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Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis

  • Authors:
    • Limin Huang
    • Chunhong Xu
    • Yining Song
    • Furong Sun
    • Xuemei Sun
    • Hanyi Yao
    • Mingchen Liu
    • Nan Luo
  • View Affiliations / Copyright

    Affiliations: Department of Oncology III, Weifang Hospital of Traditional Chinese Medicine, Weifang, Shandong 261041, P.R. China, Department of Breast Thyroid Surgery, Weifang Hospital of Traditional Chinese Medicine, Shandong 261041, P.R. China, Intensive Care Unit, Weifang Hospital of Traditional Chinese Medicine, Shandong 261041, P.R. China, First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250013, P.R. China
    Copyright: © Huang et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
  • Article Number: 61
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    Published online on: December 1, 2025
       https://doi.org/10.3892/ol.2025.15414
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Abstract

The present study aimed to construct a human epidermal growth factor receptor 2 (HER2)‑related gene risk model to predict breast cancer prognosis. Gene expression and clinical follow‑up data were extracted from The Cancer Genome Atlas database, while the GSE7390 dataset was obtained from the Gene Expression Omnibus database. Prognostic and clinical feature analyses were performed. In addition, differentially expressed genes (DEGs) between HER2‑negative and ‑positive groups were screened, followed by enrichment analysis. Subsequently, a prognostic model was established, and prognosis was predicted using a nomogram. In addition, the association of risk score with immunity was analyzed, and single‑cell analysis was performed. Next, key genes were identified by reverse transcription‑quantitive PCR (RT‑qPCR) analysis. The results revealed that HER2 was significantly associated with estrogen receptor status, progesterone receptor status, N stage, American Joint Committee on Cancer stage, mutation count and tumor mutation burden of breast cancer. AS601245, AP.24534 and roscovitine were the top three chemotherapeutic agents showing the highest sensitivity differences between the risk groups. A total of 251 DEGs between HER2‑negative and ‑positive groups were screened, which were found to be significantly involved in the Kyoto Encyclopedia of Genes and Genomes pathway of estrogen signaling, PI3K‑AKT signaling pathway and chemical carcinogenesis‑receptor activation. Eight prognostic gene models were constructed, and it was found that patients in the high‑risk group had significantly shorter survival times than those in the low‑risk group. A nomogram, incorporating risk groups and clinicopathological features, demonstrated strong predictive ability and high accuracy. The RT‑qPCR results indicated that the expression of electron transfer flavoprotein subunit α, rap guanine nucleotide exchange factor‑like 1, keratin 7, cluster of differentiation 24, proline rich 15‑like, arachidonate 15‑lipoxygenase type B, ELOVL fatty acid elongase 2 and C‑X‑C motif chemokine ligand 9 was consistent with the results of bioinformatic analysis. In conclusion, the HER2‑related risk model and nomogram developed in the present study demonstrated high accuracy in predicting patient survival.
View Figures

Figure 1

Prognosis, clinical features and
tumor microenvironment analysis. Kaplan-Meier curves of (A)
disease-free survival, (B) disease-specific survival, (C) overall
survival and (D) progression-free survival in HER2-negative and
positive groups. Note: Survival curves were constructed using the
Kaplan-Meier method, and the log-rank test was employed to compare
survival differences between groups, provided the proportional
hazards assumption held. The two-stage survival analysis method
from the two-stage Hazard Rate Comparison package was applied when
this assumption was violated. (E) Differences in stromal, immune
and ESTIMATE scores between HER2-negative and positive groups. The
(F) ‘CIBERSORT’, (G) ‘Single-sample Gene Set Enrichment Analysis
(ssGSEA)’ and (H) ‘xCELL’ algorithms revealed the fraction of
immune cells in the HER2-negative and -positive groups. The numbers
displayed in the yellow/blue legend represent the standardized
expression levels (Z-scores). Differences in (I) Immunomodulator
expression levels, (J) chemokine genes and (K) HLA family genes
between HER2-negative and -positive groups. The numerical values
displayed at the top of the panel refer to P-values. *P<0.05,
**P<0.01, ***P<0.001, ****P<0.0001. ns, -, and . indicate
no significant difference; HER2, human epidermal growth factor
receptor 2; CIBERSORT, Cell-type Identification By Estimating
Relative Subsets Of RNA Transcripts; ssGSEA, single-sample Gene Set
Enrichment Analysis; NK, natural killer; T helper; HLA, human
leukocyte antigen; CCL, Chemokine (C-C motif) ligand; CXCL,
Chemokine (C-X-C motif) ligand; CLP, common lymphoid progenitors;
MEP, megakaryocyte-erythroid progenitors; Th, T helper cells; Tgd,
Tgd-like cells; iDC, immature dendritic cells; cDC, conventional
dendritic cells; MPP, multipotent progenitors; GMP,
granulocyte-macrophage progenitors; CMP, common myeloid
progenitors; NKT, natural killer T-cells; mv, mv-like cells; HSC,
hematopoietic stem cells; MSC, mesenchymal stem cells; Tregs,
regulatory T cells.

