Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer.

BACKGROUND: The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers. METHODS: Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model. RESULTS: Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count). CONCLUSION: Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

Protective Effect and Possible Mechanisms of Astragaloside IV in Animal Models of Diabetic Nephropathy: A Preclinical Systematic Review and Meta-Analysis.

Astragaloside IV (AS-IV) has a variety of biological activities and is widely used to treat kidney diseases. We conducted a systematic review of 24 animal studies including 424 animals to evaluate the efficacy of AS-IV for diabetic nephropathy (DN); all current possible mechanisms were summarized. A search strategy was applied to eight databases from inception to June 2020. The CAMARADES 10-item quality checklist and Rev-Man 5.3 software were used to analyze the risks of bias of each study and data regarding outcome measures, respectively. The mean study quality score was 5.4 points (range 3-8 points). Meta-analyses data and comparisons between groups showed that AS-IV significantly slowed the progression of pathological signs in the kidney including glomeruli and tubules, increasing creatinine clearance rate, decreasing blood urea nitrogen, serum creatinine, 24-h urinary neutrophil gelatinase-associated lipocalin and N-acetyl-ß-D-glucosaminidase, 24-h urinary albumin, 24-h urinary microalbumin and HbA1c. There were no significant differences between experimental and control groups with respect to mortality or levels of alanine aminotransferase and aspartate aminotransferase. In terms of the possible mechanisms of treatment of DN, AS-IV acts through antifibrotic, antioxidant, and antiapoptotic mechanisms, thereby alleviating endoplasmic reticulum stress, inhibiting mitochondrial fission, and increasing autophagic activity. Taken together, our findings suggest that AS-IV is a multifaceted renoprotective candidate drug for DN.