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Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine

  • Authors:
    • Xiujuan Deng
    • Yujun Tan
    • Guangbo Tan
    • Heli Ning
  • View Affiliations / Copyright

    Affiliations: Department of Pulmonology, Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Changsha, Hunan 410006, P.R. China, Department of Cardiology, Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Changsha, Hunan 410006, P.R. China, Department of Oncology, Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Changsha, Hunan 410006, P.R. China
    Copyright: © Deng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 590
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    Published online on: October 14, 2025
       https://doi.org/10.3892/ol.2025.15336
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Abstract

Endoplasmic reticulum stress (ERS) has shown notable antitumor potential by regulating malignant tumor progression, metastasis and immune response activation. The aim of the present study was to further explore the potential function of brain‑derived neurotrophic factor (BDNF), a representative of ERS‑related gene (ERSG), in lung adenocarcinoma (LUAD) and to verify the application potential of BDNF‑related Esketamine for LUAD. A multi‑omics integrated analysis was used to analyze the relevant ERS and LUAD data obtained from public databases, systematically screening and identifying expressed ERSGs in LUAD. The role of ERSGs within the tumor microenvironment was assessed through single‑cell transcriptome data analysis, and distinct immune landscapes in LUAD were delineated based on bulk RNA‑seq data. Machine learning was used to construct a robust ERS‑related predictive model for accurately forecasting the prognosis of patients with LUAD. The causal relationship between the representative ERSG BDNF and LUAD was evaluated using summary‑data‑based Mendelian randomization (SMR) and colocalization analyses. The elevated expression of BDNF in A549 and BEAS‑2B cells was validated using reverse transcription‑quantitative (RT‑q) PCR. Loss‑of‑function experiments using small interfering RNA (siRNA) were used to knock down BDNF expression in A549 cells (A549BDNF‑siRNA) and the proliferation capacity of A549BDNF‑siRNA was verified using Cell Counting Kit‑8 (CCK‑8) and colony formation assays. Approved drugs targeting BDNF were screened, followed by molecular docking to evaluate the binding affinity of BDNF to these drugs. The inhibitory effect of Esketamine on A549 cell proliferation was examined using CCK‑8 and colony formation assays. The efficacy of Esketamine at inhibiting A549 cell invasion and migration was assessed using Transwell and wound healing assays. TUNEL assay and western blotting were used to analyze the apoptosis of A549 cells induced by Esketamine. A total of 67 ERSGs were screened and identified. Integrating prognostic analysis, single‑cell transcriptomic data and RNA‑seq, BDNF emerged as a gene of significant research potential. SMR and colocalization analyses indicated a potential causal relationship between BDNF and LUAD. RT‑qPCR revealed that BDNF was expressed at high levels in A549 cells, and BDNF knockdown markedly inhibited their proliferation. Esketamine, which can inhibit BDNF, was found to effectively suppress the proliferation, invasion and migration of A549 cells, while inducing apoptosis. BDNF is expressed at high levels in A549 cells, and the inhibition of its expression by Esketamine may have good potential in inhibiting LUAD.
View Figures

Figure 1

The main workflow of this study. A
total of 67 ERSGs were identified by combining three databases and
the ERSGs were analyzed and determined using single-cell and bulk
RNA data. The causal relationship between BDNF and LUAD was
analyzed by SMR and colocalization. The difference of BNDF
expression and its effect on A549 cells were verified. Molecular
docking and in vitro experiments verified the effectiveness
of Esketamine in inhibiting A549 cells via downregulated BDNF
level. **P<0.01, ***P<0.005, ****P<0.001. ERSGs,
endoplasmic reticulum stress related genes; BDNF, brain-derived
neurotrophic factor; LUAD, lung adenocarcinoma; SMR,
summary-data-based Mendelian randomization; ERS, endoplasmic
reticulum stress; TCGA, The Cancer Genome Atlas; GEO, Gene
Expression Omnibus; SNP, single nucleotide polymorphism.

Figure 2

Pan-cancer identification of ERSGs in
LUAD. (A) Differential expression landscape of TCGA-derived
transcriptomes (|log2FC|>1, FDR<0.05). (B) Intersection
analysis of ERS-associated genes across TCGA and GEO repositories
(Gene Cards threshold score >5). (C) Prognostically relevant
ERSGs identified through multivariate Cox regression
(Benjamini-P<0.05). (D) Gene Ontology annotation and (E) Kyoto
Encyclopedia of Genes and Genomes pathway enrichment of 67 ERSGs
(hypergeometric test; FDR<0.01). (F) Protein-protein interaction
network reconstructed via STRING database (confidence score
>0.4). ERSGs, endoplasmic reticulum stress related genes; LUAD,
lung adenocarcinoma; TCGA, The Cancer Genome Atlas; ERS,
endoplasmic reticulum stress; GEO, Gene Expression Omnibus.

