Study on potential differentially expressed genes in stroke by bioinformatics analysis

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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Effects of Serum Metabolites on the Pancreatic Transcriptome in Acute Acalculous Cholecystitis

Gastroenterology Research and Practice ◽

10.1155/2021/2368571 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yuanyuan Sun ◽

Qiang Wang ◽

Chenjun Hao ◽

Dongbo Xue

Keyword(s):

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Acalculous Cholecystitis ◽

Normal Control Group ◽

Control Group ◽

Acute Acalculous Cholecystitis ◽

Biliary Pancreatitis ◽

Metabolomic Analysis ◽

Serum Metabolites ◽

Key Genes

Background. To provide a basis for the diagnosis and treatment of acalculous biliary pancreatitis, this study investigated the impact of serum metabolites on the pancreatic transcriptome in acute acalculous cholecystitis (AAC). Methods. Fourteen rabbits were randomly divided into two groups (a normal control group of 7 rabbits and an AAC group of 7 rabbits), blood was collected from the 14 rabbits, and metabolomic analysis was performed through 1H NMR. Two pancreatic tissue chips of the AAC group and the normal control group were prepared and sequenced. We utilized the limma package of R software, the DAVID database, the STRING database, Cytoscape software, and the CFinder analysis tool to perform differential expression gene analysis, gene function enrichment analysis, protein interaction network (PPI) construction, and network module mining, and we performed gene enrichment analysis in each module. Results. Serum metabolism analysis showed that in AAC, the metabolism of sugar, lipids, and protein, that is, the three major nutrients, was affected to varying degrees, and levels of serum trimethylamine N-oxide (TMAO) increased. Bioinformatic methods were utilized to identify a total of 183 differentially expressed genes and 3 key genes. Enrichment analysis showed that differentially expressed genes were significantly enriched in cation transport, the inflammatory response, the NF-κB pathway, and the cancer signaling pathway. Conclusion. Metabolomic analysis and functional analysis of 3 key genes demonstrated that abnormal serum metabolites affected the pancreatic transcriptome and induced a sensitive state of inflammation in the pancreas. These metabolites may represent important targets for future research on the pathogenesis, clinical diagnosis, and treatment of noncalculous biliary pancreatitis.

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Identification of differentially expressed genes in Cushing's disease by integrated bioinformatics analysis

10.21203/rs.3.rs-29947/v1 ◽

2020 ◽

Author(s):

Tingting Lv ◽

Haoying Yu ◽

Shuyue Ren ◽

Jingrong Wang ◽

Lan Sun ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Cushing’S Disease ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Cushing's Disease ◽

Age Group ◽

Ppi Network ◽

Hub Genes ◽

Differential Genes

Abstract Background Cushing's disease is a rare and little-known disease, and the individualization of drug treatment varies greatly. Studies have shown that the gene expression profile of Cushing's disease is related to its clinical characteristics. Therefore, the study aims to identify key differential genes between the age and size of tumors through bioinformatics technology, thus providing a theoretical basis for personalized targeted therapy of Cushing's disease. Methods Downloading the gene expression microarray (GSE93825) data from the Gene Expression Omnibus (GEO) database and obtaining differentially expressed genes (DEGs) of different tumor sizes and ages through GEO2R. The DAVID database, Cytoscape and String platforms were utilized for functional enrichment analysis and protein-protein interaction (PPI) network analysis on selected differential genes. Results First, 96 DEGs were identified between macroadenoma (MAC) and microadenoma (MIC), which initially proved the different gene expression characteristics between them. Second, a total of 2128 DEGs were identified in MAC age group. The top five hub genes of the PPI network were GNGT2, LPAR3, PDYN, GRM3, and HTR1D. A total of 16 DEGs were identified in MIC age group. In addition, 88 DEGs were identified in younger MAC and MIC groups. The top five hub genes included LEP, PTGS2, STAT6, CXCL12, and ITPKB. 299 DEGs were identified in senior MAC and MIC groups. The first five hub genes were CCR7, LPAR2, CXCR5, ADCY3, and TAS2R14. By virtue of DAVID and Cytoscape software, the function enrichment analysis and core module analysis were performed successfully. Conclusions In summary, our research shows through bioinformatics analysis that different gene expression profiles of Cushing's disease are related to the size and age of the tumor, which may provide new insights into the molecular pathogenesis of Cushing's disease. These hub genes may be used for accurate diagnosis and treatment of Cushing's disease.

