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Article Open Access

Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions

  • Authors:
    • Qihang Yan
    • Wuguang Chang
    • Wingshing Wong
    • Li Gong
    • Dachuan Liang
    • Jie Yang
    • Junye Wang
  • View Affiliations / Copyright

    Affiliations: State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat‑sen University Cancer Center, Guangzhou, Guangdong 510060, P.R. China, Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat‑sen University, Zhuhai, Guangdong 519000, P.R. China
    Copyright: © Yan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 329
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    Published online on: September 26, 2025
       https://doi.org/10.3892/mmr.2025.13694
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Abstract

Cholesterol metabolism reprogramming serves a pivotal role in tumor onset and progression. The present study investigated lung adenocarcinoma (LUAD), focusing on the regulatory impact of cholesterol metabolism‑related genes (CMRGs). Consensus clustering identified distinct cholesterol metabolism‑related clusters in LUAD, followed by survival analysis and immune infiltration profiling for each cluster. A predictive model, constructed using cluster‑specific differentially expressed genes and the LASSO algorithm, was validated with an independent dataset. Furthermore, the model was utilized to predict potential responses to immunotherapy and chemotherapy for patients with LUAD. The functional role of the key gene GJB3 in LUAD progression was confirmed through in vitro experiments. Two distinct cholesterol metabolism‑related clusters were identified, exhibiting significant differences in prognosis, biological function and immune cell infiltration. A survival model, based on four genes, demonstrated strong predictive performance across multiple datasets. The low‑risk group showed improved responses to immunotherapy, while the high‑risk group exhibited heightened sensitivity to chemotherapy. In vitro assays revealed that GJB3 knockdown suppressed LUAD cell proliferation and invasion, significantly reducing the expression of epithelial‑mesenchymal transition‑related genes. These findings highlight CMRGs as potential prognostic biomarkers and suggest a foundation for personalized treatment strategies in LUAD.
View Figures

Figure 1

Consensus clustering identifying two
LUAD subgroups with distinct cholesterol metabolism
characteristics. (A) Differential expression of 44 prognostically
relevant cholesterol metabolism-related genes in LUAD. (B)
Consensus clustering matrix. (C) CDF curves for clusters. (D)
Relative change in the area under the CDF curve. (E) Kaplan-Meier
survival analysis. (F) Sankey diagram. *P<0.05, **P<0.01,
***P<0.001, ****P<0.0001. CDF, cumulative distribution
function; LUAD, lung adenocarcinoma.

Figure 2

Gene set enrichment analysis
revealing differences in pathways between the two cholesterol
metabolism subgroups. GO enrichment in (A) cluster 1 and (B)
cluster 2. KEGG enrichment in (C) cluster 1 and (D) cluster 2. GO,
Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 3

Tumor microenvironment assessment and
prediction of immune cell infiltration. (A) Relative infiltration
levels of 28 immune cells by single sample gene set enrichment
analysis. Tumor microenvironment evaluation by ESTIMATE algorithm,
including (B) immune score, (C) tumor purity and (D) ESTIMATE
score. (E) Expression of key immune checkpoints in the two
clusters. *P<0.05, **P<0.01, ***P<0.001,
****P<0.0001.

Figure 4

Construction and validation of the
cholesterol metabolism gene signature. (A) LASSO Cox regression
model for parameter (λ) adjustment through 10-fold
cross-validation. (B) Identification of five key CMRGs and their
corresponding coefficients. (C) KM survival analysis in TCGA
dataset. (D) Relationship between mortality rates and risk scores
in TCGA dataset. (E) Expression patterns of key CMRGs in TCGA
dataset. (F) KM survival analysis in GSE72094. (G) Relationship
between mortality rates and risk scores in GSE72094. (H) Expression
patterns of key CMRGs in GSE72094. KM, Kaplan-Meier; TCGA, The
Cancer Genome Atlas; CMRGs, cholesterol metabolism-related
genes.

Figure 5

Application of the nomogram in the
prognosis of LUAD. (A) Univariate and (B) multivariate Cox
regression analyses to evaluate the association between CMRGs risk
signals and LUAD prognosis. (C) Nomogram for quantifying the
likelihood of patient survival. (D) Receiver operating
characteristic curves of nomogram performance. (E) Calibration
curves. AUC, area under the curve; LUAD, lung adenocarcinoma;
CMRGs, cholesterol metabolism-related genes.

Figure 6

TMB analysis in the high-risk lung
adenocarcinoma group. (A) Top 20 genes with mutation frequencies in
the high-risk group. (B) Top 20 genes with mutation frequencies in
the low-risk group. (C) Comparison of TMB between high-risk and
low-risk groups. (D) Correlation analysis between risk score and
TMB. TMB, tumor mutation burden.

Figure 7

Treatment response prediction based
on the cholesterol metabolism gene signature. (A) TIDE score
comparison between the two risk groups. (B) Response rate
comparison between the two risk groups. (C) Sensitivity of
different risk groups to chemotherapy drugs. ***P<0.001. TIDE,
Tumor Immune Dysfunction and Exclusion.

Figure 8

Effect of GJB3 expression on LUAD
progression in in vitro experiments. (A) Knockdown
efficiency of GJB3 after siRNA transfection, demonstrated by
western blotting. (B) Knockdown of GJB3 enhanced LUAD cell
proliferation. (C) EdU and (D) colony formation assays showed a
decrease in the proportion of LUAD cells in the proliferative state
after GJB3 knockdown (magnification, ×200). (E) Migration and (F)
invasion of LUAD cells was decreased after GJB3 knockdown
(magnification, ×200). (G) Knockdown of GJB3 inhibited the
expression of epithelial-mesenchymal transition-related genes. (H)
Semi-quantitative analysis of western blotting in (A). **P<0.01,
****P<0.0001. LUAD, lung adenocarcinoma; NC, negative control;
siRNA, small interfering RNA.
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Yan Q, Chang W, Wong W, Gong L, Liang D, Yang J and Wang J: Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions. Mol Med Rep 32: 329, 2025.
APA
Yan, Q., Chang, W., Wong, W., Gong, L., Liang, D., Yang, J., & Wang, J. (2025). Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions. Molecular Medicine Reports, 32, 329. https://doi.org/10.3892/mmr.2025.13694
MLA
Yan, Q., Chang, W., Wong, W., Gong, L., Liang, D., Yang, J., Wang, J."Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions". Molecular Medicine Reports 32.6 (2025): 329.
Chicago
Yan, Q., Chang, W., Wong, W., Gong, L., Liang, D., Yang, J., Wang, J."Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions". Molecular Medicine Reports 32, no. 6 (2025): 329. https://doi.org/10.3892/mmr.2025.13694
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Spandidos Publications style
Yan Q, Chang W, Wong W, Gong L, Liang D, Yang J and Wang J: Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions. Mol Med Rep 32: 329, 2025.
APA
Yan, Q., Chang, W., Wong, W., Gong, L., Liang, D., Yang, J., & Wang, J. (2025). Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions. Molecular Medicine Reports, 32, 329. https://doi.org/10.3892/mmr.2025.13694
MLA
Yan, Q., Chang, W., Wong, W., Gong, L., Liang, D., Yang, J., Wang, J."Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions". Molecular Medicine Reports 32.6 (2025): 329.
Chicago
Yan, Q., Chang, W., Wong, W., Gong, L., Liang, D., Yang, J., Wang, J."Cholesterol metabolism in LUAD progression: GJB3 as a key target for cell‑based therapeutic interventions". Molecular Medicine Reports 32, no. 6 (2025): 329. https://doi.org/10.3892/mmr.2025.13694
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