scholarly journals Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8390 ◽  
Author(s):  
Weisong Cai ◽  
Haohuan Li ◽  
Yubiao Zhang ◽  
Guangtao Han
Keyword(s):  
T Cells ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Normal Controls

Background Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. Objective This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. Materials and Methods The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. Results A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. Conclusion The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.

scholarly journals Elucidating the Key Biomarkers and Immune Infiltration in Acne by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Lu Yang ◽  
Yan-hong Shou ◽  
Yong-sheng Yang ◽  
Jin-hua Xu
Keyword(s):  
Mast Cells ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Acne Vulgaris ◽  
Interaction Network ◽  
Microarray Dataset ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration

Abstract Background Acne vulgaris is a common inflammatory condition of skin. However, the landscape of immune infiltration in acne has not been entirely described. Objectives This study used a bioinformatics approach to investigate the inflammatory acne-related key biomarkers and signaling pathways, and immune infiltration in the acne lesion. Methods Two microarray datasets (GSE108110 and GSE53795) were downloaded from Gene Expression Omnibus. We used “limma” package from R software to identify the differentially expressed genes (DEGs) and perform the functional enrichment analyses. Then we built a protein-protein interaction network (PPI), performed the hub genes’ identification through STRING and Cytoscape. We applied the CIBERSORT algorithm to describe the immune infiltration in acne, and explored the correlation between biomarkers and immune infiltration. In the end, our findings in the study were verified by analyzing microarray dataset GSE6475. Results The differentially expressed genes (DEGs) including 292 upregulated genes and 150 downregulated genes in acne compared with non-lesional skin. The hub genes FPR1, C3AR1, CXCL1, CXCL8, FPR2, C3, CCR7, ITGB2 and pivotal pathways JAK-STAT signaling pathway, Toll-like receptor and NOD-like receptor signaling pathway were the most significantly associated with raising neutrophils, monocytes, activated mast cells, as well as reducing resting mast cells and Tregs. Conclusions Our study provides new insights into the pathogenesis and the targets which might be immunomodulatory potential for acne.

scholarly journals Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Da-Qiu Chen ◽  
Xiang-Sheng Kong ◽  
Xue-Bin Shen ◽  
Mao-Zhi Huang ◽  
Jian-Ping Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
High Morbidity ◽  
Hub Genes ◽  
Module Analysis

Background. Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods. From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results. 57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion. This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

scholarly journals Identification of Differentially Expressed Genes Reveals BGN Predicting Overall Survival and Tumor Immune Infiltration of Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Weizhi Chen ◽  
Zhongheng Yang
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Treatment Strategies ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Potential Biomarker ◽  
Positive Correlations ◽  
First Time

Gastric cancer (GC) is one of the most widely occurring malignancies worldwide. Although the diagnosis and treatment strategies of GC have been greatly improved in the past few decades, the morbidity and lethality rates of GC are still rising due to lacking early diagnosis strategies and powerful treatments. In this study, a total of 37 differentially expressed genes were identified in GC by analyzing TCGA, GSE118897, GSE19826, and GSE54129. Using the PPI database, we identified 17 hub genes in GC. By analyzing the expression of hub genes and OS, MFAP2, BGN, and TREM1 were related to the prognosis of GC. In addition, our results showed that higher levels of BGN exhibited a significant correlation with shorter OS time in GC. Nomogram analysis showed that the dysregulation of BGN could predict the prognosis of GC. Moreover, we revealed that BGN had a markedly negative correlation with B cells but had positive correlations with CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in GC samples. The pan-cancer analysis demonstrated that BGN was differentially expressed and related to tumor-infiltrating immune cells across human cancers. This study for the first time comprehensively revealed that BGN was a potential biomarker for the prediction of GC prognosis and tumor immune infiltration.

scholarly journals Identification of Immune Infiltration, Differentially Expressed Genes, and Signaling Pathways in Fanconi Anemia Based on Bioinformatics Analysis

