Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset

Background. Long noncoding RNAs (lncRNAs) act as competing endogenous RNAs for microRNAs in cancer metastasis. However, the roles of lncRNA-mediated competing endogenous RNA (ceRNA) networks for breast cancer (BC) are still unclear. Material and Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs with BC were extracted from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was conducted to extract differentially expressed mRNAs (DEmRNAs) that might be core genes. Through miRWalk, TargetScan, and miRDB to predict the target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network with BC was constructed. The survival possibilities of mRNAs, miRNAs, and lncRNAs for patients with BC were determined by Kaplan-Meier survival curves and Oncomine. Results. We identified 2134 DEmRNAs, 1059 differentially expressed lncRNAs (DElncRNAs), and 86 differentially expressed miRNAs (DEmiRNAs). We then compose a ceRNA network for BC, including 72 DElncRNAs, 8 DEmiRNAs, and 12 DEmRNAs. After verification, 2 lncRNAs (LINC00466, LINC00460), 1 miRNA (Hsa-mir-204), and 5 mRNAs (TGFBR2, CDH2, CHRDL1, FGF2, and CHL1) were meaningful as prognostic biomarkers for BC patients. In the ceRNA network, we found that three axes were present in 10 RNAs related to the prognosis of BC, namely, LINC00466-Hsa-mir-204-TGFBR2, LINC00466-Hsa-mir-204-CDH2, and LINC00466-Hsa-mir-204-CHRDL1. Conclusion. This study highlighted lncRNA-miRNA-mRNA ceRNA related to the pathogenesis of BC, which might be used for latent diagnostic biomarkers and therapeutic targets for BC.

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Comprehensive Analysis of Differentially Expressed Profiles of lncRNAs/mRNAs and miRNAs with Associated ceRNA Networks in Triple-Negative Breast Cancer

Cellular Physiology and Biochemistry ◽

10.1159/000494162 ◽

2018 ◽

Vol 50 (2) ◽

pp. 473-488 ◽

Cited By ~ 17

Author(s):

Rui Yang ◽

Lei Xing ◽

Min Wang ◽

Hong Chi ◽

Luyu Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Large Scale ◽

Triple Negative ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cerna Network ◽

Malignant Breast ◽

Underlying Mechanisms

Background/Aims: Triple-negative breast cancer (TNBC) is a subtype of highly malignant breast cancer with poor prognosis. Growing evidence indicates that Long noncoding RNAs (lncRNAs) play important regulatory roles in the development and progression of a variety of cancers including breast cancer. However, the underlying mechanisms remain largely unknown. Methods: Here, we compared the expression profiles of mRNAs, lncRNAs and miRNAs between 111 TNBC tissues and 104 non-cancerous tissues utilizing RNA-Seq Data from The Cancer Genome Atlas (TCGA). Gene Ontology and KEGG pathway enrichment analyses were executed to investigate the principal functions of the significantly dysregulated mRNAs. Moreover, Kaplan-Meier survival analyses were performed to determine the effects of differentially expressed lncRNAs/mRNAs/miRNAs on overall survival. Subsequently, we constructed a competing endogenous RNA (ceRNA) network, which included 66 dysregulated lncRNAs, 24 miRNAs and 55 mRNAs. The four dysregulated lncRNAs, three aberrantly expressed miRNAs and four mRNAs were confirmed in the ceRNA network by qRT-PCR in 30 pairs of samples, respectively. Results: A total of 1441 lncRNAs, 114 miRNA and 2501 mRNAs were found to be differentially expressed in TNBC tissues compared with controls. 109 lncRNAs and 124 mRNAs might serve as prognostic signature for patients with TNBC according to the survival analysis. Functional analysis revealed that 19 mRNAs in the ceRNA network were enriched in 17 cancer-related pathways. Conclusion: Taken together, we identified novel lncRNAs/miRNAs which may serve as potential biomarkers to predict the survival and therapeutic targets for TNBC patients based on a large-scale sample. More importantly, we constructed the ceRNA network of TNBC, which provides valuable information to further explore the molecular mechanism underlying tumorigenesis and development of TNBC.

