scholarly journals Co-expression Analysis Revealed Key Biomarkers Associated With the Diagnosis and Progression of Hypertrophic Cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
Chenxin Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYL7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYL7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYL7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYL7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

Hereditas ◽  
2020 ◽  
Vol 157 (1) ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach. Methods The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the “pROC” R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the “proteasome” and a “PPAR signaling pathway,” respectively. Conclusions The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the “proteasome” and the “PPAR signaling pathway,” may play an important role in the development of HCM.

scholarly journals Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals Weighted Gene Co-Expression Network Analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

2020 ◽  
Author(s):  
Xin Li ◽  
Chenxin Wang ◽  
Xiaoqing Zhang ◽  
Jiali Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
R Package ◽  
Diagnostic Value ◽  
Wilcoxon Test ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Ppar Signaling

Abstract Objective: To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.Methods: The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results: Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.Conclusions: The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.

scholarly journals Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

2020 ◽  
Vol 10 ◽  
Author(s):  
Fang-Ze Wei ◽  
Shi-Wen Mei ◽  
Zhi-Jie Wang ◽  
Jia-Nan Chen ◽  
Hai-Yu Shen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Ncbi Gene Expression Omnibus ◽  
Public Datasets

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Microarray Data ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Joint Disease ◽  
Support Vector ◽  
Diagnostic Model ◽  
Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

scholarly journals HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhixin Wu ◽  
Yinxian Wen ◽  
Guanlan Fan ◽  
Hangyuan He ◽  
Siqi Zhou ◽  
...  
Keyword(s):  
Femoral Head ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

scholarly journals The mRNA landscape profiling reveals potential biomarkers associated with acute kidney injury AKI after kidney transplantation

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10441
Author(s):  
Hui Bi ◽  
Min Zhang ◽  
Jialin Wang ◽  
Gang Long
Keyword(s):  
Acute Kidney Injury ◽  
Kidney Transplantation ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Graft Function ◽  
Kidney Injury ◽  
Diagnostic Value ◽  
Hub Genes ◽  
Key Genes ◽  
Potential Biomarkers

Background This study aims to identify potential biomarkers associated with acute kidney injury (AKI) post kidney transplantation. Material and Methods Two mRNA expression profiles from Gene Expression Omnibus repertory were downloaded, including 20 delayed graft function (DGF) and 68 immediate graft function (IGF) samples. Differentially expressed genes (DEGs) were identified between DGF and IGF group. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed. Then, a protein-protein interaction analysis was performed to extract hub genes. The key genes were searched by literature retrieval and cross-validated based on the training dataset. An external dataset was used to validate the expression levels of key genes. Receiver operating characteristic curve analyses were performed to evaluate diagnostic performance of key genes for AKI. Results A total of 330 DEGs were identified between DGF and IGF samples, including 179 up-regulated and 151 down-regulated genes. Of these, OLIG3, EBF3 and ETV1 were transcription factor genes. Moreover, LEP, EIF4A3, WDR3, MC4R, PPP2CB, DDX21 and GPT served as hub genes in PPI network. EBF3 was significantly up-regulated in validation GSE139061 dataset, which was consistently with our initial gene differential expression analysis. Finally, we found that LEP had a great diagnostic value for AKI (AUC = 0.740). Conclusion EBF3 may be associated with the development of AKI following kidney transplantation. Furthermore, LEP had a good diagnostic value for AKI. These findings provide deeper insights into the diagnosis and management of AKI post renal transplantation.

scholarly journals Effects of a Sudden Drop in Salinity on Immune Response Mechanisms of Anadara kagoshimensis

2019 ◽  
Vol 20 (18) ◽  
pp. 4365 ◽  
Author(s):  
Mo Zhang ◽  
Li Li ◽  
Ying Liu ◽  
Xiaolong Gao
Keyword(s):  
Immune Response ◽  
Signaling Pathway ◽  
Transcriptome Sequencing ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Total Count ◽  
Sudden Drop ◽  
Blood Lymphocytes ◽  
Anadara Kagoshimensis ◽  
Comparison Groups

