Identification of novel therapeutic targets for neuropathic pain based on gene expression patterns

Neuropathic pain (NP) caused by nerve injury or dysfunction is one of the most challenging neurological diseases. In-depth study of disease signatures contributes to the development of novel target treatment for NP. In this study, we analyzed expression profiles of qualified NP datasets (GSE24982 and GSE63442) deposited at Gene Expression Omnibus database by systematic bioinformatics approaches. We analyzed the differentially expressed genes of high and low pain compared with normal control group, and between spinal nerve ligation (SNL) injury model and sham-operation group. A total of 1,243 upregulated and 1,533 downregulated genes were identified in GSE24982, 380 upregulated and 355 downregulated genes were identified in GSE63442. By comparing low-pain samples with the corresponding sham-operation group, we identified 457 upregulated and 409 downregulated genes. Overlapping genes were screened out and signaling pathway and expression regulation model analyses were performed. SCN10A and SST were identified as biomarkers for NP. In conclusion, our study showed the expression pattern of gene about NP. These identified biomarkers could serve as potential therapeutic targets for treating NP.     

Identification of a risk prediction model for clinical prognosis in HER2 positive breast cancer patients

BackgroundNew biomarkers are needed to identify different clinical outcomes for HER2+ breast cancer (BC).MethodsDifferential genes of HER2+ BC were screened based on TCGA database. We used WGCNA to identify the genes related to the survival. Genetic Algorithm was used to structure risk prediction model. The prognostic model was validated in GSE data.ResultsWe constructed a risk prediction model of 6 genes to identify prognosis of HER2+ BC, including CLEC9A, PLD4, PIM1, PTK2B, AKNAD1 and C15orf27. Kaplan-Meier curve showed that the model effectively distinguished the survival of HER2+ BC patients. The multivariate Cox regression suggested that the risk model was an independent predictor for HER2+ BC. Analysis related to immune showed that significant differences in immune infiltration between high- and low-risk groups classified by the prognostic model.ConclusionsOur study identified a risk prediction model of 6 genes that could distinguish the prognosis of HER2+ BC.     

Identification of novel prognostic indicators for oral squamous cell carcinoma based on proteomics and metabolomics

BackgroundThe low 5-year survival rate of oral squamous cell carcinoma (OSCC) suggests that new prognostic indicators need to be identified to aid the clinical management of patients.MethodsSaliva samples from OSCC patients and healthy controls were collected for proteomic and metabolomic sequencing. Gene expressed profiling was downloaded from TCGA and GEO databases. After the differential analysis, proteins with a significant impact on the prognosis of OSCC patients were screened. Correlation analysis was performed with metabolites and core proteins were identified. Cox regression analysis was utilized to stratify OSCC samples based on core proteins. The prognostic predictive ability of the core protein was then evaluated. Differences in infiltration of immune cells between the different strata were identified.ResultsThere were 678 differentially expressed proteins (DEPs), 94 intersected DEPs among them by intersecting with differentially expressed genes in TCGA and GSE30784 dataset. Seven core proteins were identified that significantly affected OSCC patient survival and strongly correlated with differential metabolites (R² > 0.8). The samples were divided into high- and low-risk groups according to median risk score. The risk score and core proteins were well prognostic factor in OSCC patients. Genes in high-risk group were enriched in Notch signaling pathway, epithelial mesenchymal transition (EMT), and angiogenesis. Core proteins were strongly associated with the immune status of OSCC patients.ConclusionsThe results established a 7-protein signatures with the hope of early detection and the capacity for risk assessment of OSCC patient prognosis. Further providing more potential targets for the treatment of OSCC.     

