Effect of hyperoside on cervical cancer cells and transcriptome analysis of differentially expressed genes

Abstract Background Hyperoside (Hy) is a plant-derived quercetin 3-d-galactoside that exhibits inhibitory activities on various tumor types. The objective of the current study was to explore Hy effects on cervical cancer cell proliferation, and to perform a transcriptome analysis of differentially expressed genes. Methods Cervical cancer HeLa and C-33A cells were cultured and the effect of Hy treatment was determined using the Cell Counting Kit-8 (CCK-8) assay. After calculating the IC50 of Hy in HeLa and C-33A cells, the more sensitive to Hy treatment cell type was selected for RNA-Seq. Differentially expressed genes (DEGs) were identified by comparing gene expression between the Hy and control groups. Candidate genes were determined through DEG analysis, protein interaction network (PPI) construction, PPI module analysis, transcription factor (TF) prediction, TF-target network construction, and survival analysis. Finally, the key candidate genes were verified by RT-qPCR and western blot. Results Hy inhibited HeLa and C33A cell proliferation in a dose- and time-dependent manner, as determined by the CCK-8 assay. Treatment of C-33A cells with 2 mM Hy was selected for the subsequent experiments. Compared with the control group, 754 upregulated and 509 downregulated genes were identified after RNA-Seq. After functional enrichment, 74 gene ontology biological processes and 43 Kyoto Encyclopedia of Genes and Genomes pathways were obtained. According to the protein interaction network (PPI), PPI module analysis, TF-target network construction, and survival analysis, the key genes MYC, CNKN1A, PAX2, TFRC, ACOX2, UNC5B, APBA1, PRKACA, PEAR1, COL12A1, CACNA1G, PEAR1, and CCNA2 were detected. RT-qPCR was performed on the key genes, and Western blot was used to verify C-MYC and TFRC. C-MYC and TFRC expressions were lower and higher than the corresponding values in the control group, respectively, in accordance with the results from the RNA-Seq analysis. Conclusion Hy inhibited HeLa and C-33A cell proliferation through C-MYC gene expression reduction in C-33A cells and TFRC regulation. The results of the current study provide a theoretical basis for Hy treatment of cervical cancer.

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Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

10.21203/rs.3.rs-139481/v1 ◽

2021 ◽

Author(s):

Chengang Guo ◽

Zhimin wei ◽

Wei Lyu ◽

Yanlou Geng

Keyword(s):

Differentially Expressed Genes ◽

Principal Component ◽

Enrichment Analysis ◽

Hierarchical Cluster ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Differential Analysis ◽

Rna Seq ◽

Protein Coding ◽

Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

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Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

Lung Cancer Management ◽

10.2217/lmt-2020-0009 ◽

2020 ◽

Vol 9 (2) ◽

pp. LMT30

Author(s):

Chuanli Ren ◽

Weixiu Sun ◽

Xu Lian ◽

Chongxu Han

Keyword(s):

Lung Adenocarcinoma ◽

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Kaplan Meier ◽

Key Genes ◽

Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

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Characterization and Duodenal Transcriptome Analysis of Chinese Beef Cattle With Divergent Feed Efficiency Using RNA-Seq

Frontiers in Genetics ◽

10.3389/fgene.2021.741878 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chaoyun Yang ◽

Liyun Han ◽

Peng Li ◽

Yanling Ding ◽

Yun Zhu ◽

...

Keyword(s):

