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Article Open Access

FOSL2 drives transcriptional activation of super‑enhancer-regulated DYNC1H1 to promote hypoxia‑induced oral squamous cell carcinoma progression

  • Authors:
    • Linyu Jin
    • Jing Zhang
    • Wenjing Wang
    • Nan Zhang
    • Yang Liu
    • Alin Tian
    • Weiwei Yin
    • Yongle Qiu
  • View Affiliations / Copyright

    Affiliations: Department of Stomatology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China, Department of Medical Periodical Press, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China, Department of Stomatology, The Second Hospital of Shijiazhuang, Shijiazhuang, Hebei 050000, P.R. China, Department of Stomatology, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050000, P.R. China
    Copyright: © Jin et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 209
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    Published online on: May 25, 2026
       https://doi.org/10.3892/mmr.2026.13919
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Abstract

Hypoxia is a hallmark of oral squamous cell carcinoma (OSCC) and plays a critical role in driving tumor progression. However, the mechanisms by which OSCC cells respond to hypoxic signaling to promote malignant phenotypes remain unclear. The present study aimed to identify the key regulatory factors governing hypoxic adaptation in OSCC. Based on bulk RNA‑sequencing (RNA‑seq) data from The Cancer Genome Atlas (TCGA) database, hypoxia was identified as a prognostic risk factor for OSCC. Hierarchical clustering revealed ‘glycolysis’ and ‘hypoxia’ as the most strongly associated cancer hallmarks. Using single‑cell RNA‑seq data from the Gene Expression Omnibus (GEO) database, 104 hypoxia‑ and glycolysis‑associated genes were identified. Integration of bulk RNA‑seq data from TCGA and GEO databases enabled the development of an optimized prognostic model [stepwise Cox (backward) + elastic network (α=0.8)] through machine learning and highlighted four core genes: Heparan sulfate proteoglycan 2, immunoglobulin superfamily member 3, dihydrouridine synthase 1‑like and dynein cytoplasmic 1 heavy chain 1 (DYNC1H1). Further analysis of chromatin immunoprecipitation sequencing data from GEO database identified DYNC1H1 as the sole super‑enhancer (SE)‑regulated gene among these candidates, characterized by hypoxia‑responsive upregulation. DYNC1H1 knockdown significantly abrogated hypoxia‑induced proliferation, invasion, glucose uptake and lactate production in OSCC cells. Hypoxia‑responsive transcription factor FOS‑like 2 (FOSL2) activated DYNC1H1 transcription by binding to enhancer regions. Functional validation established that the FOSL2/DYNC1H1 axis was a pivotal regulatory hub for hypoxia‑driven malignant phenotypes in OSCC. Overall, DYNC1H1 is an SE‑regulated hypoxia‑responsive gene that undergoes transcriptional activation via FOSL2‑specific binding to typical enhancer and SE regions. The FOSL2/DYNC1H1 axis drives the hypoxia‑induced malignant progression of OSCC. The present study therefore establishes a novel target for precision therapies targeting the hypoxic tumor microenvironment in OSCC.
View Figures

Figure 1

Hypoxia as a risk factor for RFS in
OSCC. (A) A hierarchical clustering dendrogram for 50
cancer-related hallmark gene sets. Data were derived from the
TCGA-OSCC cohort. (B) Relationship between hypoxia scores and RFS
in patients with OSCC. (C) Relationship between glycolysis scores
and RFS in patients with OSCC. Patients were stratified into high-
and low-score groups based on the optimal cutoff value of the
hypoxia or glycolysis scores. Data were derived from the TCGA-OSCC
cohort. HR, hazard ratio; CI, confidence interval; OSCC, oral
squamous cell carcinoma; RFS, recurrence-free survival.

Figure 2

Single-cell transcriptomic analysis
revealing OSCC heterogeneity. Single cell RNA-sequencing data from
OSCC samples were integrated using publicly available Gene
Expression Omnibus datasets (GSE173468, GSE188737 and GSE234933).
(A) UMAP plots of single-cell transcriptomic profiles from 32 OSCC
samples. (B) Single-cell transcriptomic profiles of OSCC were
annotated into nine distinct cell clusters. (C) UMAP visualization
depicting the expression patterns of marker genes corresponding to
the nine cell clusters. (D) Dot plots for marker gene expression.
OSCC, oral squamous cell carcinoma; UMAP, Uniform Manifold
Approximation and Projection; NK, natural killer; Expr.,
expression; Aver., average.

