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Article Open Access

A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma

  • Authors:
    • Zichao Xiong
    • Zhen Zhang
    • Shaodan Cheng
    • Shaohua Liao
  • View Affiliations / Copyright

    Affiliations: Department of Rehabilitation, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200050, P.R. China, Department of Rehabilitation, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P.R. China
    Copyright: © Xiong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 436
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    Published online on: July 9, 2025
       https://doi.org/10.3892/ol.2025.15182
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Abstract

Pancreatic ductal adenocarcinoma (PDAC) represents a particularly aggressive and highly malignant neoplasm, characterized by its unfavorable prognosis and restricted treatment alternatives. The present study aimed to use bioinformatics methodologies to assess transcriptomic data sourced from The Cancer Genome Atlas and the Gene Expression Omnibus to pinpoint biomarkers associated with manganese metabolism that may forecast outcomes in PDAC. Utilizing differential expression analysis, Least Absolute Shrinkage and Selection Operator regression and multivariable Cox regression, 12 essential genes were identified that demonstrate notable associations with the prognosis of PDAC (Natriuretic Peptide A, Kynureninase, Integrin Subunit β6, Cytochrome P450 Family 27 Subfamily A Member 1, C‑X‑C Motif Chemokine Ligand 10, Protein Phosphatase 2 Regulatory Subunit Bβ, MET Proto‑Oncogene Receptor Tyrosine Kinase, Matrix Metalloproteinase 3, Keratin 19, ATPase Na+/K+ Transporting Subunit α3, Pyridoxal Phosphatase and Interleukin 1 Receptor Accessory Protein Like 2). The validation of these genes was performing using both a training cohort and external datasets (GSE62452 and GSE28735), demonstrating the robustness of the model with area under the curve values of 0.82 and 0.83 in the training set and the external validation cohort, respectively. The results of the present study further elucidated the molecular processes underlying PDAC and highlight the crucial importance of manganese metabolism in its development. These biomarkers may provide significant prognostic insights and facilitate the advancement of targeted therapeutic strategies for PDAC.
View Figures

Figure 1

Workflow diagram. TCGA, The Cancer
Genome Atlas; PDAC, pancreatic ductal adenocarcinoma; GO, Gene
Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs,
differentially expressed genes; MRGs, metabolism-related genes;
GGI, gene-gene interaction; RT-qPCR, reverse
transcription-quantitative PCR; K-M, Kaplan-Meier; LASSO, Least
Absolute Shrinkage and Selection Operator; ROC, Receiver Operating
Characteristic; GSEA, Gene Set Enrichment Analysis; GSVA, Gene Set
Variation Analysis.

Figure 2

Differential expression analysis and
candidate gene identification. (A) Volcano plot illustrating the
distribution of DEGs between tumor and normal samples. The top 10
upregulated and downregulated genes with the highest
|log2(FoldChange)| are annotated. (B) Heatmap showing
the expression levels of DEGs, with red indicating upregulation and
blue indicating downregulation. (C) Venn diagram of candidate
genes. DEGs, differentially expressed genes; MRGs,
metabolism-related genes.

Figure 3

Enrichment analysis results of
differentially expressed genes. Circos plots illustrating the
results of GO enrichment analysis for different categories: (A)
Biological Process, (B) Molecular Function and (C) Cellular
Component. (D) Circos plot showing the results of KEGG pathway
enrichment analysis. In the figure, ‘NA’ within the ‘shape’
category indicates that the shape of the data points is not
applicable or not defined for the corresponding GO terms or
pathways. The top 5 pathways with an adjusted P-value of <0.05
from both GO and KEGG enrichment analyses are annotated in the
plots. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and
Genomes; FC, fold change; MHC, major histocompatibility
complex.

Figure 4

GSEA results for manganese metabolism
pathways. (A) Overall, (B) downregulated genes and (C) upregulated
genes. GSEA, Gene Set Enrichment Analysis.

Figure 5

Results of GO and KEGG enrichment
analyses. Circos plots displaying the results of GO enrichment
analysis for the following categories: (A) Biological Process, (B)
Molecular Function and (C) Cellular Component. (D) Circos plot
illustrating the KEGG pathway enrichment analysis results. In the
figure, ‘NA’ within the ‘shape’ category indicates that the shape
of the data points is not applicable or not defined for the
corresponding GO terms or pathways. The top 5 pathways with an
adjusted P-value of <0.05 from both GO and KEGG enrichment
analyses are annotated in the plots. GO, Gene Ontology; KEGG, Kyoto
Encyclopedia of Genes and Genomes; FC, fold change.

