Establishment of a novel CNV-related prognostic signature predicting prognosis in patients with breast cancer

Abstract

Background Copy number variation (CNVs) is a key factor in breast cancer development. This study determined prognostic molecular characteristics to predict breast cancer through performing a comprehensive analysis of copy number and gene expression data. Methods Breast cancer expression profiles, CNV and complete information from The Cancer Genome Atlas (TCGA) dataset were collected. Gene Expression Omnibus (GEO) chip data sets (GSE20685 and GSE31448) containing breast cancer samples were used as external validation sets. Univariate survival COX analysis, multivariate survival COX analysis, least absolute shrinkage and selection operator (LASSO), Chi square, Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) analysis were applied to build a gene signature model and assess its performance. Results A total of 649 CNV related-differentially expressed gene obtained from TCGA-breast cancer dataset were related to several cancer pathways and functions. A prognostic gene sets with 9 genes were developed to stratify patients into high-risk and low-risk groups, and its prognostic performance was verified in two independent patient cohorts (n = 327, 246). The result uncovered that 9-gene signature could independently predict breast cancer prognosis. Lower mutation of PIK3CA and higher mutation of TP53 and CDH1 were found in samples with high-risk score compared with samples with low-risk score. Patients in the high-risk group showed higher immune score, malignant clinical features than those in the low-risk group. The 9-gene signature developed in this study achieved a higher AUC. Conclusion The current research established a 5-CNV gene signature to evaluate prognosis of breast cancer patients, which may innovate clinical application of prognostic assessment.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy

“…Hu et al developed a model to predict the prognosis of breast cancer patients based on CNV-related genes. The area of the ROC curve for this model was 0.7, 0.63, and 0.58 in the TCGA test set, while the AUC values were 0.66,0.68, and 0.71 in the TCGA all data sets 14 . In this study, we constructed a risk model based on aneuploidy-associated genes with AUC values of 0.7, 0.81, and 0.77 in the GEO cohort, respectively.…”

“…It has been reported that in cancer cells, chromosomal aneuploidy can lead to copy number alterations 13 . Hu et al 14 identified a total of nine genes to be able to independently predict the prognosis of breast cancer patients based on public databases of breast cancer and CNV data. Bian et al 15 comprehensively analyzed CNV differential data and differentially expressed gene data from TCGA and screened eight CNV driver genes (including AKR1B15 , TRIM16L , CBX2 , CDCA8 , EZH2 , FLVCR1 , EPS8L3 , and GPRIN1 ) to generate a prognostic model that could well predict the prognosis of patients with hepatocellular carcinoma.…”

Identification of necroptosis-related diagnostic biomarkers in coronary heart disease

“…With our knowledge, all the above five necroptosis-related biomarkers are protein-coding genes and are the first to be assigned significance as diagnostic markers for CHD. By reviewing the available literature, it was found that increased FAM166B expression relates to a favorable prognosis in breast cancer (BRCA) and is likely to be an independent prognostic factor [ 48 ]. Besides, this gene was shown to be significantly upregulated (3.693-fold) in multiple symmetric lipomatosis (MSL) [ 49 ].…”

Copy Number Variation of Circulating Tumor DNA (ctDNA) Detected Using NIPT in Neoadjuvant Chemotherapy-Treated Ovarian Cancer Patients

“…CNV burden in all chromosomes of NAC-sensitive patients was fewer than that of NAC-resistant patients before NAC treatment. Since the CNVs are considered a key factor in the genetic variation of tumors (Hu et al, 2021), it seems that less CNV burden indicated better response in treating patients (Walker et al, 2017). CNVs were also discovered in a variety of genes following chemotherapy, notably in genes that control drug absorption into cells and drug metabolism (Willyard, 2015).…”