Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Identification and analysis of RNA-5-methylcytosine-related key genes in osteoarthritis

BACKGROUND

5-methylcytosine (m5C) modification is widely associated with many biological and pathological processes. However, knowledge of m5C modification in osteoarthritis (OA) remains lacking. Thus, our study aimed to identify common m5C features in OA.

RESULTS

In the present study, we identified 1395 differentially methylated genes (DMGs) and 1673 differentially expressed genes (DEGs) using methylated RNA immunoprecipitation next-generation sequencing (MeRIP-seq) and RNA-sequencing. A co-expression analysis of DMGs and DEGs showed that the expression of 133 genes was significantly affected by m5C methylation. A protein-protein interaction network of the 133 genes was constructed using the STRING database, and the cytoHubba plug-in of Cytoscape was used to hub genes were screen out 11 hub genes, including MMP14, VTN, COL15A1, COL6A2, SPARC, COL5A1, COL6A3, COL6A1, COL8A2, ADAMTS2 and COL7A1. The Pathway enrichment analysis by the ClueGO and CluePedia plugins in Cytoscape showed that the hub genes were significantly enriched in collagen degradation and extracellular matrix degradation.

CONCLUSIONS

Our study indicated that m5C modification might play an important role in OA pathogenesis, and the present study provides worthwhile insight into identifying m5C-related therapeutic targets in OA.

Identification of Four Hub Genes as Promising Biomarkers for the Evaluation of Ovarian Cancer Prognosis

Ovarian malignant growth is perhaps the most lethal disease in females. There are no exact biomarkers for the early determination of ovarian disease. We obtained a total of 12 gene clusters through WGCNA and studied the azure gene modules related to the lymphatic infiltration of ovarian cancer further. What’s more, endurance investigation was utilized to decide three qualities connected with the by and large and infection-free endurance in ovarian disease patients, including GOGA8B [Hazard Ratio (HR)=1.53, p=0.037, 95% CI: 1.05–2.24], LRRC26 (HR=0.7, p =0.045, 95% CI: 0.48–1.01), and CCDC114 (HR = 0.72, p = 0.042, 95% CI: 0.53–0.98). A prognostic risk score model was built to anticipate the endurance pace of patients at 1, 3, and 5 years, individually. The area under the receiver operating characteristic (ROC) curve (AUC) of the training set was 0.749, 0.764, and 0.784, respectively; the test AUC was 0.601, 0.623, and 0.709. Our review gives a point of view on significant possible biomarkers for the determination, anticipation, and therapy of ovarian malignant growth.

Quantitative proteomic analysis of Bi Zhong Xiao decoction against collagen-induced arthritis rats in the early and late stages

Background

Rheumatoid arthritis (RA) is a chronic, progressive, systemic autoimmune inflammatory disease. Bi Zhong Xiao decoction (BZXD) performs multiple functions for rheumatoid arthritis (RA) treatment for decades. In this study, we aimed to study the protein alterations of BZXD in the early and late stages of RA.

Methods

Sprague–Dawley rats were randomly divided into the Control, collagen-induced arthritis (CIA) and BZXD groups. Clinical assessment, paw thickness, weight changes and serum inflammatory cytokine levels were used to evaluate anti-inflammatory effects. Histopathological tests were performed to assess the improvement of inflammation and synovial hyperplasia. Moreover, we analyzed the proteins profiling of synovial tissue samples with different time intervals after BZXD treatment by Isobaric Tag for Relative Absolute (ITRAQ) quantitative proteomics technology. To further explore the interrelationships among differentially expressed proteins (DEPs), we used DAVID Bioinformatics Resources v6.8 and STRING 11.0 for bioinformatics analysis. Besides, the western blot and immunohistochemistry were exerted to verify related proteins.

Results

In our study, BZXD ameliorated joint inflammation, and suppressed the pathological changes in arthrosis of CIA rats. The proteomic analysis demonstrated that CIA rats were mainly involved in two significant pathways (the focal adhesion and the ECM-receptor interaction) in the early stage. BZXD down-regulated the expression of proteins involved in these pathways, such as CAV1, CHAD, COL3A1, COL5A2, COL6A1, and COL6A5. Additionally, BZXD exerts anti-inflammatory effects in the late stage mainly by increasing the expression of FASN and affecting fatty acid metabolism.

