The role and regulation mechanisms of APOD in prognosis and subtyping of breast cancer

Background:Breast cancer is the most common cancer,and abnormal lipid metabolism is associated with cancer.APOD expression is negatively correlated with various cancers related to tumor prognosis.DNA methylation may affect APOD expression.Therefore,this paper aims to investigate the significance of APOD expression and APOD DNA methylation in breast cancer.Methods:This study utilized comprehensive bioinformatics analysis of APOD using Gene Expression database of Normal and Tumor tissues 2,UCSC Xena,etc.Clinical and survival information obtained from the The Cancer Genome Atlas and Gene Expression Omnibus datasets were extracted for data mining.Results:The correlation between APOD and breast cancer was examined,along with the connection between APOD DNA methylation and APOD expression.In the The Cancer Genome Atlas cohort,as well as GSE31448 and GSE65194 datasets,APOD expression decreased in breast cancer(P<0.0001).Clinical feature analysis results showed that APOD expression was correlated with the PAM50 subtype,with the lowest expression in the Basal subtype(P<0.0001).High APOD expression is a good prognostic marker for breast cancer(HR=0.71,P=0.037).APOD methylation level was significantly negatively correlated with expression level(R=−0.4770,P<0.001),and cg15231202,cg23720929,and cg05624196 were important regulatory targets.High APOD expression was associated with higher metabolism and extracellular matrix scores.Conclusion:APOD is an independent prognostic marker for breast cancer and is regulated by DNA methylation to modulate mRNA expression.

mvPPT:A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification.In this study,we developed Pathogenicity Prediction Tool for missense variants(mvPPT),a highly sensitive and accurate missense variant classifier based on gradient boosting.mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles,and extracts three categories of features,including scores from existing prediction tools,frequencies(allele frequencies,amino acid frequencies,and genotype frequencies),and genomic context.Compared with established predictors,mvPPT achieves superior performance in all test sets,regardless of data source.In addition,our study also provides guidance for training set and feature selection strategies,as well as reveals highly relevant features,which may further provide biological insights into variant pathogenicity.