Clinical effectiveness and safety of olaparib in BRCA-mutated, HER2-negative metastatic breast cancer in a real-world setting: final analysis of LUCY.

PURPOSE: The interim analysis of the phase IIIb LUCY trial demonstrated the clinical effectiveness of olaparib in patients with germline BRCA-mutated (gBRCAm), human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (mBC), with median progression-free survival (PFS) of 8.11 months, which was similar to that in the olaparib arm of the phase III OlympiAD trial (7.03 months). This prespecified analysis provides final overall survival (OS) and safety data. METHODS: The open-label, single-arm LUCY trial of olaparib (300 mg, twice daily) enrolled adults with gBRCAm or somatic BRCA-mutated (sBRCAm), HER2-negative mBC. Patients had previously received a taxane or anthracycline for neoadjuvant/adjuvant or metastatic disease and up to two lines of chemotherapy for mBC. RESULTS: Of 563 patients screened, 256 (gBRCAm, n = 253; sBRCAm, n = 3) were enrolled. In the gBRCAm cohort, median investigator-assessed PFS (primary endpoint) was 8.18 months and median OS was 24.94 months. Olaparib was clinically effective in all prespecified subgroups: hormone receptor status, previous chemotherapy for mBC, previous platinum-based chemotherapy (including by line of therapy), and previous cyclin-dependent kinase 4/6 inhibitor use. The most frequent treatment-emergent adverse events (TEAEs) were nausea (55.3%) and anemia (39.2%). Few patients (6.3%) discontinued olaparib owing to a TEAE. No deaths associated with AEs occurred during the study treatment or 30-day follow-up. CONCLUSION: The LUCY patient population reflects a real-world population in line with the licensed indication of olaparib in mBC. These findings support the clinical effectiveness and safety of olaparib in patients with gBRCAm, HER2-negative mBC. CLINICAL TRIAL REGISTRATION: Clinical trials registration number: NCT03286842.

Two Types of Survival Curves of Different Lines of Progeric Mice.

For most of their lifespan, the probability of death for many animal species increases with age. Gompertz law states that this increase is exponential. In this work, we have compared previously published data on the survival kinetics of different lines of progeric mice. Visual analysis showed that in six lines of these rapidly aging mutants, the probability of death did not strictly depend on age. In contrast, ten lines of progeric mice have survival curves similar to those of the control animals, that is, in agreement with Gompertz law, similar to the shape of an exponential curve upside down. Interestingly, these ten mutations cause completely different cell malfunctions. We speculate that what these mutations have in common is a reduction in the lifespan of cells and/or an acceleration of the transition to the state of cell senescence. Thus, our analysis, similar to the conclusions of many previously published works, indicates that the aging of an organism is a consequence of the aging of individual cells.

Laparoscopic versus open gastrectomy for nonmetastatic T4a gastric cancer: a meta-analysis of reconstructed individual participant data from propensity score-matched studies.

BACKGROUND: ​The applicability of laparoscopy to nonmetastatic T4a patients with gastric cancer remains unclear due to the lack of high-quality evidence. The purpose of this study was to compare the survival rates of laparoscopic gastrectomy (LG) versus open gastrectomy (OG) for these patients through a meta-analysis of reconstructed individual participant data from propensity score-matched studies. METHODS: PubMed, Embase, Web of Science, Cochrane library and CNKI were examined for relevant studies without language restrictions through July 25, 2023. Individual participant data on overall survival (OS) and disease-free survival (DFS) were extracted from the published Kaplan-Meier survival curves. One-stage and two-stage meta-analyses were performed. In addition, data regarding surgical outcomes and recurrence patterns were also collected, which were meta-analyzed using traditional aggregated data. RESULTS: Six studies comprising 1860 patients were included for analysis. In the one-stage meta-analyses, the results demonstrated that LG was associated with a significantly better DFS (Random-effects model: P = 0.027; Restricted mean survival time [RMST] up to 5 years: P = 0.033) and a comparable OS (Random-effects model: P = 0.135; RMST up to 5 years: P = 0.053) than OG for T4a gastric cancer patients. Two-stage meta-analyses resulted in similar results, with a 13% reduced hazard of cancer-related death (P = 0.04) and 10% reduced hazard of overall mortality (P = 0.11) in the LG group. For secondary outcomes, the pooled results showed an association of LG with less estimated blood loss, faster postoperative recovery and more retrieved lymph nodes. CONCLUSION: Laparoscopic surgery for patients with nonmetastatic T4a disease is associated with a potential survival benefit and improved surgical outcomes.

