Identification of potential key genes in anaplastic thyroid cancer using bioinformatics analysis

Anaplastic thyroid cancer (ATC) has a high degree of malignancy and poor prognosis. The purpose of this study was to determine differentially expressed genes (DEGs) in ATC through biometric analysis technology, clarify potential interactions between them, and screen genes related to the prognosis of ATC. Using obtained DEGs, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-protein interaction (PPI), and survival analysis were performed. After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated genes and 450 downregulated genes. Among the hub DEGs obtained from the PPI network, the expression levels of TYMS, FN1, CHRDL1, SDC2, ITGA2, COL1A1, COL9A3, and COL23A1 were associated with ATC prognosis. These results showed that the recurrence-free survival (RFS) of ATC was associated with TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 statistically significantly in the KM plotter (P<0.05). Thus, the study suggests that TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 may be used as potential biomarkers of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.     

Identification of biomarkers correlated with hypertrophic cardiomyopathy with co-expression analysis

Hypertrophic cardiomyopathy (HCM) is reported to be the most common genetic heart disease. To identify key module and candidate biomarkers correlated with clinical prognosis of patients with HCM, we carried out this study with co-expression analysis. To construct a co-expression network of hub genes correlated with HCM, the Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction network analysis of central genes was performed to recognize the interactions of central genes. Gene set enrichment analyses were carried out to discover the possible mechanisms involved in the pathways promoted by hub genes. To validate the hub genes, quantitative real-time polymerase chain reaction (RT-PCR) was performed. Based on the results of topological overlap measure based clustering, 2,351 differentially expressed genes (DEGs) were identified. Those genes were included in six different modules. Of these modules, the yellow and the blue modules showed a pivotal correlation with HCM. DEGs were enriched in immune system procedure associated GO terms and KEGG pathways. We identified nine hub genes (TYROBP, STAT3, CSF1R, ITGAM, SYK, ITGB2, LILRB2, LYN, and HCK) affected the immune system significantly. Among the genes we validated with RT-PCR, TYROBP, CSF1R, and SYK showed significant increasing expression levels in model HCM rats. In conclusion, we identified two modules and nine hub genes, which were prominently associated with HCM. We found that immune system may play a crucial role in the HCM. Accordingly, those genes and pathways might become therapeutic targets with clinical usefulness in the future.     

COPD 差异表达基因的生物信息学分析及在LUSC样本中的表达

为确定慢性阻塞性肺病(COPD)的分子标记物及COPD与肺鳞状细胞癌(LUSC)共存的差异表达基因,探寻COPD合并肺癌的预测因子,发现新的治疗靶点。本研究采用生物信息学方法,从GEO数据库中筛选3套基因芯片数据集,挖掘COPD患者小气道上皮细胞(SAEC)的差异表达基因(DEG)以及潜在的生物标记物,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)富集分析预测DEGs的功能及参与的代谢途径。继而对DEGs构建PPI网络,使用Cytoscape软件筛选子模块和Hub基因,并将Hub基因通过TCGA数据库分析其在LUSC中的差异表达情况及差异基因间的相关性。结果共获得52个上调基因和24个下调基因,代谢通路主要集中在细胞色素P450对外源物质的代谢、化学致癌、花生四烯酸代谢及甲状腺激素合成四条途径上,通过Cytoscape软件从PPI网络中筛选得到2个功能模块和10个Hub基因,进一步验证发现其中5个基因在TCGA数据库中的LUSC样本中同样差异表达。由此推测SPP1、ALDH3A1、SPRR3、KRT6A和SPRR1B 可能为COPD 分子标记物及COPD与LUSC共存的DEGs,从而为研究COPD和LUSC的发病机制及二者潜在关系奠定良好的基础。     

Bioinformatics analysis reveals disturbance mechanism of MAPK signaling pathway and cell cycle in Glioblastoma multiforme

Background & objectives

To analyze the reversal gene pairs and identify featured reversal genes related to mitogen-activated protein kinases (MAPK) signaling pathway and cell cycle in Glioblastoma multiforme (GBM) to reveal its pathogenetic mechanism.

Methods

We downloaded the gene expression profile GSE4290 from the Gene Expression Omnibus database, including 81 gene chips of GBM and 23 gene chips of controls. The t test was used to analyze the DEGs (differentially expressed genes) between 23 normal and 81 GBM samples. Then some perturbing metabolic pathways, including MAPK (mitogen-activated protein kinases) and cell cycle signaling pathway, were extracted from KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database. Cancer genes were obtained from the database of Cancer Gene Census. The reversal gene pairs between DEGs and cancer genes were further analyzed in MAPK and cell cycle signaling pathway.

Results

A total 8523 DEGs were obtained including 4090 up-regulated and 4433 down-regulated genes. Among them, ras-related protein rab-13(RAB13), neuroblastoma breakpoint family member 10 (NBPF10) and disks large homologue 4 (DLG4) were found to be involved in GBM for the first time. We obtained MAPK and cell cycle signaling pathways from KEGG database. By analyzing perturbing mechanism in these two pathways, we identified several reversal gene pairs, including NRAS (neuroblastoma RAS) and CDK2 (cyclin-dependent kinase 2), CCND1 (cyclin D1) and FGFR (fibroblast growth factor receptor). Further analysis showed that NRAS and CDK2 were positively related with GBM. However, FGFR2 and CCND1 were negatively related with GBM.