2022 ◽  
Author(s):  
Biyu Shen ◽  
Songsong Shi ◽  
Haoyang Chen ◽  
Yi Lu ◽  
Hengmei Cui ◽  
...  
Keyword(s):  
T Cells ◽  
Differentially Expressed Genes ◽  
Fanconi Anemia ◽  
Bioinformatics Analysis ◽  
Neutrophil Activation ◽  
Normal Group ◽  
Differentially Expressed ◽  
Expression Levels ◽  
Immune Infiltration ◽  
Kegg Analysis

Abstract Background and Objective: Fanconi anemia (FA) patients have a reduced ability to form blood cells, accompanied by multiple congenital malformations, mental retardation, solid tumors, and other symptoms. However, the molecular mechanism that causes FA is unclear, and few studies have addressed the regulatory mechanism of immune infiltration in FA. Here, we aimed to identify differentially expressed genes (DEGs), pathways, and immune infiltration involved in FA using integrated bioinformatics analysis and molecular mechanisms. Methods: The GEO gene chip database was searched for FA low density bone marrow tissue, and the content and proportion of 22 types of immune cells in the FA group and the normal group were analyzed using CIBERSORT. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of FA differentially expressed genes (DEGs) using R language and related package programs was also performed.Results: The expression levels of T cells regulatory (Tregs), M2 macrophages, T cells CD8, dendritic cells resting, and T cells CD4 naïve in FA were higher than in the normal group. Furthermore, the expression levels of naïve B cells, monocytes, and resting mast cells in FA were lower than in the normal group. GO analysis of FA differential genes showed that “neutrophil degranulation,” “neutrophil activation,” and “neutrophil activation involved in immune response,” were most frequently enriched among biological processes, with “specific granule,” “tertiary granule,” “tertiary granule lumen” among cellular components, and “carbohydrate binding” among molecular functions. For the KEGG analysis, “Asthma” was most often enriched.Conclusion: This study obtained useful data related to immune infiltration, DEGs, and gene pathways of FA, and provides new evidence for immunotherapy and clinical assessment of FA patients. These results are potentially a useful reference for subsequent related scientific research.

scholarly journals POS0458 IDENTIFICATION OF HUB GENES AND MOLECULAR PATHWAYS IN PATIENTS WITH RHEUMATOID ARTHRITIS BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 460.1-460
Author(s):  
L. Cheng ◽  
S. X. Zhang ◽  
S. Song ◽  
C. Zheng ◽  
X. Sun ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Bioinformatics Analysis ◽  
Metabolic Process ◽  
Shanxi Province ◽  
Molecular Pathways ◽  
Biological Processes ◽  
Hub Genes ◽  
Tissue Samples

