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Article Open Access

Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma

  • Authors:
    • Yi-Hao Liu
    • Wen-Hao Zhao
    • Ze-Xia Zhao
    • Zhi-Xuan Duan
    • Hua Huang
    • Cheng Ding
    • Ming-Hui Liu
    • Hong-Bing Zhang
    • Yong-Wen Li
    • Min Wang
    • Jun Chen
    • Hong-Lin Zhao
  • View Affiliations / Copyright

    Affiliations: Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China
    Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 494
    |
    Published online on: August 21, 2025
       https://doi.org/10.3892/ol.2025.15240
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Abstract

Lung adenocarcinoma (LUAD) remains one of the most prevalent and lethal cancers globally, making it critical to understand the mechanisms driving its progression and improve prognosis. Moreover, cuproptosis and mitochondrial dysfunction may be involved in lung cancer. Therefore, the present study aimed to identify mitochondrial genes associated with cuproptosis to develop a prognostic model for patients with LUAD, with the potential to predict survival outcomes and responses to treatment. Bulk RNA‑sequencing data was utilized from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO), and Pearson correlation analysis was employed to identify mitochondrial genes associated with cuproptosis. A prognostic model was constructed using univariate Cox regression combined with least absolute shrinkage and selection operator analysis, and a nomogram was developed to predict survival with clinical relevance. The accuracy of the model was evaluated using two independent GEO datasets. Additionally, the clinical value of the risk score model was assessed using immune infiltration analysis, tumor mutational burden and drug sensitivity predictions. Furthermore, the effects of superoxide dismutase 2 (SOD2) gene knockdown on tumor metastasis and proliferation were experimentally evaluated. A set of 22 mitochondrial genes associated with cuproptosis were identified: Metabolism of cobalamin associated D, SOD2, human immunodeficiency virus‑1 Tat interactive protein 2, cytochrome C somatic, mitochondrial pyruvate carrier 1, adenylate kinase 2, mitochondrial ribosomal protein L44, transforming growth factor β regulator 4, mitochondrial transcription factor A, tetratricopeptide repeat domain 19, coiled‑coil‑helix‑­coiled‑coil‑helix domain containing 4, sideroflexin 1, ATP binding cassette subfamily D member 1, NADH:ubiquinone oxidoreductase complex assembly factor 7, NOP2/Sun RNA methyltransferase 4, NME/NM23 nucleoside diphosphate kinase 6, X‑Prolyl aminopeptidase 3, lipoyltransferase 1, mitochondrial methionyl aminopeptidase type 1D, carbonic anhydrase 5B, kynurenine 3‑monooxygenase and alcohol dehydrogenase iron containing 1. The model was validated as an independent predictor of overall survival, dividing patients into high‑ and low‑risk groups. Immune infiltration analysis revealed that tumors in the low‑risk group displayed more active immune responses and improved immune function. Drug sensitivity analysis suggested that high‑risk patients may be more responsive to specific drug treatments. Finally, knockdown of the SOD2 gene suppressed tumor cell metabolism, proliferation and metastasis. In conclusion, the present study successfully established a prognostic model based on cuproptosis‑related mitochondrial genes and developed a nomogram to predict LUAD prognosis with high accuracy, thereby providing improved tools for treatment decision‑making and enhancing patient outcomes.
View Figures

Figure 1

Identification of cuproptosis-related
mitochondrial genes in lung adenocarcinoma. (A) Sankey diagram
showing the positive correlation (>0.3) between 13
cuproptosis-related genes and mitochondrial-related genes based on
Pearson correlation analysis. (B) Volcano plot displaying all
differentially expressed genes. (C) Venn diagram showing the
intersection of 415 genes obtained from differentially expressed
genes and cuproptosis-related mitochondrial genes. (D) Forest plot
demonstrating the 67 cuproptosis-related mitochondrial genes
identified through univariate Cox regression analysis. (E)
Chromosomal localization of these 67 genes. FC, fold change; DEG,
differentially expressed gene; Cup-mt, cuproptosis-related
mitochondrial genes; HR, hazard ratio; CI, confidence interval.