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Identification of a novel lncRNA‑miRNA‑mRNA competing endogenous RNA network associated with prognosis of breast cancer

10.21203/rs.2.24821/v1 ◽

2020 ◽

Author(s):

Xiaohui Wan ◽

Shuhong Hao ◽

Chunmei Hu ◽

Rongfeng Qu

Keyword(s):

Breast Cancer ◽

Cancer Biology ◽

Cox Regression ◽

Malignant Tumors ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Competing Endogenous Rna ◽

Cox Regression Analysis ◽

Target Mrna ◽

Cerna Network

Abstract Background: Breast cancer is one of the most common malignant tumors. Recently, the effects of competing endogenous RNA (ceRNA) on molecular biological mechanism of cancer has aroused great interext. However, research on the pathogenesis and biomarkers of breast cancer is still limited. Thus, this study is aimed to identify the competing endogenous RNA (ceRNA) network related to prognosis of patients with breast cancer. Methods: The RNA SEQ data and corresponding clinical information were downloaded from the Cancer Genome Atlas (TCGA) database, and the difference analysis was performed after data quality control. The similarity between two groups of genes with traits in the network was analyzed by weighted correlation network analysis (WGCNA) . Next, the interaction among lncRNA, miRNA, and mRNA was predicted using miRcode, TargetScan, miRDB, and miRTarBase database, and the lncRNA-miRNA-mRNA ceRNA network was constructed. Finally, the survival model of target mRNA was established by Cox regression analysis. Results: In total 5056 differentially expressed lncRNAs, 712 differentially expressed miRNAs, and 9878 differentially expressed mRNAs were identified. WGCNA predicted that 823 lncRNAs and 1813 mRNAs were closely related to the breast cancer. The lncRNA-miRNA-mRNA ceRNA network involved in breast cancer was constructed with 27 lncRNA, 14 miRNAs and 4 mRNAs. The AUC of four survival models of target mRNA (ZC3H12B + HRH1 + TMEM132C + PAG1) was 0.609, which suggested the sensitivity and specificity of prognosis prediction of breast cancer. Conclusion: This study provides insight into the ceRNA network involved in breast cancer biology, which significantly associated with gene regulation and prognosis of breast cancer.

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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Identification of novel mRNA-miRNA-lncRNA competing endogenous RNA network associated with prognosis of breast cancer

Epigenomics ◽

10.2217/epi-2019-0209 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1501-1518 ◽

Cited By ~ 6

Author(s):

Guansheng Zhong ◽

Weiyang Lou ◽

Minya Yao ◽

Chengyong Du ◽

Haiyan Wei ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Cerna Network ◽

Competing Endogenous Rna Network ◽

Cancer Genome Atlas ◽

Genome Atlas

Aim: To identify novel competing endogenous RNA (ceRNA) network related to patients prognosis in breast cancer. Materials & methods: Dysregulated mRNA based on intersection of three Gene Expression Omnibus and The Cancer Genome Atlas datasets were analyzed by bioinformatics. Results: In total 72 upregulated and 208 downregulated genes were identified. Functional analysis showed that some pathways related to cancer were significantly enriched. By means of stepwise reverse prediction and validation from mRNA to lncRNA, 19 hub genes, nine key miRNA and four key lncRNAs were identified by expression and survival analysis. Ultimately, the coexpression analysis identified RRM2-let-7a-5p- SNHG16/ MAL2 as key ceRNA subnetwork associated with prognosis of breast cancer. Conclusion: We successfully constructed a novel ceRNA network, among which each component was significantly associated with breast cancer prognosis.

