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Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies

  • Authors:
    • Hui Wang
    • Hang Chen
    • Jianjun Liu
    • Dan Zhang
    • Da Wang
    • Minshan Huang
    • Mengwei Li
    • Suyu He
    • Lanqing Ma
  • View Affiliations / Copyright

    Affiliations: Department of Gastroenterology, The First Affiliated Hospital, Yunnan Institute of Digestive Disease, Yunnan Clinical Research Center for Digestive Diseases, Kunming Medical University, Kunming, Yunnan 650032, P.R. China, Academy of Biomedical Engineering, Kunming Medical University, Kunming, Yunnan 650500, P.R. China, The Fourth Department of Digestive Disease Center, Suining Central Hospital, Suining, Sichuan 629099, P.R. China
    Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 450
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    Published online on: July 18, 2025
       https://doi.org/10.3892/ol.2025.15196
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Abstract

Ferroptosis is a novel iron‑dependent form of cell death that influences the development and progression of gastric cancer (GC), affecting its growth, invasion and metastasis. However, molecular regulatory mechanisms of ferroptosis in GC remain unclear. The present study aimed to identify key ferroptosis‑related genes associated with GC development. Ferroptosis‑related genes were collected from FerrDb, a database that collects data on genes and substances that regulate ferroptosis, and the top survival‑related genes (including progression‑free and overall survival), and differentially expressed genes were identified using data from The Cancer Genome Atlas stomach adenocarcinoma (STAD) samples. Following intersection analysis, least absolute shrinkage and selection operator analysis was performed on 140 screened important genes, and 14 key ferroptosis‑related genes in STAD were obtained using Cox regression models. By reviewing the expression of these genes through the Gene Set Cancer Analysis tool and their correlation with survival, the present study analyzed their overall role in STAD. Tumor immunity analysis was performed to identify potential microRNAs (miRs) and drugs targeting key carcinogenic ferroptosis genes in STAD. NADPH oxidase (NOX) 4, NOX5, aldo‑keto reductase family 1 member C2, RNA binding motif single stranded interacting protein 1 (RBMS1), GABA type A receptor associated protein like 2 (GABARAPL2), gap junction protein α1 (GJA1), transferrin and hydroxycarboxylic acid receptor 1 were notable risk genes. Additionally, by examining the association between these genes and tumor‑infiltrating immune cells, it was discovered that GABARAPL2, GJA1, NOX4 and RBMS1 may influence the immune microenvironment. In total, five miRs [Homo sapiens (hsa)‑miR‑6795‑5p, hsa‑miR‑6758‑5p, hsa‑miR‑501‑5p, hsa‑miR‑505‑5p and hsa‑miR‑484] with potential therapeutic implications for STAD were identified as targeting carcinogenic genes. Finally, using the Genomics of Drug Sensitivity in Cancer and Cancer Therapeutics Response Portal databases, potential drugs [(5Z)‑7‑oxozeaenol, selumetinib, RDEA119, AZ628, dabrafenib and trametinib] were identified based on the aforementioned seven key carcinogenic genes, focusing on those that targeted multiple genes. In conclusion, the present study identified 14 key ferroptosis‑related genes, and seven key carcinogenic genes, which represent promising novel molecular targets for the prognosis and treatment of GC.
View Figures

Figure 1

Flow chart of the present study. A
total of 140 key ferroptosis-related genes were obtained, and 14
key genes were screened by LASSO and prognostic models for the
following analyses: i) Optimized Cox regression model, ii) key
pathway analysis in tumors, iii) immunity analysis and iv)
identification of potential miRNAs and drug targeting key
carcinogenic ferroptosis genes in STAD. M, marker; D, driver; I,
inhibitor; LASSO, least absolute shrinkage and selection operator
regression; miRNA, microRNA; STAD, stomach adenocarcinoma; DEG,
differentially expressed gene; OS, overall survival; PFS,
progression-free survival; NOX, NADPH oxidase; MYB, MYB
proto-oncogene, transcription factor; PRR5, proline-rich protein 5;
PVT1, Pvt1 oncogene; TF, transferrin; GABARAPL2, GABA type A
receptor-associated protein-like 2; GJA1, gap junction protein α1;
HCAR1, hydroxycarboxylic acid receptor 1; AKR1C2, aldo-keto
reductase family 1 member C2; DUOX1, dual oxidase 1; FXN, frataxin;
RBMS1, RNA binding motif single stranded interacting protein 1.