Ball divergence for the equality test of crossing survival curves.

It is a very common problem to test survival equality using the right-censored time-to-event data in clinical research. Although the log-rank test is popularly used in various studies, it may become insensitive when the proportional hazards assumption is violated. As follows, there have a variety of statistical methods being proposed to identify the discrepancy between crossing survival curves or hazard functions. The omnibus tests against general alternatives are usually preferred due to their wide applicability to complicated scenarios in real applications. In this paper, we propose two novel statistics to estimate the ball divergence using the right-censored survival data, and then implement them in the equality test on survival time in two independent groups. The simulation analysis demonstrates their efficiency in identifying the survival discrepancy. Compared to the existing methods, our proposed methods present higher power in situations with complex distributions, especially when there is a scale shift between groups. Real examples illustrate its advantage in practical applications.

A comparison of different methods to adjust survival curves for confounders.

Treatment specific survival curves are an important tool to illustrate the treatment effect in studies with time-to-event outcomes. In non-randomized studies, unadjusted estimates can lead to biased depictions due to confounding. Multiple methods to adjust survival curves for confounders exist. However, it is currently unclear which method is the most appropriate in which situation. Our goal is to compare forms of inverse probability of treatment weighting, the G-Formula, propensity score matching, empirical likelihood estimation and augmented estimators as well as their pseudo-values based counterparts in different scenarios with a focus on their bias and goodness-of-fit. We provide a short review of all methods and illustrate their usage by contrasting the survival of smokers and non-smokers, using data from the German Epidemiological Trial on Ankle-Brachial-Index. Subsequently, we compare the methods using a Monte-Carlo simulation. We consider scenarios in which correctly or incorrectly specified models for describing the treatment assignment and the time-to-event outcome are used with varying sample sizes. The bias and goodness-of-fit is determined by taking the entire survival curve into account. When used properly, all methods showed no systematic bias in medium to large samples. Cox regression based methods, however, showed systematic bias in small samples. The goodness-of-fit varied greatly between different methods and scenarios. Methods utilizing an outcome model were more efficient than other techniques, while augmented estimators using an additional treatment assignment model were unbiased when either model was correct with a goodness-of-fit comparable to other methods. These "doubly-robust" methods have important advantages in every considered scenario.

A win ratio approach for comparing crossing survival curves in clinical trials.

Many clinical trials include time-to-event or survival data as an outcome. To compare two survival distributions, the log-rank test is often used to produce a P-value for a statistical test of the null hypothesis that the two survival curves are identical. However, such a P-value does not provide the magnitude of the difference between the curves regarding the treatment effect. As a result, the P-value is often accompanied by an estimate of the hazard ratio from the proportional hazards model or Cox model as a measurement of treatment difference. However, one of the most important assumptions for Cox model is that the hazard functions for the two treatment groups are proportional. When the hazard curves cross, the Cox model could lead to misleading results and the log-rank test could also perform poorly. To address the problem of crossing curves in survival analysis, we propose the use of the win ratio method put forward by Pocock et al. as an estimand for analysing such data. The subjects in the test and control treatment groups are formed into all possible pairs. For each pair, the test treatment subject is labelled a winner or a loser if it is known who had the event of interest such as death. The win ratio is the total number of winners divided by the total number of losers and its standard error can be estimated using Bebu and Lachin method. Using real trial datasets and Monte Carlo simulations, this study investigates the power and type I error and compares the win ratio method with the log-rank test and Cox model under various scenarios of crossing survival curves with different censoring rates and distribution parameters. The results show that the win ratio method has similar power as the log-rank test and Cox model to detect the treatment difference when the assumption of proportional hazards holds true, and that the win ratio method outperforms log-rank test and Cox model in terms of power to detect the treatment difference when the survival curves cross.

The probability of bacterial spores surviving a thermal process: The 12D myth and other issues with its quantitative assessment.

