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。     

PCCA表达与肾透明细胞癌患者预后相关性

[目的]探究PCCA基因与肾透明细胞癌(KIRC)患者预后的联系。[方法]下载TCGA数据库中KIRC患者基因表达数据及临床信息,利用PCCA基因表达差异分析探究KIRC患者中PCCA的表达情况;生存分析测定PCCA基因表达与患者生存时间的联系;采用逻辑回归和Cox回归分析研究PCCA基因表达与患者grade、stage、T分期和M分期等临床特征的相关性;基因集富集分析(GSEA)测定KIRC患者中与PCCA表达相关的通路。[结果]基因表达数据包含肿瘤组织539例,正常组织72例;临床数据包含537例患者信息;患者肿瘤组织中PCCA表达显著低于正常组织(P <0.000);生存分析显示PCCA低表达组患者生存时间显著低于高表达组(P <0.000); PCCA表达与临床特征显著相关(P <0.000),肿瘤等级、分期越高,PCCA表达越低;丙酸代谢,赖氨酸减少,胰岛素信号通路、色氨酸代谢、过氧化物酶体以及丙酮酸代谢等通路富集于PCCA高表达组。[结论]对于539例肿瘤组织,72例正常组织及537例病人的数据进行初步分析发现,PCCA低表达组患者6年生存率低于50%,而...     

肾透明细胞癌低氧相关基因预后模型的建立与应用

[目的]低氧是肾透明细胞癌内肿瘤微环境的重要组成部分,在肿瘤的发生与发展中起着至关重要的作用.利用癌症基因组图谱(TCGA)数据库建立低氧相关基因模型.[方法]提取低氧相关基因的表达进行单因素和多因素Cox回归分析建立模型.分析模型与临床分期以及免疫细胞浸润的相关性.[结果]构建5个低氧相关基因模型(ISG20、PLI...     

ACADSB低表达指示肾透明细胞癌患者预后不良

[目的]探究短/支链酰基辅酶a脱氢酶(A CADSB对肾透明细胞癌(KIRC)患者的生存、临床特征、通路表达等方面的影响.[方法]从肿瘤基因组图谱(TCGA)下载KIRC的基因表达数据和相应的临床信息.采用Wilcox.test分析ACADSB在正常组织和肿瘤组织中的表达差异.采用Kaplan-Meier法进行生存分析...     

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。     

透明细胞乳头状肾细胞癌的临床病理分析

目的探讨透明细胞乳头状肾细胞癌(clear cell papillary renal cell carcinoma,CCPRCC)的临床病理学特征、免疫表型、鉴别诊断及预后。方法收集2013年至2017年肾细胞癌病理切片,筛选CCPRCC 4例,收集临床资料并研究组织病理学形态及免疫组织化学特征。结果患者年龄在46岁至69岁之间(平均55.3岁),男性1例,女性3例;影像学资料显示,肿瘤平均直径为3.85 cm(1.6～7.0cm),所有肿瘤均为右肾单发肿物。4例均呈大小不等的类圆形单结节,CT显示为低密度,MRI显示为低信号。大体观察肿瘤局限于肾组织内呈单结节状生长,境界清楚,局部似带包膜,切面呈灰红至灰褐色,实性,质地中等,部分区稍韧,局灶伴有出血或囊性变。镜下见肿瘤细胞排列呈囊状、乳头状、管状/腺泡状和实性巢状结构,细胞核形态温和,远离基底膜并朝向腔面,本组病例均为WHO/ISUP分级Ⅰ-Ⅱ级,间质内可见散在数量不等的平滑肌组织。CA-9及CK7免疫组织化学染色阳性,TFE3、vimentin、CD10与AMACR阴性或局灶弱阳性。1例行FISH检测TFE3未见检测出基因相关易位...     