Background:Rheumatoid arthritis (RA) is a chronic, inflammatory synovitis based systemic disease of unknown etiology1. The genes and pathways in the inflamed synovium of RA patients are poorly understood.Objectives:This study aims to identify differentially expressed genes (DEGs) associated with the progression of synovitis in RA using bioinformatics analysis and explore its pathogenesis2.Methods:RA expression profile microarray data GSE89408 were acquired from the public gene chip database (GEO), including 152 synovial tissue samples from RA and 28 healthy synovial tissue samples. The DEGs of RA synovial tissues were screened by adopting the R software. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Protein-protein interaction (PPI) networks were assembled with Cytoscape software.Results:A total of 654 DEGs (268 up-regulated genes and 386 down-regulated genes) were obtained by the differential analysis. The GO enrichment results showed that the up-regulated genes were significantly enriched in the biological processes of myeloid leukocyte activation, cellular response to interferon-gamma and immune response-regulating signaling pathway, and the down-regulated genes were significantly enriched in the biological processes of extracellular matrix, retinoid metabolic process and regulation of lipid metabolic process. The KEGG annotation showed the up-regulated genes mainly participated in the staphylococcus aureus infection, chemokine signaling pathway, lysosome signaling pathway and the down-regulated genes mainly participated in the PPAR signaling pathway, AMPK signaling pathway, ECM-receptor interaction and so on. The 9 hub genes (PTPRC, TLR2, tyrobp, CTSS, CCL2, CCR5, B2M, fcgr1a and PPBP) were obtained based on the String database model by using the Cytoscape software and cytoHubba plugin3.Conclusion:The findings identified the molecular mechanisms and the key hub genes of pathogenesis and progression of RA.References:[1]Xiong Y, Mi BB, Liu MF, et al. Bioinformatics Analysis and Identification of Genes and Molecular Pathways Involved in Synovial Inflammation in Rheumatoid Arthritis. Med Sci Monit 2019;25:2246-56. doi: 10.12659/MSM.915451 [published Online First: 2019/03/28][2]Mun S, Lee J, Park A, et al. Proteomics Approach for the Discovery of Rheumatoid Arthritis Biomarkers Using Mass Spectrometry. Int J Mol Sci 2019;20(18) doi: 10.3390/ijms20184368 [published Online First: 2019/09/08][3]Zhu N, Hou J, Wu Y, et al. Identification of key genes in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis. Medicine (Baltimore) 2018;97(22):e10997. doi: 10.1097/MD.0000000000010997 [published Online First: 2018/06/01]Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Identification of Hub Genes and Immune Infiltration in Psoriasis by Bioinformatics Method

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenxing Su ◽  
Yuqian Wei ◽  
Biao Huang ◽  
Jiang Ji
Keyword(s):  
T Cells ◽  
B Cells ◽  
Nk Cells ◽  
Signaling Pathway ◽  
Control Sample ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Chemokine Signaling

BackgroundPsoriasis is a chronic, prolonged, and recurrent skin inflammatory disease. However, the pathogenesis of psoriasis is not completely clear, thus we aimed to explore potential molecular basis of it.MethodsTwo datasets were downloaded from the Gene Expression Omnibus database. After identifying the differentially expressed genes of psoriasis skin lesion samples and healthy controls, three kinds of analyses, namely functional annotation, protein-protein interaction (PPI) network, and immune infiltration analyses, were performed.ResultsA total of 152 up-regulated genes and 38 down-regulated genes were selected for subsequent analyses. Evaluation of the PPI network identified the most important module containing 13 hub genes. Gene ontology analysis showed that the hub genes have a significant enrichment effect on positive regulation of cell migration, defense response to the other organism and epithelial cell differentiation. KEGG signaling pathway analysis showed that the hub genes were significantly enriched in chemokine signaling, Toll-like receptor signaling pathway, and IL-17 signaling pathway. Compared with the normal control sample, naive B cells, CD8+ T cells, activated memory CD4+ T cells, follicular helper T cells, gamma delta T cells, resting NK cells, monocytes, M0 macrophages, M1 macrophages, activated dendritic cells and neutrophils infiltrated more, while memory B cells, naive CD4+ T cells, regulatory T cells (Tregs), activated NK cells, resting mast cells, and eosinophils infiltrated less.ConclusionTo conclude, the hub genes and pathways identified from psoriasis lesions and normal controls along with the immune infiltration profile may provide new insights into the study of psoriasis.

scholarly journals Bioinformatic gene analysis for potential therapeutic targets of Huntington’s disease in pre-symptomatic and symptomatic stage

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Chunchen Xiang ◽  
Shengri Cong ◽  
Bin Liang ◽  
Shuyan Cong
Keyword(s):  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Neurodegenerative Disorder ◽  
Psychiatric Symptoms ◽  
Therapeutic Targets ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Transcriptional Dysregulation