Figure 2

LASSO analysis and Cox regression
models for OS prediction. (A) Coefficients of the selected features
are shown by the λ parameter, where the horizontal coordinate
represents the value of the independent variable λ and the vertical
coordinate represents the coefficients of the independent
variables. (B) Partial likelihood deviance plotted against log(λ)
values calculated through the LASSO Cox regression model. (C)
Association between expression of nine key genes in patients with
gastric adenocarcinoma from The Cancer Genome Atlas database with
patient risk scores and survival times. (D) Median OS time of the
low- and high-risk groups, and receiver operating characteristic
curve of the Cox regression model for the prediction of OS. (E-G)
Cox regression models constructed with the key genes from TIMER
database. (E) Nomogram of the constructed COX regression model,
visually presenting the model; (F) KM curve of the model; (G) ROC
curve of the model. LASSO, least absolute shrinkage and selection
operator; OS, overall survival; HR, hazard ratio; AUC, area under
the curve; CI, confidence interval; NOX, NADPH oxidase; MYB, MYB
proto-oncogene, transcription factor; PRR5, proline rich protein 5;
PVT1, Pvt1 oncogene; GABARAPL2, GABA type A receptor associated
protein like 2; GJA1, gap junction protein α1; HCAR1,
hydroxycarboxylic acid receptor 1.

Figure 3

LASSO analysis and Cox regression
models for PFS prediction. (A) Coefficients of the selected
features are shown by the λ parameter, with the horizontal
coordinate representing the value of the independent variable λ and
the vertical coordinate representing the coefficients of the
independent variables. (B) Partial likelihood deviance plotted
against log(λ) values calculated via the LASSO Cox regression
model. (C) Associations between risk scores and survival times of
patients with expressing nine key genes in patients with stomach
adenocarcinoma from The Cancer Genome Atlas database. (D) Median
PFS of the low- and high-risk groups, and receiver operating
characteristic curve of the Cox regression model for the prediction
of PFS. LASSO, least absolute shrinkage and selection operator;
PFS, progression-free survival; HR, hazard ratio; AUC, area under
the curve; CI, confidence interval; NOX, NADPH oxidase; MYB, MYB
proto-oncogene, transcription factor; GABARAPL2, GABA type A
receptor associated protein like 2; HCAR1, hydroxycarboxylic acid
receptor 1; TF, transferrin; AKR1C2, aldo-keto reductase family 1
member C2; DUOX1, dual oxidase 1; FXN, frataxin; RBMS1, RNA binding
motif single stranded interacting protein 1.

Figure 4

Roles of key ferroptosis genes in
STAD. (A) Among the 14 key genes in STAD, MYB, NOX4, PVT1 and PRR5
expression was increased in STAD tumors compared with normal
tissue. *P<0.05. (B) Expression trend of each key gene from
stages I to IV. (C) Risk genes for DFI, DSS, OS and PFS among the
14 key genes. (D) Association between key genes and cancer
pathways. STAD, stomach adenocarcinoma; DFI, disease-free interval;
DSS, disease-specific survival; OS, overall survival; PFS,
progression-free survival; NOX, NADPH oxidase; MYB, MYB
proto-oncogene, transcription factor; PRR5, proline-rich protein 5;
PVT1, Pvt1 oncogene; TF, transferrin; GABARAPL2, GABA type A
receptor associated protein like 2; GJA1, gap junction protein α1;
HCAR1, hydroxycarboxylic acid receptor 1; AKR1C2, aldo-keto
reductase family 1 member C2; DUOX1, dual oxidase 1; FXN, frataxin;
RBMS1, RNA binding motif single stranded interacting protein 1;
TPM, transcripts per million; A, activation; I, inhibition.

Figure 5

Tumor immunity association with key
ferroptosis genes in stomach adenocarcinoma. (A) GABARAPL2 and (B)
GJA1 expression is positively associated with macrophages. (C) TF
is positively associated with CD4+ T cells. (D) NOX4
expression is positively correlated with macrophages, neutrophils
and dendritic cells. (E) RBMS1 is positively correlated with
CD4+ T cells, macrophages and dendritic cells.
GABARAPL2, GABA type A receptor associated protein-like 2; GJA1,
gap junction protein α1; TF, transferrin; NOX, NADPH oxidase;
RBMS1, RNA-binding motif single stranded interacting protein 1;
TPM, transcripts per million.

Figure 6

Key gene interaction networks. (A)
Co-expression network. (B) Genetic interaction network. (C)
Physical interaction network.