Immune-pyroptosis-related genes predict the prognosis of kidney renal clear cell carcinoma

BackgroundKidney renal clear cell carcinoma (KIRC) is a common cancer of the adult urological system. Recent developments in tumor immunology and pyroptosis biology have provided new directions for kidney cancer treatment. Therefore, there is an urgent need to identify potential targets and prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy.MethodsThe expression of immune-pyroptosis-related differentially expressed genes (IPR-DEGs) between KIRC and healthy tissues was examined using the Gene Expression Omnibus datasets. The GSE168845 dataset was selected for subsequent analyses. Data of 1793 human immune-related genes were downloaded from the ImmPort database (https://www.immport.org./home), while those of 33 pyroptosis-related genes were extracted from previous reviews. The independent prognostic value of IPR-DEGs was determined using differential expression, prognostic, and univariate and multivariate Cox regression analyses. The GSE53757 dataset was used to further verify the GSDMB and PYCARD levels. In our cohorts, the association among DEGs and clinicopathological features and overall survival was analyzed. The least absolute shrinkage and selection operator Cox regression model was established to evaluate the correlation of IPR-DEGs with the immune score, immune checkpoint gene expression, and one-class logistic regression (OCLR) score. KIRC cells and clinical tissue samples were subjected to quantitative real-time polymerase chain reaction to examine the GSDMB and PYCARD mRNA levels. The GSDMB and PYCARD levels in a healthy kidney cell line (HK-2 cells) and two KIRC cell lines (786-O and Caki-1 cells) were verified. The tissue levels of GSDMB and PYCARD were evaluated using immunohistochemical analysis. GSDMB and PYCARD were knocked down in 786-O cells using short-interfering RNA. Cell proliferation was examined using the cell counting kit-8 assay. Cell migration was measured by transwell migration assaysResultsGSDMB and PYCARD were determined to be IPR-DEGs with independent prognostic values. A risk prognostic model based on GSDMB and PYCARD was successfully established. In the GSE53757 dataset, the GSDMB and PYCARD levels in KIRC tissues were significantly higher than those in healthy tissues. The GSDMB and PYCARD expression was related to T stage and OS in our cohort. The GSDMB and PYCARD levels were significantly correlated with the immune score, immune checkpoint gene expression, and OCLR score. The results of experimental studies were consistent with those of bioinformatics analysis. The GSDMB and PYCARD levels in KIRC cells were significantly upregulated when compared with those in healthy kidney cells. Consistently, GSDMB and PYCARD in KIRC tissues were significantly upregulated when compared with those in adjacent healthy kidney tissues. GSDMB and PYCARD knockdown significantly decreased 786-O cell proliferation (p < 0.05). Transwell migration result reflects that silencing GSDMB and PYCARD inhibited 786-O cell migration (p < 0.05) .ConclusionsGSDMB and PYCARD are potential targets and effective prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy in KIRC.     

Studies on Two Factors Affecting the Production of Germline Chimeras by Using HM 1 Cells

ＥＳ细胞系统与基因定位致变相结合,进行基因敲除（ｋｎｏｃｋｏｕｔ）已成为研究基因在生物体内功能的重要手段。在ＥＳ细胞系的建立、外源基因导入ＥＳ细胞、种系嵌合鼠的获得等三个重要环节中,种系嵌合鼠的获得是最关键的一环。由于ＥＳ细胞系统技术复杂、实验条件要求很高,尽管国际上已报导了上百例的基因敲除（ｋｎｏｃｋｏｕｔ）实验,但是到目前为止,我国还无一例在国内条件下获得种系嵌合鼠的正式报道。本研究对影响种系嵌合鼠获得的两种因素（饲养层细胞、受体胚胎种类）进行了比较研究,成功地获得了种系嵌合鼠。将ＨＭ１细胞在ＳＴＯ或ＭＥＦ培养层上培养至２１３３代,注射到不同小鼠的囊胚里,经过恢复培养,移植到假孕的昆明白雌鼠子宫内。由于ＨＭ１细胞来源于粟色的的１２９品系,而胚胎供体鼠的毛色为黑或白色,仔鼠出生一周后即可辨别是否为毛色嵌合鼠。用成年嵌合鼠与其受体胚胎相同品系的小鼠交配,进行种系嵌合鼠鉴定。曾有报导：ＳＴＯ培养层会导致ＥＳ细胞发生核变。我们改用ＭＥＦ培养层,获得嵌合鼠的比率高达４８．６％（Ｔａｂｌｅ１）。不同小鼠胚胎之间存在差异,Ｃ５７ＢＬ／６Ｊ、ＩＣＲ和昆明白三者提供的受体胚胎产生嵌合鼠的比率分别为７１．４％、５５％     

