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Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high degree of malignancy that is difficult to diagnose and treat. The present study integrated PDAC cohort profile datasets to identify key candidate genes and pathways involved in the pathogenesis of the disease. The expression profiles of GSE28735 included 45 PDCA and matching pairs of adjacent non-tumor tissue. Differentially expressed genes (DEGs) were sorted and candidate genes and pathway enrichment were analyzed. A DEG-associated protein-protein interaction (PPI) network was constructed. A total of 424 DEGs were identified in PDAC, including 159 upregulated genes and 265 downregulated genes. Gene Ontology analysis results indicated that upregulated DEGs were significantly enriched in biological process, molecular function and cellular component categories. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that the upregulated DEGs were enriched in ‘pancreatic secretion’, ‘protein digestion’ and ‘absorption’. Downregulated DEGs were enriched in ‘ECM-receptor interaction’, ‘focal adhesion’ and ‘PI3K/AKT’ signaling pathways. The PPI network revealed that these genes were involved in significant pathways, including ‘ECM organization’ signaling pathways (Hippo signaling pathway, TGF-β signaling pathway, Hedgehog signaling pathway and Wnt signaling pathway), ‘serine-type peptidase activity’ signaling pathway (PI3K-Akt signaling pathway, TNF-α signaling pathway and Wnt signaling pathway) and ‘extracellular region’ signaling pathways (RTP signaling pathway, G protein-coupled receptor signaling pathway and RAS-RAF-MAPK signaling pathway). The identification of these candidate genes and pathways sheds light on the etiology and molecular mechanisms of PDAC and may guide the development of novel therapies for pancreatic cancer.


Introduction
Pancreatic cancer has a poor prognosis, with a median survival time of 3-6 month and a 5-year survival rate of less than 5% (1)(2)(3). The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), accounting for ~90% of pancreatic cancer cases (4). Although numerous studies have focused on the pathogenesis and progression of pancreatic cancer, the etiology and molecular mechanisms of pancreatic cancer remain unclear (5,6). Previous scientific studies have demonstrated that the occurrence and progression of pancreatic cancer involve the interaction of several factors, including gene mutations and environmental conditions (7,8). Thus far, there remains a lack of information regarding the molecular mechanisms that cause the development and progression of pancreatic cancer that would allow for improved precision therapies. Therefore, understanding the molecular mechanisms of pancreatic cancer can provide an effective basis for early prevention, diagnosis and treatment.
The advent of the gene chip and high-throughput gene analysis platforms allows for the rapid detection of gene expression in a microarray, which is particularly suitable for screening differentially expressed genes (DEGs) (9). With the widespread application of gene chip technology in cancer research, a large amount of genetic data has been produced and stored in public gene databases. Classification, integration and analysis of these data can provide valuable insights and evidence for cancer research. In the past few years, numerous gene chip expression profiles have been used to study the pathogenesis and development of PDAC and hundreds of DEGs have been identified (10). However, due to differences in sample size and limitations of the studies, no reliable biomarkers were identified. The combination of gene chip and biological information analysis technology can be used to monitor the expression of DEGs in the development and progression of PDAC and to elucidate the signaling pathways involved, potentially revealing targets which can be modulated to treat PDAC (11).
In the present study, the original GSE28735 data set (12) was downloaded from the Gene Expression Omnibus (GEO) database (13). The dataset contained the gene expression profiles of 45 matching pairs of pancreatic tumor and adjacent non-tumor tissues from 45 patients with PDAC. DEGs were detected by comparing the gene expression profiles between tumor tissues and paracancerous tissues in patients with PDAC. Subsequently, the DEGs were filtered using the Morpheus website (https://software.broadinstitute.org/morpheus/) with data processing standard. Then, the DEGs were screened using the Gene-Spring software (version 11.5; Agilent Technologies, Inc., Santa Clara, CA, USA), followed by Gene Ontology (GO); (www.geneontology.org) and pathway enrichment analysis. In addition, a protein-protein interaction (PPI) network was established and three significant modules were analyzed. The analysis of the biological pathways underlying the development of PDAC may provide information for its diagnosis, prognosis and treatment.

