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ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma

  • Authors:
    • Panxin Hu
    • Anan Li
  • View Affiliations / Copyright

    Affiliations: Department of Emergency Medicine, The First People's Hospital of Taizhou, Taizhou, Zhejiang 318020, P.R. China, Department of Cardiothoracic Surgery, The First People's Hospital of Taizhou, Taizhou, Zhejiang 318020, P.R. China
    Copyright: © Hu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 82
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    Published online on: February 27, 2026
       https://doi.org/10.3892/or.2026.9087
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Abstract

Lung adenocarcinoma (LUAD) remains a leading cause of cancer‑associated mortality with limited prognostic biomarkers. Fatty acid metabolism (FAM) reprogramming serves a pivotal role in tumor progression; however, the functional importance of specific FAM genes, such as ACSBG1, in LUAD remains elusive. In the present study, transcriptomics data from The Cancer Genome Atlas (n=517) and Gene Expression Omnibus‑GSE13213 (n=117) were analyzed to identify FAM‑related differentially expressed genes (DEGs). Least Absolute Shrinkage and Selection Operator regression and Shapley Additive Explanations (SHAP) interpretable analyses established a prognostic model, and in vitro experiments using A549 and H1299 cell lines with ACSBG1 overexpression (OE) and knockdown. Functional assays included Cell Counting Kit‑8, EdU, Transwell and apoptosis analyses. A total of 35 FAM‑related DEGs were identified, which were enriched in PPAR signaling and fatty acid degradation pathways (false discovery rate <0.05). Subsequently, a seven‑gene prognostic model was established, and demonstrated strong predictive power for 1‑, 3‑ and 5‑year survival (area under the curve values: 0.767, 0.769 and 0.700, respectively). SHAP analysis prioritized ACSBG1 as the dominant protective factor, and its low expression was associated with advanced Tumor‑Node‑Metastasis stages and poor survival. Mechanistically, ACSBG1 OE suppressed proliferation, migration and invasion, and promoted apoptosis. Immune profiling revealed ACSBG1 expression was positively correlated with the infiltration of CD4+ T cells, CD8+ T cells and B cells, suggesting its immunomodulatory potential. In conclusion, to the best of our knowledge, the present study established the first FAM‑based prognostic model for LUAD, and identified ACSBG1 as a novel tumor suppressor through dual mechanisms of metabolic regulation and immune microenvironment modulation. The risk score system established in the current study provides a clinically actionable tool for precision oncology, and ACSBG1‑targeted therapy represents a promising strategy against LUAD progression.
View Figures

Figure 1

Screening and functional enrichment
of differentially expressed genes related to fatty acid metabolism
in lung adenocarcinoma. (A) Heat map. (B) Volcano map. Red dots
indicate upregulated genes and green dots indicate downregulated
genes. Results of (C) Gene Ontology and (D) Kyoto Encyclopedia of
Genes and Genomes pathway enrichment analyses. BP, biological
process; CC, cellular component; FC, fold change; FDR; false
discovery rate; MF, molecular function.

Figure 2

Construction and evaluation of
prognostic models. (A) Forest plot showing the overall
survival-related fatty acid metabolism genes in LUAD. (B) Least
Absolute Shrinkage and Selection Operator regression analysis. (C)
Distribution of risk scores in patients with LUAD. Kaplan-Meier
curves show the difference in survival between low- and high-risk
patients in (D) TCGA and (E) GSE13213 datasets. LUAD, lung
adenocarcinoma; TCGA, The Cancer Genome Atlas.

Figure 3

Verification of the accuracy of the
prognostic scoring model. (A) Univariate and (B) multivariate
independent prognostic analyses. (C) ROC curves for 1-, 3- and
5-year OS. (D) ROC curves for clinical characteristics and risk
scores. (E) Heat map of clinically relevant features. *P<0.05,
**P<0.01, ***P<0.001. (F) Nomogram integrates multiple
clinicopathological characteristics, including sex, age, T stage
and risk grade, to provide individualized predictions of patient
survival probability at different time points. (G) Calibration plot
predicting the agreement between observed and predicted rates of OS
at 1, 3 and 5 years. AUC, area under the curve; OS, overall
survival; ROC, receiver operating characteristic.

Figure 4

SHAP interpretable analysis of the
contribution degree and direction of each gene feature on the
prediction results of the prognostic model. (A) Feature importance
bar chart shows the overall influence intensity of each gene
feature on the model output, ranked by the average absolute SHAP
value. The longer the bar, the more important the feature. (B)
Feature dependency swarm plot: Each point represents a sample,
showing the distribution relationship between the specific gene
feature value and its SHAP value. The more dispersed the points or
the more gradient of colors, the stronger the nonlinear effect of
the feature on the prediction result. (C) Local explanation
waterfall chart: Taking a single sample as an example, it shows how
each gene feature pushes its predicted value from the baseline to
the final output. The yellow bar represents the positive
contribution that increases the prediction risk, and the purple bar
represents the negative contribution that reduces the risk. SHAP,
Shapley Additive Explanations.

Figure 5

Functional role of ACSBG1 in
modulating cellular phenotypes. (A) mRNA and (B) protein expression
level verification post-transfection. (C) Cell Counting Kit-8 assay
was used to analyze the proliferation of cells. (D) Colony
formation assay verified the colony-forming ability of cells (×40
magnification). (E) Transwell assays verified the migration and
invasion of cells (×200 magnification). (F) EdU assay was used to
analyze the proliferation of cells (×200 magnification). (G) Wound
healing assays verified the migration of the cells (×10
magnification). (H) Annexin V-FITC/PI cell apoptosis assay verified
the apoptosis rate of cells. Ctrl, control; OE, overexpression; Sh,
short hairpin. *P<0.05; **P<0.01; ***P<0.001;
****P<0.0001.

Figure 6

Immune infiltration analysis of the
ACSBG1 gene in LUAD. (A) CD4+ T cells, (B)
CD8+ T cells, (C) B cells, (D) M2 macrophages, (E)
cancer-associated fibroblasts and (F) NK cells. LUAD, lung
adenocarcinoma; NK, natural killer; TPM, transcripts per
million.
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Spandidos Publications style
Hu P and Li A: ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma. Oncol Rep 55: 82, 2026.
APA
Hu, P., & Li, A. (2026). ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma. Oncology Reports, 55, 82. https://doi.org/10.3892/or.2026.9087
MLA
Hu, P., Li, A."ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma". Oncology Reports 55.5 (2026): 82.
Chicago
Hu, P., Li, A."ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma". Oncology Reports 55, no. 5 (2026): 82. https://doi.org/10.3892/or.2026.9087
Copy and paste a formatted citation
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Spandidos Publications style
Hu P and Li A: ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma. Oncol Rep 55: 82, 2026.
APA
Hu, P., & Li, A. (2026). ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma. Oncology Reports, 55, 82. https://doi.org/10.3892/or.2026.9087
MLA
Hu, P., Li, A."ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma". Oncology Reports 55.5 (2026): 82.
Chicago
Hu, P., Li, A."ACSBG1‑driven lipid reprogramming unveiled: A fatty acid metabolism‑associated prognostic model for lung adenocarcinoma". Oncology Reports 55, no. 5 (2026): 82. https://doi.org/10.3892/or.2026.9087
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