Background
Pancreatic adenocarcinoma (PAAD) is a prevalent malignant tumour of the digestive system, characterised by a short disease course, rapid disease deterioration and a high predisposition to death. In 2022, the number of new cases of PAAD (510,566) and deaths (467,005) worldwide was almost the same [ 1 ]. This finding not only demonstrates the high incidence rate of PAAD but also indicates its dismal prognosis. One study reported that the 5‐year survival rate of PAAD was less than 12% [ 2 ]. More than 80% of PAAD patients are in the advanced stages [ 3 ]. At this point, these PAAD patients have clinical symptoms such as abdominal pain, dyspepsia, jaundice, emaciation, anxiety and depression, which dramatically decrease their quality of life at the end of life [ 3 , 4 , 5 ]. The reason for this is that PAAD has biological behaviours, such as rapid proliferation, high invasiveness and distant metastasis [ 6 , 7 , 8 ]. Therefore, conducting further research on the biomarkers and mechanisms related to the development of PAAD is of great significance. Such research can facilitate the progress of early detection methods and therapeutic strategies, thereby enhancing the dismal clinical situation of PAAD patients.
Small ubiquitin‐like modifier 1 (SUMO1)‐activating enzyme subunit 1 (SAE1) functions as a SUMOylation by participating in the SUMO‐activating enzymes that constitute proteins [ 9 , 10 ]. The SUMO‐signalling cascade is essential for cell cycle progression and is an important target for antitumour activity [ 11 ]. Recently, some studies have shown that SAE1 plays an important role in the development of many tumours. SAE1 can promote the progression of intrahepatic cholangiocarcinoma by interacting with miR‐3185 to promote SUMOylation [ 12 ], and SAE1 can also promote the progression of hepatocellular carcinoma by facilitating the SUMOylation of the mTOR signalling pathway [ 13 ]. Reactive oxygen species (ROS) induce SAE1 upregulation by promoting helicobacter pylori to facilitate SUMOylation and thus hepatocellular carcinoma progression [ 14 ]. Some researchers not only used SAE1 to construct a risk model of nonsmall cell lung cancer but also observed in vitro that SAE1 could promote the proliferation and migration of nonsmall cell lung cancer cells [ 15 ]. Additionally, some researchers are trying to target SAE1 by SUMOylated small molecule inhibitors to treat B‐cell lymphoma [ 16 ]. However, unfortunately, the specific role or clinical significance of SAE1 in PAAD remains unclear.
For this reason, this study represents the first to comprehensively assess the role, molecular mechanism and potential clinical significance of SAE1 in PAAD by bulk RNA sequencing (bulk RNA‐Seq), single‐cell RNA sequencing (scRNA‐Seq), chromatin immunoprecipitation followed by sequencing (ChIP‐Seq), in‐house immunohistochemistry experiments, data from primary PAAD cell lines acted by the clustered regularly interspaced short palindromic repeats (CRISPR) system, drug sensitivity data and molecular docking models. The main analytical procedures of this study are elaborated in Figure 1 . These results will fill a gap in the field of PAAD research and have important implications for the advancement of the clinical diagnosis and treatment of PAAD.
Materials and Methods
Widespread Collection of mRNA Expression Profiles in PAAD and Integrated Analysis of SAE1 mRNA Expression Status in PAAD
In this study, mRNA expression profiles and corresponding clinical data of PAAD tissue available worldwide were extensively collected from databases such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Sequence Read Archive (SRA), ArrayExpress and Genotype Tissue Expression (GTEx). The specific process of searching, screening datasets and quality control is detailed in Supporting Information S1 : Figure S1. The included datasets are detailed in Supporting Information S1 : Table S1. SAE1 mRNA expression profiles were included from 1127 cases of PAAD and 702 cases of nonPAAD from 14 platforms. Fragments per kilobase per million (FPKM) was used to normalise the RNA expression matrix. Log2 ( x + 1) processing was used to normalise the mRNA expression matrix. The interquartile range method was used to identify abnormal outliers, and the values less than the lower boundary and greater than the upper boundary were replaced with the corresponding median to reduce the impact of outliers on subsequent data analysis. Due to technical limitations and biological factors, the appearance of missing values in the mRNA expression matrix is unavoidable. Upon inspection, the proportion of missing values in the expression matrix of this study was low or 0. After elimination, there will be no significant impact on the representativeness of the sample and the results of data analysis. Therefore, we remove the missing values. Then, the sva v3.46 package and limma‐voom v3.54 package of R v4.2.2 were used for batch correction [ 17 , 18 ]. We conducted Q‐tests and I 2 statistical analyses to select the calculation model needed to calculate the standardised mean deviation (SMD) of SAE1. If I 2 > 50%, there is significant heterogeneity, and a random effects model is used to calculate SMD; otherwise, a fixed effects model is chosen. Afterwards, these data were integrated, and the SMD of SAE1 was calculated to evaluate the expression status of SAE1 mRNA in PAAD.