Figure 2

Mutation analysis, drug sensitivity
analysis and immunotherapy response. Top 20 mutation genes in (A)
HER2-negative and (B) -positive samples. (C) Differences in TMB
values between the HER2-negative and -positive groups. (D) Top 3
chemotherapeutics with notable differences in their IC50
between the HER2-negative and -positive groups. Differences in (E)
TIDE, IFNG, (F) IPS, (G) TLS scores between HER2-negative and
-positive groups. ns, no obvious difference, *P<0.05,
**P<0.01, ****P<0.0001. ns, no significant difference; TMB,
tumor mutation burden; HER2, human epidermal growth factor receptor
2; Ins, insertion; Del, deletion; IC50, half maximal
inhibitory concentration; TIDE, Tumor Immune Dysfunction and
Exclusion; IFNG, interferon γ; IPS, immunophenoscore; TLS, tertiary
lymphoid structure.

Figure 3

Identification of DEGs and enrichment
analysis. (A) Gene Set Enrichment Analysis. (B) Volcano plot of
DEGs between human epidermal growth factor receptor 2-negative and
-positive groups. (C) Functional enrichment of DEGs. DEGs,
differentially expressed genes; NES, normalized enrichment score;
NP, normalized P-value; GO, Gene Ontology; BP, biological process;
MF, molecular function; p.adjust, adjusted P-value; KEGG, Kyoto
Encyclopedia of Genes and Genomes.

Figure 4

Prognostic model established in the
current study. (A) Prognosis-related genes identified. (B)
Characteristic genes screened by least absolute shrinkage and
selection operator. (C) Stepwise Cox regression analysis. (D and E)
RiskScore distribution, survival status, prognosis and ROC curves
in (D) The Cancer Genome Atlas and (E) the GSE7390 dataset.
*P<0.05, **P<0.01, ***P<0.001. AUC, area under the curve;
ROC, receiver operating characteristic.

Figure 5

Construction of a nomogram. (A)
Differences in RiskScore between different HER2-status groups and
clinical features. (B) Association between HER2 and RiskScores. (C)
Univariate and (D) multivariate analyses of clinical data and risk
group. (E) Nomogram for predicting 1-, 3- and 5-year overall
survival. (F) Calibration curve analysis of nomogram. (G)
Kaplan-Meier analysis of the nomogram. (H) AUCs for predicting 1-,
3- and 5-year OS. *P<0.05, **P<0.01. ns, no significant
difference; HER2, human epidermal growth factor receptor 2; ER,
estrogen receptor; PR, progesterone receptor; M, metastasis; N,
node; T, tumor; AJCC, American Joint Committee on Cancer; OS,
overall survival; AUC, area under the curve; ROC, receiver
operating characteristic.

Figure 6

Association analysis of immunity and
RiskScore. The (A) ‘CIBERSORT’ and (B) ‘ssGSEA’ algorithms were
adopted to compare the score differences of 22 and 28 immune cells
between the high- and low-risk groups. Association between 22
immune cell types (C), 28 immune cell types (D) and eight key
genes. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. -,
no significant difference; ssGSEA, single-sample Gene Set
Enrichment Analysis; Tregs, regulatory T cells; NK, natural
killer.

Figure 7

Characteristics of key genes (ETFA,
RAPGEFL1, KRT7, CD24, PRR15L, ALOX15B, ELOVL2 and CXCL9). (A)
Analysis of the association between the expression levels of the
eight key genes and the prognosis of patients with breast cancer
was analyzed revealed a positive trend in general. (B) CNV changes
of the above eight key genes on chromosomes. (C) CNV changes (gain
and loss trends) of the aforementioned eight key genes (there is no
relevant information about gene CD24 so a graph cannot be drawn).
(D) Heatmap of the expression of eight crucial genes. (E) Survival
analysis patients with different levels of expression of the
aforementioned eight crucial genes. Note: Survival curves were
constructed using the Kaplan-Meier method, and the log-rank test
was employed to compare survival differences between groups,
provided the proportional hazards assumption held (ETFA, RAPGEFL1,
KRT7, PRR15L, ALOX15B, and CXCL9 genes). For time-to-event data
with intermediate events, the two-stage survival analysis method
from the Two Stage Hazard Rate Comparison (TSHRC) package (Version
0.1-6) was applied when the proportional hazards assumption was
violated (CD24 and ELOVL2 genes). CNV, copy number variation; TMB,
tumor mutation burden; M, metastasis; N, node; T, tumor; AJCC,
American Joint Committee on Cancer; HER2, human epidermal growth
factor receptor 2; ER, estrogen receptor; PR, progesterone
receptor. ETFA, electron transfer flavoprotein subunitα; RAPGEFL1,
rap guanine nucleotide exchange factor-like 1; KRT7, keratin 7;
CD24, cluster of differentiation 24; PRR15L, proline rich 15-like;
ALOX15B, arachidonate 15-lipoxygenase type B; ELOVL2, ELOVL fatty
acid elongase 2; CXCL9, C-X-C motif chemokine ligand 9.

