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Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma

  • Authors:
    • Ling-Rong Zeng
    • Guang-Hui Zhu
    • Hai-Bo Mei
    • Ge Yang
  • View Affiliations / Copyright

    Affiliations: Department of Pediatric Orthopedics, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, Hunan 410007, P.R. China
    Copyright: © Zeng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 561
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    Published online on: October 1, 2025
       https://doi.org/10.3892/ol.2025.15308
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Abstract

Osteosarcoma (OS) is the most prevalent malignant bone tumor among children and adolescents, and ferroptosis has been implicated in tumor immune infiltration. The present study aimed to explore the role of ferroptosis‑related genes in immune infiltration and to develop a prognostic signature for osteosarcoma (OS). In the present study, two immune subtypes of OS were identified and demonstrated to be reproducible and effective. Building upon the distinction between high and low immunity, six common differentially expressed immune infiltration‑related ferroptosis genes (DEIIRFGs) were identified. After which, univariate Cox regression (UCR) analysis was employed to identify DEIIRFGs associated with overall survival. Next, using Gene Ontology analysis, it was revealed that the six mRNAs were enriched in the immune‑associated functions and pathway. Subsequently, using UCR analysis and the iterative Least Absolute Shrinkage and Selection Operator Cox regression analysis based on two ferroptosis genes (interferon‑γ and toll‑like receptor 4), a model was established. Additionally, it was confirmed that the risk model was an independent prognostic factor for overall survival. Furthermore, the results were verified using a Gene Expression Omnibus dataset. Finally, the content of tumor‑infiltrating immune cells demonstrated that the high‑risk group was significantly associated with immune cell infiltration, compared with the low‑risk group. Moreover, the expression of immune checkpoint inhibitors was significantly upregulated in the low‑risk cohort compared with in the high‑risk cohort. Finally, using gene set enrichment analysis, it was demonstrated that the risk score was associated with the toll‑like receptor signaling pathway, antigen processing and presentation, and cytokine‑cytokine receptor interaction. In summary, a signature of DEIIRFGs to predict prognosis in patients with OS was developed. Moreover, it was demonstrated that the molecular mechanisms responsible in the high‑risk group may influence immune infiltration‑related biological processes.
View Figures

Figure 1

Consensus clustering of osteosarcoma.
(A) The Therapeutically Applicable Research to Generate Effective
Treatments database samples were divided into two distinct clusters
when k=2. (B) Relative change in the area under the CDF curve for
k=2-9, (C) Consensus clustering CDF curve for k=2-9. CDF,
cumulative distribution function.

Figure 2

Determination of the two immune
subtypes in osteosarcoma. (A) Heatmap of single-sample Gene Set
Enrichment Analysis scores. (B) There was a significant difference
in the expression of most HLAs between the high- and low-immune
cell infiltration clusters. (C) Difference in the expression levels
of infiltrating immune cells between the two clusters. (D)
Difference in the expression levels of the immune checkpoint genes,
LAG3, VSIR, CD274, HAVCR2, NT5E, CTLA4, TIGIT and PDCD1, among the
two subtypes. (E) Patients in low immune cell infiltration cluster
showed worse overall survival than those in high immune cell
infiltration cluster. *P<0.05; **P<0.01; ***P<0.005. LAG3,
lymphocyte-activation gene 3; VSIR, V-set immunoregulatory
receptor; HAVCR2, hepatitis A virus cellular receptor 2; NT5E,
5′-nucleotidase ecto; CTLA4, cytotoxic T-lymphocyte associated
protein 4; TIGIT, T cell immunoreceptor with Ig and ITIM domains;
PDCD1, programmed cell death 1.

Figure 3

Determination of differentially
expressed immune infiltration-related ferroptosis genes. (A)
Heatmap(green, negative) Z-score; red color indicates a high
(positive) Z-score; black, expression levels at or near the mean.)
and (B) volcano plot of differentially expressed immune
infiltration-related ferroptosis genes between the high- and
low-immunity clusters. H, high; L, low; FC, fold change; FDR, false
discovery rate.

Figure 4

Overlap between immune
infiltration-related DEGs and the ferroptosis gene set. DEGs,
differentially expressed genes; IDO1, indoleamine 2,3-dioxygenase
1; IFNG, interferon γ; CYBB, cytochrome B-245 β chain; TLR4,
toll-like receptor 4; CCL5, CC motif chemokine ligand 5; AKR1C3,
aldo-keto reductase family 1 member C3.

Figure 5

GO and KEGG enrichment analysis for
the differentially expressed immune infiltration-related
ferroptosis genes in osteosarcoma. (A) GO enrichment result. (B)
KEGG enrichment result. GO, Gene Ontology; KEGG, Kyoto Encyclopedia
of Genes and Genomes.

Figure 6

Development of the immune
infiltration-related ferroptosis genes prognostic signature. (A)
Univariable Cox regression analysis of selected genes. (B) Optimal
values of the penalty parameter, defined by 1,000-round
cross-validation. (C) Least Absolute Shrinkage and Selection
Operator Cox analysis identified two genes associated with
prognosis. IDO1, indoleamine 2,3-dioxygenase 1; IFNG, interferon γ;
CYBB, cytochrome B-245 β chain; TLR4, toll-like receptor 4; CCL5,
CC motif chemokine ligand 5; AKR1C3, aldo-keto reductase family 1
member C3.

