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This work aims to analyze and construct a novel competing endogenous RNA (ceRNA) network in ankylosing spondylitis (AS) with bone bridge formation, lncRNA. Using RNA sequencing and bioinformatics, we analyzed expression profiles of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in whole blood cells from 5 AS patients and 3 healthy individuals. Next, we verified the expression levels of candidate lncRNAs in 97 samples using the ΔΔCt value of real-time quantitative polymerase chain reaction (qRT-PCR). We used multivariate logistic regression analysis to screen lncRNAs and clinical indicators for use in the prediction model. Both SPSS 24.0 and R software were used for data analysis and prediction model construction. The results showed that compared with the normal controls, 205 long noncoding RNAs (lncRNAs), 961 microRNAs (miRNAs), and 200 mRNAs (DEmRNAs) were differentially expressed in the AS patients. We identified lncRNA 122K13.12 and lncRNA 326C3.7 among 205 lncRNAs differentially expressed between AS patients and healthy humans. Then, we noted that 30 miRNAs and five mRNAs formed a ceRNA network together with these two lncRNAs. These ceRNA networks might regulate the tumor necrosis factor (TNF) signaling pathway in AS development. In addition, the expression level of lncRNA 122K13.12 and lncRNA 326C3.7 correlated with various structural damage indicators in AS. Specifically, the lncRNA 326C3.7 expression level was an independent risk factor in bone bridge formation [area under the ROC curve (AUC) = 0.739 (0.609–0.870) and p = 0.003], and the best Youden Index was 0.405 (sensitivity = 0.800 and specificity = 0.605). Moreover, we constructed a lncRNA-based nomogram that could effectively predict bone bridge formation [AUC = 0.870 (0.780–0.959) and p < 0.001, and the best Youden Index was 0.637 (sensitivity = 0.900 and specificity = 0.737)]. In conclusion, we uncovered a unique ceRNA signaling network in AS with bone bridge formation and identified novel biomarkers and prediction models with the potential for clinical applications.

BackgroundTo construct and validate a nomogram for predicting the risk of esophageal fistula in esophageal cancer patients receiving radiotherapy.MethodsA retrospective nested case–control study was performed, in which a total of 81 esophageal fistula patients and 243 controls from 2014 to 2020 in the First Affiliated Hospital of Anhui Medical University were enrolled. Factors included in the nomogram were determined by univariate and multiple logistic regression analysis. The following methods including ROC curve, C-index, calibration curves, Brier score, and decision curve analysis (DCA) were adopted to evaluate this nomogram.ResultsMultivariate logistic regression analysis showed that T4 stage, level 4 stenosis, ulcerative esophageal cancer, prealbumin, and maximum diameters of GTV and NLR were the independent risk factors of esophageal fistula. Accordingly, a nomogram incorporating the aforementioned six parameters was constructed. The AUC was 0.848 (95% CI 0.901–0.895), indicating a high prediction accuracy of this nomogram. Further evaluation of this model showed that the C-index was 0.847, while the bias-corrected C-index after internal validation was 0.833. The Brier score was 0.127. The calibration curves presented good concordance, and the DCA revealed promising clinical application.ConclusionsThe nomogram presents accurate and applicable prediction for the esophageal fistula risk in esophageal cancer patients receiving radiotherapy.

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