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. 2023 May 12;102(19):e33739.
doi: 10.1097/MD.0000000000033739.

A senescence-related signature for predicting the prognosis of breast cancer: A bioinformatics analysis

Tengfei Xing  1 , Yiyi Hu  2 , Hongying Wang  1 , Qiang Zou  1
Affiliations

A senescence-related signature for predicting the prognosis of breast cancer: A bioinformatics analysis

Tengfei Xing et al. Medicine (Baltimore). .

Abstract

Breast cancer is a heterogeneous disease with diverse prognosis and treatment outcomes. Current gene signatures for prognostic prediction are limited to specific subtypes of breast cancer. Cellular senescence is a state of irreversible cell cycle arrest that affects various physiological and pathological processes. This study aimed to develop and validate a senescence-related signature for predicting the prognosis of breast cancer patients. We retrieved 744 senescence-associated genes from the SeneQuest database and analyzed their expression profiles in 2 large datasets of breast cancer patients: The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We used univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis to derive a 29-gene senescence-related risk signature. The risk signature was significantly associated with disease-specific survival (DSS), clinical characteristics, molecular subtypes, and immune checkpoint genes expressions in both datasets. The risk signature also stratified high-risk and low-risk patients within the same clinical stage and molecular subtype. The risk signature was an independent prognostic factor for breast cancer patients. The senescence-related signature may be a useful biomarker for predicting prognosis and immunotherapy response of breast cancer patients. The risk signature may also guide adjuvant chemotherapy decisions, especially in hormone receptor positive (HR+) and human epidermal growth factor receptor type 2 (HER2)- subtypes.

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Conflict of interest statement

The authors have no funding and conflicts of interest to disclose.

Figures

Figure 1.
Figure 1.
Consensus clustering of TCGA breast cancer patients by senescence-related genes. (A) Plot of CDF. (B) Area under the CDF curve. (C) Heatmap of the consensus matrix for k = 2. (D) Kaplan–Meier survival curve. CDF = cumulative distribution function, TCGA = The Cancer Genome Atlas.
Figure 2.
Figure 2.
Construction of a senescence-related risk signature. (A) Plot of nonzero coefficients. (B) Cross-validation curve. (C–D) Receiver operating characteristic curve. AUC = area under the curve, METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, TCGA = The Cancer Genome Atlas.
Figure 3.
Figure 3.
Relationship between risk signature and clinicopathological characteristics of breast cancer patients. (A–H) Differences of the risk score in breast cancer patients stratified by age, molecular subtypes, lymph node status, and AJCC stage. The Student t test was used to determine P values. *P < .05, **P < .01, ***P < .001. AJCC = American Joint Committee on Cancer, METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, TCGA = The Cancer Genome Atlas.
Figure 4.
Figure 4.
Distribution and prognostic value of the risk score. (A and B) Distribution of the risk scores. (C and D) Heatmap of the senescence-related genes. (E and F) Kaplan–Meier survival curve. METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, TCGA = The Cancer Genome Atlas.
Figure 5.
Figure 5.
Risk signature prognosticated survival within the same clinical stage of breast cancer. (A and B) Kaplan–Meier survival curve. METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, TCGA = The Cancer Genome Atlas.
Figure 6.
Figure 6.
Risk signature prognosticated survival within the same molecular subtype of breast cancer. (A and B) Kaplan–Meier survival curve. METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, TCGA = The Cancer Genome Atlas.
Figure 7.
Figure 7.
Forest plot of hazard ratios. (A) Forest plot of TCGA. (B) Forest plot of METABRIC. *P < .05, **P < .01, ***P < .001. METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, TCGA = The Cancer Genome Atlas.
Figure 8.
Figure 8.
Relationship between risk score and expression level of immune checkpoint genes. The Student t test was used to determine P values. *P < .05, **P < .01, ***P < .001. CTLA4 = cytotoxic T-lymphocyte-associated protein 4, IDO1 = indoleamine dioxygenase 1, LAG3 = lymphocyte activation gene 3, METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, PD-1 = programmed death-1, PD-L1 = programmed death ligand-1, TCGA = The Cancer Genome Atlas.
Figure 9.
Figure 9.
Gene set enrichment analysis (GSEA). (A) GSEA of TCGA. (B) GSEA of METABRIC. METABRIC = Molecular Taxonomy of Breast Cancer International Consortium, TCGA = The Cancer Genome Atlas.
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