Figure 2

Machine learning-based prognostic
risk score model construction. (A) C-index for each machine
learning prediction model calculated for the training and test
sets, with >100 models included. (B) LASSO regression analysis
established a model containing cuproptosis-related mitochondrial
genes associated with prognosis. (C) Coefficients of the LASSO
analysis. (D) Distribution of risk scores, (E) survival status and
time distribution in high- and low-risk groups. (F) Kaplan-Meier
curves showing the OS of patients in high- and low-risk groups in
TCGA training set. (G) Distribution of risk scores, (H) survival
status and time distribution in high- and low-risk groups, and (I)
Kaplan-Meier curves showing the OS of patients in high- and
low-risk groups in the GSE26939 test set. (J) Distribution of risk
scores, (K) survival status and time distribution in high- and
low-risk groups, and (L) Kaplan-Meier curves showing the overall OS
of patients in high- and low-risk groups in the GSE31210 test set.
(M) Distribution of risk scores, (N) survival status and time
distribution in high- and low-risk groups, and (O) Kaplan-Meier
curves showing the overall OS of patients in high- and low-risk
groups in the GSE72094 test set. LASSO, least absolute shrinkage
and selection operator; TCGA, The Cancer Genome Atlas; OS, overall
survival.

Figure 3

Risk scores and survival curves for
different subgroups. (A) Risk score boxplot by survival status.
Kaplan-Meier curves for high- and low-risk groups in patients aged
(B) ≥65 years and (C) <65 years. (D) Risk score boxplot by sex.
Kaplan-Meier curves for high- and low-risk groups in (E) female and
(F) male patients. (G) Risk score boxplot by clinical stages M0 and
M1. Kaplan-Meier curves for high- and low-risk groups of patients
with clinical stage (H) M0 and (I) M1. (J) Risk score boxplot by
clinical stages N0 and N1-3. Kaplan-Meier curves for high- and
low-risk groups in patients with clinical stage (K) N0 and (L)
N1-3. (M) Risk score boxplot by stage T1-2 and T3-4. Kaplan-Meier
curves for high- and low-risk groups of patients with clinical
stage (N) T1-2 and (O) T3-4. M, metastasis; N, node; T, tumor.

Figure 4

Nomogram construction to elucidate
lung adenocarcinoma prognosis. (A) Univariate and (B) multivariate
Cox regression analyses for clinical features and risk scores. (C)
Nomogram based on clinical features and risk scores. Calibration
curves showing the accuracy of predicted and actual values in (D)
TCGA training set, (E) the GSE26939 test set, (F) the GSE31210 test
set and (G) the GSE72094 test set. Receiver operating
characteristic curves evaluating the performance of the nomogram in
(H) TCGA training set, (I) the GSE26939 test sets, (J) the GSE31210
test set and (K) the GSE72094 test set. TCGA, The Cancer Genome
Atlas; T, tumor; N, node; OS, overall survival; FPR, false-positive
rate; TPR, true-positive rate; AUC, area under the curve.

Figure 5

Immune cell infiltration in high- and
low-risk groups. (A) Expression of 23 immune cell subtypes between
high- and low-risk groups. (B) Distribution and expression of
immune-related pathways in high- and low-risk groups. (C)
Expression of checkpoint-related genes in high- and low-risk
groups. (D) Correlation heatmap of risk scores and model genes with
immune cells and pathways. *P<0.05; **P<0.01; ***P<0.001.
ns, not significant. TNFRSF, tumor necrosis factor receptor
superfamily; LAG3, Lymphocyte activation gene 3; PDCD1LG2,
Programmed cell death 1 ligand 2; CD200R1, CD200 receptor 1; BTLA,
B and T lymphocyte attenuator; IDO2, indoleamine 2,3-dioxygenase 2;
BTNL2, Butyrophilin like 2; ADORA2A, Adenosine a2a receptor.

Figure 6

Immune scores and expression of
different immune gene families. (A) ESTIMATE scores. Boxplot of the
expression of (B) MHC gene families, (C) inflammatory cytokine gene
families and (D) cytotoxic molecule-related gene families between
high- and low-risk groups. (E) Difference in TIDE scores between
high- and low-risk groups. (F) Proportion of ‘No benefits’ and
‘Responder’ between high- and low-risk groups. *P<0.05;
**P<0.01; ***P<0.001. ns, not significant; MHC, major
histocompatibility complex; TIDE, Tumor Immune Dysfunction and
Exclusion.

Figure 7

Relationship between TMB, risk scores
and gene mutations in different risk groups. (A) Comparison of TMB
between high- and low-risk groups. Kaplan-Meier survival curve for
overall survival stratified by (B) high and low TMB grouping, and
(C) TMB (high or low) and risk score (high or low). Mutation
spectrum of common gene mutations in the (D) high-risk group and
(E) low-risk group. TMB, tumor mutational burden; H-TMB, high TMB;
L-TMB, low TMB; H-RISK, high risk; L-RISK, low risk.