The concepts of "D-value," "thermal death time" and "commercial sterility," innovative and useful at their inception, are based on untenable assumptions, notably that the log-linear isothermal inactivation model has universal applicability, that extrapolation over several orders of magnitude below the detection level is permissible, and that total microbial inactivation is theoretically impossible. Almost all commonly observed inactivation patterns, the log-linear is just a special case, can be described by both deterministic and fully stochastic models, examples of which are given. Unlike the deterministic, the stochastic models predict either complete elimination of the targeted cells or spores in realistic finite time, or residual survival. In most cases, the published survival data do not contain enough information to establish which actually happens. The microbial safety of thermally processed foods can be compromised not only by under-processing but also by a variety of mishaps whose occurrence probabilities are unrelated to the inactivation kinetics. Moreover, the available sampling plans to detect microbial contamination in sterilized containers through incubation alone are insensitive to levels of potential safety concerns. In principle, many of these issues could be resolved by developing new dramatically improved detection methods and/or verifiable methods to predict very low levels of microbial survival.

Analyzing differences between restricted mean survival time curves using pseudo-values.

Hazard ratios are ubiquitously used in time to event analysis to quantify treatment effects. Although hazard ratios are invaluable for hypothesis testing, other measures of association, both relative and absolute, may be used to fully elucidate study results. Restricted mean survival time (RMST) differences between groups have been advocated as useful measures of association. Recent work focused on model-free estimates of the difference in restricted mean survival through follow-up times, instead of focusing on a single time horizon. The resulting curve can be used to quantify the association in time units with a simultaneous confidence band. In this work a model-based estimate of the curve is proposed using pseudo-values allowing for possible covariate adjustment. The method is easily implementable with available software and makes possible to compute a simultaneous confidence region for the curve. The pseudo-values regression using multiple restriction times is in good agreement with the estimates obtained by standard direct regression models fixing a single restriction time. Moreover, the proposed method is flexible enough to reproduce the results of the non-parametric approach when no covariates are considered. Examples where it is important to adjust for baseline covariates will be used to illustrate the different methods together with some simulations.

Deterministic and stochastic survival models of injured ozonated Giardia cysts.

Giardia cysts exposed to short sublethal ozonation in lake waters continue to die-off well after the ozone complete dissipation. This delayed inactivation can be the manifestation of injured cysts' mortality, which the traditional Chick-Watson-Hom type models of disinfection do not account for. But it can be described by a slightly modified version of a general microbial survival model adapted for injured cysts or other targeted microorganisms surviving disinfection. The downward concavity of the cysts' semi-logarithmic survival ratio vs. time relationships suggests that the cysts' deaths had unimodal temporal distribution. Indeed, the cumulative (CDF) forms of the Weibull and lognormal distribution functions both had excellent fit to the experimental survival data. Such a survival pattern can also be described by a fully probabilistic model devised from the injured cysts' Markov chain, where the mortality's probability rate rises linearly with time. The stochastic model explains the ubiquitous observation that microbial survival curves become increasingly irregular and irreproducible as the number of survivors dwindles, regardless of their concavity degree and direction. Although based on ozonated Giardia cyst data, the concept should be applicable to the delayed mortality of other microorganisms surviving sublethal treatments of other kinds but unable to recover and/or multiply. KEY POINTS: • Deterministic and stochastic survival models can describe delayed inactivation. • The Weibull and lognormal distributions can describe cysts' times to mortality. • Stochastic model explains the progressively growing scatter in survival curves.

Robust group sequential designs for trials with survival endpoints and delayed response.

Randomized clinical trials in oncology typically utilize time-to-event endpoints such as progression-free survival or overall survival as their primary efficacy endpoints, and the most commonly used statistical test to analyze these endpoints is the log-rank test. The power of the log-rank test depends on the behavior of the hazard ratio of the treatment arm to the control arm. Under the assumption of proportional hazards, the log-rank test is asymptotically fully efficient. However, this proportionality assumption does not hold true if there is a delayed treatment effect. Cancer immunology has evolved over time and several cancer vaccines are available in the market for treating existing cancers. This includes sipuleucel-T for metastatic hormone-refractory prostate cancer, nivolumab for metastatic melanoma, and pembrolizumab for advanced nonsmall-cell lung cancer. As cancer vaccines require some time to elicit an immune response, a delayed treatment effect is observed, resulting in a violation of the proportional hazards assumption. Thus, the traditional log-rank test may not be optimal for testing immuno-oncology drugs in randomized clinical trials. Moreover, the new immuno-oncology compounds have been shown to be very effective in prolonging overall survival. Therefore, it is desirable to implement a group sequential design with the possibility of early stopping for overwhelming efficacy. In this paper, we investigate the max-combo test, which utilizes the maximum of two weighted log-rank statistics, as a robust alternative to the log-rank test. The new test is implemented for two-stage designs with possible early stopping at the interim analysis time point. Two classes of weights are investigated for the max-combo test: the Fleming and Harrington (1981) Gρ,γ$G^{\rho , \gamma }$ weights and the Magirr and Burman (2019) modest (τ∗)$ (\tau ^{*})$  weights.