Beef Cattle ◽

Differentially Expressed Genes ◽

Feed Intake ◽

Feed Efficiency ◽

Muscle Development ◽

Dietary Factors ◽

Differentially Expressed ◽

Rna Seq ◽

Biological Regulation ◽

Key Genes

Residual feed intake (RFI) is an important measure of feed efficiency for agricultural animals. Factors associated with cattle RFI include physiology, dietary factors, and the environment. However, a precise genetic mechanism underlying cattle RFI variations in duodenal tissue is currently unavailable. The present study aimed to identify the key genes and functional pathways contributing to variance in cattle RFI phenotypes using RNA sequencing (RNA-seq). Six bulls with extremely high or low RFIs were selected for detecting differentially expressed genes (DEGs) by RNA-seq, followed by conducting GO, KEGG enrichment, protein-protein interaction (PPI), and co-expression network (WGCNA, n = 10) analysis. A total of 380 differentially expressed genes was obtained from high and low RFI groups, including genes related to energy metabolism (ALDOA, HADHB, INPPL1), mitochondrial function (NDUFS1, RFN4, CUL1), and feed intake behavior (CCK). Two key sub-networks and 26 key genes were detected using GO analysis of DEGs and PPI analysis, such as TPM1 and TPM2, which are involved in mitochondrial pathways and protein synthesis. Through WGCNA, a gene network was built, and genes were sorted into 27 modules, among which the blue (r = 0.72, p = 0.03) and salmon modules (r = −0.87, p = 0.002) were most closely related with RFI. DEGs and genes from the main sub-networks and closely related modules were largely involved in metabolism; oxidative phosphorylation; glucagon, ribosome, and N-glycan biosynthesis, and the MAPK and PI3K-Akt signaling pathways. Through WGCNA, five key genes, including FN1 and TPM2, associated with the biological regulation of oxidative processes and skeletal muscle development were identified. Taken together, our data suggest that the duodenum has specific biological functions in regulating feed intake. Our findings provide broad-scale perspectives for identifying potential pathways and key genes involved in the regulation of feed efficiency in beef cattle.

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Identification of differentially expressed genes between mucinous adenocarcinoma and other adenocarcinoma of colorectal cancer using bioinformatics analysis

Journal of International Medical Research ◽

10.1177/0300060520949036 ◽

2020 ◽

Vol 48 (8) ◽

pp. 030006052094903

Author(s):

Xue Zhang ◽

Jing Zuo ◽

Long Wang ◽

Jing Han ◽

Li Feng ◽

...

Keyword(s):

Colorectal Cancer ◽

Cell Proliferation ◽

Differentially Expressed Genes ◽

Mucinous Adenocarcinoma ◽

Wnt Signaling Pathway ◽

Interaction Network ◽

Differentially Expressed ◽

Venn Diagram ◽

Bicarbonate Transport ◽

Hub Genes

Objective As a unique histological subtype of colorectal cancer (CRC), mucinous adenocarcinoma (MC) has a poor prognosis and responds poorly to treatment. Genes and markers related to MC have not been reported. Methods To identify biomarkers involved in development of MC compared with other common adenocarcinoma (AC) subtypes, four datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using GEO2R. A protein–protein interaction network was constructed. Functional annotation for DEGs was performed via DAVID, Metascape, and BiNGO. Significant modules and hub genes were identified using Cytoscape, and expression of hub genes and relationships between hub genes and MC were analyzed. Results The DEGs were mainly enriched in negative regulation of cell proliferation, bicarbonate transport, response to peptide hormone, cell–cell signaling, cell proliferation, and positive regulation of the canonical Wnt signaling pathway. The Venn diagram revealed eight significant hub genes: CXCL9, IDO1, MET, SNAI2, and ZEB2 were highly expressed in MC compared with AC, whereas AREG, TWIST1, and ZEB1 were expressed at a low level. AREG and MET might be significant biomarkers for MC. Conclusion The identified DEGs might help elucidate the pathogenesis of MC, identify potential targets, and improve treatment for CRC.

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RNA-Seq Analysis Identifies Differentially Expressed Genes in Subcutaneous Adipose Tissue in Qaidaford Cattle, Cattle-Yak, and Angus Cattle

Animals ◽

10.3390/ani9121077 ◽

2019 ◽

Vol 9 (12) ◽

pp. 1077 ◽

Cited By ~ 2

Author(s):

Chengchuang Song ◽

Yongzhen Huang ◽

Zhaoxin Yang ◽

Yulin Ma ◽

Buren Chaogetu ◽

...

