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Article

Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer

  • Authors:
    • Dan Xiao
    • Jing Yang
    • Jianying Zhou
    • Zhenzhen Hu
    • Ting Zhang
  • View Affiliations / Copyright

    Affiliations: Department of Thoracic Oncology, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Key Laboratory of Oncology, Jiangxi Cancer Hospital and Institute, Nanchang, Jiangxi 330029, P.R. China, Department of Respiratory and Critical Care Medicine, South China Hospital Affiliated to Shenzhen University, Shenzhen, Guangdong 511800, P.R. China, Department II of Respiratory and Critical Care, Jiangxi Provincial People's Hospital (The First Affiliated Hospital of Nanchang Medical College), Nanchang, Jiangxi 330000, P.R. China
  • Article Number: 64
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    Published online on: January 19, 2026
       https://doi.org/10.3892/ijmm.2026.5735
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Abstract

Lung cancer is a highly aggressive malignancy associated with a high global mortality rate. Immunotherapy, particularly anti‑programmed cell death protein 1 (PD‑1) therapy, has offered new hope for patients; however, therapeutic resistance remains a major obstacle to clinical success. In the present study, single‑cell RNA sequencing was utilized to investigate the molecular characteristics of lung cancer and to elucidate the mechanisms underlying resistance to anti‑PD‑1 immunotherapy. Cancer‑associated fibroblasts (CAFs) were identified as key contributors to immune resistance. Functional assays, including CCK‑8, EdU, TUNEL and Transwell experiments, demonstrated that CAFs regulated the expression of lipocalin 2 (LCN2) in lung cancer cells, and elevated LCN2 levels were found to promote resistance to immunotherapy, as well as to enhance cellular proliferation and invasion. The effects of LCN2 on tumor growth, invasion, immune infiltration and ferroptosis were further validated by molecular and histological analyses. The results showed that silencing LCN2 induced ferroptosis in lung cancer cells, resulting in increased sensitivity to anti‑PD‑1 therapy, suppressed tumor growth and reduced invasiveness. These findings highlight the critical role of the CAF‑LCN2 axis in mediating resistance to anti‑PD‑1 immunotherapy and suggest that targeting this pathway may represent a promising strategy to enhance treatment efficacy in lung cancer.

View Figures

Figure 1

Analysis and annotation of scRNA-seq
data. (A) Simplified flowchart depicting the heterogeneity analysis
of cell populations and cancer-associated fibroblasts in the lung
cancer microenvironment. (B) Selection of highly variable genes
based on gene expression variance, highlighting the top 10 highly
variable genes. The top 2,000 highly variable genes are marked in
red, while low-variable genes are marked in black. (C) Distribution
of cells in PC_1 and PC_2 after Harmony batch correction, with each
point representing a cell. (D) Distribution of standard deviations
for PCs. (E) Two-dimensional tSNE clustering visualization showing
the clustering and distribution of cells from tumor and normal
samples, with tumor samples in green and normal samples in dark
blue. (F) tSNE clustering displaying the clustering and
distribution of cells within tissue, where each color represents a
cluster. (G) Expression levels of known cell lineage-specific
marker genes in clusters, with darker red indicating higher
expression levels and larger circles indicating more cells
expressing the gene. (H) Visualization of cell annotation results
from tSNE clustering, along with the distribution in normal and
tumor tissues, where each color represents a cell subpopulation.
(I) Cell trajectory construction based on pseudotime (indicated by
color gradients from dark to light corresponding to early to late
pseudotime) and cell subtypes, including T cells, monocytes,
fibroblasts, macrophages, B cells, mast cells, epithelial cells,
endothelial cells, NK cells and plasma cells (each group n=6). PC,
principal component; tSNE, the t-distributed stochastic neighbor
embedding; NK, natural killer; scRNA-seq, single-cell RNA
sequencing; CAF, cancer-associated fibroblast; myCAF,
myofibroblastic CAF; iCAF, inflammatory CAF; dCAF, desmoplastic
CAF; pCAF, proliferative CAF.

Figure 2

Reclustering and annotation of
fibroblasts in scRNA-sequencing data. (A) Two-dimensional tSNE
clustering visualization showing the clustering and distribution of
T cells from normal and tumor samples, with normal samples in dark
blue and tumor samples in green. (B) tSNE clustering displaying the
clustering and distribution of fibroblasts, where each color
represents a cluster. (C) Expression patterns of known cell
lineage-specific marker genes in clusters, with darker red
indicating higher expression levels and larger circles indicating
more cells expressing the gene. (D) Visualization of fibroblast
annotation results from tSNE clustering, where each color
represents a cell subpopulation. (E) Construction of cell
trajectories based on pseudotime. The pseudotime is indicated by
the color gradient in the figure, with darker colors representing
earlier states and lighter colors representing later pseudotime.
(F) Construction of cell trajectories based on cell subpopulations,
including dCAFs, pCAFs, iCAFs, myCAFs and unclassified fibroblasts;
each group n=6. tSNE, the t-distributed stochastic neighbor
embedding; CAF, cancer-associated fibroblast; myCAF,
myofibroblastic CAF; iCAF, inflammatory CAF; dCAF, desmoplastic
CAF; pCAF, proliferative CAF.

