Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact

BACKGROUND

Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine.

RESULTS

In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods.

CONCLUSIONS

GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Combined exposure to multiple metals on serum uric acid in NHANES under three statistical models

BACKGROUND

There are rare researches on the correlations between metals exposure and serum uric acid (SUA), and existing research has only investigated the single metal effect. This study aimed to investigate the combined effects of metal mixtures on SUA and hyperuricemia using three statistical models.

METHODS

In this study, the data were extracted from three cycle years of the National Health and Nutrition Examination Survey (NHANES). Subsequently, generalized linear regression, weighted quantile regression (WQS) and Bayesian kernel machine regression (BKMR) models were fitted to evaluate the correlations between metal mixtures and both SUA and hyperuricemia.

RESULTS

Of 3926 participants included, 19.13% participants had hyperuricemia. It was found using multi-metals generalized linear regression models that there were positive correlations of arsenic and cadmium with both outcomes. The negative correlations were identified in cobalt, iodine, and manganese with SUA concentration, whereas only cobalt was negatively correlated with hyperuricemia. Based on the WQS regression model fitted in positive direction, it was suggested that the WQS indices were significantly correlated with SUA (β = 6.64, 95% CI: 3.14-10.13) and hyperuricemia (OR = 1.25, 95% CI: 1.08-1.44); however, the result achieved by using the model fitted in negative direction indicated that the WQS indices were only significantly correlated with SUA (β = -5.29, 95%CI: 8.02 ∼ -2.56). With the use of the BKMR model, a significant increasing trend between metal mixtures and hyperuricemia was found, while no significant overall effect of metal mixtures on SUA was identified. The predominant roles of arsenic, cadmium, and cobalt in the change of SUA and hyperuricemia risk were found using all three models.

CONCLUSION

The finding of this study revealed that metal mixtures might have a positive combined effect on hyperuricemia. The mutual verification of two outcomes using the three different models provided strong public health implications for protecting people from heavy metal pollution and preventing hyperuricemia.

Association of twenty-three plasma elements with fasting serum glucose among Chinese population from four areas with different pollution level

BACKGROUND

Association between fasting serum glucose (FSG) and certain mineral elements has been extensively reported. Investigation regarding multi-element exposure among subjects with different exposure level is warranted to confirm the association and further explore dose-dependent relationship.

METHODS

A total of 3488 participants were recruited from four counties of Hunan province, South China. Basic characteristics were collected by face to face interview and 23 elements in plasma were determined by inductively coupled plasma mass spectrometry. We applied fully adjusted generalized linear regression model and multivariable restricted cubic spline function to test the association and dose-response relationship of FSG with 23 elements.

RESULTS

The results indicated that FSG was positively associated with plasma78selenium level [regression coefficient (β), 0.001; 95 % confidence interval (CI), 0.001, 0.001] in a dose-dependent manner, robust to the adjustment for suspected covariates and stratification by age, gender, BMI and smoking status. A negative association was found between FSG and plasma 208lead (β, -0.004; 95 % CI, -0.016, -0.002), 52chromium (β, -0.002; 95 % CI, -0.004, -0.001) and 47titanium (β, -0.001; 95 % CI, -0.002, -0.001).

CONCLUSION

78selenium was positively while 208lead, 52chromium and 47titanium were negatively associated with FSG in the present study. However, prospective studies are needed to confirm the results.

Impact of Switching to Fingolimod Versus Injectable Disease-Modifying Therapy Cycling on Risk of Multiple Sclerosis-Related Relapses: A Retrospective Analysis

Background

Clinical and real-world studies have shown significant reductions in multiple sclerosis (MS) relapses with fingolimod versus injectable disease-modifying therapies (DMTs). Multiple sclerosis relapse rate and incidence were compared in patients switching from an injectable DMT to fingolimod and those cycling from one injectable DMT to another or remaining on their original injectable DMT.

Methods

Retrospective analysis was performed using Commercial and Medicare Supplemental claims data (July 1, 2010, to June 30, 2016) of adults with MS receiving ≥1 injectable DMT. Relapses were identified from MS-related hospitalization, outpatient emergency department or office visit, and corticosteroid administration. Annualized relapse rate ratio was estimated.

Results

Of 16,352 patients, 1110 were switchers to fingolimod, 908 were injectable DMT cyclers, and 14,334 were nonswitchers. At baseline, rate and incidence of MS relapses were higher in switchers and injectable DMT cyclers versus nonswitchers (P < .001); mean ± SD relapse rates declined from 0.4 ± 0.7, 0.4 ± 0.7, and 0.2 ± 0.5 at baseline to 0.2 ± 0.5, 0.3 ± 0.6, and 0.1 ± 0.4 after follow-up in switchers, injectable DMT cyclers, and nonswitchers, respectively. Relapse incidence declined in each cohort. The highest reductions in relapse rate and incidence were in switchers to fingolimod, where relapse risk was significantly reduced versus injectable DMT cyclers (22%, P = .0433) and nonswitchers (47%, P < .001).

Conclusions

This study provides evidence that patients switching from an injectable DMT to fingolimod have the highest reductions in annualized rate and incidence of MS relapses and significantly reduced risk of relapse versus injectable DMT cyclers and nonswitchers.

Admissibility of simultaneous prediction for actual and average values in finite population

This paper studies the admissibility of simultaneous prediction of actual and average values of the regressand in the generalized linear regression model under the quadratic loss function. Necessary and sufficient conditions are derived for the simultaneous prediction to be admissible in classes of homogeneous and nonhomogeneous linear predictors, respectively.