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Article Open Access

Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma

  • Authors:
    • Aohui Chen
    • Ting Gao
    • Fengqi Liu
    • Mingyue Zhao
    • Ruizhen Bai
    • Quan Liu
  • View Affiliations / Copyright

    Affiliations: Wuxi Medical College, Jiangnan University, Wuxi, Jiangsu 214122, P.R. China, Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214122, P.R. China, Department of Medical Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214122, P.R. China
    Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 419
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    Published online on: July 21, 2026
       https://doi.org/10.3892/ol.2026.15774
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Abstract

Keratins (KRTs) are intermediate filament proteins expressed in epithelial cells and serve as diagnostic cancer biomarkers. They serve pivotal roles in tumor progression and metastasis, but their prognostic value in lung adenocarcinoma (LUAD) and relationship with the tumor immune microenvironment remain unclear. Gene expression of KRTs in The Cancer Genome Atlas (TCGA)‑LUAD was analyzed; candidate genes were selected using LASSO and random forest. An XGBoost classifier with SHAP interpretation distinguished tumor from normal tissues. Prognostic models were developed using 101 algorithms with 10‑fold cross‑validation and validated in two independent GEO cohorts. Immune cell composition and pathway activity were assessed by CIBERSORT, MCP‑counter, ssGSEA and GSEA; the TIDE score estimated potential immunotherapy response. Functional validation of KRT81 was performed via shRNA knockdown in LUAD cell lines, followed by proliferation, apoptosis and migration assays. A KRT‑based diagnostic and prognostic signature was established. The XGBoost model achieved high accuracy (TCGA AUC=0.996; GSE31210 AUC=0.860); SHAP identified KRT81 as a key contributor. A four‑gene prognostic model (KRT27, KRT80, KRT16, KRT81) stratified patients into high‑ and low‑risk groups. The risk score was associated with advanced tumor stage and independently predicted overall survival (multivariate HR=2.16, 95% CI 1.43‑3.28, P=2.0x10-4). High‑risk tumors enriched proliferation/stroma pathways (cell cycle, DNA repair, ECM‑receptor interaction, focal adhesion, p53 signaling); low‑risk tumors enriched immune pathways. Immune profiling revealed reduced T/B cell infiltration, increased endothelial cells and higher T‑cell exclusion scores in high‑risk patients, indicating an immunosuppressive microenvironment. Higher risk scores also associated with chemotherapy resistance. Functional validation confirmed that shRNA‑mediated KRT81 knockdown reduced LUAD cell proliferation, migration and invasion, while promoting apoptosis. These findings link KRT expression to clinical outcomes, the tumor microenvironment and therapeutic response in LUAD, suggesting roles for KRTs in cancer progression, chemotherapy resistance and predictive potential for immunotherapy response. This study provides new insights for prognostic evaluation and therapeutic decision‑making in LUAD.
View Figures

Figure 1

Differential expression and feature
selection of keratin genes in LUAD. (A) Volcano plot of keratin
genes differentially expressed between TCGA-LUAD tumors and normal
lung tissues (|log2FC|>1, FDR<0.05). (B) LASSO coefficient
profiles and 10-fold cross-validation curve for identifying
diagnostic genes. (C) Out-of-bag error rate across trees during
random forest training. (D) Top 10 keratin genes ranked by
MeanDecreaseGini importance in the random forest model. (E) ROC
curves comparing LASSO, RF, XGBoost and other models on TCGA data.
(F) ROC curve for the optimal XGBoost model in the GSE31210
validation cohort (AUC=0.860). LUAD, lung adenocarcinoma; TCGA, The
Cancer Genome Atlas; FDR, false discovery rate; LASSO, Least
Absolute Shrinkage and Selection Operator; ROC, receiver operating
characteristic; XGBoost, extreme Gradient Boosting; RF, random
forest; AUC, area under the curve; TPR, true positive rate.

Figure 2

SHAP interpretation of the diagnostic
model. (A) Force plot demonstrating the contribution of individual
genes to a single TCGA sample prediction, shifting it from the
baseline toward a tumor classification. (B) SHAP summary plot in
the TCGA training set, showing each gene's effect (SHAP value) and
expression level. SHAP, Shapley Additive explanations; TCGA, The
Cancer Genome Atlas.

Figure 3

Machine learning-based prognostic
model in LUAD. (A) Heatmap of mean C-index values for 101 model
combinations across TCGA, GSE31210 and GSE72094; the StepCox
[forward]+Ridge model is highlighted. Kaplan-Meier curves for
high-vs. low-risk groups in (B) TCGA, (C) GSE31210 and (D)
GSE72094; HR, 95% CI and P-value shown). (E) Time-dependent ROC
curves at 1, 3 and 5 years for the selected model in all three
cohorts. LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas;
HR, hazard ratio; ROC, receiver operating characteristic; CI,
confidence interval.

