BackgroundRecent studies have demonstrated that long non-coding RNAs (lncRNAs) were present in the blood of cancer patients and have shown great potential as powerful and non-invasive tumor markers. However, little is known about the value of lncRNAs in the diagnosis of esophageal squamous cell carcinoma (ESCC). We hypothesized that ESCC-related lncRNAs might be released into the circulation during tumor initiation and could be utilized to detect and monitor ESCC.MethodsTen lncRNAs (HOTAIR, AFAP1-AS1, POU3F3, HNF1A-AS1, 91H, PlncRNA1, SPRY4-IT1, ENST00000435885.1, XLOC_013104 and ENST00000547963.1) which previously found to be differently expressed in esophageal cancer were selected as candidate targets for subsequent circulating lncRNA assay. A four-stage exploratory study was conducted to test the hypothesis: (1) optimization of detected method to accurately and reproducibly measure ESCC-related lncRNAs in plasma and serum; (2) evaluation of the stability of circulating lncRNAs in human plasma or serum; (3) exploration the origin of ESCC-related lncRNAs in vitro and in vivo; (4) evaluation the diagnostic power of circulating lncRNAs for ESCC.ResultsESCC-related lncRNAs were detectable and stable in plasma of cancer patients, and derived largely from ESCC tumor cells. Furthermore, plasma levels of POU3F3, HNF1A-AS1 and SPRY4-IT1 were significantly higher in ESCC patients compared with normal controls. By receiver operating characteristic curve (ROC) analysis, among the three lncRNAs investigated, plasma POU3F3 provided the highest diagnostic performance for detection of ESCC (the area under the ROC curve (AUC), 0.842; p < 0.001; sensitivity, 72.8%; specificity, 89.4%). Moreover, use of POU3F3 and SCCA in combination could provide a more effective diagnosis performance (AUC, 0.926, p < 0.001, sensitivity, 85.7%; specificity, 81.4%). Most importantly, this combination was effective to detect ESCC at an early stage (80.8%).ConclusionsPlasma POU3F3 could serve as a potential biomarker for diagnosis of ESCC, and the combination of POU3F3 and SCCA was more efficient for ESCC detection, in particular for early tumor screening.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-4598-14-3) contains supplementary material, which is available to authorized users.
Purpose Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively. Methods In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models. Results Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923–0.973) and 0.980 (95% CI 0.959–0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797–0.947) and 0.906 (95% CI 0.821–0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model: 64.06 vs. 31.05 months, p = 0.027). Conclusion The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.
LncRNA SPRY4-IT1 has been shown to promote the progression of melanoma. However, the role of lncRNA SPRY4-IT1 in human esophageal squamous cell carcinoma (ESCC) remains unclear. The purpose of this study is to investigate the clinical significance and biological functions of SPRY4-IT1 in ESCC. The expression levels of lncRNA SPRY4-IT in 92 ESCC patients and 8 ESCC cell lines were evaluated by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). The prognostic significance was evaluated using Kaplan-Meier and Cox regression analyses. Small interfering RNA (siRNA) was used to suppress SPRY4-IT1 expression in ESCC cell lines. Both in vitro and in vivo assays were performed to further explore its role in tumor progression. SPRY4-IT1 levels were significantly higher in ESCC tissues and cells than in corresponding adjacent noncancerous tissues and nontumorigenic esophageal epithelial cells, and the ESCC patients with higher SPRY4-IT1 expression had an advanced clinical stage and poorer prognosis than those with lower SPRY4-IT1 expression. The multivariate analysis revealed that SPRY4-IT1 expression level is an independent prognostic factor in ESCC patients. In vitro assays demonstrated that knockdown of SPRY4-IT1 reduced cell proliferation, invasiveness, and migration. In vivo assays demonstrated that knockdown of SPRY4-IT1 decreases cell growth. SPRY4-IT1 is a novel molecule involved in ESCC progression, which may provide a potential prognostic biomarker and a potential target for therapeutic intervention.
PlncRNA-1 plays an important role in ESCC cell proliferation. Overexpression of PlncRNA-1 is correlated with advanced tumor stage and lymph node metastasis, and may serve as a potential prognostic marker and therapeutic target for ESCC.
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