High Trophinin‑Associated Protein Expression and Its Role in Prognosis of Pancreatic Cancer
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Abstract Background : Trophinin‑associated protein (TROAP) was known as the tastin, which originally recognized as a cytosolic protein involved in embryo implantation. Increasing studies have revealed that high expression of TROAP is related with poor outcomes in cancers. However, there have been few studies on the correlation of TROAP and pancreatic cancer. This study aimed to explore the prognostic significance of TROAP in pancreatic cancer by mining the data collected from The Cancer Genome Atlas (TCGA) dataset. Methods : Clinical information and the RNA expression data were obtained from the TCGA dataset. The correlations between clinical information and TROAP mRNA expression were performed by chi-square and Fisher exact tests. Univariate Cox analyses were used to filter the potential prognostic factors. The correlations between TROAP expression and clinical characteristics of patients with pancreatic cancer were confirmed by multivariate Cox analyses. Results : Analysis of tumor data showed that high expression of TROAP was correlated with poor outcomes in pancreatic cancer patients. Univariate and multivariate Cox analyses demonstrated that TROAP mRNA expression played an important role in shorting overall survival (OS) and relapse-free survival (RFS), which might serve as the useful biomarker and prognostic factor for pancreatic cancer. Conclusions : TROAP was an independent risk factor for pancreatic cancer. TROAP has the potential to be a biomarker, especially in predicting prognosis.Keywords:
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Clinical Significance
Univariate analysis
We examined the comparative behavior of subject-specific multivariate and univariate reference regions, using both computer-generated data and serial (semi-annual) measurements of selected analytes in subjects from a large health-maintenance program. Univariate studies under both homeostatic and random-walk time-series models were helpful in defining expected results, but only the homeostatic model was used in multivariate as well as univariate forms. Analysis of the computer-generated data and the real biochemical series produced similar findings, which showed the multivariate subject-specific reference region to be much more conservative than corresponding univariate intervals. That is, a multidimensional point of p correlated observations is quite likely to lie within the individual's multivariate reference region (based on past observation vectors), even when one or more of the observations lie outside their separate reference intervals for that individual. One consequence of this high specificity against univariate false positives in a large surveillance program is a higher than expected proportion of positive multivariate vectors in which none of the values lie outside their univariate ranges. Thus, although the development of multivariate reference regions should be encouraged, they should be used in conjunction with, not instead of, univariate ranges.
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Background/Aim: Previously, we identified predictors of survival after irradiation of grade II-IV cerebral gliomas. In this supplementary analysis, survival was calculated in a more appropriate way than the original study. Patients and Methods: Ten factors were re-evaluated for survival in patients of the original study including pre-radiotherapy seizures. In the original study, survival was calculated from the end of the last radiotherapy course (primary or re-irradiation). After re-review, this approach was considered inappropriate. Survival should have always been calculated from the first radiotherapy course, as done in this supplementary analysis. Results: On multivariate analysis, WHO-grade II (p=0.006) and upfront resection (p=0.001) were associated with better survival. Unifocal glioma was significant on univariate analysis (p=0.001), where a trend could be identified for age ≤59 years (p=0.057) and seizures (p=0.060). Conclusion: The findings of this supplementary analysis regarding the identification of prognostic factors for survival agree with the results of the original study.
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Forecasting methods are reviewed. They may be classified into univariate, multivariate and judgemental methods, and also by whether an automatic or non-automatic approach is adopted. The choice of 'best' method depends on a wide variety of considerations. The use of forecasting competitions to compare the accuracy of univariate methods is discussed. The strengths and weaknesses of different univariate methods are compared, both in automatic and non-automatic mode. Some general recommendations are made as well as some suggestions for future research.
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Univariate analysis
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The delta check methods are methods for detection of random errors in clinical laboratory tests including specimen abnormalities, specimen mix-up, problems in analysis processes, and clerical errors. Methodologically, it is known that the multivariate delta check methods are more superior to the univariate delta check methods. However, due to some problems in reality including technical difficulties, it is hard to put the multivariate delta check methods into practice. Since the univariate delta check methods are methods at hand, there has been a need for an efficient and effective univariate delta check method. In order to meet such a need, we propose "the multi-item univariate delta check (MIUDC) method". By the multi-item univariate delta check (MIUDC) method, we mean a method in which univariate delta checks are performed on multiple items and specimens with the positive univariate delta check in at least k items are put under a detailed investigation. Our research objectives are the determination of an appropriate value of such k and identification of test items deserving of more interest. Through real data and simulation studies, we concluded that an appropriate value of k is 4 because, with k = 4, we can have light checking-out volumes and high efficiency. Also, we identified total cholesterol, albumin, and total protein as items deserving of more interest because the false positive rate associated with them in the MIUDC was zero in a simulation study. We present the MIUDC method as a quality control method that is easy-to-implement and efficient.
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Univariate analysis
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Objective To explore the prognostic factors of astrocytic tumors as the theoretical reference to the clinical treatment. Methods Data of 89 patients with astrocytic tumors, admitted in Zhujiang Hospital from January to June 2000, were collected and analyzed with regard to patient age, gender, preoperative KPS score, epilepsy before surgery, histologic grade, tumor location and extension, extent of surgery, radiation, date of operation, date of death, physical state in the last follow-up (dead or alive), and cause of death. For the univariate analysis, survival probabilities were estimated based on Kaplan-Meier's survival analysis and Logrank test. Multivariate regression analysis using Cox's proportional-hazards model showed the simultaneous effect of outcome-related variables on survival. Results Univariate analysis demonstrated that patient age, KPS score, epilepsy before surgery, histologic grade and radiation were the significant factors for survival (P0.01). In contrast, multivariate survival analysis showed that patient age, histologic grade and KPS score were independent, statistically significant prognostic factors for patients with astrocytic tumors, whereas preoperative epilepsy and postoperative radiation did not reach the significance level for entry into the stepwise model. And gender, tumor location, tumor extension and extent of sugery had no association with patient survival on both univariate and multivariate analysis. Conclusion Patient age, histologic grade and KPS score are associated strongly with survival, while preoperative epilepsy and postoperative radiation appear to be of limited prognostic value. And there is no correlation among gender, extent of surgery and prognosis. Neither tumor location nor tumor extension is associated with survival.
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Log-rank test
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In univariate and in multivariate analyses, it is difficult to identify outliers in the case of skewed or heavy-tailed distributions. In this article, we propose simple univariate and multivariate outlier identification procedures that perform well with these types of distributions while keeping the computational complexity low. We describe the commands gboxplot (univariate case) and sdasym (multivariate case), which implement these procedures in Stata.
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This work compares the performances of univariate and multivariate time series models. Five time series variables from Nigeria’s gross domestic products were used for the comparative study. These series were modelled using both the univariate and multivariate time series framework. The performances of the two methods were evaluated based on the mean error incurred by each approach. The results showed that the univariate linear stationary models perform better than the multivariate models.
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