DNA-methylation signature accurately differentiates pancreatic cancer from chronic pancreatitis in tissue and plasma

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction

Background

Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR.

Methods

Firstly, differential expression genes (DEGs) were identified in two Gene Expression Omnibus (GEO) datasets, and gene set enrichment analysis was performed. Diagnosis-related key genes were identified by using three machine learning algorithms (least absolute shrinkage and selection operator, random forest, and support vector machine model), and the nomogram was then developed. The receiver operating characteristic curve, calibration curve, and decision curve analysis curve were used to verify the validity of the diagnostic model. Using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the characteristics of immune cell infiltration in placental tissue of FGR were evaluated and the candidate key immune cells of FGR were screened. In addition, this study also validated the diagnostic efficacy of TREM1 in the real world and explored associations between TREM1 and various clinical features.

Results

By overlapping the genes selected by three machine learning algorithms, four key genes were identified from 290 DEGs, and the diagnostic model based on the key genes showed good predictive performance (AUC = 0.971). The analysis of immune cell infiltration indicated that a variety of immune cells may be involved in the development of FGR, and nine candidate key immune cells of FGR were screened. Results from real-world data further validated TREM1 as an effective diagnostic biomarker (AUC = 0.894) and TREM1 expression was associated with increased uterine artery PI (UtA-PI) (p-value = 0.029).

Conclusion

Four candidate hub genes (SCD, SPINK1, TREM1, and HIST1H2BB) were identified, and the nomogram was constructed for FGR diagnosis. TREM1 was not only associated with a variety of key immune cells but also correlated with increased UtA-PI. The results of this study could provide some new clues for future research on the prediction and treatment of FGR.

Harnessing Plant Flavonoids to Fight Pancreatic Cancer

PURPOSE OF REVIEW

This review draws on the last fifteen years (2009-2024) of published data to summarize the potential effect of plant flavonoids on pancreatic carcinogenesis and discuss the possible mechanisms of action to establish their applicability as anti-cancer agents.

RECENT FINDINGS

This review found that the plant flavonoids with anti-pancreatic cancer activity mainly include chalcones, dihydrochalcones, flavanols, flavanones, flavones, isoflavonoids, flavonols, isoflavones, and flavanonols. Most of these flavonoids have anti-proliferative, pro-apoptotic, cell cycle arrest, anti-angiogenic, anti-inflammatory, anti-epithelial-mesenchymal transition, and anti-metastatic properties. Some flavonoids can also regulate autophagy, immune and glucose uptake in the context of pancreatic cancer. Several molecules and signaling pathways are associated with the pharmacological activities of plant flavonoids, including AMP-activated protein kinase, mitogen-activated protein kinases, phosphatidylinositol-3-kinase/protein kinase B, nuclear factor-κB, signal transducer, and activator of transcription 3, Smad3, epidermal growth factor receptor, and vascular endothelial growth factor. This review provides strong evidence that plant flavonoids have potential against pancreatic carcinogenesis in experimental animals through various pharmacological mechanisms. They are a promising resource for use as adjuvant anti-cancer therapy. However, randomized controlled clinical trials with those flavonoids are needed.

Identification of Chronic Pancreatitis Associated microRNAs and Genes for the Diagnosis of Pancreatic Cancer

OBJECTIVE

The timely identification of both malignant and nonmalignant pancreatic lesions has the potential to significantly enhance prognosis and implement risk management strategies across various levels. microRNAs (miRs) and their corresponding targets play a crucial role in the development of pancreatic lesions and can serve as valuable diagnostic and therapeutic targets. The objective of our study was to investigate potential diagnostic markers that can effectively differentiate between malignant and nonmalignant pancreatic lesions.

METHODS

Gene Expression Omnibus (GEO) database with GSE24279 dataset was utilized to screen differentially expressed miRNAs (DEMs). We utilized the TargetScanHuman database to predict the target genes associated with hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p. Furthermore, a cohort comprising healthy individuals (n = 52), chronic pancreatitis (CP; n = 34), and pancreatic adenocarcinoma (PAAD; n = 53) patients was recruited to ascertain the levels of plasma markers.

