ABSTRACT
However, it is still difficult for clinicians to establish prognostic stratifications and therapeutic strategies because of the lack of tools for predicting the survival of triple-negative breast cancer patients with liver metastases (TNBC-LM). Based on clinical data from large populations, a sensitive and discriminative nomogram was developed and validated to predict the prognosis of TNBC patients with LM at initial diagnosis or at the later course. Introduction/background: Liver metastasis (LM) in TNBC patients is associated with significant morbidity and mortality. The objective of this study was to construct a clinical model to predict the survival of TNBC-LM patients. Materials and methods: Clinicopathologic data were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Affiliated Hospital of Sun Yat-Sen University (FAFSYU). Based on patients with newly diagnosed TNBC with LM (nTNBC-LM) from the SEER database, a predictive nomogram was established and validated. Its predictive effect on TNBC patients with LM at later disease course by enrolling TNBC patients from FAFSYU who developed LM later. The prognostic effect of different treatment for nTNBC-LM was further assessed. Results: A prognostic model was developed and validated to predict the prognosis of TNBC-LM patients. For LM patients diagnosed at the initial or later treatment stage, the C-index (0.712, 0.803 and 0.699 in the training, validation and extended groups, respectively) and calibration plots showed the acceptable prognostic accuracy and clinical applicability of the nomogram. Surgical resection on the primary tumour and chemotherapy were found to be associated with significantly better overall survival (OS). Conclusion: A sensitive and discriminative model was developed to predict OS in TNBC-LM patients both at and after initial diagnosis.
ABSTRACT
Hypoxia preconditioning (HPC), a well-established preconditioning model, has been shown to protect the brain against severe hypoxia or ischemia caused by traumatic brain injury (TBI), but the mechanism has not been well elucidated. Anaerobic glycolysis is the major way for neurons to produce energy under cerebral ischemia and hypoxia after TBI, and it requires large amounts of glucose. We hypothesized that glucose transport, as a rate-limiting step of glucose metabolism, may play key roles in the neuroprotective effects of HPC on cerebral cortex tissue against TBI. The aim of this study was to investigate the effect of HPC on glucose transport activity of rat cerebral cortex tissue after TBI through examining the gene expression of two major glucose transporters (GLUT1 and GLUT3) and their upstream target gene hypoxia-inducible factor-1α (HIF-1α). Sprague-Dawley rats were treated with HPC (50.47 kPa, 3 h/d, 3d). Twenty-four hours after the last treatment, the rats were injured using the Feeney free falling model. Cortex tissues of injured rats were removed at 1 h, 4 h, 8 h, 12 h, 1 day, 3 days, 7 d, and 14 days post-injury for histological analysis. Compared with TBI alone, HPC before TBI resulted in the expression of HIF-1α, GLUT1, and GLUT3 to increase at 1 h; they were markedly increased at 4 h, 8 h, 12 h, 1 day, and 3 days and decreased thereafter (p < 0.05). HPC before TBI could improve neuronal survival in rats by examining NeuN staining and observing reduced apoptosis by examining TUNEL staining. The result showed that HPC before TBI could increase the expression of GLUT1 and GLUT3. And through double immunofluorescence staining for GLUT3 and NeuN, the results strongly suggest that HPC improved glucose transport activity of neurons in rats with TBI. In summary, our results further support that HPC can improve hypoxia tolerance and attenuate neuronal loss of cerebral cortex in rats after TBI. The mechanism is mainly related to the increase of glucose transport activity through inducing GLUT1 and GLUT3 expression through upregulating HIF-1α expression.