基于TCGA与Oncomine数据库挖掘MKL-1基因在KIRC中的临床意义

[目的]阐明MKL-1基因在肾透明细胞癌中的表达情况与临床意义。[方法]从TCGA与Oncomine数据库中收集与MKL-1的表达相关的数据,对收集到的数据进行荟萃分析,同时使用Kaplan–Meier方法分析MKL-1的表达量与生存的相关性。[结果]从Oncemine数据库中共收集到154项与MKL-1有关的研究,分析表明在MKL-1在9种癌症中表达上升,且在肾透明细胞癌中的表达显著性上升(P <0. 05)。进一步分析发现MKL-1与癌症患者的特征无显著性关系,但与预后成负相关性。最后通过蛋白质网络预测MKL-1相互作用的蛋白。[结论]基于不同数据库深度挖掘提示MKL-1基因在肾透明细胞癌中高表达,并与肾透明细胞癌的预后有显著相关性,有望被用作肾透明细胞癌的治疗的新靶点。     

hepaCAM和VEGF基因表达与肾透明细胞癌侵袭转移关系的探讨

研究肾癌细胞株786-0,RC-2及肾透明细胞癌组织中肝细胞黏附分子(hepatocyte cell adhesion molecule,hepaC-AM)和血管内皮生长因子(VEGF)mRNA表达及其与肾透明细胞癌侵袭转移的关系。应用逆转录聚合酶链反应(RT-PCR)检测786-0、RC-2、正常肾组织hepaCAM和VEGFmRNA表达,73例肾透明细胞癌组织及相应癌旁组织中hepaCAMmRNA表达,43例肾透明细胞癌组织及相应癌旁组织VEGFmRNA表达,并比较它们之间的差异性和相关性。与正常肾组织比较786-0,RC-2的hepaCAMmRNA显著降低(P0.05);VEGFmRNA显著升高(P0.05)。肾透明细胞癌组织hepaCAMmRNA显著低于癌旁组织(P0.05);VEGFmRNA显著高于癌旁组织(P0.05)。在肾透明细胞癌组织中临床Ⅰ+Ⅱ期和Ⅲ+Ⅳ期两组VEGFmRNA表达差异具有统计学意义(P0.05),hepaCAM与VEGFmRNA呈负相关(r=-0.329,P0.05)。提示hepaC-AM基因缺失可能参与肾透明细胞癌侵袭转移,其机制可能与调节VFGF表达改变有关,hepaCAM有望成为一种新的肾癌基因治疗的靶分子。     

肾透明细胞癌相关微小RNA的研究进展

微小核糖核酸(miRNAs)是一类长约22个核苷酸的非编码单链小核糖核酸分子,miRNA通过与靶mRNA 3'端非翻译序列完全或部分互补结合,导致靶mRNA降解或转录后翻译抑制,从而调控靶基因的表达.最新研究显示人类血清/血浆中miRNA表达稳定,并在肿瘤患者血清中发现多种miRNA,其中的一些已经被证实与肾癌发生及发展相关,以往miRNA与肾癌的研究方.向多集中于肾癌组织,尽管发现很多有差异的miRNA,但不同研究者之间的结果常难以相互验证,而最近研究证实血清miRNA具有组织相关性和器官特异性,并对某些肿瘤具有高敏感性和特异性,因此其有望成为新的肿瘤标志物.肾癌是国内泌尿系统的第二常见恶性肿瘤,而且其近年来发病率和死亡率有逐年增高的趋势.由于肾透明细胞癌是肾癌的主要亚型,因此本文就血清miRNA在肾透明细胞癌的表达及其作用的研究进展作一综述.     

