scholarly journals Bioinformatic Analysis of Key Genes Related to Tumor Microenvironment in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Jingxu Zhang ◽  
Hao Liu ◽  
Keyi Zhao ◽  
Zhiye Bao ◽  
Zhishuo Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Expression Data ◽  
Hub Genes ◽  
Oncomine Database ◽  
Key Genes ◽  
Low Expression

Abstract Background: Tumor microenvironment (TME) plays important roles in the development of different types of cancer. However, the critical regulatory members of TME related to hepatocellular carcinoma (HCC) remain unclear. In this study, a bioinformatic analysis based on Cancer Genome Atlas (TCGA) and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) datasets was conducted to predict the key genes affecting TME in HCC.Material and Methods: First, 340 patients and 20531 genes’ expression data with ESTIMATE scores were filtered and combined to identify differentially expressed genes. Next, protein-protein interaction (PPI) network and functional enrichment analysis were conducted to find hub genes. Then, log-rank test and functional enrichment analysis were conducted on the consensus genes and hub genes. Finally, Kaplan-Meier curves of the hub genes were drawn. As verification, those genes were searched on Oncomine database.Results: Among all differentially expressed genes, 916 genes were expressed in both the immune and stromal groups. The Gene Ontology (GO) terms they enriched were T cell activation, leukocyte migration, collagen-containing matrix, external side of plasma membrane, receptor ligand and activator activity. Cytokine-cytokine receptor interaction was the most significant Kyoto Encyclopedia of Genes and Genomes (KEGG) term. Furthermore, cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 hub genes were low expressed in 304 patients, participating in a variety of responses including immune response−activating cell surface receptor signaling, immune response−activating signal transduction, clathrin−coated vesicle membrane, immune receptor activity， peptide binding and amide binding pathways. Their low expression was also verified on Oncomine database.Conclusion: cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 participated in many aspects related to TME, and their low expression constructs a signature, may predict a poor 5 years’ survival in hepatocellular carcinoma.

scholarly journals Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11321
Author(s):  
Di Zhang ◽  
Pengguang Yan ◽  
Taotao Han ◽  
Xiaoyun Cheng ◽  
Jingnan Li
Keyword(s):  
Ulcerative Colitis ◽  
Mitochondrial Dysfunction ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Junction ◽  
Biological Processes ◽  
Hub Genes ◽  
Key Genes

Background Ulcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC. Methods Microarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the “limma” R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the “clusterProfiler” R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene. Results Six functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3. Conclusions Using WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

scholarly journals Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingyuan Zhang ◽  
Xinkui Liu ◽  
Wei Zhou ◽  
Shan Lu ◽  
Chao Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Differential Expression ◽  
Chronic Inflammation ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Gene Markers ◽  
Key Genes

BackgroundHepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.MethodsTwo groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.ResultsWe identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.ConclusionThe findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

scholarly journals Identification of a seven-long non-coding RNA signature associated with Jab1/CSN5 in predicting hepatocellular carcinoma

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Weijie Ma ◽  
Ye Yao ◽  
Gang Xu ◽  
Xiaoling Wu ◽  
Jinghua Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Tumors ◽  
Strong Association ◽  
Critical Role ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Clinical Value

AbstractHepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide, accounting for over 700,000 deaths each year. The lack of predictive and prognostic biomarkers for HCC, with effective therapy, remains a significant challenge for HCC management. Long non-coding RNAs (lncRNAs) play a key role in tumorigenesis and have clinical value as potential biomarkers in the early diagnosis and prediction of HCC. Jun activation domain-binding protein 1 (Jab1, also known as COP9 signalosome subunit 5, CSN5) is a potential oncogene that plays a critical role in the occurrence of HCC. Here, we performed a comprehensive analysis for Jab1/CSN5-associated lncRNAs to predict the prognosis of HCC. The differentially expressed (DE) lncRNAs between in HCC were analyzed based on the TCGA RNA-seq data. We detected 1031 upregulated lncRNAs in 371 HCC tissues and identified a seven-lncRNA signature strongly correlated with Jab1/CSN5 (SNHG6, CTD3065J16.9, LINC01604, CTD3025N20.3, KB-1460A1.5, RP13-582O9.7, and RP11-29520.2). We further evaluated the prognostic significance of these lncRNAs by GEPIA (http://gepia.cancer-pku.cn/). The expression data in 364 liver tumors indicated that this seven-lncRNA signature could better predict worse survival in HCC patients. Moreover, 35 clinical HCC samples were evaluated to assess the validity and reproducibility of the bioinformatic analysis. We found that the targeted lncRNAs were upregulated, with a strong association with Jab1/CSN5 and prognostic value in HCC. Functional enrichment analysis by Gene Ontology (GO) showed that these seven prognostic lncRNAs exhibit oncogenic properties and are associated with prominent hallmarks of cancer. Overall, our findings demonstrate the clinical implication of Jab1/CSN5 with the seven‐lncRNAs in predicting survival for patients with HCC.