Keyword(s):

Adipose Tissue ◽

Meat Quality ◽

Differentially Expressed Genes ◽

Transcriptome Analysis ◽

Cell Metabolism ◽

Differentially Expressed ◽

Receptor Interaction ◽

Control Group ◽

Rna Seq ◽

Angus Cattle

In the beef industry, fat tissue is closely related to meat quality. In this study, high-throughput RNA sequencing was utilized for adipose tissue transcriptome analysis between cattle-yak, Qaidamford cattle, and Angus cattle. The screening and identification of differentially expressed genes (DEGs) between different breeds of cattle would facilitate cattle breeding. Compared to Angus cattle adipose tissue, a total of 4167 DEGs were identified in cattle-yak adipose tissue and 3269 DEGs were identified in Qaidamford cattle adipose tissue. Considering cattle-yak as a control group, 154 DEGs were identified in Qaidamford cattle adipose tissue. GO analysis indicated the significant enrichment of some DEGs related to lipid metabolism. The KEGG pathway database was also used to map DEGs and revealed that most annotated genes were involved in ECM-receptor interaction and the PI3K-Akt signal pathway, which are closely related to cell metabolism. Eight selected DEGs related to adipose tissue development or metabolism were verified by RT-qPCR, indicating the reliability of the RNA-seq data. The results of this comparative transcriptome analysis of adipose tissue and screening DEGs suggest several candidates for further investigations of meat quality in different cattle breeds.

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Study on the Effect of Macrophages on Vascular Endothelium in Mice With Different TCM Syndromes of Dyslipidemia and its Biological Basis Based on RNA-Seq Technology

Frontiers in Pharmacology ◽

10.3389/fphar.2021.665635 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jing Chen ◽

Chao Ye ◽

Zheng Yang ◽

Tieshan Wang ◽

Bing Xu ◽

...

Keyword(s):

Quality Assessment ◽

Differentially Expressed Genes ◽

Normal Control ◽

Normal Control Group ◽

Differentially Expressed ◽

Control Group ◽

Biological Processes ◽

Rna Seq ◽

Pathogenic Factors ◽

Protective Processes

Background: “Treating the same disease with different methods” is a Traditional Chinese medicine (TCM) therapeutic concept suggesting that, while patients may be diagnosed with the same disease, they may also have different syndromes that require distinct drug administrations.Objective: This study aimed to identify the differentially expressed genes and related biological processes in dyslipidemia in relation to phlegm–dampness retention (PDR) syndrome and spleen and kidney Yang deficiency (SKYD) syndrome using transcriptomic analysis.Methods: Ten ApoE−/− mice were used for the establishment of dyslipidemic disease–syndrome models via multifactor-hybrid modeling, with five in the PDR group and five in the SKYD group. Additionally, five C57BL/6J mice were employed as a normal control group. Test model-quality aortic endothelial macrophages in mice were screened using flow cytometry. Transcriptomic analysis was performed for macrophages using RNA-Seq.Results: A quality assessment of the disease–syndrome model showed that levels of lipids significantly increased in the PDR and SKYD groups, compared to the normal control group, p < 0.05. Applying, in addition, hematoxylin and eosin staining of aorta, the disease model was also successfully established. A quality assessment of the syndrome models showed that mice in the PDR group presented with typical manifestations of PDR syndrome, and mice in the SKYD group had related manifestations of SKYD syndrome, indicating that the syndrome models were successfully constructed as well. After comparing the differentially expressed gene expressions in macrophages of the dyslipidemic mice with different syndromes, 4,142 genes were identified with statistical significance, p < 0.05. Gene ontology analysis for the differentially expressed genes showed that the biological process of difference between the PDR group and the SKYD group included both adverse and protective processes.Conclusion: The differentially expressed genes between PDR syndrome and SKYD syndrome indicate different biological mechanisms between the onsets of the two syndromes. They have distinctive biological processes, including adverse and protective processes that correspond to the invasion of pathogenic factors into the body and the fight of healthy Qi against pathogenic factors, respectively, according to TCM theory. Our results provide biological evidence for the TCM principle of “treating the same disease with different treatments.”

