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. 2023 Mar 14:14:1151800.
doi: 10.3389/fimmu.2023.1151800. eCollection 2023.

Model for predicting immunotherapy based on M2 macrophage infiltration in TNBC

Haoming Wu  1   2 , Jikun Feng  1 , Wenjing Zhong  1 , Xiazi Zouxu  1 , Zhengchong Xiong  1 , Weiling Huang  1 , Chao Zhang  1 , Xi Wang  1 , Jiarong Yi  1
Affiliations

Model for predicting immunotherapy based on M2 macrophage infiltration in TNBC

Haoming Wu et al. Front Immunol. .

Abstract

Introduction: Compared to other types of breast cancer, triple-negative breast cancer (TNBC) does not effectively respond to hormone therapy and HER2 targeted therapy, showing a poor prognosis. There are currently a limited number of immunotherapeutic drugs available for TNBC, a field that requires additional development.

Methods: Co-expressing genes with M2 macrophages were analyzed based on the infiltration of M2 macrophages in TNBC and the sequencing data in The Cancer Genome Atlas (TCGA) database. Consequently, the influence of these genes on the prognoses of TNBC patients was analyzed. GO analysis and KEGG analysis were performed for exploring potential signal pathways. Lasso regression analysis was conducted for model construction. The TNBC patients were scored by the model, and patients were divided into high- and low-risk groups. Subsequently, the accuracy of model was further verified using GEO database and patients information from the Cancer Center of Sun Yat-sen University. On this basis, we analyzed the accuracy of prognosis prediction, correlation with immune checkpoint, and immunotherapy drug sensitivity in different groups.

Results: Our findings revealed that OLFML2B, MS4A7, SPARC, POSTN, THY1, and CD300C genes significantly influenced the prognosis of TNBC. Moreover, MS4A7, SPARC, and CD300C were finally determined for model construction, and the model showed good accuracy in prognosis prediction. And 50 immunotherapy drugs with therapeutic significance in different groups were screened, which were assessed possible immunotherapeutics that have potential application and demonstrated the high precision of our prognostic model for predictive analysis.

Conclusion: MS4A7, SPARC, and CD300C, the three main genes used in our prognostic model, offer good precision and clinical application potential. Fifty immune medications were assessed for their ability to predict immunotherapy drugs, providing a novel approach to immunotherapy for TNBC patients and a more reliable foundation for applying drugs in subsequent treatments.

Keywords: M2 macrophage; TNBC; immune infiltration; immunotherapy; prognosis prediction model.

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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
Research design.
Figure 2
Figure 2
Correlation maps of the top 6 genes associated with M2 macrophage infiltration.
Figure 3
Figure 3
Gene correlation, GO, and KEGG analyses results: A shows co-expression network diagram, B shows the results of correlation analysis, C shows the results of GO analysis, D shows the results of KEGG analysis.
Figure 4
Figure 4
Model construction and verification results: A shows the results of univariate prognostic analysis, B shows the results of lasso regression analysis, C shows the results cross-validation, D shows the results of survival analysis in GEO, E shows the results of survival analysis in TCGA.
Figure 5
Figure 5
Results of further validation based on qRT-PCR: A shows the results of survival analysis based on expression of CD300C, B shows the results of survival analysis based on expression of SPARC, C shows the results of survival analysis based on expression of MS4A7, D shows the results of survival analysis based on the risk grouping.
Figure 6
Figure 6
Results of the risk classification and verification analysis as independent factors: A shows the results of univariate analysis, B shows the results of multivariate analysis, C shows the ROC curve using risk score as a single factor, D shows the ROC curve using risk score as one of the multiple factors, E shows the results of division of high- and low-risk groups, F shows the results of survival condition analysis, G shows the expression results of MS4A7, SPARC, and CD300C.
Figure 7
Figure 7
Results of the risk score assessment on the prediction of prognosis of patients: A shows the nomogram based on age, lymph node metastasis, pathological grade, risk score, and clinical stage, B shows the results of prediction accuracy validation in the model, C shows the results of further prognostic analysis, D shows the results of PFS analysis.
Figure 8
Figure 8
Relationship between risk score and immunity: A and B show the results of GSEA analysis, C shows the infiltration results of immune cells, D shows the correlation between MS4A7, SPARC and CD300C with immune cells infiltration, E shows correlation between immune checkpoints and risk score, F and G show the waterfall diagram based on risk scores and tumor mutational burden. * means p<0.05, **means p<0.01, *** means p<0.001.
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