Figure 3

HSPG2, IGSF3, DUS1L and
DYNC1H1 are identified as critical hypoxia- and
glycolysis-associated genes. (A) hdWGCNA for co-expression network
construction, with the soft threshold power set at 12. scRNA-seq
data from OSCC samples were integrated using publicly available GEO
datasets (GSE173468, GSE188737 and GSE234933). (B) Spearman's
correlation analysis between module eigengenes and
hypoxia/glycolysis scores, where ***P<0.001. scRNA-seq data from
OSCC samples were integrated using publicly available GEO datasets
(GSE173468, GSE188737 and GSE234933). (C) Heatmap displaying the
top 20 machine learning combined models ranked by descending
concordance index in the validation cohort. Training cohort:
TCGA-OSCC cohort (n=310); validation cohort: The GSE65858 dataset
(n=131). (D) Association between risk scores and recurrence-free
survival in patients with OSCC. Risk scores were calculated using
the ‘StepCox (backward) + Enet (α=0.8)’ combined model. Patients
were stratified into high- and low-risk groups according to the
median risk score. Training cohort: TCGA-OSCC cohort (n=310);
validation cohort: The GSE65858 dataset (n=131). (E) Number of
genes in the intersection of core genes screened by different
machine learning algorithms. (F) Selection frequency of core genes
across different machine learning algorithms. OSCC, oral squamous
cell carcinoma; hdWGCNA, high-dimensional weighted gene
co-expression network analysis; TCGA, The Cancer Genome Atlas;
StepCox, stepwise Cox; Enet, elastic network; RSF, random survival
forests; XgBoost, extreme gradient boosting; SVM-REF, support
vector machine recursive feature elimination; Lasso, least absolute
shrinkage and selection operator; scRNA-seq, single cell
RNA-sequencing; GEO, Gene Expression Omnibus; HSPG2, heparan
sulfate proteoglycan 2; IGSF3, immunoglobulin superfamily member 3;
DUS1L, dihydrouridine synthase 1 like; DYNC1H1, dynein cytoplasmic
1 heavy chain 1.

Figure 4

DYNC1H1 is identified as a
hypoxia-responsive SE-regulated gene and its KD is attenuated in
hypoxia-induced oncogenic phenotypes in OSCC. (A) SE scores were
calculated based on H3K27ac ChIP-sequencing data for HSC3 and SAS
cells. Data were derived from the GSE205455 dataset. Points with
curve slopes >1 (red) represent SE; points with slopes ≥1
(black) represent TE. (B) Visualization of H3K27ac peaks at the
DYNC1H1 locus in HSC3 and SAS cells. Data were derived from
the GSE205455 dataset. (C) Differential expression analysis of
DYNC1H1 in OSCC tissues (n=310) vs. normal control tissues (n=44).
Data were downloaded from The Cancer Genome Atlas-OSCC cohort.
****P<0.0001. (D) H3K27ac enrichment at the TE and SE regions of
DYNC1H1 was quantified using ChIP-qPCR. **P<0.01 vs. IgG
and ##P<0.01 vs. Nor. (E) mRNA and (F) protein levels
of DYNC1H1 in HSC3 and SAS cells were measured using RT-qPCR and
western blot analysis, respectively. **P<0.01 vs. Nor. DYNC1H1
KD efficiency was assessed using (G) RT-qPCR and (H) western blot
analysis. **P<0.01 vs. KD-ctrl. (I) CCK-8 assay was conducted to
assess the proliferation of HSC3 and SAS cells. **P<0.01 vs.
Nor/KD-ctrl and ##P<0.01 vs. Hyp/KD-ctrl. (J)
Transwell assays were performed to assess the invasion of HSC3 and
SAS cells. Scale bar, 200 µm. **P<0.01 vs. Nor/KD-ctrl and
##P<0.01 vs. Hyp/KD-ctrl. Quantification of (K)
glucose uptake and (L) lactate production in HSC3 and SAS cells.
**P<0.01 vs. Nor/KD-ctrl and ##P<0.01 vs.
Hyp/KD-ctrl. DYNC1H1, dynein cytoplasmic 1 heavy chain 1; TE,
typical enhancer; SE, super-enhancer; OSCC, oral squamous cell
carcinoma; ChIP, chromatin immunoprecipitation; qPCR, quantitative
PCR; CCK-8, Cell Counting Kit-8; chr, chromosome; H3K27ac,
histone-3 lysine-27 acetylation; Nor, normoxic conditions; Hyp,
hypoxic conditions; KD, knockdown; KD-ctrl, knockdown control.

