Oncotarget · 2017
SNORA74B gene silencing inhibits gallbladder cancer cells by inducing PHLPP and suppressing Akt/mTOR signaling.
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Xu Huiyu, Feng Guoshuang, Ma Congcong, Han Yong, Zhou Jiansuo, Song Jiatian, Su Yuan, Zhong Qun, Chen Fenghua, Cui Liyan, Li Rong
Аннотация
BACKGROUND: The anti-Müllerian hormone (AMH) is gaining attention as a key factor in determining ovarian reserve and polycystic ovarian syndrome, and its clinical applications are becoming more widespread worldwide.
OBJECTIVE: To identify the most accurate formula for converting AMH assay results between different platforms, so that the developed AMH converter can be used to reduce the need for multiple AMH tests at different hospitals.
METHODS: Assuming that the Beckman Access, Kangrun, and Roche Elecsys® AMH assays fit a linear relationship from the lowest to the highest concentration (a global relationship), we used Passing-Bablok regression to determine the conversion equation between each two assays. When the relationship between two AMH assays was a local one, spline regression was used. Bland-Altman plots were drawn to check systemic bias and heterogeneity of variance across different ranges of values. The fitting effects of the models were evaluated using the squared coefficient of determination (r2), adjusted r2, root mean square error (RMSE), Akaike information criterion (AIC), and corrected AIC.
RESULTS: The coefficient of variance for multiple controls in the Kangrun, Roche, and Beckman assays was lower than 5%, and the bias of multiple controls was lower than 7%. A global linear relationship was observed between the Kangrun and Roche assays, with the intercept being zero, for which Passing-Bablok regression was employed for data conversion between the two platforms. For the other two pairs of platforms, i.e., Roche and Kangrun or Beckman and Kangrun, spline regression was applied, with the intercepts not including zero. The six corresponding formulas were developed into an online AMH converter (http://121.43.113.123:8006/).
CONCLUSION: This is the first time Passing-Bablok plus spline regression has been used to convert AMH concentrations from one assay to another. The formulas have been developed into an online tool, which makes them convenient to use in practical applications.
OBJECTIVE: To identify the most accurate formula for converting AMH assay results between different platforms, so that the developed AMH converter can be used to reduce the need for multiple AMH tests at different hospitals.
METHODS: Assuming that the Beckman Access, Kangrun, and Roche Elecsys® AMH assays fit a linear relationship from the lowest to the highest concentration (a global relationship), we used Passing-Bablok regression to determine the conversion equation between each two assays. When the relationship between two AMH assays was a local one, spline regression was used. Bland-Altman plots were drawn to check systemic bias and heterogeneity of variance across different ranges of values. The fitting effects of the models were evaluated using the squared coefficient of determination (r2), adjusted r2, root mean square error (RMSE), Akaike information criterion (AIC), and corrected AIC.
RESULTS: The coefficient of variance for multiple controls in the Kangrun, Roche, and Beckman assays was lower than 5%, and the bias of multiple controls was lower than 7%. A global linear relationship was observed between the Kangrun and Roche assays, with the intercept being zero, for which Passing-Bablok regression was employed for data conversion between the two platforms. For the other two pairs of platforms, i.e., Roche and Kangrun or Beckman and Kangrun, spline regression was applied, with the intercepts not including zero. The six corresponding formulas were developed into an online AMH converter (http://121.43.113.123:8006/).
CONCLUSION: This is the first time Passing-Bablok plus spline regression has been used to convert AMH concentrations from one assay to another. The formulas have been developed into an online tool, which makes them convenient to use in practical applications.
Предметные рубрики MeSH
AnimalsApoptosisBiomarkers, TumorCell CycleCell ProliferationFemaleFollow-Up StudiesGallbladder NeoplasmsGene Expression Regulation, NeoplasticGene SilencingHumansLymphatic MetastasisMaleMiceMice, Inbred BALB CMice, NudeMiddle AgedNeoplasm InvasivenessNeoplasm StagingNuclear ProteinsPhosphoprotein PhosphatasesPrognosisProto-Oncogene Proteins c-aktRNA, Small NucleolarSurvival RateTOR Serine-Threonine KinasesTumor Cells, CulturedXenograft Model Antitumor Assays