The apelin gene can promote vascular endothelial cell (VEC) proliferation, migration, and angiogenesis. However, the molecular mechanism for regulation of the apelin gene is still unknown. Real-time PCR and Western blotting analysis were employed to detect the effect of all-trans retinoic acid (ATRA) in up-regulating apelin expression in human umbilical vein endothelial cells (HUVECs). Furthermore, the in vivo study also indicated that ATRA could increase apelin expression in balloon-injured arteries of rats, which is consistent with the results from the cultured HUVECs. To ensure whether retinoic acid receptor (RAR) α (RARα) could be induced by ATRA in regulating apelin, the expression of RARα was tested with a siRNA method to knock down RARα or adenovirus vector infection to overexpress RARα. The results showed that ATRA could up-regulate apelin expression time- and dose- dependently in HUVECs. ATRA could induce a RARα increase; however, the expression of RARβ and RARγ were unchanged. The blocking of RARα signaling reduced the response of apelin to ATRA when HUVECs were treated with RARα antagonists (Ro 41-5253) or the use of siRNA against RARα (si-RARα) knockdown RARα expression before using ATRA. In addition, induction of RARα overexpression by infection with pAd-GFP-RARα further increased the induction of apelin by ATRA. These results suggested that ATRA up-regulated apelin expression by promoting RARα signaling.

Background: To analyze the prognostic factors of combined small cell lung cancer (CSCLC) and construct a nomogram model for CSCLC. Methods: A total of 978 patients diagnosed with CSCLC from 2010 to 2015 were collected based on the SEER database. According to the ratio of 7:3, the patients were divided into the modeling group and the testing group. Univariate and multivariate Cox regression analyses were performed on the patients in the modeling group to analyze the independent factors affecting the prognosis of CSCLC patients and construct a nomogram prediction model, which was verified by the C-index and calibration curve in the training cohort and the validation cohort, respectively. Results: Univariate and multivariate Cox regression analysis showed that N stage, M stage, surgery, chemotherapy, radiotherapy, age, brain metastases, lung metastases, liver metastases, bone metastases, and tumor size were independent risk factors affecting the prognosis of CSCLC patients (P<0.05). A nomogram prediction model was constructed based on the above 10 risk factors through visual analysis, and the C-index was 0.753 (95%CI: 0.727~0.750). The calibration curves showed good agreement between the 1 -, 2 -, and 3-year predicted and actual survival rates of the prediction model constructed in this study. The AUC of the 1-, 2-, and 3-year prediction models was 0.813, 0.814, and 0.802, respectively. DCA showed that the nomogram model had more clinical application value in predicting survival prognosis than TNM staging. Finally, according to the total score of the nomogram survival prediction model, all the included cases were divided into low-risk, intermediate-risk, and high-risk strata and these three survival curves of each risk stratification showed significant survival differences (p< 0.05). Conclusions: The nomogram prediction model constructed in this study has higher accuracy and clinical application value than the traditional TNM staging. It can predict the 1 -, 2 - and 3-year OS of patients individually and provide a new tool for clinicians to evaluate the survival prognosis of CSCLC. The risk stratification system established by this model can identify high-risk patients more quickly, and make follow-up plans and subsequent treatment plans more targeted.