Figure 3

Expression of LCN2 in the lung cancer
anti-PD-L1 immunotherapy resistance dataset. (A) Schematic diagram
illustrating the simplified workflow for transcriptome data
analysis from the Gene Expression Omnibus and TCGA databases. (B)
Volcano plot analysis of differential gene expression in the
anti-PD-L1 resistance dataset, where red dots represent
significantly upregulated genes, green dots represent significantly
downregulated genes, and black dots represent genes with no
significant difference. (C) Volcano plot analysis of differential
gene expression in TCGA lung cancer transcriptome data, with red
dots indicating upregulated genes, green dots indicating
downregulated genes, and black dots indicating genes with no
significant difference. (D) Venn diagram analysis comparing DEGs in
NSCLC and anti-PD-L1 resistance DEGs. (E) Volcano plot depicting
the expression of intersecting genes from diagram D in the
anti-PD-L1 resistance transcriptome data, where black dots
represent significantly upregulated genes and green dots represent
significantly downregulated genes. (F) Volcano plot illustrating
the expression of intersecting genes from diagram D in lung cancer
transcriptome data, with black dots representing significantly
upregulated genes and green dots representing significantly
downregulated genes. (G) Expression profile of LCN2 in various cell
subtypes in the scRNA-sequencing data. (H) Trends in LCN2
expression across pseudo-time in the pseudo-time analysis. (I)
Receiver Operating Characteristics curve representing LCN2 in the
anti-PD-L1 resistance transcriptome data. LCN2, lipocalin 2; PD-L1,
programmed death-ligand 1; TCGA, The Cancer Genome Atlas; DEGs,
differentially expressed genes; NSCLC, non-small cell lung cancer;
AUC, area under the curve; ROC, receiver operating characteristic;
FC, fold-change; NK, natural killer; RNA-seq, RNA sequencing.

Figure 4

Impact of CAFs on lung cancer cell
resistance to anti-PD-L1 immunotherapy. (A) Diagram outlining the
influence of CAFs on the resistance of lung cancer cells to
anti-PD-L1 immunotherapy. (B) Cell Counting Kit-8 assay measuring
the sensitivity of A549 cells to anti-PD-L1 treatment. *P<0.05,
**P<0.01 and ***P<0.001 vs. N-A549. (C) EdU labeling
experiment evaluating the proliferative capacity of A549 cells
(scale bar, 25 µm), with EdU labeled in green fluorescence and DAPI
in blue fluorescence. (D) Transwell assay assessing the invasive
ability of A549 cells (scale bar, 50 µm). (E) TUNEL labeling
experiment detecting the apoptotic status of A549 cells (scale bar,
25 µm), with TUNEL staining in green fluorescence and DAPI staining
in blue fluorescence. (F) Expression of LCN2 mRNA in A549 cells
cultured with CM from NFs and CAFs. (G) Protein expression of LCN2
in A549 cells cultured with CM from NFs and CAFs. *P<0.05,
**P<0.01 and ***P<0.001. All cell experiments were conducted
in triplicate. One-way ANOVA with Tukey's multiple comparisons test
was used for (B) and unpaired two-tailed Student's t-test was used
for (C-G). LCN2, lipocalin 2; CAFs, cancer-associated fibroblasts;
PD-L1, programmed death-ligand 1; NFs, normal fibroblasts; CM,
conditioned medium; ns, not significant (P>0.05).

Figure 5

Impact of CAFs on anti-PD-L1
immunotherapy resistance in lung cancer cells via LCN2 expression.
(A) Simplified flowchart depicting how CAFs influence anti-PD-L1
immunotherapy resistance in lung cancer cells through LCN2
expression. (B) Cell Counting Kit-8 assay assessing the sensitivity
of A549 cells to anti-PD-L1 treatment. *P<0.05, **P<0.01 and
***P<0.001 vs. C-sh-NC. (C) EdU labeling experiment evaluating
the proliferative capacity of A549 cells (scale bar, 25 µm), with
EdU labeled in green fluorescence and DAPI in blue fluorescence.
(D) Transwell assay investigating the invasive ability of A549
cells (scale bar, 50 µm). (E) TUNEL staining analyzing the
apoptotic status of A549 cells (scale bar, 25 µm), with TUNEL
staining in green fluorescence and DAPI in blue fluorescence. (F)
Measurement of intracellular Fe2+ and ROS levels (scale
bar, 25 µm); *P<0.05, **P<0.01 and ***P<0.001; all cell
experiments were conducted in triplicate. One-way ANOVA with
Tukey's multiple comparisons test was used for (B-F). CAFs,
cancer-associated fibroblasts; PD-L1, programmed death-ligand 1;
ROS, reactive oxygen species; sh-, short hairpin; NC, negative
control; Fe2+, iron; ns, not significant
(P>0.05).