Conclusion

BZXD exerts therapeutic effects on RA through multi-pathways in the early and late stages. This work may provide proteomic clues for treating RA by BZXD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03663-5.

Protein N-glycosylation aberrations and glycoproteomic network alterations in osteoarthritis and osteoarthritis with type 2 diabetes

Whether the relationship between type 2 diabetes mellitus (T2DM) and osteoarthritis (OA) can be solely attributed to the shared risk factors, such as obesity, remains controversial. Several studies have revealed the critical role of abnormal glycosylation in the pathogenesis of OA and T2DM. Therefore, we speculate that T2DM may contribute to the pathogenesis of OA through the intrinsic mechanisms of N-glycosylation aberrations. Using N-glycoproteomics, we compared the changes in N-glycosylated protein abundance in cartilage samples from patients with OA without and with T2DM (DM-OA), and from patients with traumatic joint injury (NC) as controls. We identified 847 N-glycosylation sites corresponding to 729 peptides fragments from 374 proteins. The number of N-glycosylated proteins in the DM-OA group tended to decrease compared with that in the OA and NC groups. We identified 22 upregulated and 1 down-regulated N-glycosylated peptides in the OA group compared to the NC group, while only fibronectin 1 (FN1) at position N1007, cartilage intermediate layer protein 1 (CILP) at N346, and collagen type VI alpha 1 chain (COL6A1) at N804, were also identified in the DM-OA group. Compared to the OA group, the downregulation of secreted protein acidic and rich in cysteine (SPARC) at N116, collagen type VI alpha 1 chain (COL6A2) at N785, and asporin (ASPN) at N282, and the upregulation of complement component C8 alpha chain (C8α) at N437, were the most remarkable alterations in the DM-OA group. The differentially expressed N-glycosylated proteins between the OA and DM-OA groups were mainly located extracellularly and enriched in the KEGG pathways involving PI3K/Akt signaling, focal adhesion, and ECM-receptor interaction. Their predicted protein–protein interactions were also depicted. We were thus able to show the general characteristics of N-glycosylation aberrations in OA and DM-OA. Moreover, the upregulated glycosylated complement C8α in the DM-OA group might augment membrane attack complex activity, thereby exacerbating cartilage destruction. Although further confirmation is required, our hypothesis proposes a possible explanation for the deduction that T2DM is an independent risk factor for OA.

Inhibition of integrin subunit alpha 11 restrains gastric cancer progression through phosphatidylinositol 3-kinase/Akt pathway

Gastric cancer (GC) is among the most frequent malignancies originating from the digestive system worldwide, while the role and specific mechanism of integrin-subunit alpha 11 (ITGA11) in GC remain unclear. This study probes the expression characteristics and function of ITGA11 in GC. Firstly, the ITGA11 profile in GC tissues and paracancerous non-tumor tissues was assessed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot (WB), and the association between ITGA11 and GC patients' clinicopathological indicators was evaluated. ITGA11 knockdown models were set up in GC cell lines MKN45 and AGS. Cell proliferation was determined by the cell counting kit-8 (CCK-8) assay and colony formation assay. WB was utilized to gauge the expression of apoptosis-related proteins (Bax, Bcl2, Bad, and C-Caspase3) and the PI3K/AKT pathway. We discovered that the ITGA11 expression was boosted in GC tissues and was related to the unfavorable prognosis of GC patients. Additionally, ITGA11 knockdown abated GC cell proliferation, invasion and migration, and enhanced cell apoptosis. In animal experiments, the tumorigenesis of GC cells knocking down ITGA11 was reduced. Mechanically, knocking down ITGA11 notably inactivated the PI3K/AKT axis. The tumor-suppressive effect mediated by ITGA11 knockdown was attenuated after activating the PI3K/AKT pathway with insulin-like growth factor 1 (IGF-1). Overall, this study substantiated that the ITGA11 expression was heightened in GC tissues, which affected GC progression by modulating the PI3K/AKT pathway.

Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

HTR2B and SLC5A3 Are Specific Markers in Age-Related Osteoarthritis and Involved in Apoptosis and Inflammation of Osteoarthritis Synovial Cells

Objective: Osteoarthritis (OA) is a heterogeneous age-related disease, which is badly difficult to cure due to its complex regulatory networks of pathogenesis. This study explored OA-specific genes in synovial tissues and validated their roles on apoptosis and inflammation of OA synovial cells.

