Analysis of global gene expression profiles suggests a role of acute inflammation in type 3C diabetes mellitus caused by pancreatic ductal adenocarcinoma

The Impact of Pancreatic Exocrine Diseases on the β-Cell and Glucose Metabolism-A Review with Currently Available Evidence

Pancreatic exocrine and endocrine dysfunctions often come together in the course of pancreatic diseases as interdependent manifestations of the same organ. However, the mechanisms underlying the bidirectional connection of the exocrine and endocrine pancreas are not fully understood. In this review, we aimed to synthetize the current knowledge regarding the effects of several exocrine pancreatic pathologies on the homeostasis of β-cells, with a special interest in the predisposition toward diabetes mellitus (DM). We focused on the following pancreatic exocrine diseases: chronic pancreatitis, acute pancreatitis, cystic fibrosis, pancreatic cancer, pancreatic resections, and autoimmune pancreatitis. We discuss the pathophysiologic mechanisms behind the impact on β-cell function and evolution into DM, as well as the associated risk factors in progression to DM, and we describe the most relevant and statistically significant findings in the literature. An early and correct diagnosis of DM in the setting of pancreatic exocrine disorders is of paramount importance for anticipating the disease's course and its therapeutical needs.

Identification of Key Genes Involved in Pancreatic Ductal Adenocarcinoma with Diabetes Mellitus Based on Gene Expression Profiling Analysis

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

Identification of Aberrantly Methylated Differentially CpG Sites in Hepatocellular Carcinoma and Their Association With Patient Survival

This study aimed to identify aberrantly methylated differentially methylated CpG sites (DMCs) and investigate their prognostic value in hepatocellular carcinoma (HCC). A total of 2,404 DMCs were selected from Gene Expression Omnibus (GEO) and validated by The Cancer Genome Atlas (TCGA). The TCGA cohort was divided into a training cohort and a validating cohort. First, the prognostic model based on six DMCs, including cg08351331, cg02910574, cg09947274, cg17589341, cg24652919, and cg26545968, was constructed based on the least absolute shrinkage and selection operator (LASSO) regression Cox analysis. The area under the curve (AUC) of the DMC-based model was 0.765 in the training cohort and 0.734 in the validating cohort. The accuracy of a model combining the DMC signature and American Joint Committee on Cancer (AJCC) stage, with an AUC of 0.795, was better than that of the DMCs or AJCC stage alone. Second, further analysis revealed that the methylation rate of cg08351331 was negatively associated with the expression of its relative gene, lipopolysaccharide-binding protein (LBP). Besides, the gene expression of LBP was significantly associated with poor overall survival in patients with hepatitis B virus (HBV) infection. Finally, these findings were confirmed by GSE57956 data and our own cohort. In conclusion, we established an accurate DMC-based prognostic model that could be combined with AJCC stage to improve the accuracy of prognostic prediction in HCC. Moreover, our preliminary data indicate that LBP may be a new key factor in HBV-induced HCC initiation through the regulation of its methylation.

Galectin-3 and S100A9: Novel Diabetogenic Factors Mediating Pancreatic Cancer-Associated Diabetes

OBJECTIVE

Pancreatic cancer-associated diabetes (PCDM) is a paraneoplastic phenomenon accounting for 1% of new-onset diabetes. We aimed to identify the mediators of PCDM and evaluate their usefulness in distinguishing PCDM from type 2 diabetes.

RESEARCH DESIGN AND METHODS

Secreted proteins of MIA PaCa-2 cells were identified by proteomics, and those with ≥10-fold overexpression in transcriptome analysis were assessed by bioinformatics and glucose uptake assay to identify candidate factors. Expression of factors was compared between tumors with and without PCDM by immunohistochemistry. Serum levels were measured in a training set including PC with and without PCDM, type 2 diabetes, pancreatitis, other pancreatic/peripancreatic tumors, and control subjects (n = 50 each). Cutoff values for differentiation between PCDM and type 2 diabetes from the training set were validated in a test set (n = 41 each).

RESULTS

Galectin-3 and S100A9 were overexpressed in tumors with PCDM and dose-dependently suppressed insulin-stimulated glucose uptake in C2C12 myotubes. In the training set, serum galectin-3 and S100A9 levels were exclusively increased in patients with PCDM and distinguished PCDM from type 2 diabetes (area under the curve [AUC] galectin-3: 0.73 [95% CI 0.64-0.83]; S100A9: 0.79 [95% CI 0.70-0.87]). Similar results were observed in the test set (AUC galectin-3: 0.83 [95% CI 0.74-0.92]; S100A9: 0.77 [95% CI 0.67-0.87]), with sensitivity and specificity 72.1% and 86.1%, respectively, for galectin-3 and 69.8% and 58.1% for S100A9 in differentiating between PCDM and type 2 diabetes.

CONCLUSIONS

Galectin-3 and S100A9 are overexpressed in PCDM tumors and mediate insulin resistance. Galectin-3 and S100A9 distinguish PCDM from type 2 diabetes in subjects with new-onset diabetes.

Resveratrol alleviates lysophosphatidylcholine-induced damage and inflammation in vascular endothelial cells

The role of resveratrol (trans-3,5,4'-trihydroxystilbene; RES) in lysophosphatidylcholine (LPC)‑induced injury and inflammation in endothelial cells (regarded as an early event in arteriosclerosis) is unclear. The present study investigated whether RES reduces lactate dehydrogenase (LDH) activity and secretion of inflammatory cytokines such asinterleukin‑6 and tumor necrosis factor‑α, via the Toll‑like receptor (TLR)‑4/myeloid differentiation primary response gene 88 (MyD88)/nuclear factor (NF)‑κB signal transduction pathway in LPC‑induced damage and inflammation in human umbilical vein endothelial‑12 (HUVE‑12) cells. Using an ELISA and western blotting, the present study investigated the effects of RES on LDH activity and cytokine secretion. The effects of TLR‑4 short hairpin (sh)RNA and TLR‑4 cDNA transfection on NF‑κB activation during LPC‑induced damage and inflammation was also investigated in HUVE‑12 cells. The results demonstrated that RES significantly inhibited the effect of LPC on enzyme activity, pro‑inflammatory cytokine secretion, and expression of TLR‑4, MyD88 and NF‑κBp65 expression. In addition, RES and TLR‑4 shRNA transfection suppressed LPC‑induced injury and inflammation by blocking the TLR‑4/MyD88/NF‑κB signaling pathway Conversely, transfection with TLR‑4 cDNA enhanced LPC‑induced injury and inflammation, which abrogated the protective effects of RES. These data suggested that RES significantly suppressed LPC‑induced damage and inflammation, via suppression of the TLR‑4/MyD88/NF‑κB signaling pathway, which may provide a new mechanistic evidence for the treatment of arteriosclerosis by RES.

Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes

AIM

To identify novel candidate genes and gene sets for diabetes.

METHODS

We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study.

RESULTS

SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10^-8), MRPL33 (p value = 1.24 × 10^-7), and FADS1 (p value = 2.39 × 10^-7). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose.

CONCLUSION

Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases.