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Article Open Access

Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma

  • Authors:
    • Yue Chen
    • Jun Wu
    • Danxia Zhu
    • Lu Jiang
    • Jian Wang
    • Dachuan Zhang
    • Wenting He
  • View Affiliations / Copyright

    Affiliations: Department of Oncology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China, Department of Pathology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China
    Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 37
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    Published online on: January 2, 2026
       https://doi.org/10.3892/or.2026.9042
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Abstract

Cholangiocarcinoma (CCA) is an aggressive malignancy with poor prognosis and a limited number of treatments is available. Disulfidptosis, a newly identified form of cell death triggered by disulfide bond accumulation during glucose deprivation, may influence cancer progression but its role in CCA is poorly understood. The present study investigated disulfidptosis‑related genes (DRGs) and their impact on CCA prognosis and immune modulation. Differential expression analysis of 100 DRGs using RNA sequencing data from The Cancer Genome Atlas and EMBL‑EBI identified 74 dysregulated genes in CCA. Unsupervised clustering stratified patients with CCA into two distinct subtypes (Subs): i) SubA; and ii) SubB. A four‑gene prognostic signature was developed using least absolute shrinkage and selection operator regression and validated via Kaplan‑Meier survival analysis and receiver operating characteristic curves. Immune infiltration and tumor microenvironment were evaluated using Cell‑type Identification by Estimating Relative Subsets of RNA Transcripts, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data and single‑sample Gene Set Enrichment Analysis. Functional assays, including small interfering RNA knockdown of CD109 and EFNB2 in CCA cell lines were used to investigate proliferation, migration, invasion and F‑actin staining. Results showed SubB, associated with higher disulfidptosis activity, had worse prognosis, increased immune cell infiltration and elevated immune checkpoint gene expression. The four‑gene signature effectively stratified patients into risk groups. Knockdown of CD109 and EFNB2 significantly suppressed CCA cell proliferation, migration and invasion while it promoted disulfidptosis under glucose deprivation. The present study established an association between DRGs and CCA prognosis/immune dynamics, provided a robust four‑gene prognostic signature, and identified CD109 and EFNB2 as potential therapeutic targets, positioning disulfidptosis as a promising focus for precision medicine in CCA.
View Figures

Figure 1

Flow chart for the present study
analysis. DRGs (n=74) expression was assessed in the E-MTAB-6389
cohort (T=75, N=31), dividing patients into SubA and SubB based on
DRG levels. Molecular subtyping was followed by survival analysis
(KM), GSVA enrichment score, immune analysis and GSEA hallmark
enrichment. Prognosis-related DEGs were identified and used to
build a signature via univariate Cox and LASSO regression,
validated in the TCGA-CHOL cohort (T=35) with KM survival,
independent prognostic, nomogram, ROC, drug sensitivity and TMB
analyses. Disulfidptosis subtypes and risk groups were explored,
highlighting CD109 and EFNB2, validated by RT-qPCR and functional
assays. The disulfidptosis-CD109/EFNB2 mechanism was investigated
via immunofluorescence. DRGs, disulfidptosis-related genes; TCGA,
The Cancer Genome Atlas; GSVA, gene set variation analysis; GSEA,
Gene Set Enrichment Analysis; LASSO, least absolute shrinkage and
selection operator; KM, Kaplan-Meier; ROC, receiver operating
characteristic; TMB, tumor mutational burden; RT-qPCR, real-time
quantitative polymerase chain reaction; CCK-8, Cell Counting Kit-8;
TCGA-CHOL, cholangiocarcinoma cohort from The Cancer Genome Atlas
database; T, tumor; N, normal.

Figure 2

Disulfidptosis-regulated clusters and
characteristics in cholangiocarcinoma. (A) Box plots of the top 20
differentially expressed DRGs in cholangiocarcinoma vs. normal
samples; (B) heatmap of 74 DRGs in cholangiocarcinoma; (C)
correlation matrix heatmap of the top 20 DRGs; (D) waterfall plot
of mutations in 74 DRGs; (E) CDF curves for consensus clustering
(k=2-10); (F) consensus matrix heatmap at k=2; (G) relative change
in CDF Δ area for k values; (H) Kaplan-Meier survival curves for
two molecular subtypes (P=0.038; HR=1.81; 95% CI, 1.03–3.2); (I)
Violin plots of pathway enrichment score differences between
subtypes. ***P<0.001, ****P<0.0001. DRGs,
disulfidptosis-related genes; CDF, cumulative distribution
function; MutCount, mutation count.

Figure 3

Disulfidptosis subtype-associated
immune infiltration in cholangiocarcinoma. (A) Box plots depicting
significant differences in immune cell types between subtypes
analyzed by CIBERSORT; (B) violin plots comparing immune scores
between subtypes; (C) violin plots comparing stromal scores between
subtypes; (D) violin plots comparing microenvironment scores
between subtypes; (E) violin plots comparing tumor purity between
subtypes; (F) box plots showing significant differences in immune
cell types between subtypes analyzed by ssGSEA (*P<0.05;
**P<0.01; ***P<0.001; SubA: blue; SubB: red). NK, natural
killer.

Figure 4

Immune Checkpoint, HLA gene
expression and pathway enrichment in disulfidptosis subtypes of
cholangiocarcinoma. (A) box plots depicting significant differences
in immune checkpoint gene expression between subtypes; (B) box
plots showing significant differences in HLA family gene expression
between subtypes; (C) GSEA hallmark pathway analysis:
APICAL-JUNCTION; (D) GSEA hallmark pathway analysis:
TGF-β-SIGNALING. (SubA: blue; SubB: red; *P<0.05,
**P<0.01).