Materials and methods
Microarray data. The gene expression profiles of the GSE28735 dataset were downloaded from the GEO database. The GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array platform (Affymetrix; Thermo Fisher Scientific, Inc., Waltham, MA, USA) was used. The GSE28735 dataset contained 90 samples, including 45 PDAC tumor samples and 45 matching pairs of adjacent non-tumor tissue samples.
Identification of DEGs in GSE28735. The raw expression data files include TXT files (Affymetrix platform) used for analysis by processing using the Morpheus website. Data were categorized into two groups with similar expression patterns in PDAC tumor samples and matching pairs of adjacent non-tumor tissue samples. A t-test was used to identify the DEGs and |log 2 fold change|≥1 and P<0.05 were considered statistically significant.
Gene ontology and pathway enrichment analysis of DEGs. GO analysis was used to annotate genes and classify up and downregulated DEGs. GO terms are divided into three main categories: Biological process (BP), cellular component (CC) and molecular function (MF). The Kyoto Encyclopedia of Genes and Genomes (KEGG; www.kegg.jp) website is an online database which contains defined and associated gene sets and their pathways. The Database for Annotation, Visualization and Integrated Discovery (DAVID; david. ncifcrf.gov) allows analysis of gene lists and provides biological information regarding genes. To analyze the upregulated and downregulated genes in DEGs, GO and KEGG pathway analysis were used in the DAVID database. P<0.05 was considered to indicate a statistically significant difference.
Integration of PPI network. The Search Tool for the Retrieval of Interacting Genes (STRING; www.string-db. org) was used to evaluate the PPI information. The PPI network served to identify the key genes and Cytoscape software (version 3.51; www.cytoscape.org) was used to draw the network diagram. The topology of the PPI network was analyzed and the extent of the expression of each gene was calculated. P<0.05 was considered to indicate a statistically significant difference.

Results
Identification of DEGs in pancreatic cancer. A total of 45 PDAC tumor samples and 45 matching pairs of adjacent non-tumor tissue samples were analyzed. A total of 424 DEGs were identified from GSE28735, including 159 upregulated and 265 downregulated genes ( Table I). The heat map of DEG expression, presenting the top 50 upregulated and 50 downregulated genes was constructed using the web-based tool Morpheus (Fig. 1).

GO term and pathway enrichment analyses.
To further elucidate the function of the selected genes, the online software DAVID was used to perform DEG GO analysis. As aforementioned, GO analysis results classify DEG functions and pathways into three functional groups: BP, CC and MF. For BP, the upregulated DEGs were enriched in 'digestion', 'lipid digestion' and 'proteolysis', while the downregulated DEGs were enriched in 'ECM organization', 'extracellular structure organization' and 'cell adhesion' (Tables II and III). For CC, the upregulated DEGs were enriched in the 'extracellular region', and the downregulated DEGs were enriched in 'extracellular region' and 'ECM' (Tables II and III). For MF, the upregulated DEGs were enriched in 'serine-type peptidase activity', 'serine hydrolase activity' and 'peptidase activity', and the downregulated DEGs were enriched in 'ECM structural constituent', 'integrin binding' and 'cell adhesion molecule binding' (Tables II and III).
KEGG pathway analysis in pancreatic cancer. KEGG pathway analysis was used to analyze the most significantly enriched pathways of the upregulated DEGs and downregulated DEGs. The upregulated DEGs were enriched in 'pancreatic secretion', 'protein digestion and absorption' and 'fat digestion and absorption' (Table IV). The downregulated DEGs were enriched in 'ECM-receptor interaction', 'focal adhesion' and 'PI3K/Akt signaling' pathways (Table IV).