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The Genetics Analysis of Molecular Pathogenesis for Alzheimer's Disease

European Neurology ◽

10.1159/000509799 ◽

2020 ◽

Vol 83 (5) ◽

pp. 458-467

Author(s):

Guanchuan Lin ◽

Kaiyuan Ji ◽

Shiyu Li ◽

Wenli Ma ◽

Xinghua Pan

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Brain Regions ◽

Molecular Pathogenesis ◽

Differentially Expressed ◽

Control Group ◽

Membrane Structures

Introduction: The molecular pathogenesis of Alzheimer’s disease (AD) is still not clear, and the relationship between gene expression profile for different brain regions has not been studied. Objective: Bioinformatic analysis at the genetic level has become the best way for the pathogenesis research of AD, which can analyze the abovementioned relationship. Methods: In this study, the datasets of AD were obtained from the Gene Expression Omnibus (GEO), and Qlucore Omics Explorer (QOE) software was used to screen differentially expressed genes of GSE36980 and GSE9770 and verify gene expression of GSE63060. The Gene Ontology (GO) function enrichment analysis of these selected genes was conducted by Database for Annotation, Visualization, and Integrated Discovery (DAVID), and then the gene/protein interaction network was established by STRING to find the related proteins. R language was used for drafting maps and plots. Results: There were 20 differentially expressed genes related to AD selected from GSE36980 (p = 6.2e−6, q = 2.9422e−4) and GSE9770 (p = 3.3e−4, q = 0.016606). Their expression levels of the AD group were lower than those in the control group and varied among different brain regions. Cellular morphogenesis and establishment or maintenance of cell polarity were enriched, and LRRTM1 and RASAL1 were identified by the integration network. Moreover, the analysis of GSE63060 verified the expression level of LRRTM1 and RASAL1 in Alzheimer’s patients, which was much lower than that in normal people aged >65 years. Conclusions: The pathogenesis of AD at molecular levels may link to cell membrane structures and signal transduction; hence, a list of 20 genes, including LRRTM1 and RASAL1,potentially are important for the discovery of treatment target or molecular marker of AD.

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Dorsal Striatum Transcriptome Profile Profound Shift in Repeated Aggression Mouse Model Converged to Networks of 12 Transcription Factors after Fighting Deprivation

Genes ◽

10.3390/genes13010021 ◽

2021 ◽

Vol 13 (1) ◽

pp. 21

Author(s):

Vladimir Babenko ◽

Olga Redina ◽

Dmitry Smagin ◽

Irina Kovalenko ◽

Anna Galyamina ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Expression Profile ◽

Transcriptional Repression ◽

Gene Networks ◽

Dorsal Striatum ◽

Differentially Expressed ◽

Control Group ◽

Rna Seq ◽

Withdrawal Effect ◽

Transcriptome Expression

Both aggressive and aggression-deprived (AD) species represent pathologic cases intensely addressed in psychiatry and substance abuse disciplines. Previously, we reported that AD mice displayed a higher aggressive behavior score than the aggressive group, implying the manifestation of a withdrawal effect. We employed an animal model of chronic social conflicts, curated in our lab for more than 30 years. In the study, we pursued the task of evaluating key events in the dorsal striatum transcriptome of aggression experienced mice and AD species compared to controls using RNA-Seq profiling. Aggressive species were subjected to repeated social conflict encounters (fights) with regular positive (winners) experience in the course of 20 consecutive days (A20 group). This led to a profoundly shifted transcriptome expression profile relative to the control group, outlined by more than 1000 differentially expressed genes (DEGs). RNA-Seq cluster analysis revealed that elevated cyclic AMP (cAMP) signaling cascade and associated genes comprising 170 differentially expressed genes (DEGs) in aggressive (A20) species were accompanied by a downturn in the majority of other metabolic/signaling gene networks (839 DEGs) via the activation of transcriptional repressor DEGs. Fourteen days of a consecutive fighting deprivation period (AD group) featured the basic restoration of the normal (control) transcriptome expression profile yielding only 62 DEGs against the control. Notably, we observed a network of 12 coordinated DEG Transcription Factor (TF) activators from 62 DEGs in total that were distinctly altered in AD compared to control group, underlining the distinct transcription programs featuring AD group, partly retained from the aggressive encounters and not restored to normal in 14 days. We found circadian clock TFs among them, reported previously as a withdrawal effect factor. We conclude that the aggressive phenotype selection with positive reward effect (winning) manifests an addiction model featuring a distinct opioid-related withdrawal effect in AD group. Along with reporting profound transcriptome alteration in A20 group and gaining some insight on its specifics, we outline specific TF activator gene networks associated with transcriptional repression in affected species compared to controls, outlining Nr1d1 as a primary candidate, thus offering putative therapeutic targets in opioid-induced withdrawal treatment.