Figure 5

Prognostic gene signature and risk
score analysis in cholangiocarcinoma. (A) Volcano plot of
differentially expressed genes (blue, downregulated; red,
upregulated); (B) Univariate Cox regression forest plot of seven
prognostic genes; (C) LASSO coefficient distribution profile; (D)
likelihood deviance for LASSO coefficients, with vertical dashed
lines indicating λ. min (left) and λ.1se (right); (E) Kaplan-Meier
survival curves for patients stratified by the 4-gene signature in
the training cohort (Risk group; P<0.0001; HR=5.95; 95% CI
3.03–11.68); (F) Kaplan-Meier survival curves for patients
stratified by the 4-gene signature in the validation cohort; (G)
training cohort: Risk score distribution (top), survival status
(middle) and gene expression heatmap (bottom); (H) validation
cohort: Risk score distribution (top), survival status (middle) and
gene expression heatmap (bottom); (I) time-dependent ROC curves
(1-, 2-, 3-year) in the training cohort; (J) time-dependent ROC
curves (1-, 2-, 3-year) in the validation cohort. LASSO, least
absolute shrinkage and selection operator; HR, hazard ratio; AUC,
area under the curve.

Figure 6

Prognostic model and clinical
evaluation in cholangiocarcinoma. (A) Univariate Cox regression
analysis of independent prognostic factors; (B) Multivariate Cox
regression analysis of independent prognostic factors; (C)
Prognostic nomogram integrating independent factors; (D)
Time-dependent ROC curves for 1-, 2- and 3-year survival
predictions; (E) Calibration curves for 1-, 2- and 3-year survival
probabilities; (F) Clinical decision curve analysis DCA for 1-year
survival outcomes; (G) DCA for 2-year survival outcomes; (H) DCA
for 3-year survival outcomes. DCA, decision curve analysis; AUC,
area under the curve.

Figure 7

Chemotherapeutic response and genomic
characteristics in cholangiocarcinoma. (A) Comparative analysis of
IC50 levels for 10 chemotherapeutic agents across risk
subgroups; (B) waterfall plot of the 20 most frequently mutated
genes in cholangiocarcinoma samples; (C) TMB distribution across
TCGA-CHOL samples; (D) Box plots comparing TMB between high-risk
and low-risk subgroups; (E) Sankey diagram mapping disulfidptosis
subtypes to risk stratification subgroups. L, low risk group; H,
high risk group; TMB, tumor mutational burden.

Figure 8

Establishment of CD109 and EFNB2
knockdown cholangiocarcinoma cell model. (A) mRNA expression levels
of CD109 and EFNB2 in normal human intrahepatic biliary epithelial
cells and CCA cell lines; (B) RT-qPCR validation of siRNA knockdown
efficiency; (C) RT-qPCR validation of mRNA knockdown in HuCCT1 and
RBE cells; (D) Western blotting validation of protein knockdown in
HuCCT1 and RBE cells. **P<0.01; ***P<0.001. si, small
interfering; RT-qPCR, reverse transcription-quantitative PCR; NC,
negative control.

Figure 9

Functional assays following CD109 and
EFNB2 knockdown in cholangiocarcinoma cells. (A) Cell Counting
Kit-8 assay for cell proliferation; (B) clonogenic assay for colony
formation; (C) wound healing assay for cell migration; (D)
Transwell assay for cell migration; (E) Transwell assay for cell
invasion. *P<0.05, **P<0.01, ***P<0.001. Scale bar, 100
µm. si, small interfering; NC, negative control.

Figure 10

F-actin and nuclei in HuCCT1 and RBE
cells transfected with si-NC, si-CD109 or si-EFNB2 and treated with
5 µM BAY-876 or 1 µM TCEP for 24 h. si, small interfering; NC,
negative control; TCEP, Tris-(2-carboxyethyl)-phosphine
hydrochloride.
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Copy and paste a formatted citation
Spandidos Publications style
Chen Y, Wu J, Zhu D, Jiang L, Wang J, Zhang D and He W: Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma. Oncol Rep 55: 37, 2026.
APA
Chen, Y., Wu, J., Zhu, D., Jiang, L., Wang, J., Zhang, D., & He, W. (2026). Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma. Oncology Reports, 55, 37. https://doi.org/10.3892/or.2026.9042
MLA
Chen, Y., Wu, J., Zhu, D., Jiang, L., Wang, J., Zhang, D., He, W."Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma". Oncology Reports 55.2 (2026): 37.
Chicago
Chen, Y., Wu, J., Zhu, D., Jiang, L., Wang, J., Zhang, D., He, W."Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma". Oncology Reports 55, no. 2 (2026): 37. https://doi.org/10.3892/or.2026.9042
Copy and paste a formatted citation
x
Spandidos Publications style
Chen Y, Wu J, Zhu D, Jiang L, Wang J, Zhang D and He W: Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma. Oncol Rep 55: 37, 2026.
APA
Chen, Y., Wu, J., Zhu, D., Jiang, L., Wang, J., Zhang, D., & He, W. (2026). Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma. Oncology Reports, 55, 37. https://doi.org/10.3892/or.2026.9042
MLA
Chen, Y., Wu, J., Zhu, D., Jiang, L., Wang, J., Zhang, D., He, W."Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma". Oncology Reports 55.2 (2026): 37.
Chicago
Chen, Y., Wu, J., Zhu, D., Jiang, L., Wang, J., Zhang, D., He, W."Exploring the role of disulfidptosis‑related signatures in immune microenvironment, prognosis and therapeutic strategies of cholangiocarcinoma". Oncology Reports 55, no. 2 (2026): 37. https://doi.org/10.3892/or.2026.9042
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