Immunohistochemical Experiments on PAAD Tissue to Verify the Expression Status of SAE1 at the Protein Level
A total of 47 PAAD tissues and 15 independent nonPAAD tissues (from noncancer patients undergoing pancreatectomy for benign diseases) were included. Sample information is shown in Supporting Information S1 : Table S3. These 62 cases of pancreatic tissues were constructed into two tissue chips, PAC961 and PAC482, by Guilin Fanpu Biotech of Guangxi (a subsidiary wholly owned by Pantomics Inc.). SAE1 antibody (rabbit polyclonal antibody [ab97523]) was purchased from Abcam Plc (Shanghai, China). The detailed information about SAE1 antibody can be obtained through the following network link: ( https://www.abcam.cn/products/primary‐antibodies/sae1‐antibody‐ab97523.html ). All technical operations related to tissue chips and immunohistochemistry mentioned above were strictly carried out by the manufacturer's technical standards. The immunohistochemical scoring of SAE1 was independently completed by two senior pathologists. In this study, positive signals were defined as nuclear staining. Using the ‘ImageScope x64’ software to open digital images, 10 random tumour or nontumour high‐power fields were selected for manual counting. The grading criteria for staining intensity are no staining (0 points), mild (1 point), moderate (2 points) and strong staining (3 points). The scoring criteria for the proportion of positive cells are: 0%–5% (0 points), 6%–25% (1 point), 26%–50% (2 points), 51%–75% (3 points) and > 75% (4 points). The comprehensive score of each field is the product of staining intensity and positive cell ratio score, and the average of 10 fields is taken as the immunohistochemical score of the sample [ 19 ]. This study was licenced by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (No. 2023‐S382‐01) and Guilin Fanpu Biotech of Guangxi (No. Fanpu [2018] 23). All patients or family members provided informed consent. In this study, we also evaluated the expression status of SAE1 protein in PAAD tissue from The Human Protein Atlas. In addition, SAE1 mRNA expression data was integrated with internal SAE1 immunohistochemistry scores to further validate the expression status of SAE1.
Enrichment Analysis of SAE1‐Related Genes in PAAD
First, we batch calculated the SMD of each gene expression level in the mRNA dataset of PAAD from 32 datasets across 16 platforms worldwide to screen for differentially expressed genes (DEGs) of PAAD (SMD 95% confidence interval does not include 0, p < 0.05). Next, cBioPortal (date: October 8, 2023) was selected to analyse PAAD data from the TCGA to screen for co‐expressed genes of SAE1 (|Spearman's correlation| > 0.3, p < 0.05) [ 20 ]. The intersection of DEGs and SAE1 co‐expressed genes yields the intersection of genes. Afterwards, we performed gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis (date: October 10, 2023) on these intersecting genes to evaluate the potential biological functions of abnormal SAE1 in PAAD. In addition, STRING v12.0 was used to explore potential SUMO‐modified substrates regulated by SAE1 and involved in the progression of PAAD [ 21 ].
Comprehensive Analysis of Single Cells in PAAD Tissue
The single‐cell data in this study were obtained from the GSE242230 dataset of the GEO database, which includes primary PAAD samples. The data of these samples were processed successively through row integration, removal of low‐quality cells, exclusion of interfering factors, standardisation, normalisation and removal of batch effects by the Seurat v4.40 package and harmony v1.20. Next, Uniform Manifold Approximation and Projection (UMAP) and t‐distributed stochastic neighbour embedding (tSNE) dimensionality reduction were performed on the 20 principal components with the highest standard deviation. We used the SingleR v2.00 package to identify the cell types of each cluster [ 22 ]. We used the immune stromal cells of PAAD tissue as a reference and evaluated the malignancy of epithelial cells using inferCNV v1.14.2 packages [ 23 ]. We utilised the monocle v2.24 package for pseudotime analysis to explore the developmental and differentiation trajectory of malignant epithelial cells [ 24 ]. To verify the abnormal function of SAE1 in PAAD, we identified co‐expressed gene modules in malignant epithelial cells of PAAD tissue using the high‐dimensional weighted gene co‐expression network analysis (hdWGCNA) v0.2.26 package [ 25 ]. The TestSoftPowers function determines the threshold for generating the optimal topology network β. We conducted GO enrichment analysis on hub genes of co‐expressed gene modules to annotate their abnormal functions. Using the AddModuleScore function of the Seurat v4.40 package, we grouped cells based on the median score of the hub gene of the co‐expressed gene module [ 26 ]. We evaluated the differences in SAE1 expression between the high‐risk group and the low‐risk group of malignant epithelial cells to explore the possible mechanisms of SAE1 expression. The AddModuleScore_UCell function of the Seurat v4.40 package was used to perform proliferation and migration score analyses on malignant PAAD cells. The markers for proliferation include MKI67, IGF1, ITGB2, PDGFC, JAG1 and PHGDH [ 27 ]. The markers for migration include VIM, SNAI1, MMP9, AREG, ARID5B and FAT1 [ 27 ]. In addition, we used the Cellchat v1.6.1 package to analyse the cellular communication network between PAAD malignant epithelial cells and surrounding immune stromal cells [ 28 ].
