Identification of novel neutrophil-extracellular-traps-related genes as biomarkers for breast cancer prognosis and immunotherapy

Abstract

Background: Breast cancer (BC) ranks as one of the most prevalent malignancies among women globally. This study aimed to explore the involvement of neutrophil extracellular traps (NETs)-related genes (NETRGs) in BC pathogenesis, highlighting the critical role of NETs.

Methods: Differentially expressed NETRGs (DE-NETRGs) were identified by intersecting BC vs. control differentially expressed genes (DEGs) with the NETRG gene set from The Cancer Genome Atlas breast cancer (TCGA-BRCA) and GSE42568 datasets. Functional analysis elucidated their biological roles. Prognostic biomarkers were selected using least absolute shrinkage and selection operator (LASSO) and Cox regression, generating a predictive model, of which its prognostic predictive ability was evaluated through the Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) curve, and verified it in the test set and the validation set. Subsequently, the clinicopathological features were incorporated into the risk model for Cox independent prognostic analysis, and a nomogram was constructed to verify the predictive performance of the model. Finally, the mechanism of action of the biomarkers in BC was explored through immune infiltration, immunotherapy, and drug sensitivity. The biomarker expression validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results: Functional analysis revealed 37 DE-NETRGs associated with leukocyte migration and the Interleukin (IL)-17 signaling pathway. Four biomarkers [F2RL2, AZU1, IL33, neutrophil elastase (ELANE)] were used to construct the prognostic model and it was validated by the test set and the validation set. The KM curve showed significant differences in prognosis between the high- and low-risk group, while the ROC curve showed that the model had good predictive performance. Radiation, age, tumor stage, pathologic N, and risk scores were identified as independent prognostic factors. Subgroups based on risk scores exhibited distinct immune cell infiltration patterns, with the risk score positively correlated with M0 macrophages and resting mast cells. The high-risk group demonstrated lower Tumor Immune Dysfunction and Exclusion (TIDE) scores. Drug sensitivity varied between risk subgroups, and qRT-PCR confirmed the expression of ELANE and IL33.

Conclusions: This study has reported four biomarkers related to BC prognosis, namely F2RL2, AZU1, IL33, and ELANE. Our study has offered new potential biomarkers for prognosis and has identified therapeutic targets for the treatment and prognosis prediction in BC patients.

Keywords: Breast cancer (BC); Riskmodel; immunotherapy; neutrophil extracellular traps (NETs); prognosis.

Figure 1

Flowchart of bioinformatics analysis. BRCA, breast cancer; DCA, decision curve analysis; DEGs, differential expressed genes; DE-NETRGs, differentially expressed NETRGs; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; LASSO, least absolute shrinkage and selection operator; NETRGs, neutrophil extracellular trap-related genes; qRT-PCR, quantitative reverse transcription polymerase chain reaction; ROC, receiver operating characteristic; TCGA, the cancer genome atlas; TIDE, tumor immune dysfunction and exclusion.

Figure 2

Development of a predictive model using DE-NETRGs in the TCGA-BRCA dataset. (A) Forest plot depicting the HRs for DE-NETRGs in BC. (B) Plot of the relationship between partial likelihood deviance and the natural logarithm of the tuning parameter (λ) derived from the LASSO Cox regression model. (C) Coefficients of selected features in the model shown in relation to the lambda parameter. (D) Distribution of the risk score curve, survival states, and gene expression in BC cohorts from the TCGA-BRCA dataset. (E) Kaplan-Meier survival curves comparing overall survival between patients in the high-risk and low-risk groups. (F) Time-dependent ROC curves assessing the predictive performance of the risk score. AUC, area under the curve; BC, breast cancer; CI, confidence interval; DE-NETRGs, differentially expressed NETRGs; LASSO, least absolute shrinkage and selection operator; NETRGs, neutrophil extracellular trap-related genes; OS, overall survival; ROC, receiver operating characteristic.

Figure 3

Prognostic analysis of biomarkers. (A) Expression analysis of model genes based on the TCGA-BRCA dataset, with yellow-brown and blue colors representing BC and control samples, respectively. **, P<0.01; ****, P<0.0001. (B-E) Kaplan-Meier survival curves demonstrating overall survival differences between high- and low-expression groups of the biomarkers. BC, breast cancer; BRCA, breast cancer; TCGA, the cancer genome atlas.

Figure 4

Creation and assessment of a nomogram model. (A,B) Univariate (A) and multivariate (B) Cox regression analyses of the TCGA-BRCA dataset, identifying significant factors (radiation therapy, age, tumor stage, pathologic N, and risk score) related to overall survival (P<0.05). (C) Prognostic nomogram incorporating the risk score and clinical factors to predict 1-, 3-, and 5-year survival outcomes. **, P<0.01. (D,E) Calibration (D) and DCA curves (E) for the prediction of 1-, 3-, and 5-year overall survival in patients with BC using the nomogram. (F-H) ROC curves for the prediction of 1-, 3-, and 5-year overall survival in patients with BCs using the nomogram. AUC, area under the curve; BC, breast cancer; BRCA, breast cancer; CI, confidence interval; DCA, decision curve analysis; HR, hazard ratio; OS, overall survival; Pr, probability; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.

Figure 5

Immune infiltration analysis. (A) Distribution of 22 immune cell types in individual BC samples. (B) Boxplots showing the infiltration levels of 22 immune cell types between the high- and low-risk subgroups. ns, no significance; *, P<0.05; **, P<0.01; ***, P<0.001. (C) Correlation heatmap of the relationship between differentially expressed immune cells and risk score. BC, breast cancer.

Figure 6

Real-time quantitative polymerase chain reaction (qRT-PCR) analysis of biomarker expression. (A) AZU1 expression in BC and adjacent non-cancerous tissues. (B) IL33 expression in BC and adjacent non-cancerous tissues. (C) ELANE expression in BC and adjacent non-cancerous tissues. (D) F2RL2 expression in BC and adjacent non-cancerous tissues. ns, no significance; *, P<0.05. BC, breast cancer.