Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Oct;11(20):3886-3901.
doi: 10.1002/cam4.4755. Epub 2022 Apr 20.

Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

Xueting Ren  1 , Hanxiao Cui  1 , Jianhua Wu  1 , Ruina Zhou  1 , Nan Wang  1 , Dandan Liu  1 , Xin Xie  2 , Hao Zhang  2 , Di Liu  1 , Xiaobin Ma  1 , Chengxue Dang  2 , Huafeng Kang  1 , Shuai Lin  1
Affiliations

Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

Xueting Ren et al. Cancer Med. 2022 Oct.

Abstract

Background: Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia-related genes (AHGs) to predict BC prognosis and immune infiltration.

Methods: We collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single-sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs.

Results: We identified a 16-AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high-risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity.

Conclusion: We established and verified a 16-AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC.

Keywords: breast cancer; cancer genetics; immunology; microenvironment; prognosis; risk model.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

FIGURE 1
FIGURE 1
LASSO regression analysis based on differentially expressed genes. (A) Ten‐fold cross‐validation for the coefficients. (B) Parameter selection of the 31 selected AHGs in LASSO regression (λ = −4.2)
FIGURE 2
FIGURE 2
Prognostic analysis of the 16‐AHG risk model in TCGA cohort. (A) Distribution and median of the risk scores. (B) Expression heatmap of 16 AHGs in high‐ and low‐risk groups. (C) Survival status. (D) Kaplan–Meier curves of OS in high‐ and low‐risk patients. (E) Time‐dependent ROC curves of prognostic prediction performance of gene signature
FIGURE 3
FIGURE 3
Correlation between risk score and clinicopathological factors. (A) Age. (B) Pathological stage. (C) T stage. (D) N stage. (E) M stage. (F) ER status. (G) PR status. (H) HER2 status
FIGURE 4
FIGURE 4
The 16‐AHG signature is an independent prognostic factor for BC patients. (A) Univariate Cox regression analysis. (B) Multivariate Cox regression analysis
FIGURE 5
FIGURE 5
Kaplan–Meier survival analysis for predicting survival in BC patients with different clinical features. (A) Age. (B) Pathological stage. (C) T stage. (D) N stage. (E) M stage. (F) ER status. (G) PR status. (H) HER2 status
FIGURE 6
FIGURE 6
Kaplan–Meier subgroup analysis based on the 16‐AHG signature in BC patients stratified by clinical characteristics. (A) Age < =65y. (B) Age > 65y. (C) Early stage (Stage I‐II). (D) Advanced stage (Stage III‐IV). (E) T1‐2. (F) T3‐4. (G) N0. (H) N1‐3. (I) Patients without distant metastasis. (J) patients with distant metastasis metastasis. (K) ER‐Negative. (L) ER‐Positive. (M) PR‐Negative. (N) PR‐Positive. (O) HER2‐Negative. (P) HER2‐Positive
FIGURE 7
FIGURE 7
The difference in immune infiltration at high‐ and low‐risk groups based on the 16‐AHG signature. (A) The infiltration of 22 immune cell subtypes and seven immune‐related pathways in high‐ and low‐risk groups was analyzed by ssGSEA. (B) The relationship between risk score and tumor purity, immune score, stromal score, and corresponding estimated score. (C) Difference in infiltration fractions of 22 immune cell subsets in high‐ and low‐risk groups. (D) The expression levels of 14 immune checkpoint genes in different risk subgroups. (*p < 0.05, **p < 0.01, and ***p < 0.001)
FIGURE 8
FIGURE 8
Correlation between expression of 14 immune checkpoints and risk score based on the 16‐AHG signature. (A) BTLA. (B) CD27. (C) CD28. (D) CTLA4. (E) IDO1. (F) KIR3DL1. (G) LAG3. (H) PDCD1. (I) PDCD1LG2. (J) PD‐L1. (K) TNFRSF4. (L) TNFRSF18. (M) TNFSF14. (N) VSIR
FIGURE 9
FIGURE 9
Establishment and verification of a predictive nomogram model based on the 16‐AHG signature. (A) The sum of the scores of each item on the nomogram predicted the probability of survival in 3 and 5 years. (B) Kaplan–Meier survival analysis of BC patients in high‐ and low‐risk groups based on nomogram. (C) AUC of 1‐, 3‐, and 5‐year predictive power of nomogram. (D) The calibration curve of nomogram for predicting 3‐year survival. (E) The calibration curve of nomogram for predicting 5‐year survival
FIGURE 10
FIGURE 10
Representative results of GO (A) and KEGG analysis (B)
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Sung H, Ferlay J, Siegel RL. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 2021;71(3):209‐249. - PubMed
    1. Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). 2021;134(7):783‐791. - PMC - PubMed
    1. Siegel RL, Miller KD. Cancer statistics, 2022. 2021;72(1):7‐33. - PubMed
    1. Zuo S, Yu J, Pan H, Lu L. Novel insights on targeting ferroptosis in cancer therapy. Biomark Res. 2020;8:50. - PMC - PubMed
    1. D'Arcy MS. Cell death: a review of the major forms of apoptosis, necrosis and autophagy. Cell Biol Int. 2019;43(6):582‐592. - PubMed