Evaluation of the Effect of SAE1 on the Growth of PAAD Cells
In this study, Chronos score data were collected from 25 primary PAAD cell lines after knocking out SAE1 through the CRISPR system using DepMap [ 29 ]. A gene with a Chronos score less than −1 in more than 75% of PAAD cell lines was considered essential for the growth of PAAD [ 29 ].
Assessment of the Impact of SAE1 on the PAAD Mutant Landscape
This study collected 165 landscape maps of PAAD mutations from the TCGA to evaluate the impact of SAE1 on PAAD mutations. First, we used the maftools v2.18.1 package to batch calculate the tumour mutation burden of these 165 cases of PAAD and determine the relationship between SAE1 and PAAD tumour mutation burden [ 30 ]. Second, we divided these 165 cases of PAAD into a high SAE1 expression group and a low SAE1 expression group based on the median expression level to compare the difference in mutation frequency between the two groups. We also analysed the status of PAAD hotspot mutated genes and the main forms of PAAD mutations. In this study, the co‐mutation of PAAD hotspot mutation genes between the SAE1 high‐expression group and the SAE1 low‐expression group were also evaluated.
Exploration of Potential Transcriptional Regulatory Factors of SAE1 in PAAD
This study draws useful research ideas and methods from the excellent literature achievements of predecessors and then accurately screens the potential regulatory factors related to SAE1 transcription in PAAD with the help of the Citrome Data Browser database [ 31 , 32 , 33 ]. We analysed the possibility of SAE1 interacting with transcription regulatory factors based on the ChIP‐seq data. This study also predicts the potential binding sites of transcription regulatory factors to SAE1 in PAAD based on data from the JASPAR database [ 34 ]. We also comprehensively analysed the abnormal expression patterns of SAE1 transcription regulatory factors in PAAD based on the collected and organised mRNA expression profiles of PAAD tissue. The correlation between SAE1 and transcriptional regulatory factor expression levels was also evaluated to further analyse the transcriptional regulatory patterns of SAE1 abnormalities in PAAD. In addition, we also used UALCAN, cBioPortal and UCSC Xena databases to explore SAE1 gene alterations and methylation modifications in PAAD [ 35 , 36 ].
Evaluation of the Clinical and Pathological Significance of SAE1 Expression Levels in PAAD
This study used the receiver operating characteristic curve (ROC) and summary receiver operating characteristic curve (sROC) to analyse the collected PAAD SAE1 mRNA expression profile, PAAD internal protein expression levels and corresponding clinical information and evaluated the recognition ability of the SAE1 expression level on PAAD. We divided the PAAD from TCGA into a SAE1 high‐expression group and a SAE1 low‐expression group based on the median expression level of SAE1. We used GEPIA to compare the differences in overall survival and disease‐free survival between the SAE1 high‐expression group and the low‐expression group [ 37 ].
Analysis of the Potential Impact of SAE1 on the Efficacy of PAAD‐Targeted Therapy
In this study, PAAD tissue mRNA expression profiles from TCGA were analysed using the oncoPredict v0.2 package to screen for targeted therapeutics whose drug sensitivity showed a strong positive correlation ( R > 0.3, p < 0.01) with the SAE1 expression level [ 38 ]. Next, this study obtained the 3D structure of the SAE1 protein from the PDB database (PDB format) and the 3D structure of targeted therapeutic drugs from PubChem (sdf format). In this study, the 3D structures of SAE1 proteins and drugs were also subjected to the processes of repairing missing atoms and hydrogen atoms, removing water molecules and other heteroatoms, protein pocket probing, spatial parameter estimation and Vina score scoring using CB‐Dock2 to perform blind protein‐drug docking analysis [ 39 ]. Additionally, the molecular docking model with the best Vina score was selected for visual presentation. In this study, a Vina score less than −7 kcal/mol indicated a good binding ability; a Vina score greater than −7 but less than −4 kcal/mol represented a fair binding ability; and a Vina score greater than −4 kcal/mol indicated a poor binding ability.