Figure 7

Robustness of the two-gene signature
model. Kaplan-Meier survival curves of patients with OS in the
high- and low-risk cohorts in the (A) TARGET and (B) GEO datasets.
Risk score distribution in the (C) TARGET and (D) GEO datasets.
Survival status of patients with OS in the (E) TARGET and (F) GEO
datasets. Heatmap of the two signature genes in the (G) TARGET and
(H) GEO datasets. ROC curve for overall survival of patients with
OS in the (I) TARGET and (J) GEO datasets. OS, osteosarcoma;
TARGET, Therapeutically Applicable Research to Generate Effective
Treatments; GEO, Gene Expression Omnibus; ROC, receiver operating
characteristic; AUC, area under the curve; IFNG, interferon γ;
TLR4, toll-like receptor 4.

Figure 8

Univariate and multivariate Cox
analyses of clinicopathological factors associated with
osteosarcoma prognosis. Univariate Cox analysis of risk score, sex,
age and metastasis using the (A) TARGET and (B) GEO datasets.
Multivariate Cox analysis using the (C) TARGET and (D) GEO
datasets. TARGET, Therapeutically Applicable Research to Generate
Effective Treatments; GEO, Gene Expression Omnibus; M,
metastasis.

Figure 9

Analysis of immune cell infiltration
in patients with osteosarcoma. Differences in the number of (A) M0,
(B) M1 and (C) M2 macrophages between the high- and low-risk groups
in the TARGET dataset. Differences in the number of (D) M0, (E) M1
and (F) M2 macrophages between the high- and low-risk groups in the
GEO dataset. Differences in the expression of immune checkpoint
genes among the high- and low-risk groups in the (G) TARGET and (H)
GEO datasets. *P<0.05; **P<0.01; ***P<0.005. TARGET,
Therapeutically Applicable Research to Generate Effective
Treatments; GEO, Gene Expression Omnibus; LAG3,
lymphocyte-activation gene 3; VSIR, V-set immunoregulatory
receptor; KIR2DL1, killer cell Immunoglobulin-like Receptor, two Ig
domains, Long cytoplasmic tail, inhibitory receptor 1; KLRC1,
Killer cell Lectin-like Receptor subfamily C member 1; HAVCR2,
hepatitis A virus cellular receptor 2; NT5E, 5′-nucleotidase ecto;
CTLA4, cytotoxic T-lymphocyte associated protein 4; TIGIT, T cell
immunoreceptor with Ig and ITIM domains; PDCD1, programmed cell
death 1.

Figure 10

Drug sensitivity analysis of two
signature genes expression. Correlation between TLR4 expression and
the drug sensitivity of (A) Entinostat, (B) Lapatinib and (C)
Acetalax. Correlation between IFNG expression and the drug
sensitivity of (D) Decitabine, (E) Mitomycin and (F) Cisplatin.
TLR4, toll-like receptor 4; IFNG, interferon γ; cor,
correlation.

Figure 11

Gene set enrichment analysis of the
high- and low-risk groups. (A) TARGET and (B) GEO datasets. TARGET,
Therapeutically Applicable Research to Generate Effective
Treatments; GEO, Gene Expression Omnibus; KEGG, Kyoto Encyclopedia
of Genes and Genomes.

Figure 12

Reverse transcription-quantitative
PCR analysis. Relative expression levels of (A) IFNG and (B) TLR4
in hFOB1.19, MG-63,143B and U2OS cells. **P<0.01 and
***P<0.001 vs. hFOB1.19 cells. TLR4, toll-like receptor 4; IFNG,
interferon γ.

Figure 13

ELISA was used to determine the
relative IFNG expression levels in hFOB1.19, MG-63, 143B and U2OS
cells. ***P<0.001 vs. hFOB1.19 cells. IFNG, interferon γ.

Figure 14

Western blot analysis was used to
determine the relative TLR4 expression levels in hFOB1.19, MG-63,
143B and U2OS cells. ***P<0.001 vs. hFOB1.19 cells. TLR4,
toll-like receptor 4.
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Copy and paste a formatted citation
Spandidos Publications style
Zeng L, Zhu G, Mei H and Yang G: Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma. Oncol Lett 30: 561, 2025.
APA
Zeng, L., Zhu, G., Mei, H., & Yang, G. (2025). Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma. Oncology Letters, 30, 561. https://doi.org/10.3892/ol.2025.15308
MLA
Zeng, L., Zhu, G., Mei, H., Yang, G."Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma". Oncology Letters 30.6 (2025): 561.
Chicago
Zeng, L., Zhu, G., Mei, H., Yang, G."Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma". Oncology Letters 30, no. 6 (2025): 561. https://doi.org/10.3892/ol.2025.15308
Copy and paste a formatted citation
x
Spandidos Publications style
Zeng L, Zhu G, Mei H and Yang G: Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma. Oncol Lett 30: 561, 2025.
APA
Zeng, L., Zhu, G., Mei, H., & Yang, G. (2025). Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma. Oncology Letters, 30, 561. https://doi.org/10.3892/ol.2025.15308
MLA
Zeng, L., Zhu, G., Mei, H., Yang, G."Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma". Oncology Letters 30.6 (2025): 561.
Chicago
Zeng, L., Zhu, G., Mei, H., Yang, G."Signature of immune infiltration‑related ferroptosis genes to predict the prognosis of patients with osteosarcoma". Oncology Letters 30, no. 6 (2025): 561. https://doi.org/10.3892/ol.2025.15308
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