Figure 3

Single-cell resolution of ERSGs
activity. (A) Cell-type-specific expression patterns visualized
through z-score normalized heatmaps. (B) TSNE projection of eight
distinct cellular populations. (C) Cellular composition
heterogeneity across 11 LUAD specimens. (D) Violin plots
quantifying ERSG expression variance among cellular subsets
(Kruskal-Wallis; P<1.000×10−4). ERSGs, endoplasmic
reticulum stress related genes; TSNE, t-distributed stochastic
neighbor embedding; LUAD, lung adenocarcinoma.

Figure 4

Single-cell ERS activity
quantification. (A) t-distributed stochastic neighbor embedding
mapping of computed ERS-scores (Add Module Score algorithm). (B)
Comparative analysis of cytotoxic potential and T-cell exhaustion
markers between ERS-score subgroups (Mann-Whitney; P<0.001). (C)
Boxplot visualization of ERS-score distribution across cellular
compartments. ERS, endoplasmic reticulum stress.

Figure 5

Intercellular communication dynamics.
(A) Differential interaction networks between ERS-score subgroups
(permutation test FDR<0.05). (B) Cell-type-specific
ligand-receptor interaction patterns. Edge color denotes
interaction directionality (red: upregulated; blue: downregulated),
thickness reflects interaction strength. (C) Pathway activity
divergence between subgroups (Wilcoxon; P<0.05). (D)
Pseudotemporal trajectory of T-cell differentiation (Monocle3;
Q<0.01). (E and F) ICD score progression along developmental
pseudotime. ERS, endoplasmic reticulum stress; ICD, immunogenic
cell death.

Figure 6

Molecular subtyping based on ERS
signatures. (A) Consensus clustering matrix (k=2, 1000 iterations).
(B) Survival probability stratification (log-rank, P=0.008). (C)
Differential ERSGs expression heatmap (DESeq2; FDR<0.05). (D)
Gene Set Enrichment Analysis revealing immune-oncogenic pathway
activation. (E) Clinical parameter distribution between molecular
subtypes (Fisher's exact, P<0.05). ERS, endoplasmic reticulum
stress; ERSGs, endoplasmic reticulum stress related genes.

Figure 7

Immune microenvironment
characterization. Leukocyte infiltration quantification using (A)
CIBERSORTx (v1.06) and (B) MCP-counter (v2.0). (C-E) Stromal-immune
component estimation via ESTIMATE algorithm (Benjamini-P<0.01).
(F) Differential immune checkpoint expression (Welch's t-test;
*P<0.05, ****P<0.001). (G) TIDE prediction score comparison
(Wilcoxon; P=3.900×10−7). TIDE, tumor immune dysfunction
and exclusion.

Figure 8

Prognostic model development. (A)
Feature selection via random forest regression (ntree=500,
mtry=15). Inset: Variable importance metrics. (B) Risk
stratification and survival status distribution in TCGA cohort. (C)
Least absolute shrinkage and selection operator regression
coefficient trajectories (λ=0.021). (D) Survival probability
divergence between risk subgroups (log-rank, P=0.032). (E and F)
Time-dependent receiver operating characteristic analysis (1–5 year
area under the curve) in discovery cohort. (G and H) External
validation using GSE31210 dataset (log-rank,
P=1.4×10−4). TCGA, The Cancer Genome Atlas; FPR, false
positive rate.