Differential effects of larval and adult nutrition on female survival, fecundity, and size of the yellow fever mosquito, Aedes aegypti.

BACKGROUND: The yellow fever mosquito, Aedes aegypti, is the principal vector of medically-important infectious viruses that cause severe illness such as dengue fever, yellow fever and Zika. The transmission potential of mosquitoes for these arboviruses is largely shaped by their life history traits, such as size, survival and fecundity. These life history traits, to some degree, depend on environmental conditions, such as larval and adult nutrition (e.g., nectar availability). Both these types of nutrition are known to affect the energetic reserves and life history traits of adults, but whether and how nutrition obtained during larval and adult stages have an interactive influence on mosquito life history traits remains largely unknown. RESULTS: Here, we experimentally manipulated mosquito diets to create two nutritional levels at larval and adult stages, that is, a high or low amount of larval food (HL or LL) during larval stage, and a good and poor adult food (GA or PA, represents normal or weak concentration of sucrose) during adult stage. We then compared the size, survival and fecundity of female mosquitoes reared from these nutritional regimes. We found that larval and adult nutrition affected size and survival, respectively, without interactions, while both larval and adult nutrition influenced fecundity. There was a positive relationship between fecundity and size. In addition, this positive relationship was not affected by nutrition. CONCLUSIONS: These findings highlight how larval and adult nutrition differentially influence female mosquito life history traits, suggesting that studies evaluating nutritional effects on vectorial capacity traits should account for environmental variation across life stages.

Impact of shoulders on the calculus of heat sterilization treatments with different bacterial spores.

To date, heat is still the most used technology in food preservation. The calculus of heat treatments is usually based on Bigelow observations i.e. treatment time is an exponential function of the heat treatment temperature. However, a number of researchers have reported deviations from linearity in heat inactivation curves that caused errors in the calculus. This research was designed to evaluate the variability of shoulder length among different sporulated species, the impact of treatment temperature on these shoulders and the relationship between the traditional DT value and shoulder length. The heat inactivation kinetics of five bacterial spores of importance for the food industry was evaluated. B. weihenstephanensis and B. cereus did not show shoulders and DT values calculated ranged from 0.99 to 0.23 and from 1.33 to 0.56 respectively at temperatures from 100 to 102.5 °C. On the other side B. subtilis, B. licheniformis and G. stearothermophilus showed shoulders of 1.75-0.42, 1.92-0.43 and 3.22-0.78 and DT values of 1.52-0.32, 2.12-0.59 and 2.22-0.48 respectively in the range of temperatures tested. From the results obtained it was concluded that the presence and magnitude of shoulders depended on the bacterial spore species, the longest being those on the bacterial spores which showed greatest heat resistance. It has also been proved that shoulder lengths vary with treatment temperature in the same proportion of traditional DT values, with the relationship Sl/DT being constant. Thus, an equation which included the constant Sl/DT was proposed.

Adjusted Survival Curves Improve Understanding of Multivariable Cox Model Results.

Kaplan-Meier survival curves are the most common methods for unadjusted group comparison of outcomes in orthopedic research. However, they may be misleading due to an imbalance of confounders between patient groups. The Cox model is frequently used to adjust for confounders, but graphical display of adjusted survival curves is not commonly utilized. We describe the circumstances when adjusted survival curves are useful in orthopedic research, describe and use 2 different methods to obtain adjusted curves, and illustrate how they can improve understanding of the multivariable Cox model results. We further provide practical strategies for identifying the need for and performing adjusted survival curves. Please visit the followinghttps://youtu.be/ys0hy2CiMCAfor a video that explains the highlights of the paper in practical terms.

