scholarly journals Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 612 ◽  
Author(s):  
Wenzong Lu ◽  
Ning Li ◽  
Fuyuan Liao
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Differentially Expressed Genes ◽  
Topoisomerase Ii ◽  
Leucine Zipper ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Topoisomerase Ii Alpha

Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas. DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were significantly linked with poor survival in pancreatic adenocarcinoma. Conclusions: These hub genes may be used as potential targets for pancreatic cancer diagnosis and treatment.

scholarly journals Investigation of Aberrantly Methylated Differentially Expressed Genes in Pancreatic Cancer by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Cailin xue ◽  
Peng gao ◽  
Xudong zhang ◽  
Xiaohan cui ◽  
Lei jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Dna Sequences ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Biological Processes ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Abnormal methylation of DNA sequences plays an important role in the development and progression of pancreatic cancer (PC). The purpose of this study was to identify abnormal methylation genes and related signaling pathways in PC by comprehensive bioinformatic analysis of three datasets in the Gene Expression Omnibus (GEO). Methods: Datasets of gene expression microarrays (GSE91035, GSE15471) and gene methylation microarrays (GSE37480) were downloaded from the GEO database. Aberrantly methylated-differentially expressed genes (DEGs) were analysis by GEO2R software. GO and KEGG enrichment analyses of selected genes were performed using DAVID database. A protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Core module analysis was performed by Mcode in Cytoscape. Hub genes were obtained by CytoHubba app. in Cytoscape software. Results: A total of 267 hypomethylation-high expression genes, which were enriched in biological processes of cell adhesion, biological adhesion and regulation of signaling were obtained. KEGG pathway enrichment showed ECM-receptor interaction, Focal adhesion and PI3K-Akt signaling pathway. The top 5 hub genes of PPI network were EZH2, CCNA2, CDC20, KIF11, UBE2C. As for hypermethylation-low expression genes, 202 genes were identified, which were enriched in biological processes of cellular amino acid biosynthesis process and positive regulation of PI3K activity, etc. The pathways enriched were the pancreatic secretion and biosynthesis of amino acids pathways, etc. The five significant hub genes were DLG3, GPT2, PLCB1, CXCL12 and GNG7. In addition, five genes, including CCNA2, KIF11, UBE2C, PLCB1 and GNG7, significantly associated with patient's prognosis were also identified. Conclusion: Novel genes with abnormal expression were identified, which will help us further understand the molecular mechanism and related signaling pathways of PC, and these aberrant genes could possibly serve as biomarkers for precise diagnosis and treatment of PC.

scholarly journals Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6425 ◽  
Author(s):  
Yang Fang ◽  
Pingping Wang ◽  
Lin Xia ◽  
Suwen Bai ◽  
Yonggang Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Expressed Genes ◽  
Protein Protein Interaction Network

Background The elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Methods Gene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app. Results In total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA. Conclusion After comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA.

scholarly journals Screening and Identification of Differentially Expressed Genes Expressed among Left and Right Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jing Han ◽  
Xue Zhang ◽  
Yang Yang ◽  
Li Feng ◽  
Gui-Ying Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Kinase Inhibitor ◽  
Colon Adenocarcinoma ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Right Colon ◽  
Hub Genes ◽  
Expression Levels ◽  
Public Data

Purpose. Colon adenocarcinoma (COAD) is the third most common malignancy globally and is further categorized as left colon adenocarcinoma (LCOAD) or right colon adenocarcinoma (RCOAD) depending on the location of the primary tumor. The therapeutic outcome and long-term prognosis for patients with COAD are less than satisfactory, and this may be associated with tumor location. Therefore, it is important to investigate the genetic differences in COAD at different sites. Patients and Methods. Public data associated with COAD were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software (version 3.5.3), and functional annotation of DEGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction network was constructed, hub genes were identified and analyzed, and data mining using Gene Expression Profiling Interactive Analysis (GEPIA) was conducted. Results. A total of 286 DEGs were identified between LCOAD and RCOAD. Additionally, 10 hub genes associated with COAD at different locations were screened, namely, CDKN2A, IGF1R, MDM2, SMAD3, SLC2A1, GRM5, PLCB4, FGFR1, UBE2V2, and TNFRSF10B. The expression of cyclin-dependent kinase inhibitor 2A (CDKN2A) and solute carrier family 2 member 1 (SLC2A1) was significantly associated with pathological stage P<0.05. COAD patients with high expression levels of CDKN2A exhibited poorer overall survival (OS) times than those with low expression levels P<0.05. Conclusion. CDKN2A expression was significantly different between LCOAD and RCOAD and was closely related to the prognosis of COAD. It is of great value for further understanding of the pathogenesis of LCOAD and RCOAD.

