RESUMO
Identifying biomarkers linked to pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) is crucial for early detection, treatment, and prevention. Methods: Association analyses of 10 serological biomarkers involved in cell signalling (IFN-γ, IL-6, IL-8, IL-10), oxidative stress (superoxide dismutase (SOD) and glutathione peroxidase (GPx) enzyme activities, total glutathione (GSH), malondialdehyde (MDA) levels), and intestinal permeability proteins (zonulin, I-FABP2) were conducted across PDAC (n = 12), CP (n = 21) and control subjects (n = 23). A Mendelian randomisation (MR) approach was used to assess causality of the identified significant associations in two large genetic cohorts (FinnGen and UK Biobank). Results: Observational results showed a downregulation of SOD and GPx antioxidant enzyme activities in PDAC and CP patients, respectively, and higher MDA levels in CP patients. Logistic regression models revealed significant associations between CP and SOD activity (OR = 0.21, 95% CI [0.05, 0.89], per SD), GPx activity (OR = 0.28, 95% CI [0.10, 0.79], per SD), and MDA levels (OR = 2.05, 95% CI [1.36, 3.08], per SD). MR analyses, however, did not support causality. Conclusions: These findings would not support oxidative stress-related biomarkers as potential targets for pancreatic diseases prevention. Yet, further research is encouraged to assess their viability as non-invasive tools for early diagnosis, particularly in pre-diagnostic CP populations.
RESUMO
The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78−0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66−0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41−0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.
RESUMO
The gut microbiome has a fundamental role in human health and disease. However, studying the complex structure and function of the gut microbiome using next generation sequencing is challenging and prone to reproducibility problems. Here, we obtained cross-sectional colon biopsies and faecal samples from nine participants in our COLSCREEN study and sequenced them in high coverage using Illumina pair-end shotgun (for faecal samples) and IonTorrent 16S (for paired feces and colon biopsies) technologies. The metagenomes consisted of between 47 and 92 million reads per sample and the targeted sequencing covered more than 300 k reads per sample across seven hypervariable regions of the 16S gene. Our data is freely available and coupled with code for the presented metagenomic analysis using up-to-date bioinformatics algorithms. These results will add up to the informed insights into designing comprehensive microbiome analysis and also provide data for further testing for unambiguous gut microbiome analysis.