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. 2023 May 26:10:1165776.
doi: 10.3389/fmolb.2023.1165776. eCollection 2023.

Prediction of prognosis and immunotherapy response in breast cancer based on neutrophil extracellular traps-related classification

Jiajing Zhao  1   2 , Xiaojun Xie  1
Affiliations

Prediction of prognosis and immunotherapy response in breast cancer based on neutrophil extracellular traps-related classification

Jiajing Zhao et al. Front Mol Biosci. .

Abstract

Neutrophil extracellular traps (NETs), a network of DNA histone complexes and proteins released by activated neutrophils, have been demonstrated to be associated with inflammation, infection related immune response and tumorigenesis in previous reports. However, the relationship between NETs related genes and breast cancer remains controversial. In the study, we retrieved transcriptome data and clinical information of BRCA patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. The expression matrix of neutrophil extracellular traps (NETs) related genes was generated and consensus clustering was performed by Partitioning Around Medoid (PAM) to classify BRCA patients into two subgroups (NETs high group and NETs low group). Subsequently, we focus on the differentially expressed genes (DEGs) between the two NETs-related subgroups and further explored NETs enrichment related signaling pathways by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In addition, we constructed a risk signature model by LASSO Cox regression analysis to evaluate the association between riskscore and prognosis. Even more, we explored the landscape of the tumor immune microenvironment and the expression of immune checkpoints related genes as well as HLA genes between two NETs subtypes in breast cancer patients. Moreover, we found and validated the correlation of different immune cells with risk score, as well as the response to immunotherapy in different subgroups of patients was detected by Tumor Immune Dysfunction and Exclusion (TIDE) database. Ultimately, a nomogram prognostic prediction model was established to speculate on the prognosis of breast cancer patients. The results suggest that high riskscore is associated with poor immunotherapy response and adverse clinical outcomes in breast cancer patients. In conclusion, we established a NETs-related stratification system that is beneficial for guiding the clinical treatment and predicting prognosis of BRCA.