Evaluating macrophage migration inhibitory factor 1 expression as a prognostic biomarker in colon cancer.

OBJECTIVE: Several studies indicate that macrophage migration inhibitory factor 1 plays a role for tumor progression in colon cancer. We investigated whether determination of migration inhibitory factor 1 mRNA expression levels in lymph nodes of colon cancer patients could be used as a prognostic marker. METHODS: Expression levels of migration inhibitory factor 1 and carcinoembryonic antigen mRNAs were assessed in primary tumors and regional lymph nodes of 123 colon cancer patients (stages I-IV), and in colon cancer- and immune cell lines using quantitative reverse transcriptase-polymerase chain reaction. Expression of migration inhibitory factor 1 protein was investigated by two-color immunohistochemistry and immunomorphometry. RESULTS: Migration inhibitory factor 1 mRNA was expressed at 60 times higher levels in primary colon cancer tumors compared to normal colonic tissue (medians 8.2 and 0.2 mRNA copies/18S rRNA unit; p < .0001). A highly significant difference in mRNA expression levels was found between hematoxylin-eosin positive lymph nodes and hematoxylin-eosin negative lymph nodes (p < .0001). Migration inhibitory factor 1 and carcinoembryonic antigen proteins were simultaneously expressed in many colon cancer-tumor cells. Kaplan-Meier survival model and hazard ratio analysis, using a cutoff level at 2.19 mRNA copies/18S rRNA unit, revealed that patients with lymph nodes expressing high levels of migration inhibitory factor 1 mRNA had a 3.5-fold (p = .04) higher risk for recurrence, associated with a small, but significant, difference in mean survival time (7 months, p = .03) at 12 years of follow-up. CONCLUSION: Although migration inhibitory factor 1 mRNA expression levels were related to severity of disease and lymph node analysis revealed that colon cancer patients with high levels had a shorter survival time after surgery than those with low levels, the difference was small and probably not useful in clinical practice.

Comparing Kaplan-Meier curves with the probability of agreement.

The probability of agreement has been used as an effective strategy for quantifying the similarity between the reliability of two populations. By contrast to hypothesis testing approaches based on P-values, the probability of agreement provides a more realistic assessment of similarity by emphasizing practically important differences. In this article, we propose the use of the probability of agreement to evaluate the similarity of two Kaplan-Meier curves, which estimate the survival functions in two populations. This article extends the probability of agreement paradigm to right censored data and explores three different methods of quantifying uncertainty in the probability of agreement estimate. The first approach provides a convenient assessment based on large-sample normal-theory (LSNT), while the other two approaches are nonparametric alternatives based on ordinary and fractional random-weight bootstrap (FRWB) techniques. All methods are illustrated with examples for which comparing the survival curves of related populations is of interest and the efficacy of the methods are also evaluated through simulation studies. Based on these simulations we recommend point estimation using the proposed LSNT calculation and confidence interval estimation via the FRWB approach. We also provide a Shiny app that facilitates an automated implementation of the methodology.

[An analytical study of the patterns of the survival curves of experimental objects in model experiments on Daphnia magna.]

The phenomenon of multiphase survival curves found in drosophila is confirmed in experiments on daphnia. Mathematical modeling showed that the multiphase nature of the daphnia survival curves reflects abrupt changes in the intensity of death of crustaceans during the transition from phase to phase. In intact daphnia there is no phase of a sharp increase in mortality observed at an early age in drosophila. It arises as a reaction to weak negative influences, which leads to a sharp decrease in the survival of young individuals. A logical conclusion was made about the readiness for reaction of each experimental individual. The effect was observed in a series of experiments posed in different years. It is proved that in daphnia, the predisposition to reaction persists for an indefinitely large number of generations. It is postulated that this reaction has an epigenetic nature. Previous experimental materials suggest that a reaction of this type is widespread in natural surroundings.

A method for determining groups in multiple survival curves.

Survival analysis includes a wide variety of methods for analyzing time-to-event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for censored data. When the null hypothesis of equality of curves is rejected, leading to the clear conclusion that at least one curve is different, it can be interesting to ascertain whether curves can be grouped or if all these curves are different from each other. A method is proposed that allows determining groups with an automatic selection of their number. The validity and behavior of the proposed method was evaluated through simulation studies. The applicability of the proposed method is illustrated using real data. Software in the form of an R package has been developed implementing the proposed method.

