Identification of potential biomarkers associated with immune infiltration in the esophageal carcinoma tumor microenvironment

Tumor immune cell infiltration was significantly correlated with the progression and the effect of immunotherapy in cancers including esophageal carcinoma (ESCA). However, no biomarkers were identified which were associated with immune infiltration in ESCA. In the present study, a total of 128 common differentially expressed genes (DEGs) were identified between esophageal squamous cell carcinomas (ESCC) and esophageal adenocarcinomas (EAC). The results of gene ontology (GO) enrichment and Reactome pathway analysis displayed that the up-regulated DEGs were mainly involved in the regulation of extracellular matrix (ECM), while the down-regulated DEGs were mainly involved in the regulation of cornification and keratinocyte differentiation. The most significant module of up-regulated DEGs was selected by Molecular Complex Detection (MCODE). Top ten similar genes of COL1A2 were explored, then validation and the prognostic analysis of these genes displayed that COL1A2, COL1A1, COL3A1, ZNF469 and Periostin (POSTN) had the prognostic value which were up-regulated in ESCA. The expressions of COL1A2 and its four similar genes were mainly correlated with infiltrating levels of macrophages and dendritic cells (DCs) and showed strong correlations with diverse immune marker sets in ESCA. To summarize, COL1A2 and its four similar genes were identified as the potential biomarkers associated with immune infiltration in ESCA. These genes might be applied to immunotherapy for ESCA.     

Investigation of hub genes and immune status in heart transplant rejection using endomyocardial biopsies

T cell‒mediated rejection (TCMR) and antibody-mediated rejection (ABMR) are severe post-transplantation complications for heart transplantation (HTx), whose molecular and immunological pathogenesis remains unclear. In the present study, the mRNA microarray data set GSE124897 containing 645 stable, 52 TCMR and 144 ABMR endomyocardial biopsies was obtained to screen for differentially expressed genes (DEGs) between rejected and stable HTx samples and to investigate immune cell infiltration. Functional enrichment analyses indicated roles of the DEGs primarily in immune-related mechanisms. Protein-protein interaction networks were then constructed, and ICAM1, CD44, HLA-A and HLA-B were identified as hub genes using the maximal clique centrality method. Immune cell infiltration analysis revealed differences in adaptive and innate immune cell populations between TCMR, ABMR and stable HTx samples. Additionally, hub gene expression levels significantly correlated with the degree and composition of immune cell infiltration in HTx rejection samples. Furthermore, drug-gene interactions were constructed, and 12 FDA-approved drugs were predicted to target hub genes. Finally, an external GSE2596 data set was used to validate the expression of the hub genes, and ROC curves indicated all four hub genes had promising diagnostic value for HTx rejection. This study provides a comprehensive perspective of molecular and immunological regulatory mechanisms underlying HTx rejection.     

鸡胚公母性腺差异表达基因的基因芯片杂交筛选

采用Affymetrix公司鸡基因组芯片对9日龄鸡胚公母性腺总RNA进行了芯片杂交, 并对基因表达谱进行了分析。统计结果显示, 9日龄母鸡性腺表达基因数19 368个, 公鸡性腺表达基因数19 493个; 公母性腺绝对差异表达基因,即公鸡性腺表达而母鸡性腺不表达基因145个, 母鸡性腺表达而公鸡性腺不表达基因189个。绝对差异表达基因功能分类结果显示, 参与细胞组成、细胞加工和分子结合基因占多数, 部分基因参与细胞器组成、代谢加工、生物学调控以及催化反应和细胞信号转导等。值得注意的是, 本研究发现了一些已经报道同性别决定和分化有一定关联的基因, 如ASW、CHD1和SOX9等, 同时也发现了一些未知其同性腺分化和发育有关联的基因和编码假想蛋白的表达序列。进一步分析这些基因和表达序列的生物学功能和表达模式, 将对鸟类性别决定和分化机制的了解提供有益参考。     

Circadian clock genes promote glioma progression by affecting tumour immune infiltration and tumour cell proliferation

