Long non‑coding RNA (lncRNA) H19 has been suggested to serve important roles in the progression of gastric cancer (GC); however, the mechanism involved is largely unknown. The present study aimed to investigate the mechanism underlying the effect of H19 on human epidermal growth factor receptor (HER2) expression. Let‑7c belongs to the let‑7 family, which has been reported to be downregulated in cancers and considered to serve as a tumor suppressor. Let‑7c has been negatively associated with the expression of human epidermal growth factor receptor 2 (HER2). Reverse transcription‑quantitative polymerase chain reaction was used to examine the expression levels of H19 and let‑7c in GC tissues and cell lines. HER2 protein expression levels were examined using immunohistochemistry and western blot analyses. The effect of H19 on let‑7c and HER2 expression was analyzed following transfection of small interfering RNA targeting H19 in GC cells. The results indicated that the expression levels of H19 lncRNA in GC tissue samples were significantly higher when compared with that in matched benign adjacent tissue samples (P<0.001). H19‑silenced GC cells exhibited significantly increased let‑7c expression and decreased HER2 protein expression levels. Assessment of tumor diameter and pathological tumor stage suggested that increased H19 expression was associated with a poorer prognosis in patients with GC. The results of the present study suggest that H19 may function as a competing endogenous RNA to regulate HER2 expression by sequestering let‑7c in GC cells. The present study has aided the understanding of the mechanism of H19 lncRNA in GC, and has provided evidence for the application of lncRNA‑based diagnostic and therapeutic strategies for GC.
Background The main objective of this study was to investigate the risk factors for recollapse of new vertebral compression fractures (NVCFs) after percutaneous kyphoplasty (PKP) treatment for osteoporotic vertebral compression fracture (OVCF) and to construct a new nomogram model. Methods We retrospectively analysed single-level OVCFs from January 2017 to June 2020, randomizing patients to a training set and a testing set. In the training set, independent risk factors for NVCFs in OVCF patients treated with PKP were obtained by univariate and multivariate regression analyses. These risk factors were then used as the basis for constructing a nomogram model. Finally, internal validation of the built model was performed in the testing set using the consistency index (C-index), receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Results In total, 371 patients were included in this study. NVCFs occurred in 21.7% of the training set patients, and multivariate regression analysis showed that a low Hounsfield unit (HU) value, cement leakage, and thoracolumbar (TL) junction fracture were independent risk factors for NVCF after PKP. The C-index was 0.81 (95% CI: 0.74–0.81), and the validation showed that the predicted values of the established model were in good agreement with the actual values. Conclusions In this study, three independent risk factors were obtained by regression analysis. A nomogram model was constructed to guide clinical work and to make clinical decisions relatively accurately to prevent the occurrence of vertebral recollapse fractures.
Purpose New vertebral compression fractures(NVCFs) after minimally invasive surgery in patients with osteoporotic vertebral compression fracture (OVCF) is a challenging issue worldwide. Predicting the occurrence of NVCFs is key to addressing such questions. Therefore, we aimed to investigate the risk factors for patients who developed NVCFs after undergoing surgical treatment and establish a nomogram model to reduce the occurrence of NVCFs. Methods This study is a retrospective analysis that collected the general characteristics and surgical features of patients who underwent surgical treatment at 2 central institutions between January 2017 and December 2020. Patients were divided into training and testing sets based on the presence or absence of NVCFs. Independent risk factors for NVCFs were obtained in the training set of patients, and then a nomogram model was constructed. Internal and external validation of the nomogram model was performed using the consistency index (C index), receiver operating characteristic curve(ROC), calibration curves, and decision curve analysis (DCA). Results A total of 562 patients were included in this study. Patients from the first center were used for nomogram construction and internal validation, and patients from the second center were used as an external validation population. Multivariate regression analysis showed that age, Hounsfield unit (Hu) value, cement leakage, and thoracolumbar (TL) junction fracture were independent risk factors for NVCFs after minimally invasive surgery. The C index was .85, and the validation of internal and external validation shows that the predicted values of the established model is in good agreement with the actual values. Conclusions In this study, 4 independent risk factors were obtained by regression analysis, and a nomogram model was constructed to guide clinical work. The application of this model can help surgeons to make more accurate judgments to prevent the occurrence of NVCFs.
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