Identification of differentially expressed genes in clear cell renal cell carcinoma by analysis of full-length enriched cDNA library

Summary Renal cell carcinoma (RCC) is the most common malignancy in adult kidney, and accounts for 3% of malignancies worldwide with increasing incidence. Clear cell RCC (ccRCC) is the major type in RCC. Resection by surgery is the main treatment because the response of ccRCC to traditional therapies is very poor. To identify the tumor-associated genes for better understanding the molecular mechanism of ccRCC, the full-length enriched cDNA libraries of ccRCC and normal kidney tissues were constructed by the oligo-capping method. Nucleotide sequences of the cDNA libraries of ccRCC and normal kidney tissues were sequenced. From the sequence analysis of 19,425 and 12,400 clones of ccRCC and normal kidney tissues, 4356 and 3055 genes were identified, respectively. By comparing the gene-expression patterns of ccRCC and normal tissues, the up- or down-regulated genes were identified. Among these identified genes, the differential expression of annexin A2 and argininosuccinate synthetase genes were further confirmed by quantitative real-time PCR and Western blot analysis.     

肾透明细胞癌患者铁死亡相关基因的预后作用

探讨铁死亡相关基因在肾透明细胞癌患者中的表达及其预后价值。通过TCGA数据库下载KIRC的相关测序数据与检索到的铁死亡相关基因取交集,进行铁死亡相关基因的差异分析。之后利用单变量和多变量Cox回归分析,筛选具有预后价值的基因,构建预测患者生存情况的风险评分模型,并对模型进行验证。对高低风险组进行GO与KEGG通路富集,探讨风险差异的可能原因;通过ssGSEA分析,评估高低风险组间的免疫浸润情况。在KIRC患者的肿瘤组织和正常组织中,共得到21个差异的铁死亡相关基因;通过单因素Cox回归分析,获得 28 个与KIRC预后相关的基因;之后进行Lasso回归与多因素Cox回归分析,结果显示有10个基因被纳入模型,计算公式为:风险值(Risk score)=(0.024 5)×ALOX5表达值+(0.126 0)×CBS表达值+(0.199 5)×CD44表达值+(0.218 3)×CHAC1表达值+(-0.295 9)×HMGCR表达值+(0.036 7)×MT1G表达值+(0.061 4)×SLC7A11表达值+(-0.080 7)×FDFT1表达值+(0.160 3)×PEBP1表达值+(-0.220 5)×GOT1表达值。生存状态图表明,高风险组死亡病例数多于低风险组;ROC曲线表明风险评分模型具备一定预测能力;K-M生存分析显示,高风险组总体生存率低于低风险组(P=5.73×10^-13)。GO与KEGG富集分析提示,高低风险组间免疫情况及IL-17信号通路存在显著差异;进一步的ssGSEA富集显示,高低风险组间大部分免疫细胞的评分存在显著差异。基于铁死亡相关基因的预后风险评分模型可用于KIRC的预后预测,针对铁死亡相关基因设计靶点可能是治疗KIRC的一种新选择。     

KIF20B promotes the progression of clear cell renal cell carcinoma by stimulating cell proliferation

Renal cell carcinoma (RCC) is a common urinary system cancer with high morbidity and mortality rate. Clear cell renal cell carcinoma (ccRCC) is a highly aggressive and common type of RCC. More and effective therapeutic targets are badly needed for the treatment of ccRCC. Kinesin family protein (KIF)20B, also named M-phase phosphoprotein 1, was reported as a microtubule-associated, plus-end-directed kinesin. KIF20B was involved in multiple cellular processes such as cytokinesis. Multiple studies indicated the oncogenic role for KIF20B in several types of tumors, including breast cancer and bladder cancer. However, the possible role of KIF20B in the progression of renal carcinoma is still unknown. Herein, our study demonstrated that KIF20B was relatively highly expressed in ccRCC tissues. In addition, KIF20B was inversely related to the clinical features including tumor size and T stage. We further found that inhibition of the KIF20B expression by a specific short hairpin RNA obviously reduces proliferation of ccRCC cells both in vitro and in vivo. Our study reveals the involvement of KIF20B in ccRCC progression. Generally, KIF20B is a promising novel therapeutic for the treatment of clear cell RCC.     

A large-scale transcriptome analysis identified ELANE and PRTN3 as novel methylation prognostic signatures for clear cell renal cell carcinoma

DNA methylation was involved in the progress of many types of cancer including clear cell renal cell carcinomas (ccRCCs). This study aimed to identify the prognostic DNA methylation biomarkers for the ccRCCs by a large-scale RNA-seq analysis. The DNA methylation data and the corresponding clinical information of the patients with ccRCCs were extracted from TCGA database and randomly divided into the training group and the validation group. The differentially expressed CpG sites and the survival-related CpG sites were further identified, which was combined into CpG sites pair and followed by screening the survival-related pairs. The C-index and the forward search algorithms were constructed to identify the prognostic signatures for the patients with ccRCCs. The prognostic signatures were verified by the validation dataset and the protein–protein interactions (PPI) network analysis was performed on the CPG sites of the signature. A total of 9,861 differentially expressed CPG sites were identified and 567 CpG sites were found to relate to the overall survival (OS) of the patients with ccRCCs. Besides, 1,146 CPG sites pairs were found to be related to the OS of the ccRCCs samples and the signature composed of seven CpG sites pairs were obtained to predict the prognosis of patients with ccRCCs and the results were verified in the validation dataset. Besides, the PPI network analysis showed that ELANE and PRTN3 gene may be associated with the invasion and metastasis of ccRCCs and could function as potential prognostic and therapeutic signatures for ccRCCs.     

Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis

Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values.Methods: Microarray datasets from the Gene Expression Omnibus (GEO) ({"type":"entrez-geo","attrs":{"text":"GSE54129","term_id":"54129"}}GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein–protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated.Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell–cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8⁺ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC.Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.     

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.     

VNN3 is a potential novel biomarker for predicting prognosis in clear cell renal cell carcinoma

Although pathological observations provide approximate prognoses, it is difficult to achieve prognosis in patients with existing prognostic factors. Therefore, it is very important to find appropriate biomarkers to achieve accurate cancer prognosis. Renal cell carcinoma (RCC) has several subtypes, the discrimination of which is crucial for proper treatment. Here, we present a novel biomarker, VNN3, which is used to prognose clear cell renal cell carcinoma (ccRCC), the most common and aggressive subtype of kidney cancer. Patient information analyzed in our study was extracted from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts. VNN3 expression was considerably higher in stages III and IV than in stages I and II. Moreover, Kaplan–Meier curves associated high VNN3 expression with poor prognoses (TCGA, p?p?=?.00076), confirming that ccRCC prognosis can be predicted via VNN3 expression patterns. Consistent with all patient results, the prognosis of patients with higher VNN3 expression was worse in both low stage (I and II) and high stage (III and IV) (TCGA, p < 0.0001 in stage I and II; ICGC, p = 0.028 in stage I and II; TCGA, p = 0.005 in stage III and IV). Area under the curve and receiver operating characteristic curves supported our results that highlighted VNN3 expression as a suitable ccRCC biomarker. Multivariate analysis also verified the prognostic performance of VNN3 expression (TCGA, p?p?=?.017). Altogether, we suggest that VNN3 is applicable as a new biomarker to establish prognosis in patients with ccRCC.     

Systematic analysis of survival-associated alternative splicing signatures in clear cell renal cell carcinoma

Alternative splicing (AS) constitutes a major reason for messenger RNA (mRNA) and protein diversity. Increasing studies have shown a link to splicing dysfunction associated with malignant neoplasia. Systematic analysis of AS events in kidney cancer remains poorly reported. Therefore, we generated AS profiles in 533 kidney renal clear cell carcinoma (KIRC) patients in The Cancer Genome Atlas (TCGA) database using RNA-seq data. Then, prognostic models were developed in a primary cohort (N = 351) and validated in a validation cohort (N = 182). In addition, splicing networks were built by integrating bioinformatics analyses. A total of 11 268 and 8083 AS variants were significantly associated with patient overall survival time in the primary and validation KIRC cohorts, respectively, including STAT1, DAZAP1, IDS, NUDT7, and KLHDC4. The AS events in the primary KIRC cohorts served as candidate AS events to screen the independent risk factors associated with survival in the primary cohort and to develop prognostic models. The area under the curve of the receiver-operator characteristic curve for prognostic prediction in the primary and validation KIRC cohorts was 0.84 and 0.82 at 2500 days of overall survival, respectively. In addition, splicing correlation networks revealed key splicing factors (SFs) in KIRC, such as HNRNPH1, HNRNPU, KHDBS1, KHDBS3, SRSF9, RBMX, SFQ, SRP54, HNRNPA0, and SRSF6. In this study, we analyzed the AS landscape in the TCGA KIRC cohort and detected predictors (prognostic) based on AS variants with high performance for risk stratification of the KIRC cohort and revealed key SFs in splicing networks, which could act as underlying mechanisms.     

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.     

Genome-wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma with varied prognosis. We aimed to identify and assess the possible prognostic long noncoding RNA (lncRNA) biomarkers. LncRNAs expression data and corresponding clinical information of 619 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes analysis, univariate Cox regression, the least absolute shrinkage and selection operator Cox regression model were utilized to identify hub lncRNAs. Multivariate Cox regression was used to establish the risk model. Statistical analysis was performed using R 3.5.3. The expression value of five lncRNAs and the risk-score levels were significantly associated with a survival prognosis of ccRCC patients (all P < .001). In the TCGA validation cohort, the area under the curve (AUC) for the integrated nomogram was 0.905 and 0.91 for 3-, 5-year prediction separately. The AUC reached up to 0.757 in an independent ICGC cohort. Besides, the calibration plots also illustrated well curve-fitting between observation values and predictive values. Weighted gene co-expression network analysis and subsequent pathway analysis revealed that the PI3K-Akt-mTOR and hypoxia-inducible factor signaling crosstalk might function as the most essential mechanisms related to the five-lncRNAs signature. Our study suggested that lncRNA AC009654.1, AC092490.2, LINC00524, LINC01234, and LINC01885 were significantly associated with ccRCC prognosis. The prognostic model based on this five lncRNA may predict the overall survival of ccRCC.     