ObjectivesCircadian rhythm controls complicated physiological activities in organisms. Circadian clock genes have been related to tumour progression, but its role in glioma is unknown. Therefore, we explored the relationship between dysregulated circadian clock genes and glioma progression.Materials and MethodsSamples were divided into different groups based on circadian clock gene expression in training dataset (n = 672) and we verified the results in other four validating datasets (n = 1570). The GO and GSEA enrichment analysis were conducted to explore potential mechanism of how circadian clock genes affected glioma progression. The single‐cell RNA‐Seq analysis was conducted to verified previous results. The immune landscape was evaluated by the ssGSEA and CIBERSORT algorithm. Cell proliferation and viability were confirmed by the CCK8 assay, colony‐forming assay and flow cytometry.ResultsThe cluster and risk model based on circadian clock gene expression can predict survival outcome. Samples were scoring by the least absolute shrinkage and selection operator regression analysis, and high scoring tumour was associated with worse survival outcome. Samples in high‐risk group manifested higher activation of immune pathway and cell cycle. Tumour immune landscape suggested high‐risk tumour infiltrated more immunocytes and more sensitivity to immunotherapy. Interfering TIMELESS expression affected circadian clock gene expression, inhibited tumour cell proliferation and arrested cell cycle at the G0/G1 phase.ConclusionsDysregulated circadian clock gene expression can affect glioma progression by affecting tumour immune landscape and cell cycle. The risk model can predict glioma survival outcome, and this model can also be applied to pan‐cancer.     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.     

筛选差异表达基因和蛋白质的方法进展

分离和鉴定差异表达基因和蛋白质不仅有助于发现基因和蛋白质的功能,更有助于揭示某些疾病的发生机理.目前筛选差异表达基因的方法主要有差异显示PCR方法(differential display RT-PCR,DDRT-PCR)、消减杂交法(subtractive hybridization,SH)、基因芯片技术(DNA chip technique)和基因表达的系统分析(serial analysis of gene expression,SAGE)等,其中消减杂交法中又先后建立了代表性差异分析技术(representational difference analysis,RDA)、抑制消减杂交法(suppression subtractive hybridization,SSH)和获得全长基因的消减杂交法(full-length-gene-obtainable subtractive hybridization).筛选差异表达蛋白质的方法主要有双向电泳技术(two-dimentional gel electrophoresis)和噬菌体全套抗体库技术(phage display antibody repertoire library technique).这些方法各有特点,各有利弊,研究者可根据自己的需要选择适合于自己的方法.     

Identification of a four immune-related genes signature based on an immunogenomic landscape analysis of clear cell renal cell carcinoma

Renal clear cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which has strong immunogenicity. A comprehensive study of the role of immune-related genes (IRGs) in ccRCC is of great significance in finding ccRCC treatment targets and improving patient prognosis. In this study, we comprehensively analyzed the expression of IRGs in ccRCC based on The Cancer Genome Atlas datasets. The mechanism of differentially expressed IRGs in ccRCC was analyzed by bioinformatics. In addition, Cox regression analysis was used to screen prognostic related IRGs from differentially expressed IRGs. We also identified a four IRGs signature consisting of four IRGs (CXCL2, SEMA3G, PDGFD, and UCN) through lasso regression and multivariate Cox regression analysis. Further analysis results showed that the four IRGs signature could effectively predict the prognosis of patients with ccRCC, and its predictive power is independent of other clinical factors. In addition, the correlation analysis of immune cell infiltration showed that this four IRGs signature could effectively reflect the level of immune cell infiltration of ccRCC. We also found that the expression of immune checkpoint genes CTLA-4, LAG3, and PD-1 in the high-risk group was higher than that in the low-risk group. Our research revealed the role of IRGs in ccRCC, and developed a four IRGs signature that could be used to evaluate the prognosis of patients with ccRCC, which will help to develop personalized treatment strategies for patients with ccRCC and improve their prognosis. In addition, these four IRGs may be effective therapeutic targets for ccRCC.     