Interpretation & conclusions

These findings suggest that newly identified DEGs and featured reversal gene pairs participated in MAPK and cell cycle signaling pathway may provide a new therapeutic line of approach to GBM.     

Identification of key genes and pathways in benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is one of the most common causes of lower urinary tract symptoms (LUTS) in elderly man. However, the underlying molecular mechanisms of BPH have not been completely elucidated. We identified the key genes and pathways by using analysis of Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using edgeR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the DEGs by Database for Annotation, Visualization and Integrated Discovery (DAVID) database and ConsensusPathDB, respectively. Then, protein–protein interaction (PPI) networks were established by the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Finally, we identified 660 DEGs ultimately including 268 upregulated genes and 392 downregulated genes. GO analysis revealed that DEGs were mainly enriched in extracellular exosome, identical protein binding, mitochondrial adenosine triphosphate (ATP) synthesis coupled proton transport, extracelluar matrix, focal adhesion, cytosol, Golgi apparatus, cytoplasm, protein binding, and Golgi membrane. Focal adhesion pathway, FoxO signaling pathway, and autophagy pathway were selected. Ubiquitin-conjugating enzyme E2 C (UBE2C), serine/threonine kinase (AKT1), mitogen-activated protein kinase 1 (MAPK1), cyclin B1 (CCNB1), polo-like kinase 1 (PLK1) were filtrated as the hub genes according to the degree of connectivity from the PPI network. The five hub genes including UBE2C, AKT1, MAPK1, CCNB1, PLK1 may play key roles in the pathogenesis of benign prostatic hyperplasia (BPH). Focal adhesion pathway, FoxO signaling pathway, and autophagy pathway may be crucial for the progression of BPH.     

基于转录组的肝豆状核变性调控网络的构建和分析

本研究旨在利用生物信息学方法构建经铜诱导的ATP7B基因敲除HepG2细胞系的转录调控网络。探讨关键转录因子在肝豆状核变性发生、发展中的潜在作用机制。收集公共基因表达数据库(gene expression omnibus, GEO)中包含野生型、ATP7B基因敲除型、铜诱导的野生型和铜诱导的ATP7B基因敲除型HepG2细胞系数据。筛选由铜诱导产生的差异表达基因(differentially expressed genes,DEGs)后进行基因本体论(gene ontology,GO)、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)富集分析。基于蛋白相互作用网络,识别疾病关键基因和功能模块,并对关键功能模块中的基因进行富集分析。最后,构建转录调控网络,筛选核心转录因子。共筛选出1 034个差异表达基因,其中上调525个,下调509个。上、下调关键功能模块分别包括了3785个和3931个基因。关键功能模块中的基因主要定位于细胞-基质连接、染色体、剪接复合体、核糖体等区域,共同参与了mRNA加工、组蛋白修饰、RNA剪切...     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.     

Identification of Key Genes and Pathways Involved in the Heterogeneity of Intrinsic Growth Ability Between Neurons After Spinal Cord Injury in Adult Zebrafish

In the adult central nervous system (CNS), axon regeneration is a major hurdle for functional recovery after trauma. The intrinsic growth potential of an injured axon varies widely between neurons. The underlying molecular mechanisms of such heterogeneity are largely unclear. In the present study, the adult zebrafish dataset GSE56842 were downloaded. Differentially expressed genes (DEGs) were sorted and deeply analyzed by bioinformatics methods. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed with the DAVID. A DEGs-associated protein–protein interaction network was constructed from the STRING database and visualized with Cytoscape software. In total, 621 DEGs were identified. GO analysis showed that the biological processes of DEGs focused mainly on the Notch signaling pathway, cell differentiation and positive regulation of neuron differentiation. The molecular functions mainly included calcium-transporting ATPase activity and calcium ion binding and structural constituents of the cytoskeleton. The cellular components included the plasma membrane, spectrin, and cytoplasmic and membrane-bound vesicles. KEGG pathway analysis showed that these DEGs were mainly involved in the metabolic pathway and Notch signaling pathway, and subnetworks revealed that genes within modules were involved in the metabolic pathway, Wnt signaling pathway, and calcium signaling pathway. This study identified DEG candidate genes and pathways involved in the heterogeneity of the intrinsic growth ability between neurons after spinal cord injury in adult zebrafish, which could facilitate our understanding of the molecular mechanisms underlying axon regeneration, and these candidate genes and pathways could be therapeutic targets for the treatment of CNS injury.