Using the "proportion of patients covered" and the Kaplan-Meier survival analysis to describe treatment persistence.

PURPOSE: Standard Kaplan-Meier (KM) survival analysis is often used to study treatment persistence estimating the proportion of patients who have not yet experienced a treatment break by a given day after treatment initiation. This method only allows patients to be studied until their first treatment break. The "proportion of patients covered" (PPC) method is another approach to study treatment persistence. It measures the proportion of live patients currently covered by treatment. We aimed to describe the PPC method, show how the KM survival analysis and the PPC method can describe treatment persistence, and discuss the interpretation/application of the methods. METHODS: We identified new users of statins, selective serotonin reuptake inhibitors, hormone replacement therapy, and ibuprofen. We used KM estimates and the PPC to describe persistence in the 3 years post treatment initiation, using a grace period of 90 days to define a treatment break. RESULTS: Three years after statin initiation, approximately 40% of patients were still in continuous treatment (KM survival) and 60% of patients still alive were in current treatment (PPC). Corresponding numbers were 12% and 25% for selective serotonin reuptake inhibitors and 9% and 29% for hormone replacement therapy. At 1 year, numbers were 5% and 10% for ibuprofen. The PPC showed markedly less variability than the KM survival analysis with different choices of grace periods. CONCLUSIONS: The KM survival analysis and the PPC method can be used to study different aspects of treatment persistence. Together, they provide a more complete picture of treatment persistence and drug use patterns.

[Analysis of eight-year follow-up data of plateau workers by color doppler echocardiography].

Objective: To analyze the heart rate changes and risk factors, as a result of high altitude. Methods: Retrospective analysis of echocardiographic data of plateau workers at a railway maintenance company from 2006 to 2013. The survival curve method was used to analyze the abnormal rate of the heart. Kaplan-Meier method and Cox proportional hazards regression model were used to analyze the influencing factors. Results: In the first occurrence of cardiac abnormalities, the main types of abnormalities were right atrium enlargement (53.47%) , right ventricle enlargement (17.36%) , and tricuspid regurgitation (16.67%) . Cox regression analysis showed that workplace altitude and first physical examination age are two influencing factors of cardiac abnormalities, and their relative risk was 1.661 and 1.039. At high altitudes (3 600~4 000 m) , nearly 40% of workers heart has not changed. But this adaptation does not observed in the ultra-high altitudes (≥4 000 m) . Conclusion: There are individual differences in human adaptability to high altitude. We should take more stringent measures of health care for older people and those who work at more than 4000m. And we should abide by the rotation system for railways that are suitable for the plateau.

The effects of gene polymorphisms on glioma prognosis.

BACKGROUND: Malignant gliomas are the most common primary brain tumors. Various genetic factors play important roles in the development and prognosis of glioma. The present study focuses on the impact of MPHOSPH6, TNIP1 and several other genes (ACYP2, NAF1, TERC, TERT, OBFC1, ZNF208 and RTEL1) on telomere length and how this affects the prognosis of glioma. METHODS: Forty-three polymorphisms in nine genes from 605 glioma patients were selected. The association between genotype and survival outcome was analyzed using the Kaplan-Meier method, Cox regression analysis and the log-rank test. RESULTS: The 1-year overall survival (OS) rates of patients younger than 40 years of age was higher compared to those in patients older than 40 years of age. The 1-year OS rate of patients who underwent total resection was higher than that of patients whose gliomas were not completely resected. The 1-year OS rates of patients undergoing chemotherapy and of patients who did not undergo chemotherapy were 39.90% and 26.80%, respectively. Univariate analyses showed that ACYP2 rs12615793 and TERT rs2853676 loci affected progression-free survival in glioma patients; both ZNF208 rs8105767 and ACYP2 rs843720 affected the OS of patients with low-grade gliomas. Multivariate analyses suggested that MPHOSPH6 rs1056629 and rs1056654, and TERT rs2853676 loci were associated with good prognoses of patients with glioma or high-grade gliomas, whereas ZNF208 rs8105767 was associated with good prognosis of patients with low-grade glioma. CONCLUSIONS: Age, surgical resection and chemotherapy influenced the survival rates of glioma patients. TERT, MPHOSPH6, ACYP2 and ZNF208 genes were found to affect glioma prognosis.