scholarly journals Construction of a competitive endogenous RNA network and analysis of potential regulatory axis targets in glioblastoma

2020 ◽  
Author(s):  
Kai Yu ◽  
Huan Yang ◽  
Qiao-li Lv ◽  
Li-chong Wang ◽  
Zi-long Tan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Differentially Expressed Genes ◽  
Pearson Correlation ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Network Chain ◽  
Cerna Network ◽  
Potential Marker

Abstract Background Glioblastoma is the most common primary malignant brain tumor. Due to the limited understanding of its pathogenesis, the prognosis of glioblastoma is poor. The purpose of this study is to explore potential ceRNA network chains and biomarkers in glioblastoma through integrated bioinformatics analysis. Methods Transcriptome expression data from The Cancer Genome Atlas database and Gene Expression Omnibus were analyzed to identify differentially expressed genes between glioblastoma tissue and normal tissue. The potential biological pathways associated with the differentially expressed genes were explored using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and a protein-protein interaction network was established using the STRING database and Cytoscape. Survival analysis using Gene Expression Profiling Interactive Analysis was based on the Kaplan-Meier curve method. The ceRNA network chain was established using the intersection method to align data from four databases (miRTarBase, miRcode, TargetScan, and lncBace2.0), and expression differences and correlations were verified by using quantitative reverse-transcription polymerase chain reaction analysis and determining the Pearson correlation coefficient. Results A total of 2842 DEmRNAs, 2577 DElncRNAs, and 309 DEmiRNAs were dysregulated in glioblastoma. The final ceRNA network consisted of six specific lncRNAs, four miRNAs, and four mRNAs. Among them, four DEmRNAs and one DElncRNA were correlated with overall survival (p < 0.05). We found that C1S was significantly correlated with overall survival (p = 0.015) and could therefore be used as a biomarker for glioblastoma. Conclusions Four ceRNA networks were established that may influence the occurrence and development of glioblastoma. Among them, the MIR155HG/has-miR-129-5p/C1S axis may be a potential marker and therapeutic target. In particular, C1S has not yet been reported in glioblastoma studies. These findings clarify the role of the ceRNA regulatory network in glioblastoma and lay a foundation for further research.

scholarly journals Bioinformatics Analysis and Identification of Potential Biomarkers in Gastric Cancer

2020 ◽  
Author(s):  
Jingdi Yang ◽  
Bo Peng ◽  
Xianzheng Qin ◽  
Tian Zhou
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.

scholarly journals Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

2021 ◽  
Author(s):  
Yanzhi Ge ◽  
Zuxiang Chen ◽  
Yanbin Fu ◽  
Li Zhou ◽  
Haipeng Xu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Hub Genes

Abstract Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with partially common phenotypes and genotypes. This study aimed to determine the mechanistic similarities and differences between osteoarthritis and rheumatoid arthritis by analyzing the differentially expressed genes and signaling pathways. Microarray data of osteoarthritis and rheumatoid arthritis were obtained from the Gene Expression Omnibus. By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified in synovial membrane samples from patients and healthy donations. Then, the Gene ontology significant functions annotation, Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction network analysis were conducted. Moreover, CIBERSORT was used to further distinguish OA and RA in immune infiltration. Finally, animal experimentation was conducted and the establishment of model, which was verified using PCR in the mouse. As an overlapping process, we identified 1116 DEGs between OA and RA. It was indicated that specific gene signatures differed significantly between OA and RA connected with the distinct pathways. Of identified DEGs, 9 immune cell types among 22 were identified to distinguish from each other. The qRT-PCR result showed that the eight-tenths expression levels of the hub genes were significantly increased in OA samples (P < 0.05). This large-scale gene expression study provided new insights for disease-associated genes and molecular mechanisms as well as their associated function in osteoarthritis and rheumatoid arthritis, which simultaneously offer a new direction for biomarker development and the distinguishment of gene-level mechanisms between osteoarthritis and rheumatoid arthritis.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

scholarly journals Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

2021 ◽  
Author(s):  
Li Guoquan ◽  
Du Junwei ◽  
He Qi ◽  
Fu Xinghao ◽  
Ji Feihong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Treatment Options ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Chronic Lymphocytic Thyroiditis ◽  
Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

scholarly journals Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Xin Qiu ◽  
Qin-Han Hou ◽  
Qiu-Yue Shi ◽  
Hai-Xing Jiang ◽  
Shan-Yu Qin
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Pancreatic Cancer ◽  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

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