Recently, cancer research microRNA studies have drawn great attention. However, the results of these studies have been inconsistent and variable regarding the availability of circulating miRNAs in gastric cancer (GC) diagnosis. Thus, results should be interpreted cautiously. The purpose of the present study was to assess the diagnostic performance of circulating miRNAs in GC diagnosis. We conducted a systematic and comprehensive approach for the inclusion of studies. The sensitivity, specificity, and diagnostic odds ratio were pooled with random effects models, and a summary of receiver operator characteristic (SROC) curves were plotted. The potential heterogeneity was assessed with Q test and I2 statistics. Subgroup analyses and meta-regressions further investigated the sources of heterogeneity. A total of 77 studies from 48 articles were eligible for the meta-analysis. The results revealed a sensitivity of 0.76, a specificity of 0.81, and an AUC of 0.86 for gastric cancer diagnosis with circulating miRNAs. In addition, subgroup analyses indicated that multiple miRNAs assays, non-microarray screening approaches, and serum-based miRNA assays exhibited good diagnostic performance in contrast to a single miRNA assay, microarray expression profiling screening, and plasma-based miRNA group analysis. The diagnostic ability of miRNAs in early stage I–II groups and the high expression group were approximately similar to that in the stage I–IV groups and the low expression group. For the circulating miRNAs, our meta-analysis identified a combination of multiple miRNAs, non-microarray chip screening, and serum-based miRNA assays were associated with the most effective GC diagnostic performance. However, many unclear molecular mechanisms limited the accuracy of the diagnostic results, and should be interpreted with caution. Further large-scale prospective studies are required for validating the diagnostic applicability of circulating miRNAs in gastric cancer patients.

Previous studies have shown that forkhead box P4 antisense RNA 1 (FOXP4‐AS1) is dysregulated in tumor tissues and can serve as a prognostic indicator for multiple cancers. However, the clinical significance of FOXP4‐AS1 in pancreatic ductal adenocarcinoma (PDAC) remains unclear. The goal of this study is to recognize the possible clinical significance of long noncoding RNA FOXP4‐AS1 in patients with early stage PDAC. A total of 112 patients from The Cancer Genome Atlas (TCGA) PDAC cohort, receiving RNA sequencing, were involved in the study. Survival analysis, functional mechanism, and potential small molecule drugs of target therapy of FOXP4‐AS1 were performed in this study. Survival analysis in TCGA PDAC cohort suggested that patients with high FOXP4‐AS1 expression had significantly augmented possibility of death than in PDAC patients with lower FOXP4‐AS1 expression (adjusted P = .008; adjusted HR = 2.143, 95% CI = 1.221‐3.760). In this study, a genome‐wide RNA sequencing dataset was used to identify 927 genes co‐expressing with FOXP4‐AS1 in PDAC tumor tissues. A total of 676 differentially expressed genes were identified between different FOXP4‐AS1 expression groups. Functional enrichment analysis of these genes and gene set enrichment analysis for PDAC genome‐wide RNA sequencing dataset was done. We have found that FOXP4‐AS1 may function in PDAC by participating in biological processes and pathways including oxidative phosphorylation, tricarboxylic acid cycle, classical tumor‐related pathways such as NF‐kappaB as well as Janus kinase/signal transducers in addition to activators of transcription, cell proliferation, and adhesion. In addition, we also screened two potential targeted therapeutic small molecule drugs (dimenhydrinate and metanephrine) for FOXP4‐AS1 in PDAC. In conclusion, our present study demonstrated that higher expression of FOXP4‐AS1 in PDAC tumor tissues were related with an inferior medical outcome. Through multiple genome‐wide approaches, we identified the potential molecular mechanisms of FOXP4‐AS1 in PDAC and two targeted therapeutic drugs for it.

Introduction: Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM.Materials and methods: In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent scanner) for the training (testing) cohort were used for cross-validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy.Results: Forty-one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (P AUROC < .05 and/or P accuracy < .05). One radiological model performed better than random guessing during CV (P accuracy < .05). In the testing cohort, the linear SVM preprocessor + SVM, the best radiomic model (AUROC: 0.74 ± 0.08, accuracy: 0.73 ± 0.07), overperformed the best radiological model (P AUROC = .048). Conclusion: Radiomic features can predict postoperative spinal cord function in CSMpatients. The linear SVM preprocessor + SVM has great application potential in building radiomic models.Meng-Ze Zhang and Han-Qiang Ou-Yang contributed equally to this work.

2‐Bromoanilines are also efficient substrates for this reaction, though they are less reactive than 2‐iodoanilines and give slightly lower yields.