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Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer

  • Authors:
    • You Meng
    • Ze-Xin Han
    • Jie Zhu
    • Ying Wang
    • Yue-Qing Huang
    • Zhong-Hua Zou
    • Yu-Yuan Ma
    • Yi-Fan Li
    • Han Wang
    • Ying Li
    • Lian Lian
    • Wen-Jie Wang
  • View Affiliations / Copyright

    Affiliations: Department of Thyroid and Breast Surgery, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China, Department of Radio‑oncology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China, Department of Oncology, Changzhou Traditional Chinese Medicine Hospital, Changzhou, Jiangsu 213003, P.R. China, Department of Oncology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu 215001, P.R. China, Department of Gastrointestinal Surgery II, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China, Department of Oncology, Binzhou People's Hospital, Binzhou, Shandong 256601, P.R. China, Department of Oncology, Jining Cancer Hospital, Jining, Shandong 272004, P.R. China, Department of Oncology, Suzhou Xiangcheng People's Hospital, Suzhou, Jiangsu 215131, P.R. China
    Copyright: © Meng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 242
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    Published online on: April 15, 2026
       https://doi.org/10.3892/ol.2026.15597
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Abstract

Triple‑negative breast cancer (TNBC) is one of the most aggressive molecular subtypes of breast cancer and immune‑checkpoint blockade therapy has markedly changed the treatment landscape for this malignancy. Tumour‑infiltrating lymphocytes (TILs) and tumour mutational burden (TMB) predict patient response to treatment with immune checkpoint inhibitors and reflect patient outcomes. The present study aimed to develop a TIL‑based prognostic model, create a list of immune‑related genes (IRGs) to inform clinicians of possible outcome predictions and generate a clinically relevant estimate of potential benefit from immunotherapy in TNBC. The present study included a cohort of 130 patients that were classified into two groups, namely TMBhigh/CD8+ T‑cell‑rich and TMBlow/CD8+ T‑cell‑poor. Differential expression analysis using the ‘edgeR’ package identified IRGs associated with survival. The identified IRGs were included in a univariate Cox analysis to derive a prognostic signature. In addition, the present study examined how the signature genes were associated with immune cell infiltration using the Tumour IMmune Estimation Resource database. The final four‑gene signature, C‑X‑C motif chemokine ligand 13 (CXCL13), latent TGF‑β binding protein 2, placental growth factor and transporter associated with antigen processing binding protein‑like (TAPBPL), stratified risk robustly: Patients in the high‑risk group had significantly worse overall survival compared with low‑risk patients in both prognostic and validation models. Compared with high‑risk patients, low‑risk patients had greater infiltration of CD8+ T cells, M1 macrophages, resting dendritic cells and activated CD4+ T cells and less infiltration of both M0 and M2 macrophages. Higher CXCL13 and TAPBPL expression levels were significantly associated with higher CD8+ T‑cell counts and inversely associated with M0 and M2 macrophage counts. The overall risk score and CXCL13 expressions were all positively correlated with multiple immune checkpoint genes, while TAPBPL expression correlated positively with CTLA4, TIM3 and TIGIT. In summary, the present study provided a TMB‑ and T‑cell infiltration‑based IRG signature that is prognostic and may potentially support the prediction of immunotherapy responsiveness in TNBC in the future.
View Figures

Figure 1

Figure 2

Prognostic relevance of
CD8+ T cells and TMB in patients with TNBC. (A)
TMBhigh and TMBlow groups; (B)
CD8+ T cellhigh and CD8+ T
celllow groups; (C) TMBhigh/CD8+ T
cellhigh and TMBlow/CD8+ T
celllow groups. TMB, tumour mutation burden; TNBC,
triple-negative breast cancer; OS, overall survival.

Figure 3

Risk score and immune cell
infiltration association analysis using Mann-Whitney U test.
Association of high- and low-risk groups with (A) immune score, (B)
TMB, (C) CD8+ T cells, (D) Treg cells, (E) M0
macrophages, (F) M1 macrophages, (G) M2 macrophages, (H) NK cells
(resting), (I) NK cells (activated), (J) B cells, (K)
CD4+ T cells (resting), (L) CD4+ T cells
(activated), (M) DC cells (resting), (N) DCs (activated) and (O)
monocytes. TMB, tumour mutation burden; Treg, regulatory T cell;
NK, natural killer; DC, dendritic cell.