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Construction and Comprehensive Analysis of Dysregulated Long Noncoding RNA-Associated Competing Endogenous RNA Network in Moyamoya Disease

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/2018214 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Xuefeng Gu ◽

Dongyang Jiang ◽

Yue Yang ◽

Peng Zhang ◽

Guoqing Wan ◽

...

Keyword(s):

Moyamoya Disease ◽

Cerebral Artery ◽

Regulatory Network ◽

Noncoding Rna ◽

Expression Profiles ◽

Differentially Expressed ◽

Competing Endogenous Rna ◽

Cerna Network ◽

And Control ◽

Control Samples

Background. Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by chronic progressive stenosis or occlusion of the bilateral internal carotid artery (ICA), the anterior cerebral artery (ACA), and the middle cerebral artery (MCA). MMD is secondary to the formation of an abnormal vascular network at the base of the skull. However, the etiology and pathogenesis of MMD remain poorly understood. Methods. A competing endogenous RNA (ceRNA) network was constructed by analyzing sample-matched messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA) expression profiles from MMD patients and control samples. Then, a protein-protein interaction (PPI) network was constructed to identify crucial genes associated with MMD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were employed with the DAVID database to investigate the underlying functions of differentially expressed mRNAs (DEmRNAs) involved in the ceRNA network. CMap was used to identify potential small drug molecules. Results. A total of 94 miRNAs, 3649 lncRNAs, and 2294 mRNAs were differentially expressed between MMD patients and control samples. A synergistic ceRNA lncRNA-miRNA-mRNA regulatory network was constructed. Core regulatory miRNAs (miR-107 and miR-423-5p) and key mRNAs (STAT5B, FOSL2, CEBPB, and CXCL16) involved in the ceRNA network were identified. GO and KEGG analyses indicated that the DEmRNAs were involved in the regulation of the immune system and inflammation in MMD. Finally, two potential small molecule drugs, CAY-10415 and indirubin, were identified by CMap as candidate drugs for treating MMD. Conclusions. The present study used bioinformatics analysis of candidate RNAs to identify a series of clearly altered miRNAs, lncRNAs, and mRNAs involved in MMD. Furthermore, a ceRNA lncRNA-miRNA-mRNA regulatory network was constructed, which provides insights into the novel molecular pathogenesis of MMD, thus giving promising clues for clinical therapy.

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Comprehensive Analysis of lncRNAs Related to the Prognosis of Esophageal Cancer Based on ceRNA Network and Cox Regression Model

BioMed Research International ◽

10.1155/2020/3075729 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Chao Li ◽

Wu Yao ◽

Congcong Zhao ◽

Guo Yang ◽

Jingjing Wei ◽

...

Keyword(s):

Esophageal Cancer ◽

Target Genes ◽

Cox Regression ◽

Malignant Tumors ◽

Transcriptome Profiling ◽

Assessment Model ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cerna Network

Background. Esophageal cancer is one of the most deadly malignant tumors. Among the common malignant tumors in the world, esophageal cancer is ranked seventh, which has a high mortality rate. Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of various tumors. lncRNAs can competitively bind microRNAs (miRNAs) with mRNA, which can regulate the expression level of the encoded gene at the posttranscriptional level. This regulatory mechanism is called the competitive endogenous RNA (ceRNA) hypothesis, and ceRNA has important research value in tumor-related research. However, the regulation of lncRNAs is less studied in the study of esophageal cancer. Methods. The Cancer Genome Atlas (TCGA) database was used to download transcriptome profiling data of esophageal cancer. Gene expression quantification data contains 160 cancer samples and 11 normal samples. These data were used to identify differentially expressed lncRNAs and mRNAs. miRNA expression data includes 185 cancer samples and 13 normal samples. The differentially expressed RNAs were identified using the edgeR package in R software. Then, the miRcode database was used to predict miRNAs that bind to lncRNAs. MiRTarBase, miRDB, and TargetScan databases were used to predict the target genes of miRNAs. Cytoscape software was used to draw ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using DAVID 6.8. Finally, multifactor cox regression was used to screen lncRNAs related to prognosis. Results. We have screened 1331 DElncRNAs, 3193 DEmRNAs, and 162 DEmiRNAs. Among them, the ceRNA network contains 111 lncRNAs, 11 miRNAs, and 63 DEmRNAs. Finally, we established a prediction model containing three lncRNAs through multifactor Cox regression analysis. Conclusions. Our research screened out three independent prognostic lncRNAs from the ceRNA network and constructed a risk assessment model. This is helpful to understand the regulatory role of lncRNAs in esophageal cancer.

