%) plays a partial mediating role in the association between CD11c+ monocyte and pancreatic cancer, while cortisone (-26.6%, 95% CI: 138%, -84.8%) acts as a partial mediator between HLA DR+ CD4+ AC and pancreatic cancer.

Conclusion

This MR investigation provides evidence supporting the causal relationship between immune cell and pancreatic cancer, with plasma metabolites serving as mediators. Identifying immune cell phenotypes with potential causal effects on pancreatic cancer sheds light on its underlying mechanisms and suggests novel therapeutic targets.

Genetically predicted blood metabolites mediate the association between circulating immune cells and pancreatic cancer: A Mendelian randomization study

BACKGROUND

Pancreatic cancer is characterized by metabolic dysregulation and unique immunological profiles. Nevertheless, the comprehensive understanding of immune and metabolic dysregulation of pancreatic cancer remains unclear. In the present study, we aimed to investigate the causal relationship of circulating immune cells and pancreatic cancer and identify the blood metabolites as potential mediators.

METHODS

The exposure and outcome genome-wide association studies (GWAS) data used in the present study were obtained from the GWAS open-access database (https://gwas.mrcieu.ac.uk). The study used 731 circulating immune cell features, 1400 types of blood metabolites and pancreatic cancer from GWAS. We then performed bidirectional Mendelian randomization (MR) analyses to explore the causal relationships between the circulating immune cells and pancreatic cancer, and two-step MR to discover potential mediating blood metabolites in this process. All statistical analyses were performed in R software. The STROBE-MR (i.e. Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization) checklist for the reporting of MR studies was also used.

RESULTS

MR analysis identified seven types of circulating immune cells causally associated with pancreatic cancer. Furthermore, there was no strong evidence that genetically predicted pancreatic cancer had an effect on these seven types of circulating immune cells. Further two-step MR analysis found 10 types of blood metabolites were causally associated with pancreatic cancer and the associations between circulating CD39+CD8+ T cells and pancreatic cancer were mediated by blood orotates with proportions of 5.18% (p = 0.016).

CONCLUSIONS

The present study provides evidence supporting the causal relationships between various circulating immune cells, especially CD39+CD8+ T cells, and pancreatic cancer, with a potential effect mediated by blood orotates. Further research is needed on additional risk factors as potential mediators and establish a comprehensive immunity-metabolism network in pancreatic cancer.

Investigating the causal role of the gut microbiota in esophageal cancer and its subtypes: a two-sample Mendelian randomization study

BACKGROUND

Through research on the gut microbiota (GM), increasing evidence has indicated that the GM is associated with esophageal cancer (ESCA). However, the specific cause-and-effect relationship remains unclear. In this study, Mendelian randomization (MR) analysis was applied to investigate the causal relationship between the GM and ESCA, including its subtypes.

METHODS

We collected information on 211 GMs and acquired data on ESCA and its subtypes through genome-wide association studies (GWASs). The causal relationship was primarily assessed using the inverse variance weighted (IVW) method. Additionally, we applied the weighted median estimator (WME) method, MR-Egger method, weighted mode, and simple mode to provide further assistance. Subsequent to these analyses, sensitivity analysis was conducted using the MR-Egger intercept test, MR-PRESSO global test, and leave-one-out method.

RESULT

Following our assessment using five methods and sensitivity analysis, we identified seven GMs with potential causal relationships with ESCA and its subtypes. At the genus level, Veillonella and Coprobacter were positively correlated with ESCA, whereas Prevotella9, Eubacterium oxidoreducens group, and Turicibacter were negatively correlated with ESCA. In the case of esophageal adenocarcinoma (EAC), Flavonifractor exhibited a positive correlation, while Actinomyces exhibited a negative correlation.

CONCLUSION

Our study revealed the potential causal relationship between GM and ESCA and its subtypes, offering novel insights for the advancement of ESCA diagnosis and treatment.