Methods: Weighted correlation network analysis (WGCNA) was employed to explore OA-related co-expression modules in the GSE55235 and GSE55457 datasets. Then, this study screened OA-specific genes. After validation of these genes in the GSE12021 and GSE32317 datasets, HTR2B and SLC5A3 were obtained. Their expression was detected in human OA and healthy synovial tissues by RT-qPCR and western blot. OA rat models were constructed by anterior cruciate ligament transection (ACLT) operation. In OA synovial cells, HTR2B and SLC5A3 proteins were examined via western blot. After transfection with sh-HTR2B or sh-SLC5A3, apoptosis and inflammation of OA synovial cells were investigated by flow cytometry and western blot.

Results: A total of 17 OA-specific DEGs were identified, which were significantly enriched in inflammation pathways. Among them, HTR2B and SLC5A3 were highly expressed in end-than early-stage OA. Their up-regulation was validated in human OA synovial tissues and ACLT-induced OA synovial cells. Knockdown of HTR2B and SLC5A3 restrained apoptosis and increased TGF-β and IL-4 expression as well as reduced TNF-α and IL-1β expression in OA synovial cells.

Conclusion: Collectively, this study identified two OA-specific markers HTR2B and SLC5A3 and their knockdown ameliorated apoptosis and inflammation of OA synovial cells.

Identification of the potential novel biomarkers as susceptibility gene for Wilms tumor

Background

Wilms tumor (WT) is the most common malignant renal tumor in children. The aim of this study was to identify potential susceptibility gene of WT for better prognosis.

Methods

Weighted gene coexpression network analysis is used for the detection of clinically important biomarkers associated with WT.

Results

In the study, 59 tissue samples from National Cancer Institute were pretreated for constructing gene co-expression network, while 224 samples also downloaded from National Cancer Institute were used for hub gene validation and module preservation analysis. Three modules were found to be highly correlated with WT, and 44 top hub genes were identified in these key modules eventually. In addition, both the module preservation analysis and gene validation showed ideal results based on other dataset with 224 samples. Meanwhile, Functional enrichment analysis showed that genes in module were enriched to sister chromatid cohesion, cell cycle, oocyte meiosis.

Conclusion

In summary, we established a gene co-expression network to identify 44 hub genes are closely to recurrence and staging of WT, and 6 of these hub genes was closely related to the poor prognosis of patients. Our findings revealed that those hub genes may be used as potential susceptibility gene for clinical diagnosis and prognosis of this tumor.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08034-w.

Identification of lncRNA and mRNA Biomarkers in Osteoarthritic Degenerative Meniscus by Weighted Gene Coexpression Network and Competing Endogenous RNA Network Analysis

BACKGROUND

Long noncoding RNAs (lncRNAs) play a crucial role in varieties of biological processes. This study is aimed at investigating meniscal degeneration-specific lncRNAs and mRNAs and their related networks in knee osteoarthritis (KOA).

METHODS

The dataset GSE98918, which included 24 meniscus samples and related clinical data, was downloaded from the Gene Expression Omnibus database. The differentially expressed lncRNAs and mRNAs in the meniscus between KOA and control groups were identified. Based on the enriched differentially expressed lncRNAs and mRNAs, we constructed the coexpression network using WGCNA (weighted correlation network analysis) and identified the critical module related to KOA. For mRNAs in the key module, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using the DAVID database. A competing endogenous RNA network (ceRNA) based on the screened mRNAs, lncRNAs, and related miRNAs was constructed to reveal presumptive biomarkers further. Finally, the hub lncRNAs and mRNAs were screened, and the diagnostic value was evaluated using a receiver operating characteristic (ROC) curve. Hub mRNAs were validated using the dataset GSE113825.

RESULTS

We screened 208 significantly differentially expressed lncRNAs and mRNAs in menisci between the KOA and non-KOA samples, which were enriched in sixteen modules using WGCNA, especially the green module. Coexpression network based on the enriched differentially expressed lncRNAs and mRNAs in the green module uncovered 5 lncRNAs and 56 mRNAs. The lncRNA-miRNA-mRNA ceRNA network revealed that lnc-HLA-DQA1-5, lnc-RP11-127H5.1.1-1, lnc-RTN2-1, IGFBP4 (insulin-like growth factor binding protein 4), and KLF2 (Kruppel-like factor 2) were significantly correlated with the meniscus degeneration of KOA. ROC curve analysis revealed that these hub lncRNAs and mRNAs showed excellent diagnostic value for KOA.

