Circulating exosomal microRNAs as prognostic biomarkers for non-small-cell lung cancer

More than 1000 candidate-gene association studies on genetic susceptibility to lung cancer have been published over the last two decades but with few consensuses for the likely culprits. We conducted a comprehensive review, meta-analysis and evidence strength evaluation of published candidate-gene association studies in lung cancer up to November 1, 2015. The epidemiological credibility of cumulative evidence was assessed using the Venice criteria. A total of 1018 publications with 2910 genetic variants in 754 different genes or chromosomal loci were eligible for inclusion. Main meta-analyses were performed on 246 variants in 138 different genes. Twenty-two variants from 21 genes (APEX1 rs1130409 and rs1760944, ATM rs664677, AXIN2 rs2240308, CHRNA3 rs6495309, CHRNA5 rs16969968, CLPTM1L rs402710, CXCR2 rs1126579, CYP1A1 rs4646903, CYP2E1 rs6413432, ERCC1 rs11615, ERCC2 rs13181, FGFR4 rs351855, HYKK rs931794, MIR146A rs2910164, MIR196A2 rs11614913, OGG1 rs1052133, PON1 rs662, REV3L rs462779, SOD2 rs4880, TERT rs2736098, and TP53 rs1042522) showed significant associations with lung cancer susceptibility with strong cumulative epidemiological evidence. No significant associations with lung cancer risk were found for other 150 variants in 98 genes; however, seven variants demonstrated strong cumulative evidence. Our findings provided the most updated summary of genetic risk effects on lung cancer and would help inform future research direction.

Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends. Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting. ILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014-2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%). Accurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.

Better understanding and management of lung cancer are needed. Although much has been learned from known protein coding genes, long noncoding RNAs (lncRNAs), a relatively new and fast evolving large family of transcripts, have recently generated much attention for new discoveries. LncRNAs play critical regulatory functions and are emerging as new players in tumorigenesis and phenotypic determinators of lung cancer. In this review, we highlight the latest development of lncRNAs, including circular RNAs in lung cancer. We start with well-characterized lncRNAs and circular RNAs as an oncogene or tumor suppressor and then extend our discussion on the impact of SNPs in lncRNA on its functions and lung cancer risk and the clinical applications of lncRNAs as biomarkers and therapeutic targets.

Introduction: Inflammation has been recognized as an important contributing factor in the development and progression of lung cancer. However, the relationship between the magnitude of inflammation and prognosis in patients with lung cancer remains u