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Article Open Access

Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing

  • Authors:
    • Hongwei He
    • Zhiming Xiao
    • Jijun Xue
    • Yu Zhang
    • Jichen Qu
    • Peng Wu
    • Qiangjun He
  • View Affiliations / Copyright

    Affiliations: Department of Cardiothoracic Surgery, Shanghai General Hospital Jiuquan Branch (Jiuquan People's Hospital), Jiuquan, Gansu 735000, P.R. China, Department of Thoracic Surgery, Gansu Cancer Hospital (Sun Yat‑sen University Cancer Center), Lanzhou, Gansu 730000, P.R. China, Department of Thoracic Surgery, Gansu Provincial Maternity and Child Health Care Hospital (Gansu Provincial Central Hospital), Lanzhou, Gansu 730000, P.R. China, Department of Thoracic Surgery, Shanghai General Hospital, Shanghai 200001, P.R. China
    Copyright: © He et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 233
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    Published online on: April 14, 2026
       https://doi.org/10.3892/ol.2026.15588
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Abstract

The infiltration of regulatory T cells (Tregs) in lung adenocarcinoma (LUAD) is associated with a poor prognosis. The present study aimed to explore the potential function of Treg marker genes in prognosis and immunotherapy using the transcriptome profiles of five LUAD cohorts sourced from public databases. Among them, the single‑cell dataset GSE131907 was employed to identify cell types in lung cancer tissues and to identify Treg markers. The prognostic Treg markers were screened using univariate Cox and Least Absolute Shrinkage and Selection Operator regression analyses. Subsequently, a prognostic model was constructed and assessed using Kaplan‑Meier and receiver operating characteristic curves. Furthermore, the effect of prognostic Treg markers on clinical characteristics, the immune microenvironment and tumor mutation burden (TMB) were evaluated. In addition, the function of genes strongly correlated with the prognostic risk score were investigated using Spearman and functional enrichment analysis. Finally, the genes expression levels were assessed using reverse transcription‑quantitative PCR (RT‑qPCR) and western blotting. By analyzing the single‑cell data, 13 Treg markers [centromere protein M (CENPM), pituitary tumor‑transforming gene 1 protein, interleukin 1 receptor type 2, baculoviral IAP repeat containing 3, glucocorticoid induced 1 (GLCCI1), melanoma‑associated antigen H1 (MAGEH1), CD5, cytokine inducible SH2 containing protein, zinc finger protein 101 (ZNF101), Ikaros family zinc finger protein 4 (IKZF4), ankyrin repeat and SOCS box protein 2, zinc finger CCCH‑type containing 12D and C‑C motif chemokine receptor 6] were identified as prognostic features. The prognostic model constructed using these 13 genes revealed that the high‑risk group had a poorer prognosis than the low‑risk group. Moreover, it was demonstrated that the risk score could be an independent prognostic factor affecting the prognosis of patients with LUAD. Additionally, the high‑risk group had a lower ESTIMATE score, higher TMB score and lower T cell receptor richness than the low‑risk group. Finally, RT‑qPCR and western blotting showed that the expression levels of CENPM, ZNF101, MAGEH1 and IKZF4 were significantly altered in cancer tissues compared with the adjacent normal tissues. In conclusion, a reliable prognostic model based on 13 Treg markers was developed. The comprehensive characterization of the Treg markers of LUAD may help to monitor the prognosis and provide new strategies for LUAD treatment.
View Figures

Figure 1

Identification of 13 cell types in
the GSE131907 dataset. (A) UMAP plot of the distribution of 29
clusters. (B) UMAP plot of the distribution of 13 cell types (left)
with an inset showing a magnified view of the Treg cluster (right).
(C) Expression distribution of marker genes in each cell type. (D)
Average proportion of Tregs in the mLN & tL/B, PE, nLN, nLung,
tLung and mBrain groups. UMAP, uniform manifold approximation and
projection; NKT, natural killer T cells; NK, natural killer cells;
cDC, conventional dendritic cells; TSDC, tumor-specific dendritic
cells; Tregs, regulatory T cells; LUAD, lung adenocarcinoma; mLN,
metastatic lymph node; tL/B, tumor lung of advanced-stage LUAD; PE,
pleural effusion; nLN, normal lymph node; nLung, normal lung;
tLung, tumor lung of early-stage LUAD; mBrain, metastatic
brain.

Figure 2

Selection of prognostic genes and
construction of a prognostic model for LUAD. (A) Univariate Cox
analysis using the TCGA-LUAD dataset. (B) Least Absolute Shrinkage
and Selection Operator analysis using the TCGA-LUAD dataset. (C)
Risk curves and survival state in the TCGA-LUAD dataset. (D)
Kaplan-Meier curve for the TCGA-LUAD dataset. (E) Receiver
operating characteristic curves for the TCGA-LUAD dataset. TCGA,
The Cancer Genome Atlas; LUAD, lung adenocarcinoma; AUC, area under
the curve; TP, true-positive; FP, false-positive.