Prognostic evaluation and immune infiltration analysis of five bioinformatic selected genes in hepatocellular carcinoma

Despite the development in hepatocellular carcinoma (HCC) treatment in recent years, the therapeutic outcome of HCC remains unfavourable. This study examines the prognosis of HCC from a genetic level using clinical databases and single-cell data to identify genes with a high prognostic value. Three up-regulated genes (UBE2S, PTTG1, and CDC20) and two down-regulated genes (SOCS2 and DNASE1L3) in HCC tissues were identified. Various analyses confirmed its correlation with tumour stage (p < 0.01) and patient survival time (log-rank p < 0.001). Immune analysis, single-cell analysis, and gene set enrichment analysis (GSEA) were employed to provide insight on how they affect cancer progression, and we observed a close relation between these genes and tumour immune infiltration. Eventually, we constructed a risk score system that risk score = (0.0465) × UBE2S + (0.1851) × CDC20 + (−0.0461) × DNASE1L3 + (−0.2279) × SOCS2 (5-year area under curve = 0.706). The risk score system may serve as an effective novel prognostic system for HCC patients. This study might provide novel ideas for prognostic or therapeutic biomarkers for HCC.     

Bioinformatic analyses of microRNA-targeted genes and microarray-identified genes correlated with Barrett's esophagus

Barrett's esophagus (BE) is defined as a metaplasia condition in the distal esophagus, in which the native squamous epithelium lining is replaced by a columnar epithelium with or without intestinal metaplasia. It is commonly accepted that BE is a precancerous lesion for esophageal adenocarcinoma. The aim of this study was to investigate the aberrant microRNAs (miRNAs) and differentially expressed genes (DEGs) associated with BE based on online microarray datasets. One miRNA and five gene expression profiling datasets were retrieved from the Gene Expression Omnibus Database. Aberrant microRNAs and DEGs were obtained using R/Bioconductor statistical analysis language and software. 23 dysregulated miRNAs and 632 DEGs demonstrating consistent expression tendencies in the five gene microarrays were identified in BE. Moreover, 1962 target genes of aberrant miRNAs were predicted using three bioinformatic tools, namely TargetScan, RNA22-HSA and miRDB. Ultimately, 93 target DEGs were obtained, after which functional annotation was performed on DAVID Bioinformatics Resources. Among Gene Ontology (GO) biological processes, digestive tract development and epithelial cell differentiation have demonstrated significant associations with BE pathogenesis. In addition, analysis of the KEGG pathways has revealed associations with cancer. To enable further study, one miRNA-target DEGs regulatory network was constructed using Cytoscape. 6 target DEGs demonstrated higher-degree distributions in the network, and ROC analysis indicated that FNDC3B may be the best potential biomarker for BE diagnosis. The data presented herein may provide new perspectives for exploring BE pathogenesis and may offer hits with regard to potential biomarkers in BE diagnosis, prediction and therapeutic evaluation.     

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.     

Key genes and pathways in tumor-educated dendritic cells by bioinformatical analysis

Specific tumor microenvironment signaling might prevent the maturation of dendritic cells (DCs) with tolerogenic and immunosuppressive potential accounting for antigen-specific unresponsiveness in the lymphoid organs and in the periphery. In the present study, dendritic cells treated with LLC lung cancer cell or 4T1 breast cancer cell culture supernatants significantly down-regulated the expression of co-stimulatory molecules MHC-II, CD40, CD80, but up-regulated the inhibitory molecule PD-L1/L2, VISTA, and increased the messengerRNA levels of interleukin (IL)-6, arginase I, and IL-10, but decreased tumor necrosis factor-α and IL-12a. RNA was isolated from the dendritic cells with or without tumor supernatant stimulation and RNA sequencing was done. Then the differential expression genes were sorted, the candidate genes were analyzed and pathway enrichment analysis was done, and the associated protein–protein interaction network (PPI) was established. After integrated bioinformatical analysis, 405 (279 up-regulated and 126 down-regulated) consistently differential expression genes were identified. Using gene ontology and pathway analysis, it was found that differential expression genes were mainly enriched in the immune response, cell–cell interaction, hemostasis, and cell surface interactions with the vascular wall. The PPI data demonstrated that 236 nodes were classified with 1072 edges, and the most remarkable three modules involved 53 central node genes associated with cell survival, cell-substrate adhesion, chemotaxis, migration, immune response, and complement receptor mediated signaling pathway. These findings revealed the immune status of dendritic cells in the tumor environment.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。