Identification of a prognostic alternative splicing signature in oral squamous cell carcinoma
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Abstract Alternative splicing (AS) is critically associated with tumorigenesis and patient's prognosis. Here, we systematically analyzed survival‐associated AS signatures in oral squamous cell carcinoma (OSCC) and evaluated their prognostic predictive values. Survival‐related AS events were identified by univariate and multivariate Cox regression analyses using OSCC data from the TCGA head neck squamous cell carcinoma data set. The Percent Spliced In calculated by SpliceSeq from 0 to 1 was used to quantify seven types of AS events. A predictive model based on AS events was constructed by least absolute shrinkage and selection operator Cox regression assay and further validated using a training‐testing cohort design. Patient survival was estimated using the Kaplan–Meier method and compared with Log‐rank test. The receiver operating characteristics curve area under the curves was used to evaluate the predictive abilities of these predictive models. Furthermore, gene–gene interaction networks and the splicing factors (SFs)‐AS regulatory network was generated by Cytoscape. A total of 825 survival‐related AS events within 719 genes were identified in OSCC samples. The integrative predictive model was better at predicting outcomes of patients as compared to those models built with the individual AS event. The predictive model based on three AS‐related genes also effectively predicted patients’ survival. Moreover, seven survival‐related SFs were detected in OSCC including RBM4, HNRNPD, and HNRNPC, which have been linked to tumorigenesis. The SF‐AS network revealed a significant correlation between survival‐related AS genes and these SFs. Our findings revealed a systemic portrait of survival‐associated AS events and the splicing network in OSCC, suggesting that AS events might serve as novel prognostic biomarkers and therapeutic targets for OSCC.Keywords:
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To compare the surgical outcomes of adult intermittent exotropia (X(T)) patients and matched control children X(T) patients including survival analysis. Fifty-two adult X(T) patients and 129 matched control children X(T) patients were included. Clinical characteristics, survival analysis, and surgical dose-response curves were evaluated and compared between the two groups. The weighted Cox proportional hazards regression analysis was used in order to find risk factors for the recurrence. Using Kaplan-Meier survival analysis, the cumulative probability of survival rate considering recurrence as event of Adult group were 93.97% for one year, and maintained at 88.44% for two, three. four, and five years after surgery. In contrast, those of the Child group were 83.6%, 76.5%, 65.6%, 56.23%, and 40.16% for one, two, three, four, and five years after surgery, respectively. The Adult group had a better event-free survival curve than the Child group as analyzed by a Log-rank test (p = 0.020). According to multivariate weighted Cox regression analysis, the younger age at operation and the larger preoperative angle were significant risk factors for recurrence.
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Related Article, see p 971KEY POINT: Kaplan-Meier curves, log-rank-test, and Cox proportional hazards regression are common examples of “survival analysis” techniques, which are used to analyze the time until an event of interest occurs.In this issue of Anesthesia & Analgesia, Song et al1 report results of a randomized trial in which they studied the onset of labor analgesia with 3 different epidural puncture and maintenance techniques. These authors compared the techniques on the primary outcome of time until adequate analgesia was reached—defined as a visual analog scale (VAS) score of ≤30 mm—with Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression. In studies addressing the time until an event of interest occurs, some but not all patients will typically have experienced the event at the end of the follow-up period. Patients in whom the even has not occurred—or who are lost to follow-up during the observation period—are said to be “censored.” It is unknown when and, depending on the event, if the event will occur.2 Simply excluding censored patients from the analysis would bias the analysis results. Specific statistical methods are thus needed that can appropriately account for such censored patient observations. Since the event of interest is often death, these analyses are traditionally termed “survival analyses,” and the time until the event occurs is referred to as the “survival time.” However, as done by Song et al,1 these techniques can also be used for the analysis of the time to any other well-defined event. Among the many available survival analysis methods, Kaplan-Meier curves, log-rank tests to compare these