RESULTS

We identified 3 miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and 2 proteins (PCDH1 and AMN) as potential diagnostic markers for distinguishing between CP and PAAD. The area under the curve (AUC) values for all markers exceeded .800. Notably, a combination of plasma PCDH1 and AMN demonstrated excellent diagnostic performance (AUC = .921; 95% CI: .866-.977; sensitivity = .792; specificity = .941) in discriminating between CP and PAAD. In addition, the model of hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p yielded an AUC of .928, sensitivity of .830, and specificity of .912, respectively.

CONCLUSION

Plasma levels of miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and their corresponding targets (PCDH1 and AMN) hold promise as potential biomarkers for predicting PAAD in patients with CP.

Promoter Methylation Leads to Hepatocyte Nuclear Factor 4A Loss and Pancreatic Cancer Aggressiveness

Background and Aims

Decoding pancreatic ductal adenocarcinoma heterogeneity and the consequent therapeutic selection remains a challenge. We aimed to characterize epigenetically regulated pathways involved in pancreatic ductal adenocarcinoma progression.

Methods

Global DNA methylation analysis in pancreatic cancer patient tissues and cell lines was performed to identify differentially methylated genes. Targeted bisulfite sequencing and in vitro methylation reporter assays were employed to investigate the direct link between site-specific methylation and transcriptional regulation. A series of in vitro loss-of-function and gain-of function studies and in vivo xenograft and the KPC (LSL-Kras G12D/+ ; LSL-Trp53 R172H/+ ; Pdx1-Cre) mouse models were used to assess pancreatic cancer cell properties. Gene and protein expression analyses were performed in 3 different cohorts of pancreatic cancer patients and correlated to clinicopathological parameters.

Results

We identify Hepatocyte Nuclear Factor 4A (HNF4A) as a novel target of hypermethylation in pancreatic cancer and demonstrate that site-specific proximal promoter methylation drives HNF4A transcriptional repression. Expression analyses in patients indicate the methylation-associated suppression of HNF4A expression in pancreatic cancer tissues. In vitro and in vivo studies reveal that HNF4A is a novel tumor suppressor in pancreatic cancer, regulating cancer growth and aggressiveness. As evidenced in both the KPC mouse model and human pancreatic cancer tissues, HNF4A expression declines significantly in the early stages of the disease. Most importantly, HNF4 loss correlates with poor overall patient survival.

Conclusion

HNF4A silencing, mediated by promoter DNA methylation, drives pancreatic cancer development and aggressiveness leading to poor patient survival.

Discrimination of pancreato-biliary cancer and pancreatitis patients by non-invasive liquid biopsy

BACKGROUND

Current diagnostics for the detection of pancreato-biliary cancers (PBCs) need to be optimized. We therefore propose that methylated cell-free DNA (cfDNA) derived from non-invasive liquid biopsies serves as a novel biomarker with the ability to discriminate pancreato-biliary cancers from non-cancer pancreatitis patients.

METHODS

Differentially methylated regions (DMRs) from plasma cfDNA between PBCs, pancreatitis and clinical control samples conditions were identified by next-generation sequencing after enrichment using methyl-binding domains and database searches to generate a discriminatory panel for a hybridization and capture assay with subsequent targeted high throughput sequencing.

RESULTS

The hybridization and capture panel, covering around 74 kb in total, was applied to sequence a cohort of 25 PBCs, 25 pancreatitis patients, 25 clinical controls, and seven cases of Intraductal Papillary Mucinous Neoplasia (IPMN). An unbiased machine learning approach identified the 50 most discriminatory methylation markers for the discrimination of PBC from pancreatitis and controls resulting in an AUROC of 0.85 and 0.88 for a training (n = 45) and a validation (n = 37) data set, respectively. The panel was also able to distinguish high grade from low grade IPMN samples.

CONCLUSIONS

We present a proof of concept for a methylation biomarker panel with better performance and improved discriminatory power than the current clinical marker CA19-9 for the discrimination of pancreato-biliary cancers from non-cancerous pancreatitis patients and clinical controls. This workflow might be used in future diagnostics for the detection of precancerous lesions, e.g. the identification of high grade IPMNs vs. low grade IPMNs.