Bayesian Model Averaging in Proportional Hazard Models: Assessing the Risk of a Stroke

Background and Objective: Gastric cancer (GC) is a common tumor malignancy with high incidence and poor prognosis. Laminin is an indispensable component of basement membrane and extracellular matrix, which is responsible for bridging the internal and external environment of cells and transmitting signals. This study mainly explored the association of the LAMB1 expression with clinicopathological characteristics and prognosis in gastric cancer. Methods: The expression data and clinical information of gastric cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG). And we analyzed the relationship between LAMB1 expression and clinical characteristics through R. CIBERSORTx was used to calculate the absolute score of immune cells in gastric tumor tissues. Then COX proportional hazard models and Kaplan-Meier curves were performed to evaluate the role of LAMB1 and its influence on prognosis in gastric cancer patients. Finally, GO and KEGG analysis were applied for LAMB1-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. Results: In the TCGA cohort, patients with gastric cancer frequently generated LAMB1 gene copy number variation, but had little effect on mRNA expression. Both in the TCGA and ACRG cohorts, the mRNA expression of LAMB1 in gastric cancer tissues was higher than it in normal tissues. All patients were divided into high expression group and low expression group according to the median expression level of LAMB1. The elevated expression group obviously had more advanced cases and higher infiltration levels of M2 macrophages. COX proportional hazard models and Kaplan-Meier curves revealed that patients with enhanced expression of LAMB1 have a worse prognosis. GO/KEGG analysis showed that LAMB1-related genes were enriched in PI3K-Akt signaling pathway, focal adhesion, ECM-receptor interaction, etc. Conclusions: The high expression of LAMB1 in gastric cancer is related to the poor prognosis of patients, and it may be related to microenvironmental changes in tumors.

Download Full-text

Identifying Groups of Determinants in Bayesian Model Averaging Using Dirichlet Process Clustering

Scandinavian Journal of Statistics ◽

10.1111/sjos.12541 ◽

2021 ◽

Author(s):

Grün Bettina ◽

Paul Hofmarcher

Keyword(s):

Dirichlet Process ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging

Download Full-text

Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

Advances in Atmospheric Sciences ◽

10.1007/s00376-021-0265-1 ◽

2021 ◽

Vol 38 (9) ◽

pp. 1580-1595

Author(s):

Yueyue Li ◽

Li Dan ◽

Jing Peng ◽

Junbang Wang ◽

Fuqiang Yang ◽

...

Keyword(s):

Primary Production ◽

Pacific Decadal Oscillation ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

El Nino ◽

Gross Primary Production ◽

Eastern China ◽

Model Averaging ◽

Growing Season ◽

The Pacific

Download Full-text

Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽

10.3390/nu13041098 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1098

Author(s):

Ewelina Łukaszyk ◽

Katarzyna Bień-Barkowska ◽

Barbara Bień

Keyword(s):

Vitamin D ◽

Mortality Rate ◽

Plasma Level ◽

Variable Selection ◽

Lymphocyte Count ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Multivariable Analysis ◽

Model Averaging ◽

Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

Download Full-text