     

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 hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the most common subtype of head and neck cancer; however, its pathogenesis and potential therapeutic targets remain largely unknown. In the present study, we analyzed three gene expression profiles and screened differentially expressed genes (DEGs) between HNSCC and normal tissues. The DEGs were subjected to gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), protein–protein interaction (PPI), and survival analyses, while the connectivity map (CMap) database was used to predict candidate small molecules that may reverse the biological state of HNSCC. Finally, we measured the expression of the most relevant core gene in vitro and examined the effect of the top predicted potential drug against the proliferation of HNSCC cell lines. Among the 208 DEGs and ten hub genes identified, CDK1 and CDC45 were associated with unfavorable HNSCC prognosis, and three potential small molecule drugs for treating HNSCC were identified. Increased CDK1 expression was confirmed in HNSCC cells, and menadione, the top predicted potential drug, exerted significant inhibitory effects against HNSCC cell proliferation and markedly reversed CDK1 expression. Together, the findings of the present study suggest that the ten hub genes and pathways identified may be closely related to HNSCC pathogenesis. In particular, CDK1 and CDC45 overexpression could be reliable biomarkers for predicting unfavorable prognosis in patients with HNSCC, while the new candidate small molecules identified by CMap analysis provide new avenues for the development of potential drugs to treat HNSCC.     

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223 , GSE4058 , GSE4290 , GSE13276 , GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA‐seq data from TCGA‐GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up‐regulated and 51 down‐regulated genes). Subsequently, through the PPI (protein‐protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer‐related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.     

家兔前体脂肪细胞分化不同时期基因表达谱分析

脂肪的过度积累严重危害人类健康。前体脂肪细胞分化是脂肪发育的关键过程,研究前体脂肪细胞分化相关基因的表达有助于认识脂肪沉积的机理。尽管家兔是一种理想的研究脂肪发育的动物模型,但是针对其前体脂肪细胞分化不同时期基因表达谱的研究鲜见报道。本研究通过诱导家兔前体脂肪细胞分化,在分化第0 d、3 d和9 d收集脂肪细胞,利用转录组测序(RNA-seq),在分化第3 d样本与第0 d样本的比较中筛选出1352个差异表达基因(differentially expressed genes, DEGs),在分化第9 d样本与第3 d样本的比较中筛选出888个DEGs。GO (gene ontology)功能富集和KEGG (kyoto encyclopedia of genes and genomes)通路分析发现,0~3 d分化期上调的DEGs显著富集在PPAR信号通路和PI3K-Akt信号通路上,3~9d分化期上调的DEGs显著富集到与细胞周期调控有关的GO条目和KEGG信号通路,0~3d和3~9d阶段特异上调的DEGs可能分别作用于细胞质和细胞核。通过DEGs的蛋白-蛋白互作(protein-protein interaction, PPI)网络分析发现,筛选出的核心节点(hub node)基因可能通过调控细胞周期而影响家兔前体脂肪细胞分化。     

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 biomarkers in diabetic nephropathy via bioinformatic analysis

Diabetic nephropathy (DN) is a major cause of end-stage renal disease. Although intense efforts have been made to elucidate the pathogenesis, the molecular mechanisms of DN remain to be clarified. To identify the candidate genes in the progression of DN, microarray datasets GSE30122, GSE30528, and GSE47183 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. A total of 61 DEGs were identified. The enriched functions and pathways of the DEGs included glomerulus development, extracellular exosome, collagen binding, and the PI3K-Akt signaling pathway. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in acute inflammatory response, inflammatory response, and blood vessel development. Correlation analysis between unexplored hub genes and clinical features of DN suggested that COL6A3, MS4A6A,PLCE1, TNNC1, TNNI1, TNN2, and VSIG4 may involve in the progression of DN. In conclusion, DEGs and hub genes identified in this study may deepen our understanding of molecular mechanisms underlying the progression of DN, and provide candidate targets for diagnosis and treatment of DN.     

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.     

Comprehensive analysis of tumor mutation burden and immune microenvironment in gastric cancer

Tumor mutation burden (TMB) was a promising marker for immunotherapy. We aimed to investigate the prognostic role of TMB and its relationship with immune cells infiltration in gastric cancer (GC). We analyzed the mutation landscape of all GC cases and TMB of each GC patient was calculated and patients were divided into TMB-high and TMB-low group. Differentially expressed genes (DEGs) between the two groups were identified and pathway analysis was performed. The immune cells infiltration in each GC patient was evaluated and Kaplan–Meier analysis was performed to investigate the prognostic role of immune cells infiltration. At last, hub immune genes were identified and a TMB prognostic risk score (TMBPRS) was constructed to predict the survival outcome of GC patients. The relationships between mutants of hub immune genes and immune infiltration level in GC was investigated. We found higher TMB was correlated with better survival outcome and female patients, patients with T1-2 and N0 had higher TMB score. Altogether 816 DEGs were harvested and pathway analysis demonstrated that patients in TMB-high group were associated with neuroactive ligand–receptor interaction, cAMP signaling pathway, calcium signaling pathway. The infiltration of activated CD4⁺ memory T cells, follicular helper T cells, resting NK cells, M0 and M1 macrophages and neutrophils in TMB-high group were higher compared than that in TMB-low group and high macrophage infiltration was correlated with inferior survival outcome of GC patients. Lastly, the TMBPRS was constructed and GC patients with high TMBPRS had poor prognosis.