KLF8 is Associated with Poor Prognosis and Regulates Glycolysis by Targeting GLUT4 in Gastric Cancer

Mao, Anwei; Zhou, Xiang; Liu, Yanxia; Ding, Jian; Miao, Aiyu; Pan, Gaofeng

doi:10.2139/ssrn.3300411

Cited by 18 publications

(24 citation statements)

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“…SLC2A4 is an e cient glucose transporter, which is located in the cytoplasmic vesicles and can be transferred to the plasma membrane to take up glucose when stimulated by insulin [21] . It has been shown that SLC2A4 was elevated in cancer progression [22,23] , which is in conformity with our results. SLC2A4 inhibition abolished the biological effects of PC cells in proliferation, migration and invasion induced by miR-302b-3p downregulation.…”

Section: Discussionsupporting

confidence: 94%

LncRNA SNHG16 Contributes to Tumor Progression via miR-302b-3p/SLC2A4 in Pancreatic Cancer

Miao

et al. 2020

Preprint

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Background: It is reported that lncRNA SNHG16 is significantly highly expressed in pancreatic cancer (PC). However, the functions and mechanisms of SNHG16 are not clear. The aim of this study was to explore the effects of SNHG16 in PC.Methods: qRT-PCR analyze was applied to detect the expression levels of SNHG16, miR-302b-3p and SLC2A4 in PC tissues and cells. CCK8 and EdU assays were used to determine the proliferation of PC cells. Transwell assay were used to measure the capacities of PC cells migration and invasion. Apoptosis were evaluated by flow cytometry, and the expression of apoptosis related proteins (including Bax, Bcl-2, cleaved caspase-3 and cleaved caspase-9), which were tested by western blot. The interactions between miR-302b-3p and SNHG16 or miR-302b-3p and SLC2A4 mRNA 3’UTR were clarified by Dual luciferase reporter assay and RNA immunoprecipitation.Results: SNHG16 was significantly elevated in PC tissues and cell lines, which was associated with poor prognosis of PC patients. Knockdown of SNHG16 reduced PC cells proliferation, migration and invasion. SNHG16 acted as a sponge to regulate miR-302b-3p expression in PC cells. And miR-302b-3p targeted SLC2A4 directly. Conclusions: SNHG16 promoted the progression of PC via miR-302b-3p/SLC2A4 axis and was expected to be a potential target for early diagnosis and treatment of PC.

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Section: Discussionsupporting

confidence: 94%

LncRNA SNHG16 Contributes to Tumor Progression via miR-302b-3p/SLC2A4 in Pancreatic Cancer

Miao

et al. 2020

Preprint

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“…The c-index and area under the receiver operating characteristic curve (AUC) of different time were used to compare the discriminative ability of the nomogram and AJCC 7th system (IVA/IVB) in the training and validation groups. Calibration curves were created to assess the predictive accuracy in the two groups [21] .Then, we calculated the risk score of each training group patient according to the Cox regression model. The X-title program (versions 3.6.1) was used to select the optimal cut-off for the risk score to distinguish the differences in patient survival.…”

Section: Discussionmentioning

confidence: 99%

A practical nomogram from the SEER database to predict the prognosis of hepatocellular carcinoma patients with lymph node metastasis

Zhang

Tao

et al. 2020

Preprint

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Background: The presence of lymph node metastases is related to poor survival outcomes in hepatocellular carcinoma patients and because of the reported low probability of lymph node metastasis, research into the prognoses of such patients is difficult to conduct. In this study, we aimed to develop a nomogram model to predict the prognosis of HCC patients with lymph node metastasis and provided a reasonable basis for the choice of follow-up treatment.Methods: HCC patients diagnosed with LN metastasis from 2010 to 2015 were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate Cox regression and lasso regression were used to screen prognostic factors. Cox multiple-factor analysis was employed to investigate the independent prognostic factors for survival. The concordance index (C-index) and calibration curve were used to evaluate the predictive performance of our model. The clinical benefit was assessed via decision curve analysis (DCA). The survival was analyzed using Kaplan-Meier method and the differences among survival curves were compared by the log-rank test.Results: Patients were randomized into the training group (944 patients) and the validation group (402 patients) in a 70:30 ratio. Grade, T stage, surgery to the liver, chemotherapy, radiation recode, AFP, fibrosis score, tumor size group, M stage were selected as independent prognostic factors, and we developed nomograms using these variables. The c-indices of the training and validation groups were 0.70 and 0.73, respectively. The calibration curves for probability of survival showed good agreement. The DCA indicated that the nomogram had positive net benefits. Patients were divided into two risk groups according to our model, survival curves were drawn, and the log-rank test was performed, the p-value of which was <0.001.Conclusions: The nomogram can accurately predict the prognosis of HCC patients with lymph nodes metastasis and provide a reasonable basis for treatment.

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“…Then, we calculated the risk score of each training group patient according to the Cox regression model. The X-title program (versions 3.6.1) was used to select the optimal cutoff for the risk score to distinguish the differences in patient survival [20,21] . Then, the training and validationg group patients were divided into high and low risk groups according to risk score.…”

Section: Discussionmentioning

confidence: 99%

A practical nomogram predicting the prognosis of hepatocellular carcinoma patients with lymph node metastasis: A population-based analysis

Zhang

Tao

et al. 2020

Preprint

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Background: Lymph node (LN) metastasis is associated with poor survival outcomes in patients with hepatocellular carcinoma (HCC) patients and because of the reported low probability of lymph node metastasis, research into the Anchorprognoses of such patients is difficult to conduct. In this study, we aimed to develop a nomogram model to predict the prognosis of HCC patients with lymph node metastasis. Methods: HCC patients diagnosed with LN metastasis from 2010 to 2015 were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate Cox regression and lasso regression were used to screen prognostic factors. Cox multiple-factor analysis was used to investigate the independent risk factors for survival. We developed a prognostic nomograms using these independent risk factors. The predictive performance of our nomogram model was evaluated according to the concordance index (C-index) and calibration curve. The net clinical benefit was assessed via decision curve analysis (DCA). Patients were divided into different risk groups according to the model. A survival curve was drawn using the Kaplan-Meier method and the difference was compared by the log-rank test. Results: There were 944 patients in the training cohort and 402 patients in the validation cohort. Grade, T stage, surgery to the liver, chemotherapy, radiation recode, AFP, fibrosis score, tumor size group, M stage were selected as independent prognostic factors, and we developed nomograms using these variables. The calibration curves for probability of survival showed good agreement between the prediction by our model and actual observation in both the training and validation groups. DCA indicated that the nomogram had positive net benefits. Conclusions: The nomogram can accurately predict the prognosis of HCC patients with lymph nodes metastasis and provide a reasonable basis for treatment. Keywords: Hepatocellular carcinoma, prognosis, lymph nodes metastasis, prediction model, nomogram

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KLF8 is Associated with Poor Prognosis and Regulates Glycolysis by Targeting GLUT4 in Gastric Cancer

Cited by 18 publications

References 0 publications

LncRNA SNHG16 Contributes to Tumor Progression via miR-302b-3p/SLC2A4 in Pancreatic Cancer

LncRNA SNHG16 Contributes to Tumor Progression via miR-302b-3p/SLC2A4 in Pancreatic Cancer

A practical nomogram from the SEER database to predict the prognosis of hepatocellular carcinoma patients with lymph node metastasis

A practical nomogram predicting the prognosis of hepatocellular carcinoma patients with lymph node metastasis: A population-based analysis

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