Statistical Analysis
All statistical analyses involved in this study were conducted on R v4.2.2 and STATA v12. In this study, a p ‐value less than 0.05 is considered statistically significant.
Results
Comprehensive Evaluation of SAE1 mRNA Expression Status in PAAD
We collected the mRNA expression profiles of 1127 cases of PAAD and 702 cases of nonPAAD from 14 platforms worldwide to comprehensively evaluate the abnormal expression patterns of SAE1 mRNA in PAAD. There were eight platforms with significantly higher expression of SAE1 mRNA in PAAD than nonPAAD ( p < 0.05) (Supporting Information S1 : Figure S2). Given the questionable results obtained from a single dataset, this study also conducted an integrated analysis. Figure 2A shows that the expression of SAE1 mRNA in PAAD is significantly higher than that in nonPAAD (standardised mean difference [SMD] = 0.54; 95% confidence interval [CI]: 0.08–0.99; p < 0.01). Due to the wide range of sources of these datasets, there are inevitably many differences in the technical standards among them. This study recalculated the SMD of SAE1 mRNA using consecutively removed expression matrices and found no significant changes in SMD (Figure 2B ). This study also showed no significant publication bias in the analysis through Begg's test ( p > 0.05) and Egger's test ( p > 0.05) (Figure 2C ). These results further indicate that the expression matrices from 14 platforms can be integrated and analysed without significantly affecting the final results.
Verification of SAE1 Expression Status at the Protein Level in PAAD Tissue
The positive signal of the SAE1 protein is mainly located in the nuclear region of PAAD tissue (Figure 3A–C and Supporting Information S1 : Figure S3A,B). However, no significant SAE1‐positive signal was observed in nonPAAD tissue (Figure 3D–F and Supporting Information S1 : Figure S3C). A semi‐quantitative immunohistochemical score showed that the expression level of SAE1 protein in PAAD tissue was significantly higher than that in nonPAAD tissue ( p < 0.01) (Figure 3G ). Combining mRNA and protein expression level analysis showed that SAE1 was significantly overexpressed in PAAD tissue (Figure 3I ). These results further confirm the overexpression pattern of SAE1 in PAAD tissue.
Analysis of Potential Biological Behaviours of SAE1 in PAAD
We screened 521 intersecting genes for functional annotation analysis to further evaluate the abnormal biological behaviour of SAE1 in PAAD. GO enrichment analysis showed that abnormal SAE1 in PAAD may be involved in biological processes such as nuclear division, chromosome aggregation and DNA geometric change (Supporting Information S1 : Figure S4A), may involve cellular components such as condensed chromosomes, chromosomal regions and kinesin complexes (Supporting Information S1 : Figure S4B) and may have molecular functions such as adenosine triphosphate‐dependent activity on DNA, DNA helicase activity and microtubule motor activity (Supporting Information S1 : Figure S4C). In addition, KEGG enrichment analysis further confirmed that SAE1 may play a role in the occurrence and development of PAAD by participating in the cell cycle, DNA replication and P53 signalling pathways (Supporting Information S1 : Figure S4D). If the genes essential for the growth of PAAD cells are differentially expressed in PAAD (95% CIs for SMD excluding 0; p < 0.05), they may play an important role in the progression of PAAD (Supporting Information S1 : Figure S5A). The screened PAAD progression‐related genes are listed in Supporting Information S1 : Table S2. Enrichment analysis shows that these genes are involved in pathways such as ribosome biogenesis, cell cycle and DNA replication (Supporting Information S1 : Figure S5B‐C). These pathways are all important links in the progression of PAAD [ 40 , 41 , 42 ]. SAE1 may have a close interaction with genes related to PAAD progression, such as ACTL6A, PSMD14 and MYC (Supporting Information S1 : Figure S5D).
Evaluation of the Potential Impact of SAE1 on the Abnormal Single‐Cell Landscape of PAAD Tissue
Identification and Developmental Differentiation Status Analysis of Various Types of Cells in PAAD Tissue
By studying previous research studies and obtaining relevant methods from them, we further investigated the potential role of SAE1 in PAAD at the single‐cell level [ 43 , 44 , 45 ]. This study identified eight main cell types from PAAD tissue (Figure 4A,B ). SAE1 is mainly expressed in epithelial cells, B cells, T cells, NK cells and endothelial cells in PAAD tissue (Figure 4C ). InferCNV analysis of the epithelial cells of PAAD tissue showed a very high degree of malignancy (Figure 4D ). Pseudotime analysis of these malignant epithelial cells showed that they mainly developed and differentiated into two groups (Figure 4E ). The expression level of SAE1 gradually increased during this process (Figure 4F ). This finding indicates the potential regulatory role of SAE1 in the development and differentiation processes of malignant epithelial cells. The analysis of the two groups of malignant epithelial cells after the second fulcrum in Figure 4E identified genes that were significantly altered during the process of development and differentiation; they can be divided into four major categories (Figure 4G ). Analysis of the functions of these genes suggests that pathways such as cell growth (GO: 0016049), regulation of G1/S transition of the mitotic cell cycle (GO: 2000045), cell cycle G1/S phase transition (GO: 0044843), oxidative phosphorylation (GO: 0006119) and pattern specification process (GO: 0007389) may play important roles in the development and differentiation of malignant epithelial cells.
