Identification of Key Functional Modules and Immunomodulatory Regulators of Hepatocellular Carcinoma

Despite the advances in the treatment of hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfactory due to postsurgical recurrence and treatment resistance. Therefore, it is important to reveal the mechanisms underlying HCC and identify potential therapeutic targets against HCC, which could facilitate the development of novel therapies. Based on 12 HCC samples and 12 paired paracancerous normal tissues, we identified differentially expressed mRNAs and lncRNAs using the “limma” package in R software. Moreover, we used the weighted gene coexpression network analysis (WGCNA) to analyze the expression data and screened hub genes. Furthermore, we performed pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. In addition, the relative abundance of a given gene set was estimated by single-sample Gene Set Enrichment Analysis. We identified 687 differentially expressed mRNAs and 260 differentially expressed lncRNAs. A total of 6 modules were revealed by WGCNA, and MT1M and MT1E genes from the red module were identified as hub genes. Moreover, pathway analysis revealed the top 10 enriched KEGG pathways of upregulated or downregulated genes. Additionally, we also found that CD58 might act as an immune checkpoint gene in HCC via PD1/CTLA4 pathways and regulate the levels of tumor-infiltrating immune cells in HCC tissues, which might be an immunotherapeutic target in HCC. Our research identified key functional modules and immunomodulatory regulators for HCC, which might offer novel diagnostic biomarkers and/or therapeutic targets for cancer immunotherapy.

Download Full-text

CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

BioMed Research International ◽

10.1155/2019/1245072 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Wan-Xia Yang ◽

Yun-Yan Pan ◽

Chong-Ge You

Keyword(s):

Hepatocellular Carcinoma ◽

Age Of Onset ◽

Function Analysis ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Pathway Enrichment Analysis ◽

Poor Survival ◽

Hub Genes ◽

Normal Tissues

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein–protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

Download Full-text

Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.11273 ◽

2021 ◽

Vol 9 ◽

pp. e11273

Author(s):

Lei Yang ◽

Weilong Yin ◽

Xuechen Liu ◽

Fangcun Li ◽

Li Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Overall Survival ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Enrichment Score ◽

Hub Genes ◽

Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

Download Full-text

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.

Download Full-text

Blood and Brain Transcriptome Analysis Reveals APOE Genotype-mediated and Immune-related Pathways Involved in Alzheimer Disease

10.21203/rs.3.rs-892590/v1 ◽

2021 ◽

Author(s):

Rebecca Panitch ◽

Junming Hu ◽

Weiming Xia ◽

David A Bennett ◽

Thor D Stein ◽

...

Keyword(s):

Alzheimer Disease ◽

Blood Brain Barrier ◽

Vascular Injury ◽

Apoe Genotype ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Brain Barrier ◽

Gene Set Enrichment ◽

Gene Set

Abstract Background: While Alzheimer disease (AD) is generally considered as a brain disorder, blood biomarkers may be useful for diagnosis and prediction of AD brain pathology. The APOE ε4 allele has shown cerebrovascular effects including acceleration of blood brain barrier breakdown. Methods: We evaluated differential expression of previously established AD genes in brains from 344 pathologically confirmed AD cases and 232 controls and in blood from 112 pathologically confirmed AD cases and 67 controls from the Religious Orders Study and Memory and Aging Project. Differential gene expression between AD cases and controls was analyzed in the blood and brain jointly using a multivariate approach in the total sample and within APOE genotype groups. Gene set enrichment analysis was performed within APOE genotype groups using the results from the combined blood and brain analyses to identify biologically important pathways. Gene co-expression networks in brain and blood samples were investigated using weighted correlation network analysis. Top ranked genes from networks and pathways were further evaluated with vascular injury traits. Results: We observed differentially expressed genes with P<0.05 in both brain and blood for established AD genes INPP5D (upregulated) and HLA-DQA1 (downregulated). PIGHP1 and FRAS1 were differentially expressed at the transcriptome-wide level (P<3.3x10 -6 ) within ε2/ε3 and ε3/ε4 groups, respectively. Gene-set enrichment analysis revealed 21 significant pathways (false discovery rate P<0.05) in at least one APOE genotype group. Ten pathways were significantly enriched in the ε3/ε4 group, and six of these were unique to these subjects. Four pathways were enriched for AD upregulated genes in the ε3/ε4 group and AD downregulated genes in ε4 lacking subjects. We identified a co-expressed gene network in brain that reproduced in blood and showed higher average expression in ε4 carriers. Twenty-three genes from pathway and network analyses were significantly associated at P<0.05 with at least one vascular injury trait. Conclusion: These results suggest that APOE genotype contributes to unique expression network profiles in both blood and brain. Several genes in these networks are associated with measures of vascular injury and potentially contribute to ε4’s effect on the blood brain barrier.