Abstract Background Huntington’s disease (HD) is a neurodegenerative disorder characterized by psychiatric symptoms, serious motor and cognitive deficits. Certain pathological changes can already be observed in pre-symptomatic HD (pre-HD) patients; however, the underlying molecular pathogenesis is still uncertain and no effective treatments are available until now. Here, we reanalyzed HD-related differentially expressed genes from the GEO database between symptomatic HD patients, pre-HD individuals, and healthy controls using bioinformatics analysis, hoping to get more insight in the pathogenesis of both pre-HD and HD, and shed a light in the potential therapeutic targets of the disease. Methods Pre-HD and symptomatic HD differentially expressed genes (DEGs) were screened by bioinformatics analysis Gene Expression Omnibus (GEO) dataset GSE1751. A protein–protein interaction (PPI) network was used to select hub genes. Subsequently, Gene Ontology (GO) enrichment analysis of DEGs and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of hub genes were applied. Dataset GSE24250 was downloaded to verify our hub genes by the Kaplan–Meier method using Graphpad Prism 5.0. Finally, target miRNAs of intersected hub genes involved in pre-HD and symptomatic HD were predicted. Results A total of 37 and 985 DEGs were identified in pre-HD and symptomatic HD, respectively. The hub genes, SIRT1, SUZ12, and PSMC6, may be implicated in pre-HD, and the hub genes, FIS1, SIRT1, CCNH, SUZ12, and 10 others, may be implicated in symptomatic HD. The intersected hub genes, SIRT1 and SUZ12, and their predicted target miRNAs, in particular miR-22-3p and miR-19b, may be significantly associated with pre-HD. Conclusion The PSMC6, SIRT1, and SUZ12 genes and their related ubiquitin-mediated proteolysis, transcriptional dysregulation, and histone metabolism are significantly associated with pre-HD. FIS1, CCNH, and their related mitochondrial disruption and transcriptional dysregulation processes are related to symptomatic HD, which might shed a light on the elucidation of potential therapeutic targets in HD.

scholarly journals Identification of Key Immune-Related Genes, Molecular Pathways and Immune Infiltration as Diagnostic and Therapeutic Candidate Targets for RA: an integrated bioinformatics-based analysis

2021 ◽  
Author(s):  
Sheng Fang ◽  
Xiao Fang ◽  
Xin Xu ◽  
Lin Zhong ◽  
An-quan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Mast Cells ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Systemic Autoimmune Disease ◽  
Molecular Pathways ◽  
Hub Genes ◽  
Immune Infiltration

Abstract Relevance Rheumatoid arthritis (RA) is a systemic autoimmune disease with an aggressive, chronic synovial inflammation as the main pathological change. However, the specific etiology, pathogenesis, and related biomarkers in diagnosis and treatment are still not fully elucidated. This study attempts to provide new perspectives and insights into RA at the genetic, molecular, and cellular levels through the tenet of personalized medicine. Methods Gene expression profiles of four individual knee synovial tissues were downloaded from a comprehensive gene expression database, R language was used to screen for significantly differentially expressed genes (DEGs), Gene Ontology Enrichment Analysis, Kyoto Gene Encyclopedia, and Gene Set Enrichment Analysis were performed to analyze the biological functions and signaling pathways of these DEGs, STRING online database was used to establish protein-protein interaction networks, Cytoscape software to obtain ten hub genes, Goplot to get six inflammatory immune-related hub genes, and CIBERSORT algorithm to impute immune infiltration. Results Molecular pathways that play important roles in RA were obtained: Toll-like receptors, AMPK, MAPK, TNF, FoxO, TGF-beta, PI3K and NF-κB pathways, Ten hub genes: Ccr1, Ccr2, Ccr5, Ccr7, Cxcl5, Cxcl6, Cxcl13, Ccl13, Adcy2, and Pnoc. among which Adcy2 and Pnoc have not been reported in RA studies, suggesting that they may be worthy targets for further study. It was also found that among the synoviocytes in RA, the proportions of plasma cells, CD8 T cells, follicular helper T cells, monocytes, γ delta T cells, and M0 macrophages were higher, while the proportions of CD4 memory resting T cells, regulatory T cells (Tregs), activated NK cells, resting dendritic cells, M1 macrophages, eosinophils, activated mast cells, resting mast cells were lower in proportion, and each cell played an important role in RA. Conclusions This study may help understand the key genes, molecular pathways, the role of inflammatory immune infiltrating cells in RA’s pathogenesis and provide new targets and ideas for the diagnosis and personalized treatment of RA.