Figure 9

Integrative genomics prioritization
of causal ERSGs. (A) SMR visualization at BDNF locus
(hg38:11p14.1). Top: Probe passing SMR-HEIDI joint test (HEIDI;
P>0.01). Bottom: GWAS variants (gray circles) vs. lung eQTLs
(red crosses, GTEx v8). (B) Causal effect estimates (Bayes
factor>10) between gene expression and LUAD risk. (C)
Colocalization probability analysis (PP.H4>0.8) showing shared
causal variants (LD, r2 color gradient; lead
variant=purple square). (D) Quantitative PCR validation of ERSGs in
A549 (malignant) vs. BEAS-2B (normal) cells (GAPDH-normalized,
triplicates), ****P<0.001. (E) BDNF silencing efficiency (siRNA
vs. scramble, ΔΔCq method), ***P<0.005. (F) Proliferation
kinetics (CCK-8 assay) at 24/48 h post-transfection, **P<0.01,
****P<0.001. (G and H) Clonogenic capacity assessment (crystal
violet staining) with quantitative histograms (triplicate
experiments, Mann-Whitney ****P<0.001). ERSGs, endoplasmic
reticulum stress related genes; SMR, summary-Mendelian
randomization; BDNF, brain-derived neurotrophic factor; GWAS,
genome-wide association studies; eQTLs, cis-expression quantitative
trait loci; LUAD, lung adenocarcinoma; siRNA, small interfering
RNA.

Figure 10

Functional characterization of BDNF
in LUAD pathogenesis. Computational docking simulations of BDNF
(PDB 1BND) with (A) Esketamine (Glide Score=−8.2 kcal/mol) and (B)
Chondroitin (Auto Dock Vina score=−7.6). Left: 2D ligand
structures; Center: 3D binding poses; Right: Interaction
fingerprint maps. Esketamine-BDNF: −5.3 kcal/mol. Chondroitin
sulfate-BDNF: −4.9 kcal/mol. Negative controls: BSA-BDNF: −2.3
kcal/mol. Acetaminophen-BDNF: −4.1 kcal/mol. (C and D) Immunoblot
analysis (anti-BDNF 1:1,000, Abcam ab205067) under Esketamine
treatment (16.25 µM, 24 h) or siRNA knockdown (β-actin loading
control). (E) Dose-response viability curves (0–50 µM Esketamine,
72 h). (F and G) Colony formation inhibitory effects (16.25 µM, 14
days) with quantification (two-way ANOVA ***P<0.0001). (H)
Transwell assays and (I) quantified bars of A549 cells treated with
Esketamine at a concentration of 16.25 µM for 24 h, ***P<0.005.
(J and K) Bars for detection and quantification of scratch assay in
A549 cells treated with Esketamine at a concentration of 16.25 µM
for 24 h. For comparison, some experiments were also performed with
cisplatin at a concentration of 5 µM. *P<0.05. BDNF,
brain-derived neurotrophic factor; LUAD, lung adenocarcinoma.

Figure 11

Expression pattern and functional
validation of BDNF in lung adenocarcinoma. (A) Tunnel staining and
(B) fluorescence plots and quantified bars of A549 cells after
treatment with Esketamine at a concentration of 16.25 µM for 24 h
and cisplatin at a concentration of 5 µM. (C-E) Western blotting
results of Bax and Bcl-2 expression in A549 cells treated with
Esketamine at a concentration of 16.25 µM for 24 h and cisplatin at
a concentration of 5 µM. BDNF, brain-derived neurotrophic factor.
*P<0.05, **P<0.01, ****P<0.001.
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Copy and paste a formatted citation
Spandidos Publications style
Deng X, Tan Y, Tan G and Ning H: Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine. Oncol Lett 30: 590, 2025.
APA
Deng, X., Tan, Y., Tan, G., & Ning, H. (2025). Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine. Oncology Letters, 30, 590. https://doi.org/10.3892/ol.2025.15336
MLA
Deng, X., Tan, Y., Tan, G., Ning, H."Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine". Oncology Letters 30.6 (2025): 590.
Chicago
Deng, X., Tan, Y., Tan, G., Ning, H."Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine". Oncology Letters 30, no. 6 (2025): 590. https://doi.org/10.3892/ol.2025.15336
Copy and paste a formatted citation
x
Spandidos Publications style
Deng X, Tan Y, Tan G and Ning H: Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine. Oncol Lett 30: 590, 2025.
APA
Deng, X., Tan, Y., Tan, G., & Ning, H. (2025). Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine. Oncology Letters, 30, 590. https://doi.org/10.3892/ol.2025.15336
MLA
Deng, X., Tan, Y., Tan, G., Ning, H."Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine". Oncology Letters 30.6 (2025): 590.
Chicago
Deng, X., Tan, Y., Tan, G., Ning, H."Multi‑omics and experimental validation unveil BDNF as a diagnostic biomarker and therapeutic target in endoplasmic reticulum stress‑driven lung adenocarcinoma: Therapeutic potential of Esketamine". Oncology Letters 30, no. 6 (2025): 590. https://doi.org/10.3892/ol.2025.15336
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