In this experiment, the effects of a sudden drop of salinity on the immune response mechanisms of the ark shell Anadara kagoshimensis were examined by simulating the sudden drop of salinity that occurs in seawater after a rainstorm. Additionally, the differentially expressed genes (DEGs) were identified using transcriptome sequencing. When the salinity dropped from 30‰ (S30) to 14‰ (S14), the phagocytic activity of blood lymphocytes, the O2− levels produced from respiratory burst, the content of reactive oxygen species, and the activities of lysozymes and acid phosphatases increased significantly, whereas the total count of blood lymphocytes did not increase. Total count of blood lymphocytes in 22‰ salinity (S22) was significantly higher than that in any other group. The raw data obtained from sequencing were processed with Trimmomatic (Version 0.36). The expression levels of unigenes were calculated using transcripts per million (TPM) based on the effects of sequencing depth, gene length, and sample on reads. Differential expression analysis was performed using DESeq (Version 1.12.4). Transcriptome sequencing revealed 269 (101 up-regulated, 168 down-regulated), 326 (246 up-regulated, 80 down-regulated), and 185 (132 up-regulated, 53 down-regulated) significant DEGs from comparison of the S14 vs. S22, S22 vs. S30, and S14 vs. S30 groups, respectively. Gene Ontology enrichment analysis of the DEGs in these salinity comparison groups revealed that the cellular amino acid metabolic process, the regulation of protein processing, the regulation of response to stress, and other terms were significantly enriched. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that nucleotide-binding, oligomerization domain (NOD)-like receptor signaling pathway (ko04621), apoptosis-multiple species (ko04215), Toll and Imd signaling pathway (ko04624), NF-κB signaling pathway (ko04064), apoptosis (ko04210), and focal adhesion (ko04510) were significantly enriched in all salinity comparison groups. qRT-PCR verification of 12 DEGs in the above six pathways was conducted, and the results were consistent with the transcriptome sequencing results in terms of up-regulation and down-regulation, which illustrates that the transcriptome sequencing data are credible. These results were used to preliminarily explore the effects of a sudden drop of salinity on blood physiological and biochemical indexes and immunoregulatory mechanisms of A. kagoshimensis. They also provide a theoretical basis for the selection of bottom areas optimal for release and proliferation of A. kagoshimensis required to restore the declining populations of this species.

scholarly journals Using Weighted Gene Co-Expression Network Analysis to Identify Key Genes Related to the Onset of Preeclampsia

2020 ◽  
Author(s):  
Xinyang Shen ◽  
Zhijian Wang ◽  
Zhirui Zeng ◽  
Zhenqin Huang ◽  
Xiaowei Huang ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Immune Response Gene ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Ontology Term ◽  
Gene Data ◽  
Core Genes

Abstract Background: Preeclampsia is a form of hypertension in pregnancy, which induced by complicated factors. However, the pathogenesis of the disease is unclear. The present study was aimed to discover the critical biomarkers associated with the occurrence and development of preeclampsia. Methods：Gene data profile GSE75010 was downloaded from the Gene Expression Omnibus (GEO) database and used as discovery cohort to establish a WGCNA network determining significant modules which associated with clinical traits. Subsequently, functional enrichment analysis, pathway analysis and protein-protein interaction (PPI) network construction were performed on the core genes in significant modules to identify hub genes. Then, gene data profile GSE25906 was used as verified cohort to determine their diagnostic value of hub genes. The protein expression levels of these hub genes in preeclampsia and control placental tissues were verified using immunohistochemistry method. Finally, GSEA was performed to analyze their enrichment pathways. Results: Total 33 co-expression modules were identified after the establishment of WGCNA, of which 4 gene modules were identified as significant modules because they were related to multiple (>3) clinical traits. Total 75 core genes in significant modules were analyzed, and results showed that they were mainly enriched in adaptive immune response (Gene Ontology term) and platelet activation (Kyoto Encyclopedia of Genes and Genomes term). Finally, a total of 5 genes including TYROBP, PLEK, LCP2, HCK, ITGAM were identified as hub genes which scored high in PPI network and had high diagnostic value. Furthermore, the protein level of these 5 genes in placental tissues of preeclampsia was lower than that of the control group. Moreover, these 5 genes were all enriched in 17 pathways, including autoimmunity pathway. Conclusions：These 5 genes (TYROBP, PLEK, LCP2, HCK, ITGAM) may be closely related to the pathogenesis of preeclampsia, which may also help the diagnosis and therapy of preeclampsia.

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