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Analysis of miRNA signature differentially expressed in exosomes from adriamycin-resistant and parental human breast cancer cells

Bioscience Reports ◽

10.1042/bsr20181090 ◽

2018 ◽

Vol 38 (6) ◽

Cited By ~ 5

Author(s):

Wei-xian Chen ◽

Ling-yun Xu ◽

Qi Qian ◽

Xiao He ◽

Wen-ting Peng ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Cells ◽

Breast Cancer Cells ◽

Target Genes ◽

Expression Profiles ◽

Functional Enrichment ◽

Differentially Expressed ◽

Systematic Evaluation ◽

Mirna Signature ◽

Exosomal Mirnas

A major cause of failure in chemotherapy is drug resistance of cancer cells. Exosomes have been introduced to spread chemoresistance through delivering miRNAs. However, a systematic evaluation of the exosomal miRNA expression profiles responsible for chemoresistance is still lacking. In the present study, miRNA signature differentially expressed in exosomes derived from adriamycin-resistant (A/exo) and parental breast cancer cells (S/exo) were analyzed by microarray and the results were confirmed by PCR. A total of 309 miRNAs were increased and 66 miRNAs were decreased significantly in A/exo compared with S/exo. Specifically, 52 novel miRNAs with increased expression levels >16.0-fold in A/exo were identified. After prediction of target genes for 13 of 52 selected novel miRNAs, pathway analysis, gene ontology (GO) terms, and protein–protein interactions (PPIs) were constructed. The results implied that these selected exosomal miRNAs inhibited target genes involved in transcriptional misregulation in cancer, MAPK, and Wnt signaling pathways. Functional enrichment analysis demonstrated that the target genes were mainly responsible for protein phosphorylation, transcription regulation, molecular binding, and kinase activity. In summary, the current bioinformatics study of exosomal miRNAs may offer a new understanding into mechanisms of chemoresistance, which is helpful to find potential exosomal miRNAs to overcome drug insensitivity in future breast cancer treatment.

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Potential Diagnostic and Therapeutic Targets for Hepatocellular Carcinoma Based on Exosome-Derived Competitive Endogenous RNA Network

10.21203/rs.3.rs-487982/v1 ◽

2021 ◽

Author(s):

