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Cuproptosis-related gene CEP55 as a biomarker of pancreatic adenocarcinoma via multi-omics techniques and experimental validation

Riyuan Zhang

Riyuan Zhang

Department of Hepatobiliary and Pancreatic Surgery, People’s Hospital of Pingyang CountyWenzhou, China
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Zhang, Riyuan
, 
Zixia Xu

Zixia Xu

The First Clinical Medical College, Wenzhou Medical UniversityWenzhou, China
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Xu, Zixia
, 
Yurui Zhuang

Yurui Zhuang

The First Clinical Medical College, Wenzhou Medical UniversityWenzhou, China
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Zhuang, Yurui
, 
Yuzhe Shi

Yuzhe Shi

The First Clinical Medical College, Wenzhou Medical UniversityWenzhou, China
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Shi, Yuzhe
, 
Ziyi Guo

Ziyi Guo

Departments of Endocrinology, Minhang Hospital, Fudan UniversityShanghai, China
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Guo, Ziyi
 et 
Chong Chen

Chong Chen

Department of Hepatobiliary and Pancreatic Surgery, People’s Hospital of Pingyang CountyWenzhou, China
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Chen, Chong
  
18 juil. 2025
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Catégorie d'article: Research Article
Publié en ligne: 18 juil. 2025
Pages: 368 - 382
Reçu: 21 janv. 2025
Accepté: 26 avr. 2025
DOI: https://doi.org/10.2478/raon-2025-0042
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© 2025 Riyuan Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

FIGURE 1.

Single cell sequencing analysis of GSE212966. (A) Dimensionality reduction and cluster analysis. All cells in 12 samples were clustered into 14 clusters. (B) According to the surface marker genes of different cell types, the cells are annotated as D cells, endothelial cells, monocyte and macrophages, smooth muscle cells, natural killer (NK) cells and epithelial cells, respectively. (C) The expressed levels of ten cuproptosis-related genes in each cluster. (D) The percentage of necroptosis genes in each cell. The cells were divided into high-and low-cuproptosis cells. (E-H) The Weighted Co-Expression Network Analysis (WGCNA) algorithm identified gene modules associated with cuproptosis. Notably, MEred and MEcyan modules demonstrated a significant correlation with cuproptosis scores.
Single cell sequencing analysis of GSE212966. (A) Dimensionality reduction and cluster analysis. All cells in 12 samples were clustered into 14 clusters. (B) According to the surface marker genes of different cell types, the cells are annotated as D cells, endothelial cells, monocyte and macrophages, smooth muscle cells, natural killer (NK) cells and epithelial cells, respectively. (C) The expressed levels of ten cuproptosis-related genes in each cluster. (D) The percentage of necroptosis genes in each cell. The cells were divided into high-and low-cuproptosis cells. (E-H) The Weighted Co-Expression Network Analysis (WGCNA) algorithm identified gene modules associated with cuproptosis. Notably, MEred and MEcyan modules demonstrated a significant correlation with cuproptosis scores.

FIGURE 2.

Construction and validation of cuproptosis-related prognostic model. (A, B) LASSO regression identified eight genes for the prognostic model construction. (C) The Cancer Genome Atlas (TCGA) cohort survival analysis revealed poorer prognosis in the high cuproptosis-related genes (CRGs) group (P<0.0001). (D) GSE85916 Cohort survival analysis indicated a worse prognosis in the high-CRGs group (P=0.0058). (E) ROC curve of TCGA cohort. The AUC values of the model in 2, 3 and 5 years were 0.720, 0.757 and 0.846, respectively. (F) ROC curve of GSE85916 Cohort. The AUC values of the model in 2, 3 and 5 years were 0.798, 0.824 and 0.795, respectively. (G, H) Principal components analysis (PCA) analysis in TCGA and GSE85916 cohorts demonstrated effective patient grouping in both training and validation sets.
Construction and validation of cuproptosis-related prognostic model. (A, B) LASSO regression identified eight genes for the prognostic model construction. (C) The Cancer Genome Atlas (TCGA) cohort survival analysis revealed poorer prognosis in the high cuproptosis-related genes (CRGs) group (P<0.0001). (D) GSE85916 Cohort survival analysis indicated a worse prognosis in the high-CRGs group (P=0.0058). (E) ROC curve of TCGA cohort. The AUC values of the model in 2, 3 and 5 years were 0.720, 0.757 and 0.846, respectively. (F) ROC curve of GSE85916 Cohort. The AUC values of the model in 2, 3 and 5 years were 0.798, 0.824 and 0.795, respectively. (G, H) Principal components analysis (PCA) analysis in TCGA and GSE85916 cohorts demonstrated effective patient grouping in both training and validation sets.