Download Full-text

Bioinformatics analysis of potential core genes for glioblastoma

Bioscience Reports ◽

10.1042/bsr20201625 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Yu Zhang ◽

Xin Yang ◽

Xiao-Lin Zhu ◽

Jia-Qi Hao ◽

Hao Bai ◽

...

Keyword(s):

Survival Analysis ◽

Protein Interactions ◽

Poor Prognosis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Normal Brain ◽

Hub Genes ◽

Potential Core ◽

Core Genes

Abstract Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes. Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool. Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis. Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.

Download Full-text

Identification of Potential Autophagy-Related Genes in Steroid-Induced Osteonecrosis of the Femoral Head

10.21203/rs.3.rs-1066425/v1 ◽

2021 ◽

Author(s):

XueZhen LIANG ◽

Di LUO ◽

Yan-Rong CHEN ◽

Jia-Cheng LI ◽

Bo-Zhao YAN ◽

...

Keyword(s):

Femoral Head ◽

Correlation Analysis ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment

Abstract Purpose: Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people. Previous experimental studies had shown that autophagy might be involved in the pathological process of SONFH, but the pathogenesis of autophagy in SONFH remained unclear. We aim to identify and validate the key potential autophagy-related genes of SONFH to further illustrate the mechanism of autophagy in SONFH through bioinformatics analysis. Methods: The mRNA expression profile dataset GSE123568 was download from Gene Expression Omnibus (GEO) database, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples. The autophagy-related genes were obtained from the Human Autophagy Database (HADb). The autophagy-related genes of SONFH were screened by intersecting GSE123568 dataset with autophagy genes. The differentially expressed autophagy-related genes of SONFH were identified by R software. Besides, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted for the differentially expressed autophagy-related genes of SONFH by R software. Then, the correlation analysis between the expression levels of differentially expressed autophagy-related genes of SONFH was confirmed by R software. Moreover, the protein–protein interaction (PPI) network were analyzed by the Search Tool for the Retrieval of Interacting Genes (STRING), and the significant gene cluster modules were identified by the MCODE Cytoscape plugin, and hub genes of differentially expressed autophagy-related genes of SONFH were screened by the CytoHubba Cytoscape plugin. Finally, the expression levels of hub genes of differentially expressed autophagy-related genes of SONFH was validated in hip articular cartilage specimens from necrosis femur head (NFH) by GSE74089 dataset. Results: A total of 34 differentially expressed autophagy-related genes were identified between the peripheral blood of SONFH samples and non-SONFH Samples based on the defined criteria, including 25 up-regulated genes and 9 down-regulated genes. The GO and KEGG pathway enrichment analysis revealed that these 34 differentially expressed autophagy-related genes of SONFH were concentrated in death domain receptors, FOXO signaling pathway and apoptosis. The correlation analysis revealed a significant correlation among the 34 differentially expressed autophagy-related genes of SONFH. The PPI results demonstrated that the 34 differentially expressed autophagy-related genes interacted with each other. There were 10 hub genes identified by the MCC algorithms of Cytohubba. The results of GSE74089 dataset showed TNFSF10, PTEN and CFLAR were significantly upregulated while BCL2L1 were significantly downregulated in the hip cartilage specimens, which were consistent with the GSE123568 dataset. Conclusions: There were 34 potential autophagy-related genes of SONFH identified using bioinformatics analysis. TNFSF10, PTEN, CFLAR and BCL2L1 might serve as potential drug targets and biomarkers by regulating autophagy. These results would expand new insights into the autophagy-related understanding of SONFH and might be useful in the diagnosis and prognosis of SONFH.