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer

As the most commonly diagnosed malignant tumor in female population, the prognosis of breast cancer is affected by complex gene interaction networks. In this research weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network to identify potential biomarkers for prediction the prognosis of patients with breast cancer. We downloaded GSE25065 from Gene Expression Omnibus database as the test set. GSE25055 and GSE42568 were utilized to validate findings in the research. Seven modules were established in the GSE25065 by utilizing average link hierarchical clustering. Three hub genes, RSAD2, HERC5, and CCL8 were screened out from the significant module (R ² = 0.44), which were considerably interrelated to worse prognosis. Within test dataset GSE25065, RSAD2, and CCL8 were correlated with tumor stage, grade, and lymph node metastases, whereas HERC5 was correlated with lymph node metastases and tumor grade. In the validation dataset GSE25055 and RSAD2 expression was correlated with tumor grade, stage, and size, whereas HERC5 was related to tumor stage and tumor grade, and CCL8 was associated with tumor size and tumor grade. Multivariable survival analysis demonstrated that RSAD2, HERC5, and CCL8 were independent risk factors. In conclusion, the WGCNA analysis conducted in this study screened out novel prognostic biomarkers of breast cancer. Meanwhile, further in vivo and in vitro studies are required to make the clear molecular mechanisms.     

SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析

应用生物信息学方法筛选新型冠状病毒肺炎（corona virus disease 2019,COVID-19）感染的潜在关键分子生物标志物并分析其免疫浸润特征。从GEO数据库下载GSE152418数据集,其中COVID-19患者17例,健康对照17例。用加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）方法筛选出COVID-19最相关的模块基因。与差异基因取交集得到共同基因,进行功能及信号通路富集分析,构建蛋白互作网络筛选关键基因,构建关键基因的miRNA-TF-mRNA调控网络,用CIBERSORT算法预测样本免疫细胞浸润特征。差异分析得到2 049个差异基因。WGCNA分析7个模块中“土耳其蓝色”模块与COVID-19相关性最高（r=0.91,P<0.001）。模块中基因显著性和模块隶属度呈显著正相关（r=0.96,P<0.001）。得到共同基因766个,主要参与有丝分裂、微管结合、阳离子通道活性及卵母细胞减数分裂、细胞衰老等。蛋白互作网络筛选到前10位关键基因分别为CDK1、BUB1、CCNA2、CDC20、KIF11、BUB1B、CDCA8、TOP2A、CCNB2、KIF20A,构建的miRNA-TF-mRNA网络包含51个miRNA、5个TF、10个mRNA。COVID-19患者较健康对照组幼稚B细胞、嗜酸性粒细胞浸润水平显著降低（P<0.05）,浆细胞、活化肥大细胞浸润水平显著升高（P<0.05）。通过WGCNA及蛋白互作网络分析筛选出10个关键基因,并预测到调控关键基因的5个TF及51个miRNA,且COVID-19患者与健康对照的免疫浸润特征存在统计学差异,这些与免疫细胞相关的分子标志物可能作为COVID-19免疫治疗的潜在靶标。     

Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis

Hepatocellular carcinoma (HCC) is the most common subtype in liver cancer whose prognosis is affected by malignant progression associated with complex gene interactions. However, there is currently no available biomarkers associated with HCC progression in clinical application. In our study, RNA sequencing expression data of 50 normal samples and 374 tumor samples was analyzed and 9225 differentially expressed genes were screened. Weighted gene coexpression network analysis was then conducted and the blue module we were interested was identified by calculating the correlations between 17 gene modules and clinical features. In the blue module, the calculation of topological overlap was applied to select the top 30 genes and these 30 genes were divided into the green group (11 genes) and the yellow group (19 genes) through searching whether these genes were validated by in vitro or in vivo experiments. The genes in the green group which had never been validated by any experiments were recognized as hub genes. These hub genes were subsequently validated by a new data set GSE76427 and KM Plotter Online Tool, and the results indicated that 10 genes (FBXO43, ARHGEF39, MXD3, VIPR1, DNASE1L3, PHLDA1, CSRNP1, ADR2B, C1RL, and CDC37L1) could act as prognosis and progression biomarkers of HCC. In summary, 10 genes who have never been mentioned in HCC were identified to be associated with malignant progression and prognosis of patients. These findings may contribute to the improvement of the therapeutic decision, risk stratification, and prognosis prediction for HCC patients.     