Figure 3

Validation of prognostic model. (A)
KM and (B) ROC curves for the GSE30219 dataset. (C) KM and (D) ROC
curves for the GSE26939 dataset. KM, Kaplan-Meier; ROC, receiver
operating characteristic; AUC, area under the curve; TP,
true-positive; FP, false-positive.

Figure 4

(A) Kaplan-Meier and (B) receiver
operating characteristic curves for the GSE72094 dataset. AUC, area
under the curve; TP, true-positive; FP, false-positive.

Figure 5

Clinical prognostic model and
differences in risk scores between different clinical subgroups in
the TCGA-LUAD dataset. (A) Univariate Cox analysis. (B)
Multivariate Cox analysis. (C) Differences in the risk scores
between different clinical subgroups. T stage, tumor stage; N
stage, lymph node stage; M stage, metastasis stage.

Figure 6

Tumor microenvironment differences
between the high- and low-risk groups in the TCGA-LUAD dataset. (A)
Infiltration of 22 immune cells. (B) Infiltration differences
between the 2 risk groups. (C) Immune, stromal and ESTIMATE scores
between the 2 risk groups.

Figure 7

Association between risk score and
immune-system-related metagene clusters, TCR richness and TMB in
the TCGA-LUAD dataset. (A) Spearman's correlation analysis between
7 immune-system-related metagene clusters and risk score.
Association between (B) TCR Shannon diversity, (C) TCR richness and
(D) TMB scores between the two risk groups. TCR, T cell receptor;
TMB, tumor mutational burden.

Figure 8

Identification and functional
enrichment analysis of risk genes in the TCGA-LUAD dataset. (A)
Spearman's correlation analysis between risk genes and risk score.
(B) Gene Ontology enrichment analysis of risk genes. (C) Kyoto
Encyclopedia of Genes and Genomes enrichment analysis of risk
genes.

Figure 9

Expression levels of prognostic genes
in LUAD tumor and adjacent tissues. (A) The expression levels of 13
prognostic genes in the TCGA-LUAD cohort. (B) The mRNA expression
levels of 8 prognostic genes in clinic LUAD tumor and adjacent
tissues were examined using quantitative PCR. (C) The protein
expression levels of 4 prognostic genes in clinic LUAD tumor and
adjacent tissues were examined using western blotting. (D)
Semi-quantification of the western blotting results. *P<0.05,
**P<0.01, ***P<0.001, ****P<0.0001. LUAD, lung
adenocarcinoma; TCGA, The Cancer Genome Atlas; NC, normal control;
ASB2, ankyrin repeat and SOCS box protein 2; BIRC3, baculoviral IAP
repeat containing 3; CCR6, C-C motif chemokine receptor 6; CENPM,
centromere protein M; CISH, cytokine inducible SH2 containing
protein; GLCCI1, glucocorticoid induced 1; IKZF4, Ikaros family
zinc finger protein 4; MAGEH1, melanoma-associated antigen H1;
PTTG1, pituitary tumor-transforming gene 1 protein; ZNF101, zinc
finger protein 101; ZC3H12D, zinc finger CCCH-type containing
12D.
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Copy and paste a formatted citation
Spandidos Publications style
He H, Xiao Z, Xue J, Zhang Y, Qu J, Wu P and He Q: Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing. Oncol Lett 31: 233, 2026.
APA
He, H., Xiao, Z., Xue, J., Zhang, Y., Qu, J., Wu, P., & He, Q. (2026). Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing. Oncology Letters, 31, 233. https://doi.org/10.3892/ol.2026.15588
MLA
He, H., Xiao, Z., Xue, J., Zhang, Y., Qu, J., Wu, P., He, Q."Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing". Oncology Letters 31.6 (2026): 233.
Chicago
He, H., Xiao, Z., Xue, J., Zhang, Y., Qu, J., Wu, P., He, Q."Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing". Oncology Letters 31, no. 6 (2026): 233. https://doi.org/10.3892/ol.2026.15588
Copy and paste a formatted citation
x
Spandidos Publications style
He H, Xiao Z, Xue J, Zhang Y, Qu J, Wu P and He Q: Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing. Oncol Lett 31: 233, 2026.
APA
He, H., Xiao, Z., Xue, J., Zhang, Y., Qu, J., Wu, P., & He, Q. (2026). Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing. Oncology Letters, 31, 233. https://doi.org/10.3892/ol.2026.15588
MLA
He, H., Xiao, Z., Xue, J., Zhang, Y., Qu, J., Wu, P., He, Q."Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing". Oncology Letters 31.6 (2026): 233.
Chicago
He, H., Xiao, Z., Xue, J., Zhang, Y., Qu, J., Wu, P., He, Q."Unveiling the prognostic and immunotherapeutic role of Tregs in lung cancer using integrated analysis of single‑cell and bulk RNA‑sequencing". Oncology Letters 31, no. 6 (2026): 233. https://doi.org/10.3892/ol.2026.15588
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