Figure 8

Single cell analysis. (A) Levels of
eight key genes in 11 cell types based on the single-cell dataset
GSE161529. (B) Distribution and percentage of 11 cell types. (C)
Distribution of RiskScore in the above 11 cell types. (D)
Expression pattern of the aforementioned eight key genes in immune
cells. NK, natural killer; BRCA, breast invasive carcinoma; Tconv,
Tconvoluted; Mono/Macro, monocyte/macrophage; ETFA, electron
transfer flavoprotein subunitα; RAPGEFL1, rap guanine nucleotide
exchange factor-like 1; KRT7, keratin 7; CD24, cluster of
differentiation 24; PRR15L, proline rich 15-like; ALOX15B,
arachidonate 15-lipoxygenase type B; ELOVL2, ELOVL fatty acid
elongase 2; CXCL9, C-X-C motif chemokine ligand 9.

Figure 9

RT-qPCR was performed to validate the
expression of eight crucial genes (ETFA, RAPGEFL1, KRT7, CD24,
PRR15L, ALOX15B, ELOVL2 and CXCL9). *P<0.05, **P<0.01,
***P<0.001. HER2, human epidermal growth factor receptor 2;
ETFA, electron transfer flavoprotein subunitα; RAPGEFL1, rap
guanine nucleotide exchange factor-like 1; KRT7, keratin 7; CD24,
cluster of differentiation 24; PRR15L, proline rich 15-like;
ALOX15B, arachidonate 15-lipoxygenase type B; ELOVL2, ELOVL fatty
acid elongase 2; CXCL9, C-X-C motif chemokine ligand 9.

Figure 10

OE-ELOVL2 inhibits the proliferation
of human epidermal growth factor receptor 2-positive breast cancer
cells. (A) Expression level of ELOVL2 in MDA-MB-468, BT474 and
SKBR-3 cells. (B) Reverse transcription-quantitative PCR was used
to detect the transfection efficiency of OE-ELOVL2 in BT474 and
SKBR-3 cells. (C) OE-ELOVL2 inhibited the proliferation of BT474
and SKBR-3 cells. (D) Effect of OE-ELOVL2 on the levels of PI3K,
p-PI3K, AKT and p-AKT in BT474 and SKBR-3 cells. **P<0.01,
***P<0.001. OD, optical density; p, phosphorylated; NC, negative
control; OE, overexpression.
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Copy and paste a formatted citation
Spandidos Publications style
Huang L, Xu C, Song Y, Sun F, Sun X, Yao H, Liu M and Luo N: Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis. Oncol Lett 31: 61, 2026.
APA
Huang, L., Xu, C., Song, Y., Sun, F., Sun, X., Yao, H. ... Luo, N. (2026). Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis. Oncology Letters, 31, 61. https://doi.org/10.3892/ol.2025.15414
MLA
Huang, L., Xu, C., Song, Y., Sun, F., Sun, X., Yao, H., Liu, M., Luo, N."Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis". Oncology Letters 31.2 (2026): 61.
Chicago
Huang, L., Xu, C., Song, Y., Sun, F., Sun, X., Yao, H., Liu, M., Luo, N."Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis". Oncology Letters 31, no. 2 (2026): 61. https://doi.org/10.3892/ol.2025.15414
Copy and paste a formatted citation
x
Spandidos Publications style
Huang L, Xu C, Song Y, Sun F, Sun X, Yao H, Liu M and Luo N: Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis. Oncol Lett 31: 61, 2026.
APA
Huang, L., Xu, C., Song, Y., Sun, F., Sun, X., Yao, H. ... Luo, N. (2026). Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis. Oncology Letters, 31, 61. https://doi.org/10.3892/ol.2025.15414
MLA
Huang, L., Xu, C., Song, Y., Sun, F., Sun, X., Yao, H., Liu, M., Luo, N."Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis". Oncology Letters 31.2 (2026): 61.
Chicago
Huang, L., Xu, C., Song, Y., Sun, F., Sun, X., Yao, H., Liu, M., Luo, N."Construction of a human epidermal growth factor receptor 2‑related gene risk model for predicting breast cancer prognosis". Oncology Letters 31, no. 2 (2026): 61. https://doi.org/10.3892/ol.2025.15414
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