Keywords: breast cancer; clustering; immunotherapy response; neutrophil extracellular traps; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Consensus clustering of NETs-related subgroups (A) Protein-Protein Interaction Network (PPI) of NETs-related genes was generated by Cytoscape software (B) Heatmap of NETs-related gene expression between normal and tumor tissues (C)The relative change of the area under the cumulative distribution function (CDF) curve for k = 2 to 10 (D) A heatmap shows the consensus clustering solution of 22 NETs-related genes in breast cancer samples when k = 2 (E) Delta area reflects relative change in area under the CDF curve when k = 2 to 10 (F) PCA analysis of two NETs-related subgroups in TCGA database (G) Heatmap of NETs-related gene expression between C1 and C2 subgroups (H) Kaplan-Meier curve of OS prognosis between NETs high and NETs low subgroups.
FIGURE 2
FIGURE 2
Differentially expressed genes and Gene Set Enrichment Analysis between NETs subgroups (A, B) Volcano plot and heatmap are used to show differentially expressed genes (DEGs) between NETs high and NETs low subgroups (C, D) KEGG and GO analysis of DEGs between two subgroups (E, H) GSEA analysis respectively suggested that upregulated DEGs in neutrophil degranulation (E), adaptive immune system (F), extracellular matrix organization (G) and enrichment of PD-1signaling pathway (H).
FIGURE 3
FIGURE 3
Tumor microenvironment landscape in BRCA patients (A–C) Violin plot is generated to show the estimate score (A), immune score (B) and somatic score (C) between NETs high and NETs low subtypes (D, E) Stack and box plots were used to represent 22 types of tumor-related immune cells between the two NETs subgroups in BRCA (F–G) Violin plot and box plot showed the expression of immune checkpoint related genes (F) and HLA genes (G) between two different NETs subgroups, respectively (*p < 0.05, **p < 0.01, ***p < 0.001. ****p < 0.0001).
FIGURE 4
FIGURE 4
Somatic mutation and construction of NETs-related risk signature model (A, B) LASSO Cox regression analysis revealed four NETs-related genes associated with the OS prognosis (C) Venn diagram was generated to show 4 NETs-related genes related to the prognosis of OS (D) Survival time and status distribution heatmap of 4 NETs genes associated with OS prognosis (E–F) Kaplan-Meier curves associated with OS prognosis of the TCGA cohort (E) and the GSE21653 (F) cohort in different risk score subgroups (G) Waterfall plot of somatic mutations in the TCGA cohort.
FIGURE 5
FIGURE 5
Association of riskscore signature with tumor associated immune cells and immunotherapy prediction of NETs subgroups (A) Scatter plot of risk score correlation between native B cell and macrophage M1 in TCGA cohort (B) Box plots are used to represent risk score related immunotherapy responses predicted by the TIDE database in TCGA cohort (C) Scatter plot of risk score correlation between native B cell and macrophage M1 in GSE21653 cohort (D) Box plots are used to represent risk score related immunotherapy responses predicted by the TIDE database in GSE21653 cohort (E–F) Forest plots were generated to show clinical features and risk score associated with OS in univariate regression analysis (E) and multivariate regression analysis (F) (***p < 0.001).
FIGURE 6
FIGURE 6
Construction of prognosis related risk model (A) Nomogram is generated to predict 1-,3-, and 5-year overall survival in patients with BRCA patients (B) Calibration curves were powered to evaluate the reliability of the model (C) ROC curves were drawn to evaluate the accuracy of the model in predicting 1-, 3- and 5-year overall survival.
FIGURE 7
FIGURE 7
Validation of hub genes (A–D) Expression levels of LFT (A), LCP1 (B), AZU1 (C), and ENO1 (D) in paired breast cancer samples from the TCGA database. Expression levels of LFT (E), LCP1 (F), AZU1 (G), and ENO1 (H) in unpaired breast cancer samples from the TCGA database (***p < 0.001).
FIGURE 8
FIGURE 8
Validation of hub gene protein level (A–D) Total protein levels of LFT (A), LCP1 (B), AZU1 (C), and ENO1 (D) in paired breast cancer samples from the CPTAC database (E–H) Expression of LTF (E), LCP1 (F), AZU1 (G), and ENO1 (H) in various grades of BRCA patients (*p < 0.05, **p < 0.01, ***p < 0.001. ****p < 0.0001).
FIGURE 9
FIGURE 9
Validation of hub genes between breast tumor and normal tissue (A–D) Representative results of immunohistochemistry for LTF (A), ENO1 (B), LCP1 (C) and AZU1 (D).
FIGURE 10
FIGURE 10
PPI networks of LTF, LCP1, AZU1, and ENO1 from GeneMANIA database.
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References

    1. Fouret P., du Bois R. M., Bernaudin J. F., Takahashi H., Ferrans V. J., Crystal R. G. (1989). Expression of the neutrophil elastase gene during human bone marrow cell differentiation. J. Exp. Med. 169 (3), 833–845. 10.1084/jem.169.3.833 - DOI - PMC - PubMed
    1. Franzoi M. A., Romano E., Piccart M. (2021). Immunotherapy for early breast cancer: Too soon, too superficial, or just right? Ann. Oncol. official J. Eur. Soc. Med. Oncol. 32 (3), 323–336. 10.1016/j.annonc.2020.11.022 - DOI - PubMed
    1. Fridlender Z. G., Albelda S. M. (2012). Tumor-associated neutrophils: Friend or foe? Carcinogenesis 33 (5), 949–955. 10.1093/carcin/bgs123 - DOI - PubMed
    1. Fridlender Z. G., Sun J., Kim S., Kapoor V., Cheng G., Ling L., et al. (2009). Polarization of tumor-associated neutrophil phenotype by TGF-beta: "N1" versus "N2" TAN. Cancer Cell 16 (3), 183–194. 10.1016/j.ccr.2009.06.017 - DOI - PMC - PubMed
    1. Gardinassi L. G., DeSouza-Vieira T. S., da Silva N. O., Garcia G. R., Borges V. M., Campos R. N. S., et al. (2017). Molecular signatures of neutrophil extracellular traps in human visceral leishmaniasis. Parasites vectors 10 (1), 285. 10.1186/s13071-017-2222-5 - DOI - PMC - PubMed