Figure 6

Regression analysis results. (A)
Univariate Cox regression analysis. The coefficient represents the
regression coefficient of a predictor variable (such as gene or
clinical feature) in the Cox regression model. This coefficient
indicates the direction and magnitude of the effect of a variable
on survival. The hazard ratio reflects the relative risk associated
with a one-unit change in the predictor variable. A hazard ratio=1
indicates no effect on survival, >1 suggests an increased risk
(adverse factor) and <1 suggests a decreased risk (protective
factor). (B) Least Absolute Shrinkage and Selection Operator
regression analysis. The optimal penalty coefficient (λ) is
selected through 10-fold cross-validation to optimize the model.
CI, confidence interval.

Figure 7

Evaluation of risk scores in training
and testing cohorts. (A) PCA analysis, with each point representing
an individual sample, (B) K-M survival analysis and (C) ROC curve
analysis of high and low-risk scores in the training cohort. The
AUC reflects the accuracy of the model, with higher AUC values
indicate an greater model performance. Risk curves, survival status
and heatmap of prognostic gene expression in high and low-risk
groups of the (D) training and (E) testing cohorts. (F) PCA
analysis, (G) K-M survival analysis and (H) ROC curve analysis of
high and low-risk scores in the testing cohort. PCA, principal
component analysis; K-M, Kaplan-Meier; ROC, Receiver Operating
Characteristic; AUC, area under the curve.

Figure 8

Development and evaluation of the
nomogram model. (A) Univariate and (B) multivariate Cox regression
analysis. The hazard ratio reflects the relative risk for a
one-unit change in a variable. A hazard ration of =1 indicates no
effect on survival time, >1 indicates an increased risk (adverse
factor) and <1 indicates a decreased risk (protective factor).
(C) Construction of the nomogram. Total Points represents the sum
of all factors, which can be used to estimate the 1-, 3- and 5-year
survival rates. Points represent the score corresponding to each
factor. (D) Receiver Operating Characteristic curve analysis of the
nomogram model. (E) Calibration curve analysis of the nomogram
model. The closer the slope of the curve is to the ideal gray line,
the greater the predictive performance of the nomogram. (F)
Decision curve analysis of the nomogram model. Each curve
represents the performance of a prediction model, with the higher
the curve, the greater the net benefit. CI, confidence interval; T,
tumor; N, lymph node; M, metastasis.

Figure 9

Heatmap of the clinical correlation
analysis Red represents upregulated genes and blue represents
downregulated genes. T, tumor; N, lymph node; M, metastasis.

Figure 10

Enrichment and survival analysis of
signaling pathways. (A) GO enrichment analysis. (B) KEGG pathway
enrichment analysis. (C) GSVA of pathways. The x-axis represents
the t-value, calculated using a t-test: Larger t-values indicate
more significant differences between groups. Blue bars represent
upregulated pathways, whilst green bars represent downregulated
pathways. (D) Univariate Cox regression analysis of the
GSVA-enriched pathways (P<0.05). The hazard ratio reflects the
relative risk for a one-unit change in a variable. A hazard ratio
of =1 indicates no effect on survival time, >1 indicates
increased risk (adverse factor) and <1 indicates decreased risk
(protective factor). GO, Gene Ontology; KEGG, Kyoto Encyclopedia of
Genes and Genomes; GSVA, Gene Set Variation Analysis; CI,
confidence interval.

Figure 11

Differences in immune infiltration
between high-risk and low-risk groups evaluated by seven
algorithms.

Figure 12

Immune checkpoint and immune cell
infiltration analysis. (A) Differential analysis of 46 immune
checkpoints between high-risk and low-risk groups. Differences in
the infiltration of (B) 22 immune cell types and (C) 28 immune cell
types between high-risk and low-risk groups. (D) Correlation
heatmap between immune cells and Least Absolute Shrinkage and
Selection Operator-selected genes. Blue indicates negative
correlations and red indicates positive correlations. (E)
Differences in dysfunction, exclusion and TIDE scores between
high-risk and low-risk groups. TIDE, Tumor Immune Dysfunction and
Exclusion. *P<0.05; **P<0.01; ***P<0.001;
****P<0.0001.

Figure 13

TMB analysis and correlation with
risk scores. Waterfall plot of mutations in the (A) high- and (B)
low-risk groups, with the % mutation rate of each gene presented.
(C) Correlation scatter plot of TMB scores and risk scores. The
blue density plot shows the distribution of risk scores, whilst the
orange density plot represents the distribution of TMB scores. (D)
Kaplan-Meier survival analysis between different TMB and risk score
combinations. (E) Differences in TMB scores between the high- and
low-risk groups. TMB, Tumor Mutational Burden.

Figure 14

Drug sensitivity analysis based on
risk scores. (A) Difference in IC50 values of top 10
drugs between high-risk and low-risk groups. Lower IC50
values indicate higher drug sensitivity. (B) Correlation between
IC50 of top 10 drugs and risk scores. Higher
IC50 values indicate lower drug sensitivity.
****P<0.0001.