Elucidating the role of blood metabolites on pancreatic cancer risk using two-sample Mendelian randomization analysis

Pancreatic ductal adenocarcinoma (PDAC) is an uncommon but highly fatal malignancy. Identifying causal metabolite biomarkers offers an opportunity to facilitate effective risk assessment strategies for PDAC. In this study, we performed a two-sample Mendelian randomization (MR) study to characterize the potential causal effects of metabolites in plasma on PDAC risk. Genetic instruments were determined for a total of 506 metabolites from one set of comprehensive genome-wide association studies (GWAS) involving 913 individuals of European ancestry from the INTERVAL/EPIC-Norfolk cohorts. Another set of genetic instruments was developed for 483 metabolites from an independent GWAS conducted with 8299 individuals of European ancestry from the Canadian Longitudinal Study on Aging (CLSA) cohort. We analyzed GWAS data of the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), comprising 8275 PDAC cases and 6723 controls of European ancestry. The association of metabolites with PDAC risk was assessed using the inverse-variance weighted (IVW) method, and complemented with sensitivity analyses of MR-Egger and MR-PRESSO tests. Potential side effects of targeting the identified metabolites for PDAC intervention were further evaluated by a phenome-wide MR (Phe-MR) analysis. Forty-four unique metabolites were identified to be significantly associated with PDAC risk, of which four top-ranking metabolites (X: 12798, X: 11787, X: 11308 and X: 19141) showed replication evidence when using instruments developed from both two cohorts. Our results highlight novel blood metabolites related to PDAC risk, which may help prioritize metabolic features for PDAC mechanistic research and further evaluation of their potential role in PDAC risk assessment.

Regulome-wide association study identifies genetically driven accessible regions associated with pancreatic cancer risk

Genome-wide association studies (GWAS) have identified two dozen genetic variants that are associated with the risk of pancreatic ductal adenocarcinoma (PDAC), a deadly malignancy. However, a majority of these variants are located in noncoding regions of the genome, which limits the translation of GWAS findings into clinical applications. The regulome-wide association study (RWAS) is a recently developed method for identifying TF binding-induced accessibility regions for diseases. However, their potential connection to PDAC has yet to be fully explored. We evaluated the associations between genetically predicted levels of chromatin accessibility and risk of PDAC by using pan-cancer chromatin accessibility genetic prediction models. Our analysis included 8275 cases and 6723 controls from the PanScan (I, II, and III) and PanC4 consortia. To further refine our results, we also integrated genes associated to allele-specific accessibility quantitative trait loci (as-aQTL) and TF motifs located in the as-aQTL. We found that 50 chromatin accessibility features were associated with PDAC risk at a false discovery rate (FDR) of less than 0.05. A total of 28 RWAS peaks were identified as conditionally significant. By integrating the results from as-aQTL, motif analysis, and RWAS, we identified candidate causal regulatory elements for two potential chromatin accessibility regions (THCA_89956 and ESCA_89167) that are associated with PDAC risk. Our study identified chromatin accessibility features in noncoding genomic regions that are associated with PDAC risk. We also predicted the associated genes and disrupt motifs. Our findings provide new insights into the regulatory mechanisms of noncoding regions for pancreatic tumorigenesis.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models

BACKGROUND

Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets.

METHODS

We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent.

RESULTS

We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment.

CONCLUSIONS

Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.

SIGNIFICANCE

PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

Gut microbiome and frailty: insight from genetic correlation and mendelian randomization

Observational studies have shown that the gut microbiome is associated with frailty. However, whether these associations underlie causal effects remains unknown. Thus, this study aimed to assess the genetic correlation and causal relationships between the genetically predicted gut microbiome and frailty using linkage disequilibrium score regression (LDSC) and Mendelian Randomization (MR). Summary statistics for the gut microbiome were obtained from a genome-wide association study (GWAS) meta-analysis of the MiBioGen consortium (N = 18,340). Summary statistics for frailty were obtained from a GWAS meta-analysis, including the UK Biobank and TwinGene (N = 175,226). We used LDSC and MR analyses to estimate the genetic correlation and causality between the genetically predicted gut microbiome and frailty. Our findings indicate a suggestive genetic correlation between Christensenellaceae R-7 and frailty. Moreover, we found evidence for suggestive causal effects of twelve genus-level gut microbes on frailty using at least two MR methods. There was no evidence of horizontal pleiotropy or heterogeneity in the MR analysis. This study provides suggestive evidence for a potential genetic correlation and causal association between several genetically predicted gut microbes and frailty. More population-based observational studies and animal experiments are required to clarify this association and the underlying mechanisms.