Yuan Tian ◽

Dongliang Yang ◽

Tieshan Wang ◽

He Bu ◽

JinBao Wu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Target Genes ◽

Immune Escape ◽

Malignant Tumors ◽

Expression Profiles ◽

Therapeutic Targets ◽

Differentially Expressed ◽

Cerna Network ◽

Differentially Expressed Mirnas ◽

Competitive Endogenous Rna

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most malignant tumors in the world. The pathogenesis of HCC is complex and closely related to chronic uncontrollable inflammation. As a bridge between inflammation and cancer, circulating exosomes play a vital role in early tumorigenesis, metastasis, and immune escape. Studies have shown that exosomes containing specific RNAs may be potential diagnostic and therapeutic targets for HCC. Purpose The current research investigated key circRNA through exosome-derived competitive endogenous RNA network based on the ExoRBase database. Methods: The circRNA, lncRNA, and mRNA expression profiles of human blood samples were downloaded from the ExoRBase database. At the standard of P<0.05 and log FC>0, differentially expressed genes (DEGs) were further identified between normal human and HCC patients. The co-expressed pairs of DEGs were predicted by TargetScan, miRcode, and StarBase databases. The ceRNA network was constructed by Cytoscape software. Subsequently, target genes corresponding to circRNA in the ceRNA network were annotated and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG). The potential transcription factors were screened by FunRich database. Results: At the criterion of P<0.05 and log FC>0, 13 differentially expressed circRNAs(DECs) were identified with 9 up-regulated and 4 down-regulated. The co-expressed differentially expressed miRNAs-mRNAs (DEMis-DEMs) (620 pairs), differentially expressed miRNAs- LncRNAs (DEMis-DElncRNA) (684 pairs) and DEMis-DECs (53 pairs) were finally predicted to construct the ceRNA network. The GO analysis indicated that target genes in the ceRNA network were mainly enriched in the molecular function of protein serine/threonine kinase activity. KEGG pathway analysis suggested target genes were enriched in two pathways of MAPK and central carbon metabolism. Conclusion: The study provides a valuable reference for HCC through the ceRNA network in exosomes. Besides, hsa_circ_0000284 may be potential diagnostic markers of HCC.

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An integrated analysis of the competing endogenous RNA network and co-expression network revealed seven hub long non-coding RNAs in osteoarthritis

Bone and Joint Research ◽

10.1302/2046-3758.93.bjr-2019-0140.r2 ◽

2020 ◽

Vol 9 (3) ◽

pp. 90-98 ◽

Cited By ~ 1

Author(s):

Haitao Chen ◽

Liaobin Chen

Keyword(s):

Extracellular Matrix ◽

Expression Profiles ◽

Osteoclast Differentiation ◽

Signalling Pathway ◽

Differentially Expressed ◽

Integrated Analysis ◽

Knee Cartilage ◽

Competing Endogenous Rna ◽

Cerna Network ◽

Non Coding Rnas

Aims This study aimed to uncover the hub long non-coding RNAs (lncRNAs) differentially expressed in osteoarthritis (OA) cartilage using an integrated analysis of the competing endogenous RNA (ceRNA) network and co-expression network. Methods Expression profiles data of ten OA and ten normal tissues of human knee cartilage were obtained from the Gene Expression Omnibus (GEO) database (GSE114007). The differentially expressed messenger RNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the edgeR package. We integrated human microRNA (miRNA)-lncRNA/mRNA interactions with DElncRNA/DEmRNA expression profiles to construct a ceRNA network. Likewise, lncRNA and mRNA expression profiles were used to build a co-expression network with the WGCNA package. Potential hub lncRNAs were identified based on an integrated analysis of the ceRNA network and co-expression network. StarBase and Multi Experiment Matrix databases were used to verify the lncRNAs. Results We detected 1,212 DEmRNAs and 49 DElncRNAs in OA and normal knee cartilage. A total of 75 dysregulated lncRNA-miRNA interactions and 711 dysregulated miRNA-mRNA interactions were obtained in the ceRNA network, including ten DElncRNAs, 69 miRNAs, and 72 DEmRNAs. Similarly, 1,330 dysregulated lncRNA-mRNA interactions were used to construct the co-expression network, which included ten lncRNAs and 407 mRNAs. We finally identified seven hub lncRNAs, named MIR210HG, HCP5, LINC00313, LINC00654, LINC00839, TBC1D3P1-DHX40P1, and ISM1-AS1. Subsequent enrichment analysis elucidated that these lncRNAs regulated extracellular matrix organization and enriched in osteoclast differentiation, the FoxO signalling pathway, and the tumour necrosis factor (TNF) signalling pathway in the development of OA. Conclusion The integrated analysis of the ceRNA network and co-expression network identified seven hub lncRNAs associated with OA. These lncRNAs may regulate extracellular matrix changes and chondrocyte homeostasis in OA progress. Cite this article: Bone Joint Res. 2020;9(3):90–98.

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