Download Full-text

Potential Novel Therapeutic Targets In Epilepsy From The View of Intestinal Comorbidities - Inflammatory Bowel Disease

10.21203/rs.3.rs-960451/v1 ◽

2021 ◽

Author(s):

Mi Jiang ◽

Jia Li ◽

Zhi Song

Keyword(s):

Gene Expression ◽

Inflammatory Bowel Disease ◽

Bowel Disease ◽

Refractory Epilepsy ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Inflammatory Bowel ◽

Novel Therapeutic

Abstract Background: Epilepsy is a complicated neurological disorder with almost 30% refractory. Recent years, several studies showed that epilepsy is associated with its comorbidities by shared molecular mechanisms. However, the association of epilepsy and digestive comorbidities are still unclear. In this study, we aim to explore the association between inflammatory bowel disease (IBD) and epilepsy, and to find promising therapeutic targets for refractory epilepsy. Methods: Two gene expression profiles (GSE134697 and GSE59071) were selected from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R and the DESeq2 package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of intersection DEGs and Gene Set Enrichment Analysis (GSEA) were conducted by clusterProfiler package. The protein-protein interactions (PPI) network was established by using STRING and visualized by Cytoscape. Genes in the most significant module identified by MCODE plug-in were considered as candidate hub genes. Validation of hub genes were performed by using the GSE143272 dataset. Results: Cytokine-cytokine receptor interaction pathway is identified as a communal pathway between IBD and epilepsy. CXCL8, CXCR4 and ITGAX were identified as the hub genes. Conclusions: The identification of the communal pathway and hub genes in this study contributes to a potential novel therapeutic target in refractory epilepsy.

Download Full-text

Investigation of the miRNA and mRNA Coexpression Network and Their Prognostic Value in Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2020/8726567 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Hao Zhang ◽

Xi Chen ◽

Yufeng Yuan

Keyword(s):

Hepatocellular Carcinoma ◽

Gene Ontology ◽

Cell Adhesion ◽

Target Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Positive Regulation ◽

Differentially Expressed Mirnas ◽

Cell Cell

Purpose. To identify pivotal differentially expressed miRNAs and genes and construct their regulatory network in hepatocellular carcinoma. Methods. mRNA (GSE101728) and microRNA (GSE108724) microarray datasets were obtained from the NCBI Gene Expression Omnibus (GEO) database. Then, we identified the differentially expressed miRNAs and mRNAs. Sequentially, transcription factor enrichment and gene ontology (GO) enrichment analysis for miRNA were performed. Target genes of these differential miRNAs were obtained using packages in R language ( R package multiMiR). After that, downregulated miRNAs were matched with target mRNAs which were upregulated, while upregulated miRNAs were paired with downregulated target mRNA using scripts written in Perl. An miRNA-mRNA network was constructed and visualized in Cytoscape software. For miRNAs in the network, survival analysis was performed. And for genes in the network, we did gene ontology (GO) and KEGG pathway enrichment analysis. Results. A total of 35 miRNAs and 295 mRNAs were involved in the network. These differential genes were enriched in positive regulation of cell-cell adhesion, positive regulation of leukocyte cell-cell adhesion, and so on. Eight differentially expressed miRNAs were found to be associated with the OS of patients with HCC. Among which, miR-425 and miR-324 were upregulated while the other six, including miR-99a, miR-100, miR-125b, miR-145, miR-150, and miR-338, were downregulated. Conclusion. In conclusion, these results can provide a potential research direction for further studies about the mechanisms of how miRNA affects malignant behavior in hepatocellular carcinoma.