CONCLUSIONS

These hub lncRNAs and mRNAs were potential prognostic biomarkers for the meniscus degeneration of KOA. Further studies are required to validate these new biomarkers and better understand the pathological process of the meniscus degeneration of KOA.

Sex Differences in Osteoarthritis Pathogenesis: A Comprehensive Study Based on Bioinformatics

Background

Osteoarthritis (OA) is a common disorder in the elderly. OA influences the daily life of patients and has become a worldwide health problem. It is still unclear whether the pathogenesis mechanism is different between males and females. This study investigated the differentially expressed genes (DEGs) and explored the different signaling pathways of OA between males and females.

Material/Methods

Data sets of GSE55457, GSE55584, and GSE12021 were retrieved from Gene Expression Omnibus to conduct DEGs analysis. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics tool. The protein interaction network was constructed in Cytoscape 3.7.2. qRT-PCR was then performed to validate the expression of hub genes in OA patients and healthy people.

Results

In total, 4 co-upregulated and 10 co-downregulated genes were identified. We found that enriched pathways were different between males and females. BCL2L1, EEF1A1, EEF2, HNRNPD, and PABPN1 were considered as hub genes in OA pathogenesis in males, while EEF2, EEF1A1, RPL37A, FN1 were considered as hub genes in OA pathogenesis in females. Consistent with the bioinformatics analysis, the qRT-PCR analysis also showed that the gene expression of BCL2L1, HNRNPD, and PABPN1 was significantly lower in male OA patients. In contrast, EEF2, EEF1A1, and RPL37A were significantly lower in female OA patients.

Conclusions

The DEGs identified may be involved in different OA disease progression mechanisms between males and females, and they are considered as treatment targets or prognosis markers for males and females. The pathogenesis mechanism is sex-dependent.

The PI3K/AKT/mTOR signaling pathway in osteoarthritis: a narrative review

Osteoarthritis (OA) is a complicated degenerative disease that affects whole joint tissue. Currently, apart from surgical approaches to treat late stage OA, effective treatments to reverse OA are not available. Thus, the mechanisms leading to OA, and more effective approaches to treat OA should be investigated. According to available evidence, the PI3K/AKT/mTOR signaling pathway is essential for normal metabolism of joint tissues, but is also involved in development of OA. To provide a wide viewpoint to roles of PI3K/AKT/mTOR signaling pathway in osteoarthritis, a comprehensive literature search was performed using PubMed terms 'PI3K OR AKT OR mTOR' and 'osteoarthritis'. This review highlights the role of PI3K/AKT/mTOR signaling in cartilage degradation, subchondral bone dysfunction, and synovial inflammation, and discusses how this signaling pathway affects development of the disease. We also summarize recent evidences of therapeutic approaches to treat OA by targeting the PI3K/AKT/mTOR pathway, and discuss potential challenges in developing these strategies for clinical treatment of OA.

Identification of Key Modules, Hub Genes, and Noncoding RNAs in Chronic Rhinosinusitis with Nasal Polyps by Weighted Gene Coexpression Network Analysis

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory disease with relatively easy recurrence. However, the precise molecular mechanisms of this disease are poorly known. Based on gene sequencing data obtained from the Gene Expression Omnibus (GEO) database, we constructed coexpression networks by weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The core gene of pathogenesis, CRSwNP, was screened by protein-protein interaction data (PPI) from the HPRD database. Unsupervised clustering was applied to screen hub genes related to the phenotype of CRSwNP. Blue and turquoise modules were found to be most significantly related to the pathogenicity of CRSwNP. Functional enrichment analysis showed that cell proliferation in the blue modules, the apoptotic process in the turquoise module, and the cancer pathway in both modules were mostly significantly correlated with the development of CRSwNP. The noncoding RNAs (long noncoding RNA and microRNA) and the top 10 core genes in each module were found to be associated with the pathogenesis of CRSwNP. A total of nine hub genes were identified to be related to the CRSwNP phenotype. By qRT-PCR analysis, AKT1, CDH1, PIK3R1, CBL, LRP1, MALAT1, and XIST were proven to be associated with the pathogenesis of CRSwNP. AGR2, FAM3D, PIP, DSE, and TMC were identified to be related to the CRSwNP phenotype. Further exploration of these genes will reveal more important information about the mechanisms of CRSwNP.