Figure 4

CXCL13 and immune cell infiltration
association analysis using Mann-Whitney U test. Association of
CXCL13 expression with (A) immune score, (B) TMB, (C)
CD8+ T cells, (D) Treg cells, (E) M0 macrophages, (F) M1
macrophages, (G) M2 macrophages, (H) NK cells (resting), (I) NK
cells (activated), (J) B cells, (K) CD4+ T cells
(resting), (L) CD4+ T cells (activated), (M) DC cells
(resting), (N) DCs (activated) and (O) monocytes. CXCL13, C-X-C
motif chemokine ligand 13; TMB, tumour mutation burden; Treg,
regulatory T cells; NK, natural killer; DC, dendritic cell.

Figure 5

TAPBPL and immune cell infiltration
association analysis by Mann-Whitney U test. Association of TAPBPL
expression with (A) immune score, (B) TMB, (C) CD8+ T
cells, (D) Treg cells, (E) M0 macrophages, (F) M1 macrophages, (G)
M2 macrophages, (H) NK cells (resting), (I) NK cells (activated),
(J) B cells, (K) CD4+ T cells (resting), (L)
CD4+ T cells (activated), (M) DC cells (resting), (N)
DCs (activated) and (O) monocytes. TAPBPL, transporter associated
with antigen processing binding protein like; TMB, tumour mutation
burden; Treg, regulatory T cells; NK, natural killer; DC, dendritic
cell.

Figure 6

PGF and immune cell infiltration
association analysis by Mann-Whitney U test. Association of PGF
expression with (A) immune score, (B) TMB, (C) CD8+ T
cells, (D) Treg cells, (E) M0 macrophages, (F) M1 macrophages, (G)
M2 macrophages, (H) NK cells (resting), (I) NK cells (activated),
(J) B cells, (K) CD4+ T cells (resting), (L)
CD4+ T cells (activated), (M) DC cells (resting), (N)
DCs (activated) and (O) monocytes. PGF, placental growth factor;
TMB, tumour mutation burden; Treg, regulatory T cells; NK, natural
killer; DC, dendritic cell.

Figure 7

LTBP2 and immune cell infiltration
association analysis by Mann-Whitney U test. Association of LTBP2
expression with (A) immune score, (B) TMB, (C) CD8+ T
cells, (D) Treg cells, (E) M0 macrophages, (F) M1 macrophages, (G)
M2 macrophages, (H) NK cells (resting), (I) NK cells (activated),
(J) B cells, (K) CD4+ T cells (resting), (L)
CD4+ T cells (activated), (M) DC cells (resting), (N)
DCs (activated) and (O) monocytes. LTBP2, latent TGF-β binding
protein 2; TMB, tumour mutation burden; Treg, regulatory T cell;
NK, natural killer; DC, dendritic cell.

Figure 8

Risk score and immune
checkpoint-related genes correlation analysis. Correlation between
risk score and (A) PD-L1, (B) CTLA4, (C) LAG3, (D) TIM3 and (E)
TIGIT. PD-L1, programmed cell death-ligand 1; CTLA4, cytotoxic
T-lymphocyte antigen 4; TIM3, T-cell immunoglobulin and mucin
domain-containing protein 3; LAG3, lymphocyte activation gene 3;
TIGIT, T-cell immunoreceptor with Ig and immunoreceptor
tyrosine-based inhibitory motif domains.

Figure 9

CXCL13 and immune checkpoint-related
genes correlation analysis. Correlation between CXCL13 expression
and (A) PD-L1, (B) CTLA4, (C) LAG3, (D) TIM3 and (E) TIGIT. CXCL13,
C-X-C motif chemokine ligand 13; PD-L1, programmed cell
death-ligand 1; CTLA4, cytotoxic T-lymphocyte antigen 4; TIM3,
T-cell immunoglobulin and mucin domain-containing protein 3; LAG3,
lymphocyte activation gene 3; TIGIT, T-cell immunoreceptor with Ig
and immunoreceptor tyrosine-based inhibitory motif domains.