Figure 6

Influence of LCN2 on the growth of
lung cancer in mice. (A) Simplified flowchart outlining the
establishment of an anti-PD-L1 immunotherapy tolerance model in
mice with lung cancer. (B) Western blot analysis of LCN2, COL1A1
and THY1 expression in tumor tissues from drug-resistant and
drug-sensitive mice. (C) Immunohistochemical detection of LCN2,
COL1A1 and THY1 expression in tumor tissues from drug-resistant and
drug-sensitive mice (scale bar, 100 µm). (D) Establishment of a
model for anti-PD-L1 immunotherapy in LCN2-silenced mice. (E)
Evaluation of LCN2 mRNA expression in tumor tissues after LCN2 gene
intervention in mice. (F) Assessment of LCN2 protein expression in
tumor tissues following LCN2 gene intervention in mice. (G) Tumor
size in LCN2 gene intervention mice (n=10 in each group).
*P<0.05, **P<0.01 and ***P<0.001. Unpaired two-tailed
Student's t-test was used for (B) and (C) and one-way ANOVA with
Tukey's multiple comparisons test was used for (E) and (G). LCN2,
lipocalin 2; PD-L1, programmed death-ligand 1; sh-, short hairpin;
NC, negative control; COL1A1, collagen α-1(I) chain; THY1, Thy-1
membrane glycoprotein; i.p., intraperitoneal.

Figure 7

Impact of LCN2 on proliferation,
invasion and inflammatory infiltration in mouse lung cancer cells.
(A) Immunohistochemical analysis of Ki-67, cleaved caspase 3,
E-cadherin, Vimentin, IL-6 and TNF-α expression in mouse tumor
tissues (scale bar, 20 µm). (B) Evaluation of intratumoral
Fe2+ levels. (C) Measurement of intratumoral ROS levels.
(D) Detection of ferroptosis-related proteins ACSL4, SLC7A11 and
GPX4 expression in tissues. *P<0.05, **P<0.01 and
***P<0.001. One-way ANOVA with Tukey's multiple comparisons test
was used for (A-C). LCN2, lipocalin 2; sh-, short hairpin; NC,
negative control; PD-L1, programmed death-ligand 1; ROS, reactive
oxygen species; Fe2+, iron; ns, not significant
(P>0.05); ACSL4, acyl-CoA synthetase long-chain family member 4;
SLC7A11, solute carrier family 7 member 11; GPX4, glutathione
peroxidase 4.

Figure 8

Induction of ferroptosis in lung
cancer cells with anti-programmed death-ligand 1 immunotherapy
resistance via CAF regulation of LCN2 expression. CAF,
cancer-associated fibroblast; LCN2, lipocalin 2; ROS, reactive
oxygen species; PD-1, programmed cell death protein 1;
Fe2+, iron; Treg, regulatory T cell; NK, natural
killer.
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Copy and paste a formatted citation
Spandidos Publications style
Xiao D, Yang J, Zhou J, Hu Z and Zhang T: <p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>. Int J Mol Med 57: 64, 2026.
APA
Xiao, D., Yang, J., Zhou, J., Hu, Z., & Zhang, T. (2026). <p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>. International Journal of Molecular Medicine, 57, 64. https://doi.org/10.3892/ijmm.2026.5735
MLA
Xiao, D., Yang, J., Zhou, J., Hu, Z., Zhang, T."<p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>". International Journal of Molecular Medicine 57.3 (2026): 64.
Chicago
Xiao, D., Yang, J., Zhou, J., Hu, Z., Zhang, T."<p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>". International Journal of Molecular Medicine 57, no. 3 (2026): 64. https://doi.org/10.3892/ijmm.2026.5735
Copy and paste a formatted citation
x
Spandidos Publications style
Xiao D, Yang J, Zhou J, Hu Z and Zhang T: <p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>. Int J Mol Med 57: 64, 2026.
APA
Xiao, D., Yang, J., Zhou, J., Hu, Z., & Zhang, T. (2026). <p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>. International Journal of Molecular Medicine, 57, 64. https://doi.org/10.3892/ijmm.2026.5735
MLA
Xiao, D., Yang, J., Zhou, J., Hu, Z., Zhang, T."<p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>". International Journal of Molecular Medicine 57.3 (2026): 64.
Chicago
Xiao, D., Yang, J., Zhou, J., Hu, Z., Zhang, T."<p>Deciphering the CAF‑LCN2 axis: Key to overcoming anti‑PD‑L1 immunotherapy resistance in lung cancer</p>". International Journal of Molecular Medicine 57, no. 3 (2026): 64. https://doi.org/10.3892/ijmm.2026.5735
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