scholarly journals Screening and Identification of Potential Biomarkers Associated with Cutaneous Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Wenxing Su ◽  
Biao Huang ◽  
Qingyi Zhang ◽  
Wei Han ◽  
Lu An ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Functional Enrichment ◽  
Hub Genes

Abstract Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with non-melanoma skin cancers (NMSC). However, unclear pathogenesis of cSCC limits the application of molecular targeted therapy. We downloaded three microarray data (GSE2503, GSE45164 and GSE66359) from Gene Expression Omnibus (GEO) and screened out their common difference genes between tumor and non-tumor tissues. Functional enrichment analysis was performed using DAVID. The STRING online website was used to construct a protein-protein interaction network (PPI), and then Cytoscape performed module analysis and degree calculation.A total of 146 DEGs was identified with significant differences, including 113 up-regulated genes and 33 down-regulated genes. The enriched functions and pathways of the DEGs include microtubule-based movement, ATP binding, cell cycle, p53 signaling pathway, oocyte meiosis and PLK1 signaling events. Nine hub genes were identified, namely CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, ASPM. The differential expression of these genes has been verified in other data sets. By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC, and are expected to become future biomarkers or therapeutic targets.

scholarly journals Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9462
Author(s):  
Dao wei Zhang ◽  
Shenghai Zhang ◽  
Jihong Wu
Keyword(s):  
Gene Expression ◽  
Optic Nerve ◽  
Optic Nerve Head ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Hub Genes ◽  
Gene Modules ◽  
Key Genes

Purpose Glaucoma is the second commonest cause of blindness. We assessed the gene expression profile of astrocytes in the optic nerve head to identify possible prognostic biomarkers for glaucoma. Method A total of 20 patient and nine normal control subject samples were derived from the GSE9944 (six normal samples and 13 patient samples) and GSE2378 (three normal samples and seven patient samples) datasets, screened by microarray-tested optic nerve head tissues, were obtained from the Gene Expression Omnibus (GEO) database. We used a weighted gene coexpression network analysis (WGCNA) to identify coexpressed gene modules. We also performed a functional enrichment analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Genes expression was represented by boxplots, functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all the key genes. Then the key genes were validated by the external dataset. Results A total 8,606 genes and 19 human optic nerve head samples taken from glaucoma patients in the GSE9944 were compared with normal control samples to construct the co-expression gene modules. After selecting the most common clinical traits of glaucoma, their association with gene expression was established, which sorted two modules showing greatest correlations. One with the correlation coefficient is 0.56 (P = 0.01) and the other with the correlation coefficient is −0.56 (P = 0.01). Hub genes of these modules were identified using scatterplots of gene significance versus module membership. A functional enrichment analysis showed that the former module was mainly enriched in genes involved in cellular inflammation and injury, whereas the latter was mainly enriched in genes involved in tissue homeostasis and physiological processes. This suggests that genes in the green–yellow module may play critical roles in the onset and development of glaucoma. A LASSO regression analysis identified three hub genes: Recombinant Bone Morphogenetic Protein 1 gene (BMP1), Duchenne muscular dystrophy gene (DMD) and mitogens induced GTP-binding protein gene (GEM). The expression levels of the three genes in the glaucoma group were significantly lower than those in the normal group. GSEA further illuminated that BMP1, DMD and GEM participated in the occurrence and development of some important metabolic progresses. Using the GSE2378 dataset, we confirmed the high validity of the model, with an area under the receiver operator characteristic curve of 85%. Conclusion We identified several key genes, including BMP1, DMD and GEM, that may be involved in the pathogenesis of glaucoma. Our results may help to determine the prognosis of glaucoma and/or to design gene- or molecule-targeted drugs.