Figure 4

Clinical nomogram incorporating the
keratin risk score. (A) Forest plot from univariate Cox regression
for clinical variables and the risk score. (B) Forest plot from
multivariate Cox regression confirming the risk score's independent
prognostic value. (C) Nomogram combining clinical stage and risk
score to predict 1-, 3- and 5-year OS. (D) ROC curves assessing the
nomogram's accuracy at each time point. (E) Calibration curves
comparing predicted vs. observed survival for the nomogram. OS,
overall survival; ROC, receiver operating characteristic; AUC, area
under the curve.

Figure 5

Immune landscape associated with the
keratin risk score. (A) CIBERSORT heatmap of 22 immune cell
fractions in high- and low-risk TCGA samples. (B) Boxplots of
selected immune cell proportions (such as CD8+ T cells,
fibroblasts) between risk groups. (C) MCP-counter violin plots of
eight major immune/stromal populations. (D) ssGSEA violin plots for
enrichment scores of immune-related pathways. (E) GSEA enrichment
plots for representative pathways in high-vs. low-risk groups.
Violin plots of (F) TIDE total score and (G) T cell exclusion score
by risk group. TCGA, The Cancer Genome Atlas.

Figure 6

KRT81 enhances proliferation and
inhibits apoptosis in A549 and H1299 cells. (A) qPCR analysis
revealed relatively high endogenous expression of KRT81 in A549 and
H1299 cells (P<0.05). (B) qPCR validation of KRT81
downregulation following shRNA transfection (left panel, A549;
right panel, H1299). (C) Western blotting confirming KRT81
knockdown at the protein level (top panel, A549; bottom panel,
H1299). (D) CCK-8 assays demonstrated significantly reduced cell
viability upon KRT81 silencing (top panel, A549; bottom panel,
H1299). (E) Flow cytometry and (F) analysis showed a significant
increase in apoptosis rates in KRT81 knockdown groups, with bar
graphs comparing apoptosis percentages between groups (left panel,
A549; right panel, H1299). *P<0.05, **P<0.01, ***P<0.001,
****P<0.0001; ns, not significant. qPCR, quantitative PCR.

Figure 7

KRT81 knockdown inhibits migration
and invasion of A549 and H1299 cells. (A) Colony formation assay
was used to assess the proliferative capacity of lung cancer cells.
(B) Scratch wound healing assays were performed to evaluate cell
migration capacity. (C) Transwell invasion assay was performed to
evaluate the invasion capacity of A549 and (D) H1299 cells
following KRT81 knockdown. Magnification: 200×; scale bar: 100 µm.
***P<0.001, ****P<0.0001; ns, not significant. sh, short
hairpin.
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Copy and paste a formatted citation
Spandidos Publications style
Chen A, Gao T, Liu F, Zhao M, Bai R and Liu Q: Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma. Oncol Lett 32: 419, 2026.
APA
Chen, A., Gao, T., Liu, F., Zhao, M., Bai, R., & Liu, Q. (2026). Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma. Oncology Letters, 32, 419. https://doi.org/10.3892/ol.2026.15774
MLA
Chen, A., Gao, T., Liu, F., Zhao, M., Bai, R., Liu, Q."Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma". Oncology Letters 32.3 (2026): 419.
Chicago
Chen, A., Gao, T., Liu, F., Zhao, M., Bai, R., Liu, Q."Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma". Oncology Letters 32, no. 3 (2026): 419. https://doi.org/10.3892/ol.2026.15774
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Spandidos Publications style
Chen A, Gao T, Liu F, Zhao M, Bai R and Liu Q: Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma. Oncol Lett 32: 419, 2026.
APA
Chen, A., Gao, T., Liu, F., Zhao, M., Bai, R., & Liu, Q. (2026). Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma. Oncology Letters, 32, 419. https://doi.org/10.3892/ol.2026.15774
MLA
Chen, A., Gao, T., Liu, F., Zhao, M., Bai, R., Liu, Q."Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma". Oncology Letters 32.3 (2026): 419.
Chicago
Chen, A., Gao, T., Liu, F., Zhao, M., Bai, R., Liu, Q."Keratin gene expression signature predicts prognosis and immunotherapy efficacy in lung adenocarcinoma". Oncology Letters 32, no. 3 (2026): 419. https://doi.org/10.3892/ol.2026.15774
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