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Complement C3a Not C4a Activates Osteoclasts By Regulating the PI3K/PDK1/SGK3 Pathway in Patients with Multiple Myeloma

Blood ◽

10.1182/blood-2019-127022 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4416-4416

Author(s):

Rong Fu ◽

Fengjuan Jiang ◽

Hui Liu ◽

Zhaoyun Liu ◽

Siyang Yan ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Bone Disease ◽

Cathepsin K ◽

Differentially Expressed ◽

Control Group ◽

Rna Seq ◽

Relative Expression ◽

Absorption Area ◽

Osteoclast Resorption

Objective: Myeloma bone disease (MBD) is the most common complication of multiple myeloma (MM). We found that the serum levels of C3/C4 in MM patients were significantly positive correlated with the severity of bone disease in our previously study. Thus, we conduct detailed studies to explore the effect and potential mechanism of C3a/C4a on osteoclasts in patients with MM. Methods: By adding C3a/C4a to culture system of osteoclasts induced from mononuclear cells in vitro, and the expression of related gene, number and function of osteoclasts were detected. RNA-Seq analysis was used to detect the differentially expressed genes on osteoclasts between the complement group and control group, and the possible signal pathways were analyzed. Quantitative real-time PCR (qRT-PCR), Western blot and pathway inhibitors were used for further validation. R esults: In vitro, the osteoclasts area per view induced by C3a 1μg/ml (52.794±13.027 %) and 10μg/ml (51.797±12.464 %) was significantly increased than control group (0μg/ml) (33.668±8.427 %) (P<0.001; P<0.001), the mRNA relative expression of osteoclasts related genes OSCAR/TRAP/RANKL/Cathepsin K induced by C3a 1μg/ml (median 5.041; 3.726; 1.638; 4.752) and 10μg/ml (median 5.140; 3.702; 2.250; 5.172) was significantly increased than control group (0μg/ml) (median 3.137; 2.004; 0.573; 2.257) (1μg/ml P=0.001; P=0.003; P<0.001; P=0.008; 1μg/ml P<0.001; P=0.019; P<0.001; P=0.002), and the absorption area of osteoclast resorption pit per view induced by C3a 1μg/ml (51.464±11.983 %) and 10μg/ml (50.219±12.067 %) was also significantly increased than control group (0μg/ml) (33.845±8.331 %) (P<0.001; P<0.001) in NDMM patients. There was no difference among the osteoclasts area, relative expression of osteoclasts related genes and absorption area of osteoclast resorption pit between C4a (1μg/ml and 10μg/ml) group and control group (0μg/ml). RNA-Seq was performed on total RNA of osteoclasts induced by C3a in 1μg/ml group and 0μg/ml group of 4 patients with NDMM. There were 184 differentially expressed genes that were detected by RNA-Seq analysis. KEGG Pathway enrichment bubble chart shows C3a may through Phosphoinositide 3-kinase (PI3K) signaling pathways (including PI3K-Akt pathway and AKT-independent signaling pathway) promotes the proliferation of osteoclast. Upregulated differentially expressed genes in this pathway among at least 3 patients with sequencing were validated by qRT-PCR and Western Blot. It was found that the relative expression level of Phosphoinositide dependent kinase-1 (PDK1) / Serum and glucocorticoid inducible protein kinases (SGK3) genes (median 2.078; 4.428) in C3a group (1μg/ml) was significantly higher than control group (0μg/ml) (median 1.336; 1.714) (P<0.001; P=0.001). The relative grayscale levels of PDK1/ P-SGK3 protein (1.785±0.323; 2.190±0.274) in C3a group (1μg/ml) was significantly stronger than control group (0μg/ml) (0.8653±0.588; 0.176±0.152) (P=0.034; P<0.001). Under the action of C3a in patients with NDMM, osteoclasts area per view in SGK inhibitor (EMD638683) 1μM group (39.244±9.089 %) and 10μM group (39.299±9.587 %) significantly reduced than control group (0μM) (54.884±12.837 %) (P<0.001; P<0.001), the relative expression of osteoclast related genes OSCAR/RANKL/TRAP/Cathepsin K in EMD638683 1μM group (median 0.869; 1.097; 0.902; 1.328) and 10μM group (median 0.703; 1.391; 0.843; 1.418) significantly decreased than control group (0μM) (median 2.270; 3.024; 2.208; 3.237) (1μM P=0.015; P=0.002; P=0.003; P=0.015; 10μM P=0.012; P=0.006; P<0.001; P=0.017), and the absorption area per view of osteoclast resorption pit in EMD638683 1μM (35.383±7.794 %) group and 10μM group (32.886±8.993 %) significantly reduced than control group (0μM) (49.358±11.856 %) (P < 0.001; P < 0.001). Conclusions:ComplementC3a activates osteoclasts by regulating the PI3K/PDK1/SGK3 pathway in patients with MM, thus leading to the occurrence of bone diseases. SGK inhibitor has a significant inhibitory effect on osteoclasts in patients with MM. This study provide important evidences for the search for new therapeutic targets and strategies for myeloma bone disease patients. Disclosures No relevant conflicts of interest to declare.