Identification of clinical trait-related lncRNA and mRNA biomarkers with weighted gene co-expression network analysis as useful tool for personalized medicine in ovarian cancer

RELEVANCE

The pathogenesis and biomarkers of ovarian cancer (OC) remain not well-known in diagnosis, effective therapy, and prognostic assessment in OC personalized medicine. The novel identified lncRNA and mRNA biomarkers from gene co-expression modules associated with clinical traits provide new insight for effective treatment of ovarian cancer.

PURPOSE

Long non-coding RNAs (lncRNAs) are relevant to tumorigenesis via multiple mechanisms. This study aimed to investigate cancer-specific lncRNAs and mRNAs, and their related networks in OCs.

METHODS

This study comprehensively analyzed lncRNAs and mRNAs with associated competing endogenous RNA (ceRNA) network and lncRNA-RNA binding protein-mRNA network in the OC tissues in the Cancer Genome Atlas, including 2562 cancer-specific lncRNAs (n = 352 OC tissues) and 5000 mRNAs (n = 359 OC tissues). The weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression gene modules and their relationship with clinical traits. The statistically significant difference of identified lncRNAs and mRNAs was confirmed with qRT-PCR in OC cells.

RESULTS

An lncRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tissues source site, and vascular invasion, and identified 16 lncRNAs (ACTA2-AS1, CARD8-AS1, HCP5, HHIP-AS1, HOTAIRM1, ITGB2-AS1, LINC00324, LINC00605, LINC01503, LINC01547, MIR31HG, MIR155HG, OTUD6B-AS1, PSMG3-AS1, SH3PXD2A-AS1, and ZBED5-AS1) that were significantly related to overall survival in OC patients. An mRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tumor residual disease, and vascular invasion; and identified 21 hub-mRNA molecules and 11 mRNAs (FBN3, TCF7L1, SBK1, TRO, TUBB2B, PLCG1, KIAA1549, PHC1, DNMT3A, LAMA1, and C10orf82) that were closely linked with OC patients' overall survival. Moreover, the prognostic model of five-gene signature (OTUD6B-AS1, PSMG3-AS1, ZBED5-AS1, SBK1, and PLCG1) was constructed to predict risk score in OC patients. Furthermore, starBase bioinformatics constructed the lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA networks in OCs.

CONCLUSION

These new findings showed that lncRNA-related networks in OCs are a useful resource for identification of biomarkers in OCs.

Identification of key gene modules and transcription factors for human osteoarthritis by weighted gene co-expression network analysis

Osteoarthritis (OA) is one of the most prevalent causes of joint disease. However, the pathological mechanisms of OA have remained to be completely elucidated, and further investigation into the underlying mechanisms of OA development and the identification of novel therapeutic targets are urgently required. In the present study, the dataset GSE114007 was downloaded from the Gene Expression Omnibus database. Based on weighted gene co-expression network analysis (WGCNA) and the identification of differentially expressed genes (DEGs), the microarray data were further analyzed to identify hub genes, key transcription factors (TFs) and pivotal signaling pathways involved in the pathogenesis of OA. A total of 1,898 genes were identified to be differentially expressed between OA samples and normal samples. Based on WGCNA, the present study identified 5 hub modules closely associated with OA, and the potential key TFs for hub modules were further explored based on CisTargetX. The results demonstrated that B-Cell Lymphoma 6, Myelin Gene Expression Factor 2, Activating Transcription Factor 3, CCAAT Enhancer Binding Protein γ, Nuclear Factor Interleukin-3-Regulated, FOS Like Antigen-2, FOS-Like Antigen-1, Fos Proto-Oncogene, JunD Proto-Oncogene, Transcription Factor CP2 Like 1, RELA proto-oncogene NF-kB subunit, SRY-box transcription factor 3, V-Ets Avian Erythroblastosis Virus E26 Oncogene Homolog 2, Interferon Regulatory Factor 4 and REL proto-oncogene, NF-kB subunit were the potential key TFs. In addition, osteoclast differentiation, FoxO, MAPK and PI3K/Akt signaling pathways were revealed to be imperative for the pathogenesis of OA, as these 4 pivotal signaling pathways were observed to be tightly linked through 4 key TFs Fos Proto-Oncogene, JUN, JunD Proto-Oncogene and MYC, and 4 DEGs Vascular Endothelial Growth Factor A, Growth Arrest and DNA Damage Inducible α, Growth Arrest and DNA Damage Inducible β and Cyclin D1. The present study identified a set of potential key genes and signaling pathways, and provided an important opportunity to advance the current understanding of OA.