肾透明细胞癌中免疫检查点相关的免疫逃逸机制的研究进展

摘要：近年来,免疫治疗在晚期肾透明细胞癌的治疗中异军突起,使人们对于肾癌治疗有了全新的认识。肿瘤免疫治疗药物是通过抑制免疫检查点从而抑制肿瘤细胞免疫逃逸,使免疫细胞可以杀伤肿瘤细胞来发挥治疗作用。因此,了解肾透明细胞癌中免疫检查点相关免疫逃逸机制对于制定有效的治疗策略以及开发新的免疫治疗药物至关重要。本文对目前肾透明细胞癌中主要的免疫检查点（PD-1/PD-L1、CTLA-4、B7-H4、LAG-3、TIM-3和HLA-G）相关的免疫逃逸机制进行综述。     

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

ABSTRACT

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.     

IDUA,NDST1, SAP30L,CRYBA4, and SI as novel prognostic signatures clear cell renal cell carcinoma

Carcinoma of the kidney is one of the most prevalent carcinoma worldwide. The majority types of carcinoma are clear cell renal cell carcinoma (CCRCC), which consist more than 80% of the cases. As a genetically diverse disease, identification of prognosis-related genes has utmost importance in the early diagnosis and prognosis of the CCRCC. In this study, we performed gene expression profiling to identify prognosis-related genes for CCRCC. In addition, we developed and validated a gene signature-based risk score to comprehensively assess the prognostic function of differentially expressed genes. Furthermore, we performed a ROC analysis to identify the optimal cut-off point for classification risk level of the patients. Univariate Cox regression models were used to assess the association between differentially expressed genes in relation to the prognosis of patients with different stages of CCRCC. Five genes were identified significantly differentially expressed in CCRCC and associated with their survival time, namely: IDUA, NDST1, SAP30L, CRYBA4, and SI. A 5-gene signature-based risk score was developed based on the Cox coefficient of the individual genes. The prognostic value of this risk score was validated in an internal testing data set. In summary, a gene-based risk score was identified and validated, which can predict CCRCC patient survival. The potential functions of this gene expression signature and individual differentially expressed gene as prognostic targets of CCRCC were revealed by this study. Furthermore, these findings may have important implications in the understanding of the potential therapeutic method for the CCRCC patients.     

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.     

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.     

Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

There is growing evidence that alternative splicing (AS) plays an important role in cancer development. However, a comprehensive analysis of AS signatures in kidney renal clear cell carcinoma (KIRC) is lacking and urgently needed. It remains unclear whether AS acts as diagnostic biomarkers in predicting the prognosis of KIRC patients. In the work, gene expression and clinical data of KIRC were obtained from The Cancer Genome Atlas (TCGA), and profiles of AS events were downloaded from the SpliceSeq database. The RNA sequence/AS data and clinical information were integrated, and we conducted the Cox regression analysis to screen survival-related AS events and messenger RNAs (mRNAs). Correlation between prognostic AS events and gene expression were analyzed using the Pearson correlation coefficient. Protein-protein interaction analysis was conducted for the prognostic AS-related genes, and a potential regulatory network was built using Cytoscape (version 3.6.1). Meanwhile, functional enrichment analysis was conducted. A prognostic risk score model is then established based on seven hub genes (KRT222, LENG8, APOB, SLC3A1, SCD5, AQP1, and ADRA1A) that have high performance in the risk classification of KIRC patients. A total 46,415 AS events including 10,601 genes in 537 patients with KIRC were identified. In univariate Cox regression analysis, 13,362 survival associated AS events and 8,694 survival-specific mRNAs were detected. Common 3,105 genes were screen by overlapping 13,362 survival associated AS events and 8,694 survival-specific mRNAs. The Pearson correlation analysis suggested that 13 genes were significantly correlated with AS events (Pearson correlation coefficient >0.8 or <−0.8). Then, We conducted multivariate Cox regression analyses to select the potential prognostic AS genes. Seven genes were identified to be significantly related to OS. A prognostic model based on seven genes was constructed. The area under the ROC curve was 0.767. In the current study, a robust prognostic prediction model was constructed for KIRC patients, and the findings revealed that the AS events could act as potential prognostic biomarkers for KIRC.     

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.