Download Full-text

Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis

BioMed Research International ◽

10.1155/2020/4251761 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Yuqin Tang ◽

Yongqiang Zhang ◽

Xun Hu

Keyword(s):

Hepatocellular Carcinoma ◽

Hepatitis B Virus ◽

Hepatitis B ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Diagnosis And Prognosis ◽

Prognostic Analysis ◽

B Virus

Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121248) were used to screen the 309 overlapping differentially expressed genes (DEGs), including 100 upregulated genes and 209 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to explore the biological function of DEGs. A PPI network based on the STRING database was constructed and visualized by the Cytoscape software, consisting of 209 nodes and 1676 edges. Then, we recognized 17 hub genes by CytoHubba plugin, which were further validated on additional three datasets (GSE14520, TCGA-LIHC, and ICGC-LIRI-JP). The diagnostic effectiveness of hub genes was assessed with receiver operating characteristic (ROC) analysis, and all hub genes displayed good performance in discriminating TNM stage I patient samples and normal tissue ones. For prognostic analysis, two prognostic key genes (TOP2A and KIF11) out of the 17 hub genes were screened and used to develop a prognostic signature, which showed good potential for overall survival (OS) stratification of HBV-HCC patients. Gene Set Enrichment Analysis (GSEA) was performed in order to better understand the function of this prognostic gene signature. Finally, the miRNA–mRNA regulatory relationships of all hub genes in human liver were predicted using miRNet. In conclusion, the current study gives further insight on the pathogenesis and carcinogenesis of HBV-HCC, and the identified DEGs provide a promising direction for improving the diagnostic, prognostic, and therapeutic outcomes of HBV-HCC.

Download Full-text

Deciphering Expression Profiling and Functional Signatures of Individualized Obesity-Associated Genes in Ossification of Ligamentum Flavum Through RNA-Sequence Data Mining

10.21203/rs.3.rs-955257/v1 ◽

2021 ◽

Author(s):

Baoliang Zhang ◽

Lei Yuan ◽

Guanghui Chen ◽

Xi Chen ◽

Xiaoxi Yang ◽

...

Keyword(s):

Correlation Analysis ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Immune Cells ◽

Ligamentum Flavum ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Ossification Of Ligamentum Flavum

Abstract Background: Obese individuals predispose to ossification of ligamentum flavum (OLF), whereas the underlying connections between obesity phenotype and OLF pathomechanism are not fully understood, especially during early life. This study aimed to explore obesity-associated genes and their functional signatures in OLF. Methods: Gene microarray expression data related to OLF were downloaded from the GSE106253 dataset in the Gene Expression Omnibus (GEO) database. The potential obesity-related differentially expressed genes (ORDEGs) in OLF were screened. Then, gene-ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied for these genes. Furthermore, protein-protein interactions (PPI) were used to identify hub ORDEGs, and Metascape was used to further verify the key signaling pathways and immune-related function signatures of hub ORDEGs. Finally, correlation analysis of hub ORDEGs and identified OLF-related infiltrating immune cells (OIICs) was constructed to understand the possible mechanical link among obesity, immune response and OLF. Results: OLF-related differentially expressed genes and 2051 obesity-related genes from four databases were intersected to obtain 99 ORDEGs, including 54 upregulated and 55 downregulated genes. GO and KEGG analysis revealed that these genes were mainly involved in metabolism, inflammation and immune-related biological functions and pathways. A PPI network was established to determine 14 hub genes (AKT1, CCL2, CCL5, CXCL2, ICAM1, IL10, MYC, PTGS2, SAA1, SOCS1, SOCS3, STAT3, TNFRSF1B and VEGFA). The co-expression network demonstrated that this module was associated with cellular response to biotic stimulus, regulation of inflammatory response, regulation of tyrosine phosphorylation of STAT protein. Furthermore, Metascape functional annotations showed that hub genes were mainly involved in receptor signaling pathway via JAK-STAT, response to TNF and regulation of defense response, and their representative enriched pathways were TNF, adipocytokine and JAK-STAT signaling pathways. Subgroup analysis indicated that T cell activation might be potential immune function processes involved, and correlation analysis revealed that cDCs, memory B-cells and preadipocytes were highly correlated infiltrating immune cells. Conclusions: Our study deciphered individualized obesity-associated gene signature for the first time, which may facilitate exploring the underlying cellular and molecular pathogenesis and novel therapeutic targets of obesity-related early-onset OLF.

Download Full-text