Figure 10

TAPBPL and immune checkpoint-related
genes correlation analysis. Correlation between TAPBPL expression
and (A) PD-L1, (B) CTLA4, (C) LAG3, (D) TIM3 and (E) TIGIT. TAPBPL,
transporter associated with antigen processing binding protein
like; PD-L1, programmed cell death-ligand 1; CTLA4, cytotoxic
T-lymphocyte antigen 4; TIM3, T-cell immunoglobulin and mucin
domain-containing protein 3; LAG3, lymphocyte activation gene 3;
TIGIT, T-cell immunoreceptor with Ig and immunoreceptor
tyrosine-based inhibitory motif domains.

Figure 11

PGF and immune checkpoint-related
genes correlation analysis. Correlation between PGF expression and
(A) PD-L1, (B) CTLA4, (C) LAG3, (D) TIM3 and (E) TIGIT. PGF,
placental growth factor; PD-L1, programmed cell death-ligand 1;
CTLA4, cytotoxic T-lymphocyte antigen 4; TIM3, T-cell
immunoglobulin and mucin domain-containing protein 3; LAG3,
lymphocyte activation gene 3; TIGIT, T-cell immunoreceptor with Ig
and immunoreceptor tyrosine-based inhibitory motif domains.

Figure 12

LTBP2 and immune checkpoint-related
genes correlation analysis. Correlation between LTBP2 expression
and (A) PD-L1, (B) CTLA4, (C) LAG3, (D) TIM3 and (E) TIGIT. LTBP2,
latent TGF-β binding protein 2; PD-L1, programmed cell death-ligand
1; CTLA4, cytotoxic T-lymphocyte antigen 4; TIM3, T-cell
immunoglobulin and mucin domain-containing protein 3; LAG3,
lymphocyte activation gene 3; TIGIT, T-cell immunoreceptor with Ig
and immunoreceptor tyrosine-based inhibitory motif domains.

Figure 13

TIDE score analysis using a t-test.
Association of TIDE score with (A) risk score and (B) CXCL13, (C)
TAPBPL, (D) PGF and (E) LTBP2 expression. CXCL13, C-X-C motif
chemokine ligand 13; TAPBPL, transporter associated with antigen
processing binding protein like; PGF, placental growth factor;
LTBP2, latent TGF-β binding protein 2; TIDE, tumour dysfunction and
exclusion.
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Copy and paste a formatted citation
Spandidos Publications style
Meng Y, Han Z, Zhu J, Wang Y, Huang Y, Zou Z, Ma Y, Li Y, Wang H, Li Y, Li Y, et al: Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer. Oncol Lett 31: 242, 2026.
APA
Meng, Y., Han, Z., Zhu, J., Wang, Y., Huang, Y., Zou, Z. ... Wang, W. (2026). Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer. Oncology Letters, 31, 242. https://doi.org/10.3892/ol.2026.15597
MLA
Meng, Y., Han, Z., Zhu, J., Wang, Y., Huang, Y., Zou, Z., Ma, Y., Li, Y., Wang, H., Li, Y., Lian, L., Wang, W."Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer". Oncology Letters 31.6 (2026): 242.
Chicago
Meng, Y., Han, Z., Zhu, J., Wang, Y., Huang, Y., Zou, Z., Ma, Y., Li, Y., Wang, H., Li, Y., Lian, L., Wang, W."Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer". Oncology Letters 31, no. 6 (2026): 242. https://doi.org/10.3892/ol.2026.15597
Copy and paste a formatted citation
x
Spandidos Publications style
Meng Y, Han Z, Zhu J, Wang Y, Huang Y, Zou Z, Ma Y, Li Y, Wang H, Li Y, Li Y, et al: Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer. Oncol Lett 31: 242, 2026.
APA
Meng, Y., Han, Z., Zhu, J., Wang, Y., Huang, Y., Zou, Z. ... Wang, W. (2026). Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer. Oncology Letters, 31, 242. https://doi.org/10.3892/ol.2026.15597
MLA
Meng, Y., Han, Z., Zhu, J., Wang, Y., Huang, Y., Zou, Z., Ma, Y., Li, Y., Wang, H., Li, Y., Lian, L., Wang, W."Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer". Oncology Letters 31.6 (2026): 242.
Chicago
Meng, Y., Han, Z., Zhu, J., Wang, Y., Huang, Y., Zou, Z., Ma, Y., Li, Y., Wang, H., Li, Y., Lian, L., Wang, W."Identification of a prognostic model using immune related genes combined with tumour mutational burden and T cell infiltration in triple‑negative breast cancer". Oncology Letters 31, no. 6 (2026): 242. https://doi.org/10.3892/ol.2026.15597
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