基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

脑胶质瘤(glioma)是中枢神经系统最常见的内在肿瘤,具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤(glioblastoma multiforme,GBM)和低级别胶质瘤(lower-grade gliomas, LGG)之间的差异表达基因(differentially expressed genes, DEGs),以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据,其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析,经过滤得到21 071个细胞,通过基因本体分析、京都基因与基因组百科全书途径分析,从差异表达基因中筛选出70个基因,我们通过查阅文献,聚焦到delta样典型Notch配体3 (delta like canonical Notch ligand 3,DLL3)这个基因。基于TCGA的基因表达谱交互分析(gene expression profiling interactive analysis, GEPIA)数据库用于探索LGG和GBM中DLL3基因的表达差异,采用基因表达...     

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets.Method and ResultsIn this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cellsConclusionThis research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.     

Identification of novel LncRNA targeting Smad2/PKCα signal pathway to negatively regulate malignant progression of glioblastoma

Glioblastoma multiforme (GBM) is a highly proliferative cancer with generally poor prognosis and accumulating evidence has highlighted the potential of long noncoding RNAs (lncRNAs) in the biological behaviors of glioma cells. This study focused on the identification of lncRNAs to identify targets for possible GBM prognosis. Microarray expression profiling found that 1,759 lncRNAs and 3,026 messenger RNAs (mRNAs) were upregulated, and 1932s lncRNA and 2,979 mRNAs were downregulated in GBM. Bioinformatics analysis and experimental verification identified TCONS_00020456 (TCON) for further analysis. In situ hybridization, along with immunohistochemical and receiver operating characteristic analysis determined TCON (truncation value = 3.5) as highly sensitive and specific in GBM. Grade IV patients with glioma life span with different lncRNA staining scores were analyzed. TCON staining scores below 3.5 indicated poor prognosis (life span ranging from 0.25 to 7 months), even if the glioma was surgically removed. TCON decreased significantly in GBM, and showed a coexpressional relationship with Smad2 and protein kinase C α (PKCα). Overexpression of TCON reduced the proliferation on one hand and migration, invasion on the other. TCON also inhibited epithelial–mesenchymal transformation and glioma progression in vivo, based on a nude mouse tumorigenicity assay. In addition, we predicted a potential binding site and intersection that microRNAs targeting Smad2, PKCα, and TCON through RACE pretest and bioinformatics analysis. Taken together, TCON, regarded as oncosuppressor, targeting the Smad2/PKCα axis plays a novel role in inhibiting the malignant progression of glioma. Moreover, it also demonstrates that the level of TCON can be used as a prognostic and diagnostic biomarker for GBM.     

The overexpression of GRASP might inhibit cell proliferation and invasion in hepatocellular carcinoma

This study aimed to validate the methylation of key genes in hepatocellular carcinoma (HCC) screened by bioinformatics analysis and explore whether they affected HCC cell proliferation, migration, and invasion. Using The Cancer Genome Atlas (TCGA) database, HCC-related differentially methylated positions (DMPs) were screened, genes corresponding to DMPs were selected, and prognosis-related genes were identified. A representative DMP was used to divide the DMPs into hyper- and hypomethylated groups. Expression of key genes in cell lines was detected using quantitative real-time polymerase chain reaction and western blot analysis. After treatment of HepG2 cells with 5-Aza-2′-deoxycytidine (5-Aza-DC), gene expression was observed. Bisulfite sequencing PCR assay was used to detect methylation frequency. Overexpressed GRASP lentiviral vectors were constructed to analyze their influence on cell proliferation, migration, and invasion using cell counting kit-8 and transwell assays. Forty-three HCC prognosis-related genes were screened using the TCGA database. cg00249511 (SCT) was used to divide the DMPs into hyper- and hypomethylated groups, distinguishing between high- and low-risk samples. The prognosis survival model constructed using 12 genes revealed the prognosis type. GRASP messenger RNA was downregulated in HepG2 and upregulated after 5-Aza-DC treatment. In HCC tissues, methylation frequency of GRASP was upregulated. GRASP overexpression inhibited HepG2 cell proliferation, invasion, and G-CSFR expression. Thus, GRASP might be a prognosis-related gene controlled by methylation.     

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.     

Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF-kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Identification of a six-gene prognostic signature for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancies worldwide, and its morbidity and mortality have increased in the near term. Consequently, the purpose of the present study was to identify the notable differentially expressed genes (DEGs) involved in their pathogenesis to obtain new biomarkers or potential therapeutic targets for OSCC. The gene expression profiles of the microarray datasets GSE85195, GSE23558, and GSE10121 were obtained from the Gene Expression Omnibus (GEO) database. After screening the DEGs in each GEO dataset, 249 DEGs in OSCC tissues were obtained. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathway enrichment analysis was employed to explore the biological functions and pathways of the above DEGs. A protein–protein interaction network was constructed to obtain a central gene. The corresponding total survival information was analyzed in patients with oral cancer from The Cancer Genome Atlas (TCGA). A total of six candidate genes (CXCL10, OAS2, IFIT1, CCL5, LRRK2, and PLAUR) closely related to the survival rate of patients with oral cancer were identified, and expression verification and overall survival analysis of six genes were performed based on TCGA database. Time-dependent receiver operating characteristic curve analysis yields predictive accuracy of the patient's overall survival. At the same time, the six genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients recruited to the present study. In conclusion, the present study identified the prognostic signature of six genes in OSCC for the first time via comprehensive bioinformatics analysis, which could become potential prognostic markers for OCSS and may provide potential therapeutic targets for tumors.     

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

NEK2 enhances malignancies of glioblastoma via NIK/NF-κB pathway

Glioblastoma (GBM) is one of the most lethal primary brain tumor with a poor median survival less than 15 months. Despite the development of the clinical strategies over the decades, the outcomes for GBM patients remain dismal due to the strong proliferation and invasion ability and the acquired resistance to radiotherapy and chemotherapy. Therefore, developing new biomarkers and therapeutic strategies targeting GBM is in urgent need. In this study, gene expression datasets and relevant clinical information were extracted from public cancers/glioma datasets, including TCGA, GRAVENDEEL, REMBRANDT, and GILL datasets. Differentially expressed genes were analyzed and NEK2 was picked as a candidate gene for subsequent validation. Human tissue samples and corresponding data were collected from our center and detected by immunohistochemistry analysis. Molecular biological assays and in vivo xenograft transplantation were performed to confirm the bioinformatic findings. High-throughput RNA sequencing, followed by KEGG analysis, GSEA analysis and GO analysis were conducted to identify potential signaling pathways related to NEK2 expression. Subsequent mechanism assays were used to verify the relationship between NEK2 and NF-κB signaling. Overall, we identified that NEK2 is significantly upregulated in GBM and the higher expression of NEK2 exhibited a poorer prognosis. Functionally, NEK2 knockdown attenuated cell proliferation, migration, invasion, and tumorigenesis of GBM while NEK2 overexpression promoted the GBM progression. Furthermore, High-throughput RNA sequencing and bioinformatics analysis indicated that NEK2 was positively related to the NF-κB signaling pathway in GBM. Mechanically, NEK2 activated the noncanonical NF-κB signaling pathway by phosphorylating NIK and increasing the activity and stability of NIK. In conclusion, NEK2 promoted the progression of GBM through activation of noncanonical NF-κB signaling, indicating that NEK2- NF-κB axis could be a potential drug target for GBM.Subject terms: CNS cancer, Nuclear receptors     

MicroRNA-145 Is Downregulated in Glial Tumors and Regulates Glioma Cell Migration by Targeting Connective Tissue Growth Factor

Glioblastomas (GBM), the most common and aggressive type of malignant glioma, are characterized by increased invasion into the surrounding brain tissues. Despite intensive therapeutic strategies, the median survival of GBM patients has remained dismal over the last decades. In this study we examined the expression of miR-145 in glial tumors and its function in glioma cells. Using TCGA analysis and real-time PCR we found that the expression of miR-145/143 cluster was downregulated in astrocytic tumors compared to normal brain specimens and in glioma cells and glioma stem cells (GSCs) compared to normal astrocytes and neural stem cells. Moreover, the low expression of both miR-145 and miR-143 in GBM was correlated with poor patient prognosis. Transfection of glioma cells with miR-145 mimic or transduction with a lentivirus vector expressing pre-miR 145 significantly decreased the migration and invasion of glioma cells. We identified connective tissue growth factor (CTGF) as a novel target of miR-145 in glioma cells; transfection of the cells with this miRNA decreased the expression of CTGF as determined by Western blot analysis and the expression of its 3′-UTR fused to luciferase. Overexpression of a CTGF plasmid lacking the 3′-UTR and administration of recombinant CTGF protein abrogated the inhibitory effect of miR-145 on glioma cell migration. Similarly, we found that silencing of CTGF decreased the migration of glioma cells. CTGF silencing also decreased the expression of SPARC, phospho-FAK and FAK and overexpression of SPARC abrogated the inhibitory effect of CTGF silencing on cell migration. These results demonstrate that miR-145 is downregulated in glial tumors and its low expression in GBM predicts poor patient prognosis. In addition miR-145 regulates glioma cell migration by targeting CTGF which downregulates SPARC expression. Therefore, miR-145 is an attractive therapeutic target for anti-invasive treatment of astrocytic tumors.     