Identification of CXCL13 as a potential biomarker in clear cell renal cell carcinoma via comprehensive bioinformatics analysis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system. However, there are still no reliable biomarkers for the diagnosis and prognosis of ccRCC. In this study, we aimed to screen candidate biomarkers and potential therapeutic targets for ccRCC.

METHODS

Differentially expressed genes (DEGs) were screened using NetworkAnalyst. Protein-protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) were utilized to identify hub genes. Then, we assessed the prognostic and diagnostic values of hub genes to screen candidate biomarkers. Gene Set Enrichment Analysis (GSEA) was applied to reveal potential mechanisms of candidate biomarkers in ccRCC. Oncomine database and The Human Protein Atlas were used to verify the expression of candidate biomarkers online. In addition, qRT-PCR, Enzyme linked immunosorbent assay (ELISA) and Immunohistochemistry (IHC) assays were performed to validate the expression level of candidate biomarkers in ccRCC cells and tissues.

RESULTS

A total of 771 genes were identified as DEGs. GO function analysis showed that DEGs were mostly enriched in excretion, apical part of cell and monovalent inorganic cation transmembrane transporter activity. KEGG pathway analysis demonstrated that DEGs were mostly involved in Neuroactive ligand-receptor interaction. After utilizing PPI network and WGCNA, nine genes (IFNG, CXCR3, PMCH, CD2, FASLG, CXCL13, CD8A, CD3D and GZMA) were identified as the hub genes. Moreover, survival analysis exhibited that high expression of CXCL13 predicted poor survival in both overall survival (OS) and disease free survival (DFS). The ROC curves indicated that CXCL13 could distinguish ccRCC samples from normal kidney samples. High expression of CXCL13 group was mostly associated with RB and MEL18 pathways by GSEA. Furthermore, qRT-PCR, ELISA and IHC results showed that the expression of CXCL13 was elevated in ccRCC.

CONCLUSIONS

Our study illustrated that CXCL13 had good diagnostic and prognostic value, which may become a candidate biomarker and therapeutic target for ccRCC.

Identification of the Biomarkers and Pathological Process of Osteoarthritis: Weighted Gene Co-expression Network Analysis

Osteoarthritis (OA) is a joint disease resulting in high rates of disability and low quality of life. The initial site of OA (bone or cartilage) is uncertain. The aim of the current study was to explore biomarkers and pathological processes in subchondral bone samples. The gene expression profile GSE51588 was downloaded from the Gene Expression Omnibus database. Fifty subchondral bone [knee lateral tibial (LT) and medial tibial (MT)] samples from 40 OA and 10 non-OA subjects were analyzed. After data preprocessing, 5439 genes were obtained for weighted gene co-expression network analysis. Highly correlated genes were divided into 19 modules. The yellow module was found to be highly correlated with OA (r = 0.71, p = 1e-08) and the brown module was most associated with the differences between the LT and MT regions (r = 0.77, p = 1e-10). Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment indicated that the yellow module was enriched in a variety of components including proteinaceous extracellular matrix and collagen trimers, involved in protein digestion and absorption, axon guidance, ECM-receptor interaction, and the PI3K-Akt signaling pathway. In addition, the brown module suggests that the differences between the early (LT) and end (MT) stage of OA are associated with extracellular processes and lipid metabolism. Finally, 45 hub genes in the yellow module (COL24A1, COL5A2, COL3A1, MMP2, COL6A1, etc.) and 72 hub genes in the brown module (LIPE, LPL, LEP, SLC2A4, FABP4, ADH1B, ALDH4A1, ADIPOQ, etc.) were identified. Hub genes were validated using samples from cartilage (GSE57218). In summary, 45 hub genes and 72 hub genes in two modules are associated with OA. These hub genes could provide new biomarkers and drug targets in OA. Further studies focusing on subchondral bone are required to validate these hub genes and better understand the pathological process of OA.