肾透明细胞癌预后相关miRNAs的生物信息学分析

目的寻找可作为肾透明细胞癌（ccRCC）生物标志物的miRNA,以及ccRCC与正常组织间miRNA差异表达情况。 方法利用TCGA数据库下载ccRCC中miRNA表达数据,分析肿瘤与正常组织间差异表达miRNA。使用Kaplan-Meier曲线对患者进行生存分析,筛选出表达情况与临床预后相关的miRNA。通过生物信息学对miRNA的靶基因进行预测,然后运用FunRich软件和ClueGO对靶基因进行GO和KEGG富集分析。 结果通过TCGA数据库分析发现,ccRCC较正常组织差异表达miRNA共54个,其中上调33个,下调21个。通过生存分析发现hsa-miR-21和hsa-miR-155与患者预后相关,P≤0.05。进一步通过Perl软件在Targetscan、miRDB、miRTarBase、miRPath这四个数据库中预测miRNA靶基因并将结果取交集,共发现129个靶基因。GO和KEGG分析结果表明,这些靶基因主要与转录因子活性、信号转导以及FoxO、TNF等信号通路密切相关。 结论通过生物信息学分析发现了ccRCC与正常组织的差异表达miRNA;其中hsa-miR-21和hsa-miR-155与患者总体生存率相关,并通过调控靶基因参与相关的信号通路进而影响ccRCC的发生发展进程,提示hsa-miR-21和hsa-miR-155可能是ccRCC潜在的生物标志物。     

Stratification of clear cell renal cell carcinoma by signaling pathway analysis

Investigation of cell signaling pathways in 16 clear cell renal cell carcinomas to identify groups based on commonly shared phosphorylation-driven signaling networks. Using laser capture microdissection and reverse-phase protein arrays, we profiled 75 key nodes spanning signaling pathways important in tumorigenesis. Analysis revealed significantly different (P < 0.05) signaling levels for 27 nodes between two groups of samples, designated A (4 samples; high EGFR, RET, and RASGFR1 levels, converging to activate AKT/mTOR) and B (12 samples; high ERK1/2 and STAT phosphorylation). Group B was further partitioned into groups C (7 samples; elevated expression of LC3B) and D (5 samples; activation of Src and STAT). Network analysis indicated that group A was characterized by signaling pathways related to cell cycle and proliferation, and group B by pathways related to cell death and survival. Homogeneous clear cell renal cell carcinomas could be stratified into at least two major functional groups.     

利用TCGA数据库构建肾透明细胞癌相关miRNA预后模型

利用TCGA数据库中肾透明细胞癌的miRNA与mRNA数据及临床信息,构建由miRNA组成的预后风险评分模型,并筛选与生存预后相关的miRNA-mRNA调控关系对,为研究提供理论依据。下载并整理TCGA[JP+1]数据库中肾透明细胞癌的miRNA与mRNA数据;对数据进行差异分析,将差异表达的miRNA与临床信息进行合并,利用单因素与多因素Cox回归分析,构建预后模型并进行模型评价;对模型中的miRNA进行靶基因预测,结果与差异表达的mRNA进行取交集,构建miRNA-mRNA调控网络;对网络中的mRNA进行生存分析,筛选生存相关的miRNA-mRNA调控关系对。共得到49个差异表达的miRNA与3 613个差异表达的mRNA;预后模型计算公式为:风险值(risk score)=hsa-miR-21-5p表达量×0.603＋hsa-miR-1251-5p表达量×-0.093;调控网络中共纳入31个miRNA-mRNA调控关系对;对mRNA进行生存分析,共得到7个有价值的关系对。所构建预后模型可有效预测肾透明细胞癌患者生存预后情况,筛选到的miRNA-mRNA调控关系对可为相关研究与治疗提供参考。     

Integrated glycoproteomic characterization of clear cell renal cell carcinoma

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Key modules and hub genes identified by coexpression network analysis for revealing novel biomarkers for larynx squamous cell carcinoma

Larynx squamous cell carcinoma (LSCC) is the second most aggressive head and neck squamous cell carcinoma. Numerous genes have been identified to be aberrantly expressed during the development of LSCC. However, currently, researchers focus more on the individual molecule and downstream genes, leaving the coexpression among genes and key upstream disease driver genes unexploited. In this study, we applied weighted gene coexpression analysis (WGCNA) to decipher potential hub genes driving the development of LSCC. After downloading of LSCC microarray profile from gene expression omnibus, different expression analysis was performed, which was used to conduct functional enrichment analysis. Then, we applied WGCNA to highlight the hub genes which were relevant to the carcinogenesis and progression. A total of 2858 differentially expressed genes were identified in LSCC samples compared with adjacent non-neoplastic tissues. WGCNA revealed three LSCC set-specific modules having significant Kyoto Encyclopedia of Genes and Genomes enrichment effect, including pink, cyan, and black module. Nine hub genes were identified to be crucial in LSCC onset and progression, which may assist clinical decisions and serve as potential targets for LSCC treatment.