No abstract

NF‐κB is constitutively activated in most human pancreatic adenocarcinoma, which is a deadly malignancy with a 5‐year survival rate of about 5%. In this work, we investigate whether microRNAs (miRNAs) contribute to NF‐κB activation in pancreatic cancer. We demonstrate that miR‐301a down‐regulates NF‐κB‐repressing factor (Nkrf) and elevates NF‐κB activation. As NF‐κB promotes the transcription of miR‐301a, our results support a positive feedback loop as a mechanism for persistent NF‐κB activation, in which miR‐301a represses Nkrf to elevate NF‐κB activity, which in turn promotes miR‐301a transcription. Nkrf was found down‐regulated and miR‐301a up‐regulated in human pancreatic adenocarcinoma tissues. Moreover, miR‐301a inhibition or Nkrf up‐regulation in pancreatic cancer cells led to reduced NF‐κB target gene expression and attenuated xenograft tumour growth, indicating that miR‐301a overexpression contributes to NF‐κB activation. Revealing this novel mechanism of NF‐κB activation by an miRNA offers new avenues for therapeutic interventions against pancreatic cancer.

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the Zhou et al. Clinical prediction models with R

Long non-coding RNAs (lncRNAs) have been shown to be implicated in the complex network of cancer including malignant melanoma and play important roles in tumorigenesis and progression. However, their functions and downstream mechanisms are largely unknown. This study aimed to investigate whether BRAF-activated non-coding RNA (BANCR), a novel and potential regulator of melanoma cell, participates in the proliferation of malignant melanoma and elucidate the underlying mechanism in this process. We found that BANCR was abnormally overexpressed in human malignant melanoma cell lines and tissues, and increased with tumor stages by quantitative PCR. BANCR knockdown induced by shRNA transfection significantly inhibited proliferation of tumor cells and inactivated MAPK pathway, especially by silencing the ERK1/2 and JNK component. Moreover, combination treatment of BANCR knockdown and suppression ERK1/2 or JNK (induced by specific inhibitors U0126 or SP600125 respectively) produced synergistic inhibitory effects in vitro. And the inhibitory effects induced by ERK1/2 or JNK could be rescued by BANCR overexpression. By tumorigenicity assay in BALB/c nude mice, we further found that BANCR knockdown inhibited tumor growth in vivo. In addition, patients with high expression of BANCR had a lower survival rate. Taken together, we confirmed the abnormal upregulation of a novel lncRNA, BANCR, in human malignant melanoma. BANCR was involved in melanoma cell proliferation both in vitro and in vivo. The linkage between BANCR and MAPK pathway may provide a novel interpretation for the mechanism of proliferation regulation in malignant melanoma.

Glycosylation is one of the most common protein modifications and is involved in many functions of glycoproteins. Investigating aberrant protein glycosylation associated with diseases is useful in improving disease diagnostics. Due to the non-template nature of glycan biosynthesis, the glycans attached to glycoproteins are enormously complex; thus, a method for comprehensive analysis of glycans from biological or clinical samples is needed. Here, we describe a novel method for glycomic analysis using glycoprotein Immobilization for glycan extraction (GIG). Proteins or peptides from complex samples were first immobilized on solid support, and other non-conjugated molecules were removed. Glycans were enzymatically or chemically modified on solid-phase before releasing from glycoproteins/glycopeptides for mass spectrometry analysis. The method was applied to the glycomic analysis of both N- and O-glycans.