Figure 8

Correlation between prognostic model
and drug sensitivity prediction. (A) Correlation between risk
score, model genes and drugs. Blue indicates a negative
correlation, whilst orange denotes a positive correlation. (B)
Boxplot showing the difference in IC50 of A.770041
between high- and low-risk groups. (C) Scatter plot showing the
correlation between risk score and A.770041. (D) Boxplot showing
the difference in IC50 of CGP.082996 between high- and
low-risk groups. (E) Scatter plot showing the correlation between
risk score and CGP.082996. (F) Boxplot showing the difference in
IC50 of Obatoclax.Mesylate between high- and low-risk
groups. (G) Scatter plot showing the correlation between risk score
and Obatoclax.Mesylate. (H) Boxplot showing the difference in
IC50 of SL.0101.1 between high- and low-risk groups. (I)
Scatter plot showing the correlation between risk score and
SL.0101.1. (J) Boxplot showing the difference in IC50 of
Thapsigargin between high- and low-risk groups. (K) Scatter plot
showing the correlation between risk score and Thapsigargin. (L)
Boxplot showing the difference in IC50 of WZ.1.84
between high- and low-risk groups. (M) Scatter plot showing the
correlation between risk score and WZ.1.84. *P<0.05;
**P<0.01; ***P<0.001.

Figure 9

Expression levels and biological
functions of SOD2 in lung adenocarcinoma cell lines. (A) mRNA
levels of SOD2 in the NC and knockdown groups in A549 and PC9 cell
lines. Cell Counting Kit-8 analysis showing the effects of SOD2
knockdown on the (B) A549 and (C) PC9 cell lines. Comparison of (D)
migration, (E) invasion and (F) proliferation in A549 and PC9 cells
between NC and SOD2 knockdown groups. Comparison of mitochondrial
fluorescence between the si-NC and si-SOD2 groups in (G) A549 and
(H) PC9 cells. ****P<0.0001. SOD2, superoxide dismutase 2; NC,
negative control; si, small interfering; ns, not significant OD,
optical density.
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Copy and paste a formatted citation
Spandidos Publications style
Liu Y, Zhao W, Zhao Z, Duan Z, Huang H, Ding C, Liu M, Zhang H, Li Y, Wang M, Wang M, et al: Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma. Oncol Lett 30: 494, 2025.
APA
Liu, Y., Zhao, W., Zhao, Z., Duan, Z., Huang, H., Ding, C. ... Zhao, H. (2025). Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma. Oncology Letters, 30, 494. https://doi.org/10.3892/ol.2025.15240
MLA
Liu, Y., Zhao, W., Zhao, Z., Duan, Z., Huang, H., Ding, C., Liu, M., Zhang, H., Li, Y., Wang, M., Chen, J., Zhao, H."Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma". Oncology Letters 30.5 (2025): 494.
Chicago
Liu, Y., Zhao, W., Zhao, Z., Duan, Z., Huang, H., Ding, C., Liu, M., Zhang, H., Li, Y., Wang, M., Chen, J., Zhao, H."Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma". Oncology Letters 30, no. 5 (2025): 494. https://doi.org/10.3892/ol.2025.15240
Copy and paste a formatted citation
x
Spandidos Publications style
Liu Y, Zhao W, Zhao Z, Duan Z, Huang H, Ding C, Liu M, Zhang H, Li Y, Wang M, Wang M, et al: Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma. Oncol Lett 30: 494, 2025.
APA
Liu, Y., Zhao, W., Zhao, Z., Duan, Z., Huang, H., Ding, C. ... Zhao, H. (2025). Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma. Oncology Letters, 30, 494. https://doi.org/10.3892/ol.2025.15240
MLA
Liu, Y., Zhao, W., Zhao, Z., Duan, Z., Huang, H., Ding, C., Liu, M., Zhang, H., Li, Y., Wang, M., Chen, J., Zhao, H."Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma". Oncology Letters 30.5 (2025): 494.
Chicago
Liu, Y., Zhao, W., Zhao, Z., Duan, Z., Huang, H., Ding, C., Liu, M., Zhang, H., Li, Y., Wang, M., Chen, J., Zhao, H."Machine learning‑based model identifies a novel cuproptosis‑related mitochondrial gene signature with a key role in the prognosis and treatment of lung adenocarcinoma". Oncology Letters 30, no. 5 (2025): 494. https://doi.org/10.3892/ol.2025.15240
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