Discussion
The incidence of PDAC is increasing worldwide (14). The clinical signs and symptoms may be difficult to diagnose in the initial stages of the disease (7). Patients are often diagnosed at a late stage, when regional invasion or distant metastasis have occurred, resulting in a 5-year survival rate of ~5% (15,16). An insight into the molecular mechanisms of PDAC would allow for earlier diagnosis and more effective treatment. The rapid development of gene chips and high-throughput sequencing can rapidly and accurately provide gene expression data for thousands of genes in the human genome. Previous studies have identified some of the genes and signaling pathways that serve a role in the development of pancreatic cancer from chip analysis (17,18). In the present study, the chip data in the GSE28735 dataset was comprehensively analyzed and 424 common DEGs (159 upregulated and 265 downregulated) between PDAC and matching pairs of adjacent non-tumor tissue were identified using bioinformatics analysis. GO analysis is an international standardized gene function classification system that provides the molecular function of genes involved in a variety of biological processes (19). In the current study, GO term analysis revealed that the upregulated genes were mainly involved in 'digestion', 'lipid digestion' and 'proteolysis', and downregulated DEGs were involved in 'extracellular matrix organization', 'extracellular structure organization' and 'cell adhesion'. The pancreas mainly secretes trypsin and pancreatic lipase and abnormalities in secretions can interfere with protein and lipid metabolism, leading to chronic pancreatitis which is one of the important contributing factors for pancreatic cancer (20). The stability of cell structure and cell adhesion is also a major factor in the formation of pancreatic cancer (21). Furthermore, KEGG pathway analysis indicated that the upregulated DEGs were involved in pancreatic secretion pathways and protein and lipid digestion and absorption pathways. Existing studies revealed that metabolic change is considered one of the characteristics of cancer, especially the dysfunction of pancreatic secretion (11,22). In pancreatic cancer, metabolic changes are prominent in protein and lipid digestion and absorption pathways (23). The downregulated DEGs were associated with 'ECM-receptor interaction', 'focal adhesion' and the 'PI3K-Akt signaling' pathways.
In CFTR knockout mice, mucosal barrier function was impaired, including tight junction disruption, which resulted in impaired tolerance to bacterial colonization and infection, abnormal innate and adaptive immune responses, and inflammation (33,34). It has been reported that CFTR is a negative regulator of the pro-inflammatory nuclear factor k-light-chain-enhancer of activated B cells-mediated innate immune response, including interleukin-8, and evokes a positive feedback loop of cyclooxygenase 2-prostaglandin E2 in inflammation, and therefore, these factors may work together to promote tumorigenesis (35,36). The pancreas is a digestive organ that secretes a variety of substances to regulate the digestive fluids through exocrine and endocrine methods (37). At the same time, the abovementioned 10 hub genes can also regulate the development and progression of pancreatic cancer by regulating immune and inflammatory processes, protein glycosylation and energy metabolism which affect multiple signaling pathways (38)(39)(40)(41)(42)(43). Therefore, these genes can be an important target for the precise treatment of pancreatic cancer.
For the upregulated DEGs, module analysis of the PPI network revealed that they were associated with pancreatic secretion signaling pathways and 'protein digestion and absorption' and 'lipid digestion and absorption' signaling pathways. Stimulation of the pancreas by secretagogues, including acetylcholine and cholecystokinin, results in intracellular Ca 2+ signals, leading to the polarized secretion of enzymes (44). However, activation of the CFTR Clchannel and the CFTR-dependent Cl -/HCO 3 exchange is responsible for cAMP-induced HCO 3 secretion (44). The secretory function of the pancreas is directly associated with both protein and lipid metabolism in the body, the disruption of which may lead to chronic inflammation of the pancreas, developing into pancreatic cancer (45).
The downregulated DEGs were associated with ECM-receptor interactions, focal adhesion and the PI3K-Akt signaling pathway (46). The ECM serves an important role in the morphogenesis of tissues and organs, and in the maintenance of cell and tissue structures and functions (47). These interactions lead to direct or indirect control of cell activity, including adhesion, migration, differentiation, proliferation, and apoptosis (48). Furthermore, the focal adhesion signaling pathway is the key signaling pathway of cell matrix adhesion, which serves an important role in cell movement, cell proliferation, cell differentiation, gene expression regulation and cell survival (49). The proliferation and metastasis of cancer cells depend on the regulation of this pathway (50,51). The PI3K-Akt signaling pathway serves as a bridge between extracellular signals and intracellular responses (52,53). Once activated, Akt phosphorylation can be involved in apoptosis, matrix control, important cellular processes, protein synthesis, metabolism and the cell cycle (54). The results obtained in the DEGs, differentially expressed genes; PPI, protein-protein interactions; PDAC, pancreatic ductal adenocarcinoma.
current study suggest that pancreatic secretory dysfunction, the imbalance of ECM-associated signaling pathways and the PI3K-Akt signaling pathway may result in cell cycle disruption and metabolism-associated microenvironmental changes, which can trigger the development of pancreatic cancer.
In conclusion, the current study investigated the biological pathways involved in PDAC by providing a comprehensive bioinformatics map of DEGs. These DEGs are involved in the development and progression of PDAC and provide a basis for the effective study of the molecular mechanisms of pancreatic cancer. Further molecular biological experiments and animal studies are required to confirm the functions and roles of these DEGs in PDAC.

Acknowledgements
Not applicable.

Funding
This study was supported by the Key Foundation of Sichuan Municipal Commission of Health and Family Planning Foundation of China (grant no. 17ZD008) and the Sichuan Medical Research Project Foundation of China (grant no. S16007).

Availability of data and materials
The datasets analyzed during the current study are available in the GSE28735 repository (www.ncbi.nlm.nih.gov/geo).

Authors' contributions
YH, YL and HW conceived of and designed the experiments. YH, YL, JG, CL and HZ performed the experiments. YH, YL and HW acquired, analyzed and interpreted the data and wrote the paper.

Ethics approval and consent to participate
Not applicable.