Preoperative and postoperative complications as risk factors for delayed gastric emptying following pancreaticoduodenectomy: A single-center retrospective study.

BACKGROUND: Mortality rates after pancreaticoduodenectomy (PD) have significantly decreased in specialized centers. However, postoperative morbidity, particularly delayed gastric emptying (DGE), remains the most frequent complication following PD. AIM: To identify risk factors associated with DGE after the PD procedure. METHODS: In this retrospective, cross-sectional study, clinical data were collected from 114 patients who underwent PD between January 2015 and June 2018. Demographic factors, pre- and perioperative characteristics, and surgical complications were assessed. Univariate and multivariate analyses were performed to identify risk factors for post-PD DGE. RESULTS: The study included 66 males (57.9%) and 48 females (42.1%), aged 33-83 years (mean: 62.5), with a male-to-female ratio of approximately 1.4:1. There were 63 cases (55.3%) of PD and 51 cases (44.7%) of pylorus-preserving pancreatoduodenectomy. Among the 114 patients who underwent PD, 33 (28.9%) developed postoperative DGE. Univariate analysis revealed significant differences in four of the 14 clinical indexes observed: pylorus preservation, retrocolonic anastomosis, postoperative abdominal complications, and early postoperative albumin (ALB). Logistic regression analysis further identified postoperative abdominal complications [odds ratio (OR) = 4.768, P = 0.002], preoperative systemic diseases (OR = 2.516, P = 0.049), and early postoperative ALB (OR = 1.195, P = 0.003) as significant risk factors. CONCLUSION: Postoperative severe abdominal complications, preoperative systemic diseases, and early postoperative ALB are identified as risk factors for post-PD DGE.

Long non-coding RNA XIST accelerates hepatic carcinoma progression by targeting the microRNA-320a/PIK3CA axis.

The aim of the present study was to reveal the new molecular mechanism of long non-coding (lnc)RNA XIST in the development of hepatic carcinoma. A total of 69 patients with hepatic carcinoma were included. Hepatoma cell lines (SUN449), hepatoblastoma cell line (HepG2, Huh-6), liver cancer cell line (HepG2) and transformed human liver epithelial-2 cells (THLE-2) were used in the present study. A total 3 short hairpin RNA (sh)-lncRNA XIST sequences, overexpression vector (oe)-lncRNA XIST, microRNA (miR)-320a mimic, miR-320a inhibitor, PIK3CA inhibitor, and their corresponding controls were transfected in hepatic carcinoma cells. Reverse transcription-quantitative polymerase chain reaction was conducted to detect lncRNA-XIST, miR-320a and PIK3CA expression. Cell Counting Kit-8 assay and flow cytometry were undertaken to measure proliferation and apoptosis. Cell invasion and migration were detected by Transwell assays. Moreover, the binding of lncRNA XIST, PIK3CA and miR-320a were verified by luciferase reporter experiment and pull-down assay. Finally, a rescue assay was processed to confirm the effect of lncRNA-XIST, miR-320a and PIK3CA in the aforementioned processes. lncRNA XIST was highly expressed in hepatic carcinoma tissues and cells. The survival rate was significantly lower in the highly expressed lncRNA XIST group. shlncRNA XIST attenuated cell proliferation, invasion and migration, while increasing the apoptosis of hepatic carcinoma cells. The lncRNA XIST negatively targeted miR-320a, and miR-320a negatively regulated the expression of PIK3CA. The miR-320a mimic and PIK3CA inhibitor could recover the effect of oe-lncRNA in terms of the proliferation, invasion, migration and apoptosis of hepatic carcinoma cells. lncRNA XIST accelerates hepatic carcinoma progression by targeting the miR-320a/PIK3CA axis, which might provide the theoretical basis for the potential targeted therapy of hepatic carcinomas.

MiR-200b Suppresses Gastric Cancer Cell Migration and Invasion by Inhibiting NRG1 through ERBB2/ERBB3 Signaling.

PURPOSE: Accumulating evidence indicates that miRNAs (miRs) play crucial roles in the modulation of tumors development. However, the accurately mechanisms have not been entirely clarified. In this study, we aimed to explore the role of miR-200b in the development of gastric cancer (GC). METHODS: Western blot and RT-PCR were applied to detect epithelial-mesenchymal transition (EMT) marker expression and mRNA expression. Transwell assay was used for measuring the metastasis and invasiveness of GC cells. TargetScan system, luciferase reporter assay, and rescue experiments were applied for validating the direct target of miR-200b. RESULTS: MiR-200b was prominently decreased in GC tissues and cells, and its downregulation was an indicator of poor prognosis of GC patients. Reexpression of miR-200b suppressed EMT along with GC cell migration and invasion. Neuregulin 1 (NRG1) was validated as the target of miR-200b, and it rescued miR-200b inhibitory effect on GC progression. In GC tissues, the correlation of miR-200b with NRG1 was inverse. CONCLUSION: MiR-200b suppressed EMT-related migration and invasion of GC through the ERBB2/ERBB3 signaling pathway via targeting NRG1.

Asthma and the risk of prostate cancer.

OBJECTIVE: The association between asthma and the risk of prostate cancer remains elusive. We aimed to evaluate the relationship between asthma and the onset of prostate cancer. METHODS: We searched PubMed, Embase, and Cochrane databases for articles that assessed the association of asthma with the risk of prostate cancer through October 2015. We extracted odds ratio (OR) and calculated the corresponding 95% confidence interval (CI). We used random-effects models to calculate a pooled association between asthma and the risk of prostate cancer. RESULTS: Fourteen studies were involved in the assessment of the association between asthma and prostate cancer risk. Asthma was not associated with the risk of prostate cancer in overall populations (OR 0.994, 95% CI 0.836-1.182), Caucasians (OR 0.922, 95% CI 0.825-1.030), and Asians (OR 5.022, 95% CI 0.415-60.793). The cumulative analysis also suggested a lack association between asthma and the risk of prostate cancer. Exclusion of any single study did not change the results significantly. No evidence of marked publication bias was observed. CONCLUSIONS: Our investigation indicated that asthma was not associated with prostate cancer risk in overall populations, Caucasians, and Asians.

Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas.

AIM: To evaluate the prognostic power of different molecular data in liver cancer. METHODS: Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis. RESULTS: The mRNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index (C-index) of 0.61. For the copy number variation, methylation and miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone (P < 0.05). On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself (P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models. CONCLUSION: In all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.