The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: Evidence from bioinformatics analysis of high-throughput data

Overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty-eight nodes with 603 interactions were obtained from the establishment of protein-protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients’ prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS-275 (enrichment score = −0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG.     

Human Cytomegalovirus Gene Expression in Long-Term Infected Glioma Stem Cells

The most common adult primary brain tumor, glioblastoma (GBM), is characterized by fifteen months median patient survival and has no clear etiology. We and others have identified the presence of human cytomegalovirus (HCMV) gene products endogenously expressed in GBM tissue and primary cells, with a subset of viral genes being consistently expressed in most samples. Among these viral genes, several have important oncomodulatory properties, regulating tumor stemness, proliferation, immune evasion, invasion and angiogenesis. These findings lead us to hypothesize that a specific HCMV gene signature may be associated with GBM pathogenesis. To investigate this hypothesis, we used glioma cell lines and primary glioma stem-like cells (GSC) infected with clinical and laboratory HCMV strains and measured relative viral gene expression levels along several time points up to 15 weeks post-infection. While HCMV gene expression was detected in several infected glioma lines through week 5 post-infection, only HCMV-infected GSC expressed viral gene products 15 weeks post-infection. Efficiency of infection across time was higher in GSC compared to cell lines. Importantly, HCMV-infected GSC outlived their uninfected counterparts, and this extended survival was paralleled by increased tumorsphere frequency and upregulation of stemness regulators, such as SOX2, p-STAT3, and BMX (a novel HCMV target identified in this study). Interleukin 6 (IL-6) treatment significantly upregulated HCMV gene expression in long-term infected glioma cultures, suggesting that pro-inflammatory signaling in the tumor milieu may further augment HCMV gene expression and subsequent tumor progression driven by viral-induced cellular signaling. Together, our data support a critical role for long-term, low-level HCMV infection in promoting survival, stemness, and proliferation of GSC that could significantly contribute to GBM pathogenesis.     

PPFIBP1 induces glioma cell migration and invasion through FAK/Src/JNK signaling pathway

Glioblastoma multiforme (GBM) is the most aggressive brain tumor, with a 5-year survival ratio <5%. Invasive growth is a major determinant of the poor prognosis in GBM. In this study, we demonstrate that high expression of PPFIA binding protein 1 (PPFIBP1) correlates with remarkable invasion and poor prognosis of GBM patients. Using scratch and transwell assay, we find that the invasion and migration of GBM cells are promoted by overexpression of PPFIBP1, while inhibited by knockdown of PPFIBP1. Then, we illustrate that overexpression of PPFIBP1 facilitates glioma cell infiltration and reduces survival in xenograft models. Next, RNA-Seq and GO enrichment analysis reveal that PPFIBP1 regulates differentially expressed gene clusters involved in the Wnt and adhesion-related signaling pathways. Furthermore, we demonstrate that PPFIBP1 activates focal adhesion kinase (FAK), Src, c-Jun N-terminal kinase (JNK), and c-Jun, thereby enhancing Matrix metalloproteinase (MMP)-2 expression probably through interacting with SRCIN1 (p140Cap). Finally, inhibition of phosphorylation of Src and FAK significantly reversed the augmentation of invasion and migration caused by PPFIBP1 overexpression in GBM cells. In conclusion, these findings uncover a novel mechanism of glioma invasion and identify PPFIBP1 as a potential therapeutic target of glioma.Subject terms: Oncogenes, Molecular neuroscience