Figure 15

Analysis of prognostic genes and
their regulatory networks. (A) Correlation analysis between
prognostic genes. Purple represents a positive correlation and
green represents a negative correlation. The plot shows the
correlation matrix between the 12 prognostic genes, highlighting
significant relationships. (B) Chromosomal localization of
prognostic genes. The outer circle represents the chromosomes, with
lines connecting each prognostic gene to its respective chromosomal
location. The plot illustrates the genomic distribution of the
identified genes. (C) Differential expression of 12 prognostic
genes between normal and tumor tissues. (D) lncRNA-miRNA-mRNA
network of prognostic genes. Yellow nodes represent miRNAs, green
nodes represent mRNAs and red nodes represent lncRNAs. (E)
Gene-gene interaction network of prognostic genes. ‘Functions’
refers to the biological pathways identified through enrichment
analysis. The plot visualizes the functional relationships between
the prognostic genes and their associated pathways. lncRNA, long
noncoding RNA; miRNA/miR, microRNA.

Figure 16

Expression and prognostic analysis of
CYP27A1 and KYNU in pancreatic ductal adenocarcinoma. (A)
Expression levels of CYP27A1 and KYNU in 8988, HPNE and PANC1 cell
lines, detected using reverse transcription-quantitative PCR. HPNE
represents the human pancreatic ductal epithelial cell line, whilst
8988 and PANC1 represent human pancreatic cancer cell lines.
***P<0.001; ****P<0.0001. (B) K-M survival analysis comparing
high- and low-risk groups based on CYP27A1 expression in the
training cohort. The survival curves are stratified by CYP27A1
expression levels. (C) K-M survival analysis comparing high- and
low-risk groups based on KYNU expression in the training cohort.
The survival curves are stratified by KYNU expression levels. KYNU,
Kynureninase; CYP27A1, Cytochrome P450 Family 27 Subfamily A Member
1; K-M, Kaplan-Meier.

Figure 17

Transfection efficiency validation.
Reverse transcription-quantitative PCR analysis demonstrated the
efficacy of KYNU knockdown and CYP27A1 overexpression. GAPDH was
used as internal control. **P<0.01. KYNU, Kynureninase; CYP27A1,
Cytochrome P450 Family 27 Subfamily A Member 1; OE, overexpression;
NC, negative control; siR, small interfering RNA.

Figure 18

Functional roles of KYNU and CYP27A1
in PDAC progression. (A) Cell Counting Kit-8 assay evaluating the
effects of KYNU knockdown and CYP27A1 overexpression on PDAC cell
proliferation. *P<0.05; **P<0.01; ***P<0.001;
****P<0.0001. (B) Scratch assay assessing KYNU knockdown and
CYP27A1 overexpression on cell migration (4X). (C) Colony formation
assay detecting changes in cell proliferation after KYNU knockdown
and CYP27A1 overexpression. *P<0.05; **P<0.01; ***P<0.001;
****P<0.0001. KYNU, Kynureninase; CYP27A1, Cytochrome P450
Family 27 Subfamily A Member 1; PDAC, pancreatic ductal
adenocarcinoma; OD, optical density; NC, negative control; siR,
small interfering RNA; OE, overexpression.
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Copy and paste a formatted citation
Spandidos Publications style
Xiong Z, Zhang Z, Cheng S and Liao S: A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma. Oncol Lett 30: 436, 2025.
APA
Xiong, Z., Zhang, Z., Cheng, S., & Liao, S. (2025). A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma. Oncology Letters, 30, 436. https://doi.org/10.3892/ol.2025.15182
MLA
Xiong, Z., Zhang, Z., Cheng, S., Liao, S."A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma". Oncology Letters 30.3 (2025): 436.
Chicago
Xiong, Z., Zhang, Z., Cheng, S., Liao, S."A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma". Oncology Letters 30, no. 3 (2025): 436. https://doi.org/10.3892/ol.2025.15182
Copy and paste a formatted citation
x
Spandidos Publications style
Xiong Z, Zhang Z, Cheng S and Liao S: A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma. Oncol Lett 30: 436, 2025.
APA
Xiong, Z., Zhang, Z., Cheng, S., & Liao, S. (2025). A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma. Oncology Letters, 30, 436. https://doi.org/10.3892/ol.2025.15182
MLA
Xiong, Z., Zhang, Z., Cheng, S., Liao, S."A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma". Oncology Letters 30.3 (2025): 436.
Chicago
Xiong, Z., Zhang, Z., Cheng, S., Liao, S."A novel defined manganese metabolism‑related gene signature for predicting the prognosis of pancreatic ductal adenocarcinoma". Oncology Letters 30, no. 3 (2025): 436. https://doi.org/10.3892/ol.2025.15182
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