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Identification of key genes in relapsed multiple myeloma by weighted gene co-expression network analysis

10.21203/rs.3.rs-153300/v1 ◽

2021 ◽

Author(s):

Lele Cao¹ ◽

Ye Meng¹ ◽

Rui Huang ◽

Xiaoxue Wang ◽

Hui Qin

Keyword(s):

Multiple Myeloma ◽

Survival Analysis ◽

Differentially Expressed Genes ◽

Interaction Network ◽

R Package ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Hub Genes ◽

Hematologic Disorder ◽

Key Genes

Abstract Background Multiple myeloma is a hematologic disorder of abnormal plasma cell proliferation. Although there are some agents with different mechanisms in the clinic, the treatment of multiple myeloma is still challenging for the reason that its recurrence and progression are common. Therefore, it is critical to determine novel biomarkers to improve the prognosis of patients. Methods Firstly, raw data of GSE82307 was collected from the Gene Expression Omnibus database. Secondly, the top 50% of most variant genes were employed to construct a gene co-expression network in the R.WGCNA algorithm, and module significance and module membership were utilized to identify hub modules and hub genes respectively. The gene ontology enrichment and Kyoto encyclopedia of genes and genomes pathway analysis were carried out to assess biological characteristics. Then a protein-protein interaction network was conducted based on the STRING website and Cytoscape software. Next, differentially expressed genes were analyzed using the limma R package. Finally, survival analysis was performed by Kaplan–Meier plotter to evaluate prognosis. Results 10826 genes were used to construct a co-expression network. In this network, the blue module was identified as a hub module in which 68 genes were identified as hub genes. Furthermore, 46 differentially expressed genes were screened in samples of GSE82307. Integrating hub genes and differentially expressed genes, we determined 14 key genes. Finally, survival analysis revealed that ten genes CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 were related to the relapse and prognosis of multiple myeloma. Conclusion Our study suggested that CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 may be potential biomarkers for predicting the relapse and prognosis of multiple myeloma.

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