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AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1773 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1037.2-1038

Author(s):

X. Sun ◽

S. X. Zhang ◽

S. Song ◽

T. Kong ◽

C. Zheng ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Shanxi Province ◽

Receptor Interaction ◽

Term Therapy ◽

Pathway Enrichment Analysis ◽

Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].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

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Identification of Hub Genes Associated with Development of Lung Adenocarcinoma by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-550498/v1 ◽

2021 ◽

Author(s):

Jinglei Li ◽

Wei Hou

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Network Analysis ◽

Lung Adenocarcinoma ◽

Normal Control ◽

Gene Expression Analysis ◽

Normal Control Group ◽

Control Group ◽

Differential Gene Expression Analysis ◽

Differential Gene

Abstract Purpose: Lung adenocarcinoma (LUAD) has high heterogeneity and poor prognosis, posing a major challenge to human health worldwide. Therefore, it is necessary to improve our understanding of the molecular mechanism of LUAD in order to be able to better predict its prognosis and develop new therapeutic strategies for target genes.Methods: The Cancer Genome Atlas and Gene Expression Omnibus, were selected to comprehensively analyze and explore the differences between LUAD tumors and adjacent normal tissues. Critical gene information was obtained through weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and survival analysis.Results: Using WGCNA and differential gene expression analysis, 29 differentially expressed genes were screened. The functional annotation analysis showed these genes to be mainly concentrated in heart trabecula formation, regulation of inflammatory response, collagen-containing extracellular matrix, and metalloendopeptidase inhibitor activity. Also, in the protein–protein interaction network analysis, 10 central genes were identified using Cytoscape's CytoHubba plug-in. The expression of CDH5, TEK, TIMP3, EDNRB, EPAS1, MYL9, SPARCL1, KLF4, and TGFBR3 in LUAD tissue was found to be lower than that in the normal control group, while the expression of MMP1 in LUAD tissue was higher than that in the normal control group. According to survival analysis, the low expression of MYL9 and SPARCL1 was correlated with poor overall survival in patients with LUAD. Finally, through the verification of the Oncomine database, it was found that the expression levels of MYL9 and SPARCL1 were consistent with the mRNA levels in LUAD samples, and both were downregulated.Conclusion: Two survival-related genes, MYL9 and SPARCL1, were determined to be highly correlated with the development of LUAD. Both may play an essential role in the development LUAD and may be potential biomarkers for its diagnosis and treatment in the future.

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

10.21203/rs.3.rs-1023446/v1 ◽

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.

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Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

PeerJ ◽

10.7717/peerj.8831 ◽

2020 ◽

Vol 8 ◽

pp. e8831 ◽

Cited By ~ 1

Author(s):

Xiaojiao Guan ◽

Yao Yao ◽

Guangyao Bao ◽

Yue Wang ◽

Aimeng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Esophageal Cancer ◽

Esophageal Carcinoma ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Diagnostic Model ◽

Link Type ◽

Key Genes

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

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

10.21203/rs.3.rs-87182/v1 ◽

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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Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis

PeerJ ◽

10.7717/peerj.11320 ◽

2021 ◽

Vol 9 ◽

pp. e11320

Author(s):

Ying Pan ◽

Ye Meng ◽

Zhimin Zhai ◽

Shudao Xiong

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Normal Control ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Clinical Parameters ◽

Hub Genes ◽

Key Genes

Background Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles. Methods Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters. Results GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients. Conclusion In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.

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Bioinformatic Identification of Hub Genes and Biological Pathways for Sepsis-Associated Acute Lung Injury

10.21203/rs.3.rs-623296/v1 ◽

2021 ◽

Author(s):

Chao Zhang ◽

Feng Xu ◽

Fang Fang

Keyword(s):

Gene Expression ◽

Acute Lung Injury ◽

Lung Injury ◽

Function Analysis ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Biological Pathways ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Hub Genes

Abstract Background: Sepsis-associated acute lung injury (ALI) is a potentially lethal complication associated with a poor prognosis and high mortality worldwide, especially in the outbreak of COVID-19. However, the fundamental mechanisms of this complication were still not fully elucidated. Thus, we conducted this study to identify hub genes and biological pathways of sepsis-associated ALI, mainly focus on two pathways of LPS and HMGB1. Methods: Gene expression profile GSE3037 were downloaded from Gene Expression Omnibus (GEO) database, including 8 patients with sepsis-induced acute lung injury, with 8 unstimulated blood neutrophils, 8 LPS- induced neutrophils and 8 HMGB1-induced neutrophils. Diﬀerentially expressed genes (DEGs) identifications, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network constructions were performed to obtain hub genes and relevant biological pathways.Results: We identified 534 and 317 DEGs for LPS- and HMGB1-induced ALI, respectively. The biological pathways involved in LPS- and HMGB1-induced ALI were also identified accordingly. By PPI network analysis, we found that ten hub genes for LPS-induced ALI (CXCL8, TNF, IL6, IL1B, ICAM1, CXCL1, CXCL2, IL1A, IL1RN and CXCL3) and another ten hub genes for HMGB1-induced ALI (CCL20, CXCL2, CXCL1, CCL4, CXCL3, CXCL9, CCL21, CXCR6, KNG1 and SST). Furthermore, by combining analysis, the results revealed that genes of TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3 were potential biomarkers for sepsis-associated ALI. Conclusions: Our study revealed that ten hub genes associated with sepsis-induced ALI were TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3, which may serve as genetic biomarkers and be further verified in prospective experimental trials.

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