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Neoadjuvant therapy (NAT) for pancreatic cancer (PC) has achieved certain results. This article was aimed to analyze the trends in NAT in PC over the past 20 years using bibliometric analysis and visualization tools to guide researchers in exploring future research hotspots. Articles related to NAT for PC were retrieved from the Web of Science Core Collection for the period 2002–2021. The information was analyzed and visualized using VOSviewer, Citespace, Microsoft Excel and R software. The number of articles per year has continued to increase over the past 20 years. Of the 1,598 eligible articles, the highest number was from the United States (760), and an analysis of institutions indicated that the University of Texas System (150) had the highest number of articles. Matthew H. G. Katz had the highest number of citations and the highest H-index. "Pancreatic cancer" (981), "Resection" (623), "Cancer" (553), "Neoadjuvant therapy" (509) and "Survival" (484) were the top five ranked keywords. Combined with the keywords-cluster analysis and citation burst analysis, current research hotspots were the optimal NAT regimen, NAT response assessment, NAT for resectable PC and management of complications. NAT has received increasing attention in the field of PC over the past 20 years, but greater collaboration between countries and additional multicenter randomized clinical trials are needed. Overall, we have revealed current research hotspots and provided valuable information for the choice of future research directions. [ABSTRACT FROM AUTHOR]

Background: Long non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer., Objective: We sought to establish an lncRNA signature to improve postoperative risk stratification for patients with localized clear cell renal cell carcinoma (ccRCC)., Design, Setting, and Participants: Based on the RNA-seq data of 444 stage I-III ccRCC tumours from The Cancer Genome Atlas project, we built a four-lncRNA-based classifier using the least absolute shrinkage and selection operation (LASSO) Cox regression model in 222 randomly selected samples (training set) and validated the classifier in the remaining 222 samples (internal validation set). We confirmed this classifier in an external validation set of 88 patients with stage I-III ccRCC from a Japan cohort and using quantitative reverse transcription polymerase chain reaction (RT-PCR) in another three independent sets that included 1869 patients from China with stage I-III ccRCC., Outcome Measurements and Statistical Analysis: Univariable and multivariable Cox regression, Harrell's concordance index (c-index), and time-dependent receiver operating characteristic curves were used to evaluate the association of the classifier with overall survival, disease-specific survival, and disease-free survival., Results and Limitations: Using the LASSO Cox regression model, we built a classifier named RCClnc4 based on four lncRNAs: ENSG00000255774, ENSG00000248323, ENSG00000260911, and ENSG00000231666. In the RNA-seq and RT-PCR data sets, the RCClnc4 signature significantly stratified patients into high-risk versus low-risk groups in terms of clinical outcome across and within subpopulations and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.34 [95% confidence interval {CI}: 1.03-1.75; p=0.028] to 1.89 [95% CI, 1.55-2.31; p<0.001]) after adjusting for clinical and pathologic factors. The RCClnc4 signature achieved a higher accuracy (mean c-index, 0.72) than clinical staging systems such as TNM (mean c-index, 0.62) and the stage, size, grade, and necrosis (SSIGN) score (mean c-index, 0.64), currently reported prognostic signatures and biomarkers for the estimation of survival. When integrated with clinical characteristics, the composite clinical and lncRNA signature showed improved prognostic accuracy in all data sets (TNM + RCClnc4 mean c-index, 0.75; SSIGN + RCClnc4 score mean c-index, 0.75). The RCClnc4 classifier was able to identify a clinically significant number of both high-risk stage I and low-risk stage II-III patients., Conclusions: The RCClnc4 classifier is a promising and potential prognostic tool in predicting the survival of patients with stage I-III ccRCC. Combining the lncRNA classifier with clinical and pathological parameters allows for accurate risk assessment in guiding clinical management., Patient Summary: The RCClnc4 classifier could facilitate patient management and treatment decisions., (Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.)