FIGURE 3.

The construction of a nomogram. (A) Nomogram for the patient predicting mortality rates at 1, 3 and 5 years: 0.576, 0.973, and 0.994, respectively. (B) Nomogram ROC curve indicating AUC values at 1, 3 and 5 years as 0.71, 0.8, and 0.84. (C) Decision curve analysis demonstrated superior performance of the nomogram over other clinical indicators.
The construction of a nomogram. (A) Nomogram for the patient predicting mortality rates at 1, 3 and 5 years: 0.576, 0.973, and 0.994, respectively. (B) Nomogram ROC curve indicating AUC values at 1, 3 and 5 years as 0.71, 0.8, and 0.84. (C) Decision curve analysis demonstrated superior performance of the nomogram over other clinical indicators.

FIGURE 4.

Immune microenvironment and mutation correlation analysis. (A) Heatmap depicts immune cell infiltration in high and low cuproptosis-related genes (CRGs) groups, with seven methods employed to assess the cancer immune microenvironment in corresponding risk groups. (B-E) The results of the mutation types in high-and low-CRGs groups. (F) Further analysis revealed that there were variations in the mutation rates of the same genes in high-and low-CRGs groups. (G) The CRGs risk level positive correlates with tumor mutational burden.
Immune microenvironment and mutation correlation analysis. (A) Heatmap depicts immune cell infiltration in high and low cuproptosis-related genes (CRGs) groups, with seven methods employed to assess the cancer immune microenvironment in corresponding risk groups. (B-E) The results of the mutation types in high-and low-CRGs groups. (F) Further analysis revealed that there were variations in the mutation rates of the same genes in high-and low-CRGs groups. (G) The CRGs risk level positive correlates with tumor mutational burden.

FIGURE 5.

Single-cell sequencing analysis to investigate the cellular localization of 8 model genes. (A–H) Expression patterns of 8 genes in single cells. (I–P) Univariate cox analysis of the prognostic value of 8 genes.
Single-cell sequencing analysis to investigate the cellular localization of 8 model genes. (A–H) Expression patterns of 8 genes in single cells. (I–P) Univariate cox analysis of the prognostic value of 8 genes.

FIGURE 6.

Cell experiment and screening of low cuproptosis-related genes (CRGs). (A) quantitative real time-PCR (qRT-PCR) to assess the expression of 8 cuproptosis-related genes (CRGs) in pancreatic epithelial cells (HPDE6-C7) and two pancreatic cancer cell lines (ASPC-1 and BXPC-3). (B) Immunohistochemical analysis revealed elevated protein expression of CEP55, FAM111B, MRPL3, MET, and KNSTRN in pancreatic cancer tissues, while DHX30 exhibited significantly higher expression in normal pancreatic tissues than in pancreatic cancer tissues among the 8 CRGs.
Cell experiment and screening of low cuproptosis-related genes (CRGs). (A) quantitative real time-PCR (qRT-PCR) to assess the expression of 8 cuproptosis-related genes (CRGs) in pancreatic epithelial cells (HPDE6-C7) and two pancreatic cancer cell lines (ASPC-1 and BXPC-3). (B) Immunohistochemical analysis revealed elevated protein expression of CEP55, FAM111B, MRPL3, MET, and KNSTRN in pancreatic cancer tissues, while DHX30 exhibited significantly higher expression in normal pancreatic tissues than in pancreatic cancer tissues among the 8 CRGs.

FIGURE 7.

CEP55 knockdown inhibits pancreatic adenocarcinoma (PAAD) in ASPC1 cell line. (A) quantitative real time-PCR (qRT-PCR) to assess the expression of CEP55. (B) The results of Cell Counting Kit-8 (CCK8). (C-D) CEP55 knockdown inhibits cell invasive ability by transwell assay. (E-F) CEP55 knockdown inhibits colony formation in ASPC1 cell line.
CEP55 knockdown inhibits pancreatic adenocarcinoma (PAAD) in ASPC1 cell line. (A) quantitative real time-PCR (qRT-PCR) to assess the expression of CEP55. (B) The results of Cell Counting Kit-8 (CCK8). (C-D) CEP55 knockdown inhibits cell invasive ability by transwell assay. (E-F) CEP55 knockdown inhibits colony formation in ASPC1 cell line.
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Sujets de la revue:
Médecine, Médecine clinique, Médecine interne, Hématologie, oncologie, Radiologie
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