Genetically Predicted Causal Effects of Gut Microbiota and Gut Metabolites on Digestive Tract Cancer: A Two-Sample Mendelian Randomization Analysis

Background

Evidence from numerous observational studies and clinical trials has linked gut microbiota and metabolites to digestive tract cancer. However, the causal effect between these factors remains uncertain.

Methods

Data for this study were obtained from the MiBioGen, TwinsUK Registry, and FinnGen (version R8). Two-sample Mendelian randomization analysis with inverse variance weighting method was primarily used, and the results were validated by heterogeneity analysis, pleiotropy test, and sensitivity analysis.

Results

At P < 5 × 10-8, our analysis identified four gut microbiotas as risk factors for digestive tract cancer and six as risk factors for colorectal cancer. Conversely, one gut microbiota exhibited protection against bile duct cancer, and two showed protective effects against stomach cancer. At P < 1 × 10-5, our investigation revealed five, six, three, eight, eight, and eight gut microbiotas as risk factors for esophageal, stomach, bile duct, liver, pancreatic, and colorectal cancers, respectively. In contrast, four, two, eight, two, two, and five gut microbiotas exhibited protective effects against these cancers. Additionally, GABA, a metabolite of gut microbiota, displayed a significant protective effect against colorectal cancer.

Conclusion

In conclusion, specific gut microbiota and metabolites play roles as risk factors or protective factors for digestive tract cancer, and a causal relationship between them has been established, offering novel insights into gut microbiota-mediated cancer development.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Molecular Characterisation of Blood Microbiome in Patients with Ankylosing Spondylitis and Healthy Controls

Background

In human and animal studies, ankylosing spondylitis (AS) has been increasingly linked to changes in the microbial inhabitants in the human body (microbiome). These studies have primarily now concentrated on the microbial communities that live in the gastrointestinal tract. However, evidence suggests that various molecular techniques can be used to detect microbial DNA in blood circulation. This DNA might be an unknown reservoir of biomarkers with the potential to track alterations in the microbiomes of remote locations, such as the gut. To this end, we compared the presence and identity of microbial DNA in blood samples taken from ankylosing spondylitis patients to healthy control subjects by amplifying and sequencing the bacterial 16S rRNA variable region four.

Methods

The study's design is a case study based on the presence and identity of bacterial DNA in the blood of Ankylosing spondylitis (AS) patients (n = 10) and healthy control subjects (n = 10) was investigated by amplifying and sequencing the bacterial 16S rRNA gene. Blood concentrations of the cytokines TNF alpha, IL-17A, and IL-23 were determined by the Human Magnetic Luminex Screening, and data were analysed using an Unpaired T-test.

Results

Using PCR amplification, 8 of 10 AS patients (80%) and 8 of 10 healthy control samples (80%) had microbial 16S rRNA in their blood. At the phylum level, Proteobacteria (Control = 48.5%, AS = 52%), Firmicutes (Control = 27.8%, AS = 26.1%), Actinobacteria (Control = 15.4%, AS = 10.7%), and Bacteroidetes (Control = 6.5%, AS = 10%) dominated the blood microbiome. A two-tailed Mann-Whitney test found that Ankylosing Spondylitis was associated with significantly elevated Bacteroides (P < 0.05), Prevotella (P < 0.001), and Micrococcus (P < 0.01), and significantly reduced levels of Corynebacterium 1 (P < 0.001), Gemella (P < 0.01), and Alloprevotella (P < 0.05), compared to healthy controls. Additionally, it was shown that the presence of the Prevotella genus was highly positively correlated with higher levels of TNF-alpha (P < 0.05; r = 0.8) in AS patients' blood.

Conclusion

This article reveals that a blood microbiome exists in healthy individuals and identifies particular taxa modulated in disease. These blood-derived signatures indicate that this field needs more research and may be helpful as disease biomarkers.

The coagulation-related genes for prognosis and tumor microenvironment in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC.

METHODS

We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data.

RESULTS

Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC.

CONCLUSIONS

Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.