RESUMO
Cancer cells modulate their metabolic activities to adapt to their growth and proliferation. Despite advances in breast cancer biology having led to the widespread use of molecular targeted therapy and hormonal drugs, the molecular mechanisms in metabolism related to the regulation of breast cancer cell proliferation are still poorly understood. Here, we investigate the possible role of SHMT2, a key enzyme in serine metabolism, in breast cancer. Firstly, SHMT2 is found highly expressed in both breast cancer cells and tissues, and patients with high expression of SHMT2 have a worse prognosis. Moreover, the intervention of SHMT2 by either knockdown or over-expression in vitro induces the effect on breast cancer proliferation. Mechanistically, RNA-seq shows that over-expression of SHMT2 affect multiple signaling pathways and biological process in breast cancer cells. Furthermore, we confirm that SHMT2 promotes breast cancer cell growth through MAPK and VEGF signaling pathways. Finally, we verify the role of SHMT2 in promoting breast cancer growth in the xenograft tumor model. Our results indicate that SHMT2 plays a critical role in regulating breast cancer growth through MAPK, and VEGF signaling pathways, and maybe serve as a therapeutic target for breast cancer therapy.
RESUMO
Background: To build a predictive scoring model based on simple immune and inflammatory parameters to predict postoperative survival in patients with breast cancer. Methods: We used a brand-new immuno-inflammatory index-pan-immune-inflammation value (PIV)-to retrospectively evaluate the relationship between PIV and overall survival (OS), and based on the results of Cox regression analysis, we established a simple scoring prediction model based on several independent prognostic parameters. The predictive accuracy of the model was evaluated and independently validated. Results: A total of 1,312 patients were included for analysis. PIV was calculated as follows: neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L). According to the best cutoff value of PIV, we divided the patients into two different subgroups, high PIV (PIV > 310.2) and low PIV (PIV ≤ 310.2), associated with significantly different survival outcomes (3-year OS, 80.26% vs. 86.29%, respectively; 5-year OS, 62.5% vs. 71.55%, respectively). Six independent prognostic factors were identified and used to build the scoring system, which performed well with a concordance index (C-index) of 0.759 (95% CI: 0.715-0.802); the calibration plot showed good calibration. Conclusions: We have established and verified a simple scoring system for predicting prognosis, which can predict the survival of patients with operable breast cancer. This system can help clinicians implement targeted and individualized treatment strategies.
RESUMO
BACKGROUND: Using the current tumor lymph node metastasis (TNM) staging system to make treatment decisions and predict survival in patients with nasopharyngeal carcinoma (NPC) lacks sufficient accuracy. Patients at the same stage often have different survival prognoses. METHODS: In the current study 802 NPC patients who underwent concurrent radiotherapy and chemotherapy from January 2010 to December 2014 at Sun Yat-sen University Cancer Center in China were retrospectively assessed. The optimal cut-off points for skeletal muscle index (SMI) and monocyte-to-lymphocyte ratio (MLR) were determined via receiver operating characteristic curves. SMI-MLR (S-M) grade and a nomogram were developed and used as clinical indicators in NPC patients. The consistency index (C-index) and a calibration curve were used to measure the accuracy and discriminative capacity of prediction. RESULTS: The predictive performance of S-M grade was better than that of TNM staging (C-index 0.639, range 0.578-0.701 vs. 0.605, range 0.545-0.665; p = 0.037). In multivariate analysis S-M grade, T stage, and N stage were independent prognostic factors. These three factors were then combined, yielding a nomogram with a C-index of 0.71 (range 0.64-0.77), indicating good predictive capacity. CONCLUSION: We developed and validated a prognostic parameter, S-M grade, which increased prediction accuracy significantly and can be combined with TNM staging to predict survival in patients with NPC undergoing concurrent chemoradiotherapy.