Identification of Co‐Expressed Gene Modules in Malignant Epithelial Cells in PAAD Tissue
The threshold for co‐expression analysis β takes 16 (Figure 5A ) and identifies the co‐expressed gene modules of 15 malignant epithelial cells (Figure 5B,C ). We found that the co‐expression modules of red, salmon, greenyellow, pink, green, yellow and black genes have strong specificity for malignant epithelial cells in PAAD (Figure 5C ). Interestingly, the black module of malignant epithelial cells participates in pathways such as regulation of chromosome organisation, positive regulation of the cell cycle, chromosome aggregation and regulation of DNA repair (Figure 5D ). This study also used the expression level of the nuclear hub gene in the black module to score various types of cells in PAAD tissue (Figure 5E ). Further research revealed a significant difference ( p < 0.01) in the expression level of SAE1 between the high‐risk group and the low‐risk group of malignant epithelial cells (Figure 5F ). This finding further confirms that SAE1 is involved in the occurrence and development of PAAD through chromosome segregation and cell cycle‐related pathways.
Cellular Communication Analysis Between Malignant Epithelial Cells and Immune Matrix Cells in PAAD Tissue
In addition, we used the hub gene of the black module to divide malignant epithelial cells into high‐ and low‐score groups to further explore deeper biological functions. Epithecal_cell_DOWN has a stronger effect on the annexin and MIF signalling pathways than Epithecal_cell_UP (Supporting Information S1 : Figure S6). In the MK signalling pathway, the opposite was true (Supporting Information S1 : Figure S6). This finding further emphasises the potential role of SAE1 in the occurrence and development of PAAD through black module‐related pathways.
Effects of SAE1 on the Proliferation and Migration of PAAD Cells
As shown in Figure 6A , among 25 PAAD cell lines, 23 had chronos scores lower than −1. Subsequently, the identified malignant epithelial cells were extracted and further subjected to cluster analysis, resulting in 12 clusters (Figure 6B ). Figure 6C depicts the expression status of SAE1 in PAAD cells. The results indicate that there is a significant difference in the expression level of SAE1 between PAAD cells with high and low proliferation scores ( p < 0.05) (Figure 6D,E ). The migration scores of PAAD cells also exhibit a similar trend (Figure 6F,G ). In conclusion, SAE1 has a certain impact on the proliferation and migration processes of PAAD cells.
Analysis of the PAAD Mutation Landscape
The tumour mutation burden of PAAD with high expression of SAE1 was significantly higher than that of the low‐expression group ( p < 0.01), and the expression level of SAE1 mRNA showed a strong positive correlation with the tumour mutation burden of PAAD ( R > 0, p < 0.01) (Figure 7A,B ). The frequency of PAAD mutations in the high‐expression group of SAE1 was also higher than that in the low‐expression group, especially in TP53, KRAS, SAMD4 and CDKN2A (Figure 7C,D ). The mutation form of PAAD was mainly a missense mutation, and C > T base substitution was its main form (Figure 7C,D ). However, the co‐mutation intensity of hotspot‐mutated genes in PAAD was relatively weak (Figure 7E,F ). These results suggest the potential role of SAE1 in the PAAD mutant landscape.
Analysis of Transcription Regulation Patterns of SAE1 in PAAD Tissue
The potential transcription regulatory factors of SAE1 included ETS2, MED1 and FOXA1 (Figure 8A ). The phenomenon of FOXA1 regulating SAE1 as a transcription factor can be observed in PAAD. Further research found that a distinct FOXA1 signal peak was observed upstream of SAE1 in PAAD (Figure 8B ). Figure 8C shows the binding site of FOXA1 upstream of SAE1. In addition, combined with the previously included expression profile analysis of pancreatic tissue, it was found that FOXA1 expression in PAAD was significantly higher than that in nonPAAD (SMD = 0.5; 95% CI: 0.14–0.87; p < 0.01) (Figure 8D ). A strong positive correlation was observed between FOXA1 and SAE1 expression levels in five PAAD expression profiles ( R > 0.3, p < 0.05) (Figure 8E ). In the five PAAD cohorts, only about 2% of PAAD patients exhibited SAE1 gene amplification (Supporting Information S1 : Figure S7A). Although the promoter methylation level of SAE1 in PAAD was higher than that in nonPAAD ( p < 0.05) (Supporting Information S1 : Figure S7B), there was no significant correlation between the modification level of methylation sites in the transcription start site region and the expression level of SAE1 ( p > 0.05) (Supporting Information S1 : Figure S7C). The above results may indicate that genetic alterations and methylation modifications have little impact on the abnormal expression pattern of SAE1. This result further confirms the positive transcriptional regulation mode of SAE1 by FOXA1 in PAAD.