Figure 5

FOSL2 was characterized as a
hypoxia-responsive transcription factor that activates
DYNC1H1 transcription through binding to TE and SE regions.
(A) Transcription factor prediction for DYNC1H1 using the
Toolkit for Cistrome Data Browser (http://dbtoolkit.cistrome.org). (B) Spearman's
correlation between predicted transcription factors and
DYNC1H1 expression. Data were derived from The Cancer Genome
Atlas-oral squamous cell carcinoma cohort. A significant positive
correlation was defined as a correlation coefficient r>0.2 and
P<0.05. **P<0.01 vs. DYNC1H1 expression. (C) qPCR analysis of
MED1, POLR2A, SMARCC2, FOSL2, POLR2B, XRN2, DCP1A, ASXL1, RNF2,
PPARD, RBPJ and MAX transcription in HSC3 and SAS cells. *P<0.05
and **P<0.01 vs. Nor. (D) Western blot analysis of FOSL2 and
ASXL1 protein levels in HSC3 and SAS cells. (E) Chromatin
Immunoprecipitation-qPCR quantification of anti-FOSL2 enrichment at
DYNC1H1 TE and SE regions in HSC3 and SAS cells. **P<0.01
vs. IgG, ##P<0.01 and #P<0.05 vs. Nor.
(F) FOSL2 OE efficiency was assessed using qPCR. **P<0.01 vs.
OE-ctrl. (G) FOSL2 OE efficiency was assessed using western blot.
(H) Effect of FOSL2 OE on DYNC1H1 mRNA and (I) DYNC1H1 protein
levels in HSC3 and SAS cells under normoxic conditions. **P<0.01
vs. OE-ctrl. DYNC1H1, dynein cytoplasmic 1 heavy chain 1; qPCR,
quantitative PCR; TE, typical enhancer; SE, super-enhancer; OE,
overexpression; Nor, normoxic conditions; Hyp, hypoxic conditions;
ctrl, control; FOSL2, FOS-like 2; NS, non-significant.

Figure 6

FOSL2/DYNC1H1 axis drives
hypoxia-induced proliferation, invasion, glucose uptake and lactate
production in oral squamous cell carcinoma cells. FOSL2 KD
efficiency was assessed using (A) qPCR and (B) western blotting.
**P<0.01 vs. KD-ctrl. (C) DYNC1H1 OE efficiency was assessed
using qPCR and (D) western blotting. **P<0.01 vs. OE-ctrl. (E)
Cell proliferation was analyzed using a Cell Counting Kit-8 assay.
**P<0.01 vs. Nor/KD-ctrl, ##P<0.01 vs. Hyp/KD-ctrl
and &&P<0.01 vs. Hyp/KD-FOSL2. (F) Cell
invasion was measured using a Transwell assay. Scale bar, 200 µm.
**P<0.01 vs. Nor/KD-ctrl; ##P<0.01 vs.
Hyp/KD-ctrl; &&P<0.01 vs. Hyp/KD-FOSL2. (G)
Relative glucose uptake and (H) lactate production in HSC3 and SAS
cells. **P<0.01 vs. Nor/KD-ctrl, ##P<0.01 vs.
Hyp/KD-ctrl and &&P<0.01 vs. Hyp/KD-FOSL2.
DYNC1H1, dynein cytoplasmic 1 heavy chain 1; qPCR, quantitative
PCR; OD, optical density; KD, knockdown; OE, overexpression; Nor,
normoxic conditions; Hyp, hypoxic conditions; ctrl, control; FOSL2,
FOS-like 2.
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Copy and paste a formatted citation
Spandidos Publications style
Jin L, Zhang J, Wang W, Zhang N, Liu Y, Tian A, Yin W and Qiu Y: FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression. Mol Med Rep 34: 209, 2026.
APA
Jin, L., Zhang, J., Wang, W., Zhang, N., Liu, Y., Tian, A. ... Qiu, Y. (2026). FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression. Molecular Medicine Reports, 34, 209. https://doi.org/10.3892/mmr.2026.13919
MLA
Jin, L., Zhang, J., Wang, W., Zhang, N., Liu, Y., Tian, A., Yin, W., Qiu, Y."FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression". Molecular Medicine Reports 34.1 (2026): 209.
Chicago
Jin, L., Zhang, J., Wang, W., Zhang, N., Liu, Y., Tian, A., Yin, W., Qiu, Y."FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression". Molecular Medicine Reports 34, no. 1 (2026): 209. https://doi.org/10.3892/mmr.2026.13919
Copy and paste a formatted citation
x
Spandidos Publications style
Jin L, Zhang J, Wang W, Zhang N, Liu Y, Tian A, Yin W and Qiu Y: FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression. Mol Med Rep 34: 209, 2026.
APA
Jin, L., Zhang, J., Wang, W., Zhang, N., Liu, Y., Tian, A. ... Qiu, Y. (2026). FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression. Molecular Medicine Reports, 34, 209. https://doi.org/10.3892/mmr.2026.13919
MLA
Jin, L., Zhang, J., Wang, W., Zhang, N., Liu, Y., Tian, A., Yin, W., Qiu, Y."FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression". Molecular Medicine Reports 34.1 (2026): 209.
Chicago
Jin, L., Zhang, J., Wang, W., Zhang, N., Liu, Y., Tian, A., Yin, W., Qiu, Y."FOSL2 drives transcriptional activation of super‑enhancer-regulated <em>DYNC1H1</em> to promote hypoxia‑induced oral squamous cell carcinoma progression". Molecular Medicine Reports 34, no. 1 (2026): 209. https://doi.org/10.3892/mmr.2026.13919
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