Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis

Abstract

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

Keywords: Luminal A breast cancer; differentially expressed genes; multigene prognostic assay; significant pathways.

Figure 1.

The volcanic map of 300 DEGs. The abscissa represents log₂ FC and the ordinate represents the negative logarithm of P-value. DEG, differentially expressed genes; FC, fold-change.

Figure 2.

The heat map of 300 DEGs in the dead and alive samples. The abscissa represents the expression value of 300 DEGs in all samples and the ordinate represents the two sets of samples. Blue represents high expression, and yellow represents low expression. Meanwhile, the dead and alive samples were marked with red and green labels, respectively. DEG, differentially expressed genes.

Figure 3.

The annotation results and the heat map of 18 biomarkers in Luminal A breast cancer samples from the TCGA database. (A) The annotation results of 18 biomarkers in the TCGA database. Red represents upregulation and blue represents downregulation. The proportion of each gene mutation is also marked. (B) The heat map for the changes of the 18 genes in TCGA breast cancer samples. Red represents upregulation and blue represents downregulation. TCGA, The Cancer Genome Atlas; TCF7L2, transcription factor 7-like 2; APC, anterior parietal cortex; LEF1, lymphocyte enhancer factor-1; CCNE1, cyclin E1; SKP2, S-phase kinase-associated protein 2; FZD7, human frizzled-7; PLK1, polo-like kinase 1; BCL2, B cell lymphoma 2; PSME4, proteasome activator subunit 4; PDSS1, prenyldiphosphate synthase, subunit 1; PRKDC, promoters for human DNA-PK cs; TTK, TTK protein kinase; MCM4, minichromosome maintenance deficient 4; PGR, progesterone receptor; PSMA7, proteasome subunit α7; MDM2, MDM2 proto-oncogene; LAMB2, laminin subunit β2; PSMD7, proteasome 26S subunit, non-ATPase 7.

Figure 4.

Survival analysis for the 18 biomarkers in train set obtained from TCGA database. (A) Survival curve for two sets of samples from TCGA database using the 18 biomarkers. The red curve represents the samples with differently expressed biomarkers, and the blue curve represents the samples with normally expressed biomarkers. (B) ROC curve for 18 biomarkers in train set. The abscissa represents sensitivity and the ordinate represents specificity. ROC, receiver operating characteristic; TGCA, The Cancer Genome Atlas; TPR, true positive rate; FPR, false positive rate; AUC, area under the curve.

Figure 5.

Survival analysis for the 18 biomarkers in test set obtained from Gene Expression Omnibus database. (A) Survival curve for the samples from the gene expression profiles of GSE2034 using the 18 biomarkers. The red curve represents samples in high risk group with differently expressed biomarkers, and the blue curve represents samples in low risk group with normally expressed biomarkers. (B) ROC curve for 18 biomarkers in test set. The abscissa represents sensitivity and the ordinate represents specificity. ROC, receiver operating characteristic; TPR, true positive rate; FPR, false positive rate; AUC, area under the curve.