Characterizing heterogeneity of non‐small cell lung tumour microenvironment to identify signature prognostic genes

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non‐small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 (''low infiltration'' cluster), cluster 2 (''heterogeneous infiltration'' cluster) and cluster 3 (''high infiltration'' cluster). The immune cells were responsible for a significantly favourable prognosis for the ''high infiltration'' community. Cluster 1 had the lowest cytotoxic activity, tumour‐infiltrating lymphocytes and interferon‐gamma (IFN‐γ), as well as immune checkpoint molecules expressions. In addition, MHC‐I and immune co‐stimulator were also found to have lower cluster 1 expressions, indicating a possible immune escape mechanism. A total of 43 differentially expressed genes (DEGs) that overlapped among the groups were determined based on three clusters. Finally, based on a univariate Cox regression model, prognostic immune‐related genes were identified and combined to construct a risk score model able to predict overall survival (OS) rates in the validation datasets.     

Comprehensive analysis of microRNA-messenger RNA regulatory network in gemcitabine-resistant bladder cancer cells

Chemotherapy is still a standard treatment of unresectable bladder cancer or distant metastases. The chemotherapy resistance always occurs after a period of treatment indicating poor prognosis. The current study aimed to explore the molecular mechanism of chemoresistance in bladder cancer cells. The gene expression profiles of GSE77883, including three untreated T24 cells samples and three gemcitabine-resistant T24 cells samples, was downloaded from Gene Expression Omnibus database. The screening of differentially expressed genes (DEGs), gene function analysis, and interaction prediction between microRNAs (miRNAs) and DEGs were performed by R software. The protein-protein interaction (PPI) and miRNA-DEGs networks were constructed and visualized by Cytoscape software. Then, the small molecules, with potential synergistic or antagonistic effects to gemcitabine resistance, were identified using the Connectivity Map database. Finally, gemcitabine-resistant T24 cell line was established and key genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR). In total, 536 upregulated and 513 downregulated genes were screened and mainly enriched in oxidative stress response and signaling pathways related to extracellular matrix–receptor interaction and focal adhesion. PPI network showed interleukin 6, tumor necrosis factor, kinesin family member 11, and BUB1 mitotic checkpoint serine/threonine kinase B were key genes. The miRNA-DEGs regulatory networks included 18 miRNAs and 185 DEGs, including miR-182-5p, miR-590-3p, miR-320a and serum- and glucocorticoid-regulated kinase 1 (SGK1). Then, the related key genes and miRNAs were confirmed by qRT-PCR. Furthermore, 81 small molecules with antagonistic or synergistic effect to GEM were screened. We have investigated the molecular mechanisms driving GEM-resistance in bladder cancer cells that would contribute to the development of chemotherapy for advanced bladder cancer.     

G Protein γ subunit 7 loss contributes to progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common urinary neoplasm, looking for useful candidates to establish scientific foundation for the therapy of ccRCC is urgent. We downloaded genomic profiles of GSE781, GSE6244, GSE53757, and GSE66271 from the Gene Expression Omnibus (GEO) database. GEO2R was used to analyze the derivative genes, while hub genes were screened by protein-protein interactions and cytoscape. Further, overall survival, gene methylation, gene mutation, and gene expression were all analyzed using bioinformatics tools. Colony formation and cell-cycle assay were used to detect the biological function of GNG7 in vitro. We found that GNG7 was downregulated in ccRCC tissues and negatively associated with overall survival in ccRCC patients. We also found that promoter methylation and frequent gene mutation were responsible for GNG7 gene suppression. GNG7 low expression was related to upregulation of enhancer of zeste homolog 2 and downregulation of disabled homolog 2-interacting protein. Further, Gene Set Enrichment Analysis results showed that mTOR1, E2F, G2M, and MYC pathways were all significantly altered in response to GNG7 low expression. In vitro, A498 and 786-O cells in which GNG7 expression was silenced, exhibited a lower G1 phase when compared to the negative control cells. Taken together, our findings suggest that GNG7 is a tumor suppressor gene in ccRCC progression and represents a novel candidate for ccRCC treatment.     