scholarly journals Prognostic value of aberrantly expressed methylation genes in human hepatocellular carcinoma

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Limin Zhen ◽  
Gang Ning ◽  
Lina Wu ◽  
Yongyuan Zheng ◽  
Fangji Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Methylation Status ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Reference Database ◽  
Hub Genes

Abstract Objectives: To identify the prognostic value of aberrantly methylated differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC) and to explore the underlying mechanisms of tumorigenesis. Methods: Gene expression profiles (GSE65372 and GSE37988) were analyzed using GEO2R to obtain aberrantly methylated DEGs. Functional enrichment analysis of screened genes was performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Cytoscape software was used to analyze the PPI network and to select hub genes. Transcriptional and proteinic expression data of hub genes were obtained through UALCAN and the Human Protein Reference Database. Finally, we analyzed the prognostic value of hub genes with the Kaplan–Meier Plotter and MethSurv database. Results: In total, 24 up-hypomethylated oncogenes and 37 down-hypermethylated tumor suppressor genes (TSGs) were identified, and 8 hub genes, including 4 up-hypomethylated oncogenes (CDC5L, MERTK, RHOA and YBX1) and 4 down-hypermethylated TSGs (BCR, DFFA, SCUBE2 and TP63), were selected by PPI. Higher expression of methylated CDC5L-cg05671347, MERTK-cg08279316, RHOA-cg05657651 and YBX1-cg16306148, and lower expression of methylated BCR-cg25410636, DFFA-cg20696875, SCUBE2-cg19000089 and TP63-cg06520450, were associated with better overall survival (OS) in HCC patients. Multivariate analysis also showed they were independent prognostic factors for OS of HCC patients. Conclusions: In summary, different expression of methylated genes above mentioned were associated with better prognosis in HCC patients. Altering the methylation status of these genes may be a therapeutic target for HCC, but it should be further evaluated in clinical studies.

scholarly journals Identification of mRNA-miRNA-lncRNA Competing Endogenous RNA Network in Adrenocortical Carcinoma Using Bioinformatics Analysis 

2020 ◽  
Author(s):  
Senlin Ye ◽  
Haohui Wang ◽  
Wei Li ◽  
Lu Yi
Keyword(s):  
Adrenocortical Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Node Degree ◽  
Pathway Enrichment Analysis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Key Genes ◽  
Worse Prognosis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare malignant tumor originating from the adrenal cortex. However, there are no effective therapies to treat patients with ACC. LncRNA participates in a variety of biological processes of cancers. We constructed ceRNA network and identify key competing endogenous RNAs (ceRNAs) in adrenocortical carcinoma (ACC) using bioinformatic processing tools. Methods: Firstly, the differentially expressed genes (DEGs) were identified by analyzing GSE12368 and GSE19750 datasets. SangerBox was used to generate volcano maps. DAVID database was used for functional enrichment analysis. STRING database was used to conduct Protein-protein interaction (PPI) network, and hub genes were identified by Cytoscape plug-in CytoHubba. UCSC database was used to construct hierarchical clustering of hub genes. Upstream miRNAs of mRNAs were predicted by miRTarBase and upstream lncRNAs of miRNA by miRNet. Expression analysis for lncRNAs were performed via GEPIA. Prognostic analysis for genes, miRNAs and lncRNAs were performed via cBioPortal, OncomiR and GEPIA, respectively. Results: In this study, 49 and 276 upregulated and downregulated significant DEGs were identified. KEGG pathway enrichment analysis showed that they were significantly enriched in cancer-associated pathways. According to node degree, the top 10 upregulated genes and downregulated genes were classfied as hub genes. However, only 9 hub genes were defined as key genes because alteration was significantly associated with worse prognosis and all the 9 key genes were upregulated hub genes. Then, 15 miRNAs were predicted to target the 7 out of 9 key genes. But only 4 miRNAs were defined as key miRNAs because alteration significantly influenced prognosis in cancer. 185 lncRNAs were predicted to potentially interaction with the 4 miRNAs. Only 3 lncRNAs(XIST, HOXA11-AS and TMPO-AS1) were up-regulated and only 1 lncRNA (HOXA11-AS ) indicated alteration was significantly associated with worse prognosis in adrenocortical carcinoma. HOXA11-AS were finally identified as key lncRNA. Finally, RRM2-miR-24-3p/let-7a-5p-HOXA11-AS, CDK1/MCM4-miR-24-3P-HOXA11-AS competing endogenous RNA (ceRNA) sub-networks were constructed in adrenocortical carcinoma. Conclution：This study has constructed RRM2-miR-24-3p/let-7a-5p-HOXA11-AS, CDK1/MCM4-miR-24-3p-HOXA11-AS competing endogenous RNA (ceRNA) sub-networks. Our results suggested that these sub-networks might be potential therapeutic targets or prognostic biomarkers in ACC.