Evaluation of the Clinical Significance of SAE1 in Clinical Pathology and Targeted Therapy in PAAD Tissue
Given the important role of SAE1 in the occurrence and development of PAAD, its potential clinical value is attractive. First, both the mRNA and protein expression levels of SAE1 had a strong ability to recognise PAAD (Figures 2 , 3 , Supporting Information S1 : Figures S8 and S9A,B). Second, the overall survival and disease‐free survival indicated that the prognosis of the SAE1 low‐expression group was significantly better than that of the SAE1 high‐expression group (Figure 9A ).
In terms of targeted therapy, this study showed a very strong positive correlation ( R > 0.3, p < 0.01) for four targeted therapeutic agents: axitinib, AZD6482, fludarabine and tozasertib (Figure 9B ). The binding energy of these four drugs to the molecular docking model of the SAE1 protein demonstrated the good binding ability of these four pairs of molecules (Figure 9C ). Further analysis revealed that these four pairs of molecules formed strong interaction forces, such as hydrogen bonds, ionic interactions and hydrophobic interactions (Figure 9C ).
Discussion
It is well known that cell cycle, chromosome segregation and DNA replication‐related pathways play important roles in the occurrence and development of tumours [ 46 , 47 , 48 , 49 , 50 ]. The role of SAE1 in promoting tumour occurrence and development has been reported in some tumours. Examples include nonsmall cell lung cancer, multiple myeloma and hepatocellular carcinoma [ 15 , 35 , 51 ]. However, few studies have focused on SAE1 in PAAD at this stage, resulting in a lack of systematic and comprehensive analysis of the specific biological functions and potential clinical value of SAE1 in PAAD. In view of this, this study pioneered the integration of large‐scale sample evidence from multiple centres around the world, covering PAAD mRNA expression profiles, single‐cell abnormal landscapes, transcriptional regulation patterns, mutation maps, internal immunohistochemistry experimental results, CRISPR data, drug sensitivity data and molecular docking models. From the three key dimensions of SAE1 overexpression characterisation in PAAD, potential molecular biological mechanisms of action and clinical application potential, this study aims to provide new and in‐depth theoretical insights for this field, fill the current research gaps and help optimise the development of PAAD precision diagnosis and treatment strategies.
In this study, 1829 mRNA expression profiles from 14 platforms were integrated for the first time to accurately evaluate the overexpression of SAE1 in PAAD—the origins and preparation processes of each external dataset may be significantly different, such as the technical specifications followed by different research institutions being uneven. However, after rigorous computational analysis, it was found that the merging of such datasets for analysis did not cause significant bias in the results. At the same time, the 62 pancreatic tissue samples in this study were verified by protein immunohistochemistry, which further confirmed the accuracy and rationality of the results obtained. Therefore, the extensive and diverse sources of research data strongly support the accuracy and universality of the argument that SAE1 is overexpressed in PAAD. In addition, with the help of the CRISPR system, SAE1 gene knockout was implemented in 25 PAAD cell lines, and a comprehensive analysis was performed, which deeply explained the key efficacy of SAE1 in the proliferation and growth processes of PAAD cells. Given that SAE1 may play a key role in promoting the occurrence and development of PAAD, this study integrated multiple analytical methods to provide strong evidence support. Multidimensional exploration based on bulk RNA‐Seq enrichment analysis, scRNA‐Seq pseudotime analysis, WGCNA analysis and cell communication analysis showed that abnormally expressed SAE1 in PAAD is closely associated with key pathways such as cell cycle, chromosome separation and DNA repair. In terms of chromosome separation and DNA replication, although there is currently no special research on the molecular biological mechanism of SAE1 in PAAD‐related pathways, a small number of explorations in other tumour types have provided certain reference information for understanding this mechanism, suggesting that its potential mode of action in the context of PAAD may have something in common that urgently needs to be further explored. Previous studies have shown that under in vitro conditions, the downregulation of SAE1 can slow down the chromosomal segregation and mitotic process of tumour cells [ 52 ]. In the case of triple‐negative breast cancer, SAE1 is closely associated with DNA damage‐ and DNA repair‐related pathways [ 53 ]. Within the scope of PAAD research, the correlation between chromosomal segregation and DNA repair‐related pathways and the progression of PAAD has been frequently reported. For example, the transferrin receptor can deeply participate in the occurrence and evolution of PAAD through pathways related to chromosome condensation, chromosomal segregation and cell cycle checkpoints [ 54 ]. Additionally, DNA damage and repair responses play an indispensable and crucial role in the onset, development and implementation of various treatment modalities for PAAD [ 55 ]. Based on the above information, it is highly reasonable and likely that the interaction between SAE1 and chromosomal segregation and DNA repair‐related pathways drive the progression of PAAD.