Differential RNA expression of immune-related genes and tumor cell proximity from intratumoral M1 macrophages in acral lentiginous melanomas treated with PD-1 blockade

Immune checkpoint inhibitors (ICIs) offer improved survival for patients with advanced malignant melanomas. However, only a subset of these patients exhibit an objective response rate of 10–40 % with ICIs. We aimed to ascertain the effects of RNA signatures and the spatial distribution of immune cells on the treatment outcomes of patients with malignant melanomas undergoing ICI therapy. Clinical data were retrospectively collected from ICI-treated patients with malignant melanoma; RNA expression profiles were examined via next-generation sequencing, whereas the composition, density, and spatial distribution of immune cells were determined via multiplex immunohistochemistry. Patients with poor and good responses to ICIs showed significant differences in mRNA expression profiles. Different spatial distributions of T-cells, macrophages, and NK cells as well as RNA signatures of immune-related genes were found to be closely related to therapeutic outcomes in ICI-treated patients with malignant melanomas. The spatial distributions of PD-1+ T-cells and activated M1 macrophages showed a significant correlation with favorable responses to ICIs. Our findings highlight the clinical relevance of the spatial proximity of immune cell subsets in the treatment outcomes of metastatic malignant melanoma.     

Differential expression and bioinformatics analysis of exosome circRNAs in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a fatal malignant tumor with an unfavorable prognosis. Increasing evidence indicated circRNAs were associated with the pathogenesis and progression of tumors, but data on the expression of serum exosomal circRNAs in PDAC are scarce. This study attempted to explore the prognostic value and function of serum exosomes in PDAC patients.MethodsMicroarray-based circRNA expression was determined in PDAC and paired with normal serum samples, and the intersection of differentially expressed circRNAs (DECs) in serum exosomal samples and GSE79634 tissue samples was conducted. A specific CircRNA database was applied to investigate DECs binding miRNAs. Target genes were predicted using the R package multiMiR. Cox regression analyses were applied for constructing a prognostic model. The immunological characteristics analysis was carried out through the TIMER, QUANTISEQ, XCELL, EPIC, and ssGSEA algorithms.Results15 DECs were finally identified, and a circRNA-miRNA-mRNA network was established. A prognostic risk model was developed to categorize patients according to the risk scores. Furthermore, the association between risk score and immune checkpoint genes including CD80, TNFSF9, CD276, CD274, LGALS9, and CD44 were significantly elevated in the high-risk group, while ICOSLG and ADORA2A were upregulated in the low-risk group.ConclusionsOur results may provide new clues for the prognosis and treatment of PDAC.     

Interaction network analysis revealed biomarkers in myocardial infarction

Myocardial infarction (MI) is a serious heart disease. The cardiac cells of patients with MI will die due to lack of blood for a long time. In this study, we aimed to find new targets for MI diagnosis and therapy. We downloaded GSE22229 including 12 blood samples from healthy persons and GSE29111 from Gene Expression Omnibus including 36 blood samples from MI patients. Then we identified differentially expressed genes (DEGs) in patients with MI compared to normal controls with p value < 0.05 and |logFC| > 1. Furthermore, interaction network and sub-network of these of these DEGs were constructed by NetBox. Linker genes were screened in the Global Network database. The degree of linker genes were calculated by igraph package in R language. Gene ontology and kyoto encyclopedia of genes and genomes pathway analysis were performed for DEGs and network modules. A total of 246 DEGs were identified in MI, which were enriched in the immune response. In the interaction network, LCK, CD247, CD3D, FYN, HLA-DRA, IL2, CD8A CD3E, CD4, CD3G had high degree, among which CD3E, CD4, CD3G were DEGs while others were linker genes screened from Global Network database. Genes in the sub-network were also enriched in the immune response pathway. The genes with high degree may be biomarkers for MI diagnosis and therapy.