Figure 7

Potential miRNAs that target key
carcinogenic ferroptosis genes in stomach adenocarcinoma. (A) Top
miRNAs with the most matching pairs to key genes. (B) Top miRNAs
with the most targeted genes. (C) A total of five miRNAs
(hsa-miR-6795-5p, hsa-miR-6758-5p, hsa-miR-501-5p, hsa-miR-505-5p
and hsa-miR-484) targeted the majority of genes (six targets each).
Relative mRNA expression of miR-484 in (D) GC cells with miR-484
overexpression and (E) tumor and surrounding normal tissue.
Relative mRNA expression of miR-501-5p in (F) GC cell line treated
with siRNAs and (G) tumor and surrounding normal tissue. (H) A
total of four risk genes for GC (AKR1C2, RBMS1, NADPH oxidase 4 and
TF) were detected via western blotting. *P<0.05; **P<0.01;
****P<0.001. miRNA/miR, microRNA; hsa, Homo sapiens; GC,
gastric cancer; siRNA, small interfering RNA; nc, negative control;
oe, overexpression; GABARAPL2, GABA type A receptor-associated
protein-like 2; TF, transferrin; AKR1C2, aldo-keto reductase family
1 member C2; NOX, NADPH oxidase; HCAR1, hydroxycarboxylic acid
receptor 1; RBMS1, RNA binding motif single stranded interacting
protein 1.

Figure 8

Potential drugs based on key
ferroptosis genes in stomach adenocarcinoma. Top 30 ranked drugs in
(A) GDSC and (B) CTRP database based on the seven key carcinogenic
genes. (C) In the CTRP database, the following four drugs had the
most targets (each targeting three genes): PHA-793887, SNS-032,
CR-1-31B and saracatinib. (D) In the GDSC database, six drugs
targeted four genes simultaneously, namely (5Z)-7-oxozeaenol,
selumetinib, RDEA119, AZ628, dabrafenib and trametinib. Drug
toxicity of (E) (5Z)-7-oxozeaenol, (F) selumetinib, (G) RDEA119,
(H) AZ628, (I) dabrafenib and (J) trametinib towards gastric cancer
cells. (K) Expression of four risk genes (AKR1C2, RBMS1, NOX4 and
TF) was downregulated in gastric cancer cells treated with the
aforementioned drugs. *P<0.05; **P<0.01; ***P<0.005;
****P<0.001. GDSC, Genomics of Drug Sensitivity in Cancer; CTRP,
Cancer Therapeutics Response Portal; AKR1C2, aldo-keto reductase
family 1 member C2; RBMS1, RNA-binding motif single stranded
interacting protein 1; NOX, NADPH oxidase; TF, transferrin; FDR,
false discovery rate; ns, not significant.
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Copy and paste a formatted citation
Spandidos Publications style
Wang H, Chen H, Liu J, Zhang D, Wang D, Huang M, Li M, He S and Ma L: Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies. Oncol Lett 30: 450, 2025.
APA
Wang, H., Chen, H., Liu, J., Zhang, D., Wang, D., Huang, M. ... Ma, L. (2025). Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies. Oncology Letters, 30, 450. https://doi.org/10.3892/ol.2025.15196
MLA
Wang, H., Chen, H., Liu, J., Zhang, D., Wang, D., Huang, M., Li, M., He, S., Ma, L."Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies". Oncology Letters 30.4 (2025): 450.
Chicago
Wang, H., Chen, H., Liu, J., Zhang, D., Wang, D., Huang, M., Li, M., He, S., Ma, L."Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies". Oncology Letters 30, no. 4 (2025): 450. https://doi.org/10.3892/ol.2025.15196
Copy and paste a formatted citation
x
Spandidos Publications style
Wang H, Chen H, Liu J, Zhang D, Wang D, Huang M, Li M, He S and Ma L: Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies. Oncol Lett 30: 450, 2025.
APA
Wang, H., Chen, H., Liu, J., Zhang, D., Wang, D., Huang, M. ... Ma, L. (2025). Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies. Oncology Letters, 30, 450. https://doi.org/10.3892/ol.2025.15196
MLA
Wang, H., Chen, H., Liu, J., Zhang, D., Wang, D., Huang, M., Li, M., He, S., Ma, L."Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies". Oncology Letters 30.4 (2025): 450.
Chicago
Wang, H., Chen, H., Liu, J., Zhang, D., Wang, D., Huang, M., Li, M., He, S., Ma, L."Identification of key ferroptosis‑related genes associated with the development of gastric cancer: Prognostic models, molecular mechanisms and potential treatment strategies". Oncology Letters 30, no. 4 (2025): 450. https://doi.org/10.3892/ol.2025.15196
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