scholarly journals Identification of Key Genes and Evaluation of Their Prognosis Potential in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Jankun Liu ◽  
zy liu ◽  
Wei Li ◽  
Xinghua Pan ◽  
Zongjiang Fan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Target Genes ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Survival Curve ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Key Genes

Abstract Background Hepatocellular carcinoma (HCC) is a malignancy causing highly death rate in the world. Despite the development of treatment strategies for HCC, prognosis of this malignancy remains unsatisfactory. In this study, we aimed to identify the target genes associated with the prognosis of HCC patients. Methods Three expression profiles of HCC tissues were extracted from the Gene Expression Omnibus database to explore the differentially expressed genes (DEGs) using GEO2R method. Functional enrichment analysis was performed to reveal the biological characteristics of DEGs. Protein-protein interaction (PPI) network was constructed using Cytoscape software. The survival curve of identified DEGs were tested by Kaplan-Meier analysis. Results We identified 15 DEGs (CYP39A1, CYR61, FOS, FOXO1, GADD45B, ID1, IL1RAP, MT1M, PHLDA1, RND3, SDS, SOCS2, TAT, S100P, and SPINK1) in HCC tissues. Prognosis analysis showed that 4 DEGs (FOXO1,SPINK1༌SOCS2, and TAT) correlated with overall survival time of HCC patients, which might serve as therapeutic targets for HCC patients. Conclusions By integrated bioinformatics analysis, we proposed a novel way to reveal key genes that closely relate to HCC development.

scholarly journals WGCNA reveals key gene modules regulated by the combined treatment of colon cancer with PHY906 and CPT11

2020 ◽  
Vol 40 (9) ◽  
Author(s):  
Shuqin Xing ◽  
Yafei Wang ◽  
Kaiwen Hu ◽  
Fen Wang ◽  
Tao Sun ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Side Effects ◽  
Molecular Mechanism ◽  
Combined Treatment ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Pcr Analysis ◽  
Hub Genes ◽  
Key Genes

Abstract Irinotecan (CPT11) is one of the most effective drugs for treating colon cancer, but its severe side effects limit its application. Recently, a traditional Chinese herbal preparation, named PHY906, has been proved to be effective for improving therapeutic effect and reducing side effects of CPT11. The aim of the present study was to provide novel insight to understand the molecular mechanism underlying PHY906-CPT11 intervention of colon cancer. Based on the GSE25192 dataset, for different three treatments (PHY906, CPT11, and PHY906-CPT11), we screened out differentially expressed genes (DEGs) and constructed a co-expression network by weighted gene co-expression network analysis (WGCNA) to identify hub genes. The key genes of the three treatments were obtained by merging the DEGs and hub genes. For the PHY906-CPT11 treatment, a total of 18 key genes including Eif4e, Prr15, Anxa2, Ddx5, Tardbp, Skint5, Prss12 and Hnrnpa3, were identified. The results of functional enrichment analysis indicated that the key genes associated with PHY906-CPT11 treatment were mainly enriched in ‘superoxide anion generation’ and ‘complement and coagulation cascades’. Finally, we validated the key genes by Gene Expression Profiling Interactive Analysis (GEPIA) and RT-PCR analysis, the results indicated that EIF4E, PRR15, ANXA2, HNRNPA3, NCF1, C3AR1, PFDN2, RGS10, GNG11, and TMSB4X might play an important role in the treatment of colon cancer with PHY906-CPT11. In conclusion, a total of 18 key genes were identified in the present study. These genes showed strong correlation with PHY906-CPT11 treatment in colon cancer, which may help elucidate the underlying molecular mechanism of PHY906-CPT11 treatment in colon cancer.

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.

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