In addition, an abnormally dysregulated cell cycle is an important factor in promoting tumour occurrence and development [ 56 , 57 ]. This study links SAE1 in PAAD with dysregulated cell cycle‐related pathways. There are research reports that the overexpression pattern of SAE1 in tumour cells promotes AKT SUMOylation and cell cycle progression to promote tumour progression [ 58 ]. The downregulation of SAE1 can lead to a decrease in cyclin D1 expression, and p‐NF‐ κ B/NF‐ κ B reduces the proliferation of tumour cells and enhances their radiosensitivity [ 9 ]. In mantle cell lymphoma cells, the upregulation of SAE1 is strongly correlated with cell cycle, chromosome segregation and overexpression of centromere‐related genes [ 59 ]. P53, KRAS and CDKN2A mutations play important roles in cell cycle‐related pathways and tumour progression [ 60 , 61 , 62 ]. Through landscape analysis of PAAD mutations, this study found that overexpressing SAE1 was associated with higher mutation frequencies of these genes. In addition, this study revealed the potential regulatory roles of SAE1 and cell cycle‐related pathways in the annexin and MK signalling pathways of malignant epithelial cells in PAAD tissue. The specific inhibition of annexin A1 (ANXA1) in the annexin pathway and its interaction with formalin peptide receptors 1 and 2 can block the cell cycle progression of PAAD cells, leading to significant inhibition of PAAD proliferation [ 63 ]. Integrin β1 can enhance the progression of tumours by accelerating the cell cycle process in the MK signalling pathway [ 64 ]. Other important molecules in the MK signalling pathway, midkine and nucleolin, are essential for the cell cycle progression of cancer cells [ 65 , 66 ]. These are similar to the results of our study on cell communication. In summary, it is reasonable for overexpressed SAE1 to promote the occurrence and development of PAAD through dysregulated cell cycle‐related pathways.
To gain deeper insights into the regulatory mechanism underlying the abnormal biological behaviour of SAE1, this study, through ChIP‐Seq analysis, revealed that aberrant FOXA1 is likely to exert a positive regulatory effect on the abnormal biological manifestations of SAE1 within the context of PAAD. It has been previously documented in the literature that the overexpression of FOXA1 can activate its affiliated transcriptional network, consequently facilitating the metastasis of PAAD [ 67 ]. FOXA1 can also participate in promoting the progression of PAAD by inducing the upregulation of MIR99AHG [ 68 ]. FOXA1 is involved in regulating lipid and fatty acid metabolism related to the therapeutic resistance and poor prognosis of PAAD [ 69 ]. In addition, ivermectin can affect cell cycle checkpoints and inhibit tumour cell proliferation by interacting with FOXA1 [ 70 ]. FOXA1 can participate in the biological processes of the tumour cell cycle and DNA replication by regulating the expression status of BRG1 [ 71 ]. FOXA1 prolongs the S phase of cell cycle progression and promotes tumour progression by upregulating CDC5L and activating the ERK1/2 and JAK2 pathways [ 72 ]. Therefore, SAE1, which participates in pathways such as the cell cycle and DNA replication, is likely to be regulated by FOXA1 and to promote the occurrence and development of PAAD.