scholarly journals A competing endogenous RNA mechanism in glioblastoma is investigated by bioinformatics analysis

2020 ◽  
Author(s):  
Jinsheng Wang ◽  
Yutao Wang ◽  
Lei Gao ◽  
Yuhua Zhao ◽  
Junhua Liu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Dendritic Cell Maturation ◽  
Vital Role ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
P53 Signaling

Abstract Background Glioblastoma (GBM) is the most aggressive and most lethal primary malignant brain tumor, the 5-year survival rate of which is less than 5%. Novel potential molecular and mechanism of GBM need to investigate.Materials and methods Microarray data of GSE15824 was downloaded from GEO. Differentially expressed genes and lncRNAs were screened by Limma package in R studio, and pathway enrichment analysis was performed by clusterprofiler package in R studio and IPA. The ceRNA mechanism was analyzed and predicted by several kinds of online public databases.ResultsThere were 567 differentially expressed genes and 121 differentially expressed lncRNAs in GBM. And differentially expressed genes were mainly enriched in Tuberculosis, Staphylococcus aureus infection, Systemic lupus erythematosus, Basal cell carcinoma, TGF-beta signaling pathway and p53 signaling pathway. Besides, Neuroinflammation signaling pathway, Role of NFAT in regulation of the immune response, and Dendritic cell maturation were significantly activated in GBM. According to the analysis of target miRNAs of SEM4D and OSER1-AS1, a possible ceRNA mechanism OSER1-AS1/hsa-miR-520h/SEMA4D axis was predicted in GBM.Conclusion Bioinformatics analysis was employed to analyze GSE15824 chip, and predict the potential mechanism. The results revealed that the ceRNA mechanism, OSER1-AS1/hsa-miR-520h/SEMA4D axis, might play a vital role in GBM.

scholarly journals Identification of critical pathways and potential therapeutic targets in Poorly differentiated duodenal papilla adenocarcinoma

2020 ◽  
Author(s):  
Yuanxiang Lu ◽  
Wensen Li ◽  
Ge Liu ◽  
Erwei Xiao ◽  
Senmao Mu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Therapeutic Targets ◽  
Malignant Neoplasm ◽  
Differentially Expressed ◽  
High Recurrence Rate ◽  
Rna Seq ◽  
Duodenal Papilla ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Duodenal papilla carcinoma (DPC) is a rare malignancy of the gastrointestinal tract with high recurrence rate, and the pathogenesis of this highly malignant neoplasm is yet to be fully elucidated. This study aims to identify key genes to further understand the biology and pathogenesis underlying the molecular alterations driving DPC, which could be potential diagnostic or therapeutic targets.Methods: Tumor samples of three DPC patients were collected and integrating RNA-seq analysis of tumor tissues and matched normal tissues were performed to discover differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to understand the potential bio-functions of the DPC differentially expressed genes (DEGs) and protein–protein interaction (PPI) network was constructed for functional modules analysis and dentification of hub genes. Results: A total of 110 DEGs were identified from our RNA-Seq data, GO and KEGG analyses showed that the DEGs were mainly enriched in multiple cancer-related functions and pathways, such as cell proliferation, IL-17signaling pathway, Jak-STAT signaling pathway, PPAR signaling pathway. The PPI network screened out six hub genes including IL-6, LEP, LCN2, CCND1, FABP4 and MMP1, which were identified as core genes in the network and potential therapeutic targets of DPC. Discussion: The current study provides new insight into the exploration of DPC pathogenesis and the screened hub genes may serve as potential diagnostic indicator and novel therapeutic target.

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