Breast Cancer Prognosis Prediction and Immune Pathway Molecular Analysis Based on Mitochondria-Related Genes

Abstract

Background: Mitochondria play an important role in breast cancer (BRCA). We aimed to build a prognostic model based on mitochondria-related genes.

Method: Univariate Cox regression analysis, random forest, and the LASSO method were performed in sequence on pretreated TCGA BRCA datasets to screen out genes from a Gene Set Enrichment Analysis, Gene Ontology: biological process gene set to build a prognosis risk score model. Survival analyses and ROC curves were performed to verify the model by using the GSE103091 dataset. The BRCA datasets were equally divided into high- and low-risk score groups. Comparisons between clinical features and immune infiltration related to different risk scores and gene mutation analysis and drug sensitivity prediction were performed for different groups.

Result: Four genes, MRPL36, FEZ1, BMF, and AFG1L, were screened to construct our risk score model in which the higher the risk score, the poorer the prognosis. Univariate and multivariate analyses showed that the risk score was significantly associated with age, M stage, and N stage. The gene mutation probability in the high-risk score group was significantly higher than that in the low-risk score group. Patients with higher risk scores were more likely to die. Drug sensitivity prediction in different groups indicated that PF-562271 and AS601245 might be new inhibitors of BRCA.

Conclusion: We developed a new workable risk score model based on mitochondria-related genes for BRCA prognosis and identified new targets and drugs for BRCA research.

Figure 1

Screening of mitochondria-related genes and establishment of a risk score model. (a) Differential (increased or reduced) mitochondria-related gene expression in the hazard group compared with that in the normal group. (b) Error rates of randomly generated trees (upper panel). Variable relative importance of the four selected mitochondria-related genes. (c) LASSO analysis: partial likelihood deviance values were plotted against log (λ) (upper panel). The relative abundance of the selected genes varies with the risk score.

Figure 2

Establishment and verification of the risk score model. (a, d) Distribution of risk score and survival status in the corresponding datasets. (b, e) Survival analysis between high- and low-risk score groups in the different datasets. (c, f) 1-, 3-, and 5-year ROC curves for the different datasets. (a–c) The results from the analysis of the TCGA BRCA dataset (TCGA dataset); (d–f) results from the analysis of the GSE103091 dataset. RFS: relapse-free survival.

Figure 3

Univariate, multivariate, and mutation analyses of the different risk score groups. (a) Univariate and multivariate Cox analyses of the risk scores and the clinical characteristics of the patients. (b) Mutation patterns of mitochondria-related genes from different risk score groups.

Figure 4

Clinical features and drug sensitivity prediction in breast cancer (BRCA) datasets. (a) Clinical characteristics of patients with BRCA, sorted by risk scores. (b) Predicted responses to drugs that might be used to treat BRCA.

Figure 5

Immune infiltration analyses of BRCA datasets with different algorithms.

Figure 6

GSEA of the risk score model. (a) GSEA of GO processes. (b) GSEA of KEGG pathways. NES : normalized enrichment score.