curves, and Cox proportional hazards regression are most commonly used. The Kaplan-Meier method estimates the survival function, which is the probability of “surviving” (ie, the probability that the event has not yet occurred) beyond a certain time point. The corresponding Kaplan-Meier curve is a plot of probability (y-axis) against time (x-axis) (Figure). This curve is a step function in which the estimated survival probability drops vertically whenever one or more outcome events occurred with a horizontal time interval between events. Plotting several Kaplan-Meier curves in 1 figure allows for a visual comparison of estimated survival probabilities between treatment or exposure groups; the curves can formally be compared with a log-rank test. The null hypothesis tested by the log-rank test is that the survival curves are identical over time; it thus compares the entire curves rather than the survival probability at a specific time point.Figure.: Kaplan-Meier plot of the percentage of patients without adequate analgesia, redrawn from Figure 2 in Song et al.1 Note that the original figure plotted the probability of adequate analgesia, as this is easily interpretable for readers in the context of the study research aim. In contrast, we present the figure as conventionally done in a Kaplan-Meier curve or plot, with the estimated probability (here expressed as percentage) of “survival” plotted on the y-axis. Vertical drops in the plot indicate that one or more patients reached the end point of experiencing adequate analgesia at the respective time point. CEI indicates continuous epidural infusion; DPE, dural puncture epidural; EP, conventional epidural; PIEB, programmed intermittent epidural bolus.The log-rank test assesses statistical significance but does not estimate an effect size. Moreover, while there is a stratified log-rank test that can adjust the analysis for a few categorical variables, the log-rank test is essentially not useful to simultaneously analyze the relationships of multiple variables on the survival time. Thus, when researchers either desire (a) to estimate an effect size3 (ie, the magnitude of the difference between groups)—as done in the study by Song et al1—or (b) to test or control for effects of several independent variables on survival time (eg, to adjust for confounding in observational research),4 a Cox proportional hazards model is typically used. The Cox proportional hazards regression5 technique does not actually model the survival time or probability but the so-called hazard function. This function can be thought of as the instantaneous risk of experiencing the event of interest at a certain time point (ie, the probability of experiencing the event during an infinitesimally small time period). The event risk is inversely related to the survival function; thus, “survival” rapidly declines when the hazard rate is high and vice versa. The exponentiated regression coefficients in Cox proportional hazards regression can conveniently be interpreted in terms of a hazard ratio (HR) for a 1-unit increase in the independent variable, for continuous independent variables, or versus a reference category, for categorical independent variables. While the HR is not the same as a relative risk, it can for all practical purposes be interpreted as such by researchers who are not familiar with the intricacies of survival analysis techniques. For those wishing to delve deeper into the details and learn more about survival analysis—including but not limited to the topics that we briefly touch on here—we refer to our tutorial on this topic previously published in Anesthesia & Analgesia.2 Importantly, even though the techniques discussed here do not make assumptions on the distribution of the survival times or survival probabilities, these analysis methods have other important assumptions that must be met for valid inferences, as also discussed in more detail in the previous tutorial.2
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Kaplan-Meier analysis is largely used in nephrology to estimate a population survival curve from a sample. If patients are followed-up until death, the survival curve can be estimated simply by computing the fraction survival at each time point. Kaplan-Meier analysis allows the estimation of survival over time, even when patients drop out or are studied for different lengths of time. In Kaplan-Meier analysis, the comparison between two survival curves is made by the log-rank test.
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Survival analysis of time to an event such as death or sickness. The survivorship function is estimated via the actuarial method and the Kaplan-Meier curve. Both are compared graphically and via the log-rank test. Stratification is needed when confounding is present. The Cox proportional hazard model incorporates covariates into the survival model. Other parametric and non-parametric models are discussed.