Given the above understanding of the biological basis of SAE1 in the occurrence and development of PAAD, this study also attempts to evaluate the attractive clinical potential of SAE1 in PAAD. In terms of clinical pathology, SAE1 exhibits a strong ability to recognise PAAD and has the potential to predict prognosis. Therefore, in the future, we can develop more efficient diagnostic and prognostic strategies for PAAD. In terms of targeted therapy, studies have attempted to target SAE1 to treat B‐cell lymphoma and multiple myeloma [ 16 , 51 ]. This finding indicates that SAE1 is a promising target in tumour‐targeted therapy. Therefore, this study demonstrated through sensitivity analysis and stable molecular docking model construction of four targeted therapeutic drugs—axitinib, AZD6482, fludarabine and tozasertib—that these four drugs may exert anti‐PAAD therapeutic effects by specifically targeting the SAE1‐related pathway. There have been many reports on the antitumour effects of these drugs in other tumours. It has been reported that axitinib can block the cell cycle process of renal cell carcinoma cells, lung carcinoid cells and breast cancer cells and can promote cell apoptosis to have an antitumour effect [ 73 , 74 , 75 ]. AZD6482 can specifically target PI3K β to further block cell cycle progression and inhibit cell proliferation to exert antitumour effects [ 76 ]. Fludarabine is a first‐line treatment drug for chronic lymphocytic leukaemia [ 77 ]. Fludarabine can disrupt the cell cycle process of lymphoma cells by blocking the G0/G1 phase and promoting cell apoptosis [ 78 ]. Tozasertib treats tumours by inhibiting the intensity of the MIF signalling pathway between melanoma cells and lymphocytes by inhibiting the cell cycle checkpoint Aurora kinase B [ 79 ]. Tozasertib can inhibit tumour cell proliferation and migration by upregulating mitochondrial permeability, increasing the production of ROS and promoting cell cycle arrest [ 80 ]. These studies indicate that these drugs exert antitumour effects by acting on cell cycle‐related pathways. Therefore, these drugs are likely potential drugs for the targeted treatment of PAAD.
The conclusion of this study is based on multi‐platform integration analysis, which has the advantage of improving statistical effectiveness through a large sample size. However, the heterogeneity of individual datasets suggests the need to further expand the single platform queue or adopt higher sensitivity detection techniques (such as single‐cell sequencing) to validate the expression pattern of SAE1 in the future. Furthermore, the potential impact of PAAD subtypes or molecular stratification deserves further exploration. Despite many exciting new discoveries in this study, there are still some drawbacks worth noting. Firstly, although this study used a large amount of external and internal validation data to illustrate the overexpression pattern of SAE1 in PAAD and its role in the occurrence and development of PAAD, there is still a lack of relevant in vitro and in vivo experiments to supplement and validate our results. Secondly, due to objective limitations, we are unable to comprehensively evaluate the clinical significance of SAE1 in other aspects from multiple perspectives. Thirdly, existing technology limits our understanding of the deeper mechanisms of SAE1 in PAAD.
Conclusion
This study reveals for the first time that SAE1 may promote the occurrence and development of PAAD through cell cycle‐related pathways. FOXA1 may be a regulatory factor in the abnormal biological behaviour of SAE1.
Abbreviations
pancreatic adenocarcinoma
SUMO1 activating enzyme subunit 1
Author Contributions
Yi Chen and Tong Wu conducted computational biology analysis and wrote the manuscript. Qi Li and Ming‐Jie L conducted immunohistochemical experiments. Na Yu, Li‐Jueyi Meng, Xian‐Jin Chen, Bang‐Teng Chi, Shi‐De Li and Su‐Ning Huang collected and integrated data and samples. Gang Chen, Dan‐Ming Wei and Yu‐Ping Ye designed the study and corrected the draft. All authors have reviewed and endorsed the final manuscript.
Ethics Statement
This study was licenced by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (No. 2023‐S382‐01) and Guilin Fanpu Biotech of Guangxi (No. Fanpu [2018] 23).
Consent
All patients or family members have provided informed consent to participate in the study.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supporting Information S1
Acknowledgements
We would like to thank the Guangxi Zhuang Autonomous Region Clinical Medical Molecular Pathology and Intelligent Pathology Precision Diagnosis Research Centre for its technical support in computational pathology and clinical pathology.
Handling Editor: Hao Wu
Funding: The work was supported by Future Academic Star of Guangxi Medical University (WLXSZX24118), China Undergraduate Innovation and Entrepreneurship Training Program (S202410598212), Guangxi Zhuang Autonomous Region Administration of Traditional Chinese Medicine Scientific Research Project (GXZYA20230267), Guangxi Zhuang Autonomous Region Administration of Traditional Chinese Medicine Scientific Research Project (GXZYA20230270), Guangxi Higher Education Undergraduate Teaching Reform Project (2022JGA146), Guangxi Educational Science Planning Key Project (2022ZJY2791), Guangxi Medical University Undergraduate Education and Teaching Reform Project (2023Z10) and Natural Science Foundation of Guangxi Province (2025GXNSFAA069660).
Yi Chen and Tong Wu contributed equally to this work.
Contributor Information
Yu‐Ping Ye, Email: ypye18@foxmail.com.
Dan‐Ming Wei, Email: weidanming@gxmu.edu.cn.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
Supplementary Materials
Supporting Information S1
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.