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บทคดยอ งานวจยนนำเทคนคการวเคราะหการอยรอด (survival analysis) มาศกษาการออกกลางคนของนกศกษาโครงการปรญญาตรภาคพเศษ ภาควชาคณตศาสตรและสถต คณะวทยาศาสตรและเทคโนโลย มหาวทยาลยธรรมศาสตร เพอศกษาฟงกชนความอยรอด (survival function) มธยฐานระยะเวลาความอยรอด (median survival time) และอตราความเสยงอนตราย (hazard rate) ของการออกกลางคนของนกศกษา ดวยวธของ Kaplan-Meier และเปรยบเทยบระยะเวลาการอยรอดของนกศกษาโครงการปรญญาตรภาคพเศษ ภาควชาคณตศาสตรและสถต และภาควชาวทยาการคอมพวเตอร ดวยวธ Log-rank test โดยใชขอมลการจดทะเบยนเรยนของนกศกษารนปการศกษา 2550 ตงแตปการศกษา 2550 ถง 2556 รวมทงสนจำนวน 203 คน จากสำนกทะเบยนและประมวลผล มหาวทยาลยธรรมศาสตร และประมวลผลขอมลดวยโปรแกรมสำเรจรป SPSS for Window version 21.0 จากการศกษาพบวาไมสามารถคำนวณคามธยฐานระยะเวลาความอยรอดของนกศกษาสาขาวทยาการคอมพวเตอรได สวนนกศกษาสาขาคณตศาสตรและสาขาสถตมมธยฐานระยะเวลาความอยรอดมากกวาภาคเรยนท 2/2552 และภาคเรยนท 2/2553 ตามลำดบ นกศกษาสาขาวทยาการคอมพวเตอรมอตราความเสยงตอการออกกลางคนมากทสดเทากบ 0.27 ในภาคเรยนท 3/2550 โดยมโอกาสทจะอยรอดในการศกษาไดนานกวาภาคเรยนท 3/2550 เทากบ 69 % นกศกษาสาขาคณตศาสตรมอตราความเสยงตอการออกกลางคนมากทสดเทากบ 0.27 ในภาคเรยนท 2/2551 โดยมโอกาสทจะอยรอดในการศกษาไดนานกวาภาคเรยนท 2/2551 เทากบ 60 % นกศกษาสาขาสถตมอตราความเสยงตอการออกกลางคนมากทสดเทากบ 0.17 ในภาคเรยนท 3/2550 โดยมโอกาสทจะอยรอดในการศกษาไดนานกวาภาคเรยนท 3/2550 เทากบ 83 % และพบวานกศกษาโครงการปรญญาตรภาคพเศษทง 3 สาขา มระยะเวลาการอยรอดไมแตกตางกน (P-value = 0.532) คำสำคญ : ระยะเวลาการอยรอด; ฟงกชนความอยรอด; มธยฐานระยะเวลาความอยรอด; อตราความเสยง; การออกกลางคน Abstract Survival analysis was used to study the dropping out of undergraduate students (special program) of the Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University. Survival function, median survival time and hazard rate of dropping out by Kaplan-Meier method were analyzed. Survival times among mathematics students, statistics students and computer science students were compared by log-rank test. The registration data from academic year 2007 to 2013 of the total 203 undergraduate students entering in academic year 2007, were obtained from the office of the registrar. They were analyzed through survival analysis procedure by using SPSS for Window version 21.0. The results showed that the median survival time of the computer science students could not be calculated. The median survival time of Mathematics students and Statistics students were higher than the 1 st semester of academic year 2009 and the 1 st semester of academic year 2010, respectively. The highest hazard rate of the computer science students was 0.27 and the survival time of 69 % occurred in the 2 nd semester of academic year 2007. The highest hazard rate of the mathematics students was 0.27 and the survival time of 60 % occurred in the 1st semester of academic year 2008. The highest hazard rate of the statistics students was 0.17 and the survival time of 83 % occurred in the 2 nd semester of academic year 2007. The whole result indicated that there were no differences between the survival time of mathematics, statistics and computer science students (P-value = 0.532). Keywords: survival time; survival function; median survival time; hazard rate; drop out
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