Analysis of methylation-driven genes for predicting the prognosis of patients with head and neck squamous cell carcinoma

To help provide evidence for prognosis prediction and personalized targeted therapy for patients with head and neck squamous cell carcinoma (HNSCC), we investigated prognosis-specific methylation-driven genes in HNSCC. Survival time data, RNA sequencing data, and methylation data for HNSCC patients were downloaded from The Cancer Genome Atlas. The MethylMix R package based on the β mixture model was utilized to screen genes with different methylation statuses in tumor tissues and adjacent normal tissues, and a total of 182 HNSCC-related methylation-driven genes were then identified. A survival prediction scoring model based on multivariate Cox analysis was developed to screen the genes related to the prognosis of HNSCC, and a linear risk model of the methylation status of six genes (INA, LINC01354, TSPYL4, MAGEB2, EPHX3, and ZNF134) was constructed. The prognostic values of the six genes were further independently explored by survival analysis combined with methylation and gene expression analyses. The 5-year survival rate in the high-risk group of patients in the test set was 30.4% (95% CI: 22.7%-40.8%) and that in the low-risk group of patients was 65.5% (95% CI: 56.1%-76.5%). The area under the receiver operating characteristic curve for the model was 0.723, which further verified the specificity and sensitivity of the model. In addition, subsequent combined survival analysis revealed that all six genes could be used as independent prognostic markers and thus might be potential drug targets. The innovative method provides new insight into the molecular mechanism and prognosis of HNSCC.     

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.     

A two-CpG-based prognostic signature for oral squamous cell carcinoma overall survival

Oral squamous cell carcinoma (OSCC) represents one of the most common head and neck cancer that with dire prognosis due partly to the lack of reliable prognostic biomarker. Here, we aimed to develop a CpG site–based prognostic signature through which we could accurately predict overall survival (OS) of patients with OSCC. We obtained OSCC-related DNA methylation and gene expression data sets from the public accessible Gene Expression Omnibus. Correlations between methylation level of CpG sites and OS of patients with OSCC were assessed by univariate Cox regression analysis followed by robust likelihood-based survival analysis on those CpG sites with permutation P < 0.05 for further screening the optimal CpG sites for OSCC OS prediction based on the risk score formula that composed of the methylation level of optimal CpG sites weighted by their regression coefficients. Besides, differential expression genes (DEGs) and differential methylation genes (DMGs) in OSCC samples compared with normal samples were obtained and shared genes were considered as vital genes in OSCC tumorgenesis and progression. As a result, two CpG sites including cg17892178 and cg17378966 that located in NID2 and IDO1, respectively, were identified as the optimal prognostic signatures for OSCC OS. In addition, 12 overlapping genes between DEGs and DMGs that closely associated with inflammation or blood and tissue development–related biological processes were obtained. In conclusions, this study should provide valuable signatures for OSCC diagnosis and treatment.     

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.     

FBXL6 depletion restrains clear cell renal cell carcinoma progression

BackgroundF-box proteins play important roles in cell cycle and tumorigenesis. However, its prognostic value and molecular function in clear cell renal cell carcinoma (ccRCC) remain unclear. In this study, we established a survival model to evaluate the prognosis of patients with ccRCC using the F-box gene signature and investigated the function of FBXL6 in ccRCC.MethodsComprehensive bioinformatics analyses were used to identify differentially expressed F-box and hub genes associated with ccRCC carcinogenesis. Based on the F-box gene signature, we constructed a risk model and nomogram to predict the overall survival (OS) of patients with ccRCC and assist clinicians in decision-making. Finally, we verified the function and underlying molecular mechanisms of FBXL6 in ccRCC using CCK-8 and EdU assays, flow cytometry, and subcutaneous xenografts.ResultsA risk model based on FBXO39, FBXL6, FBXO1, and FBXL16 was developed. In addition, we drew a nomogram based on the risk score and clinical features to assess the prognosis of patients with ccRCC. Subsequently, we identified FBXL6 as an independent prognostic marker that was highly expressed in ccRCC cell lines. In vivo and in vitro assays revealed that the depletion of FBXL6 inhibited cell proliferation and induced apoptosis. We also demonstrated that SP1 regulated the expression of FBXL6.ConclusionsFBXL6 was first identified as a diagnostic and prognostic marker in patients with ccRCC. Loss of FBXL6 attenuates proliferation and induces apoptosis in ccRCC cells. SP1 was also found to regulate the expression of FBXL6.     

Expression of CIDE proteins in clear cell renal cell carcinoma and their prognostic significance

Clear cell renal cell carcinoma (ccRCC) is the major and aggressive subtype of renal cell carcinoma. It is known to derive its histologic appearance from accumulation of abundant lipids and glycogens. The cell death-inducing DFF45-like effector (CIDE) family has been characterized as the lipid droplet proteins involved in the metabolism of lipid storage droplets. The purpose of this study was to evaluate the expression of CIDE proteins in ccRCC cells and to investigate their prognostic significance. We examined consecutive patients with sporadic ccRCC, who underwent nephrectomy, to measure their mRNA and protein expression of CIDE proteins. We found that Cidec and ADRP expression were significantly up-regulated in ccRCC, compared with normal kidney tissues. Cideb was down-regulated. We also found that Cideb was expressed more in low-grade ccRCC than in high-grade tumors. To further clarify the relationship between Cideb expression and patient prognosis, we evaluated 57 ccRCC patients followed up for 120 months. Reduced ccRCC Cideb expression was associated with a higher Fuhrman nuclear grade. Patients with high Cideb expression had better overall survival rate than those with low expression (p < 0.05). Cideb expression was an independent predictor of survival (p = 0.001). Although the biologic function of Cideb in ccRCC remains unknown, the expression level of Cideb might be a novel predictor of prognosis in ccRCC.     

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.     

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.     

Identification of a 4-microRNA signature for clear cell renal cell carcinoma metastasis and prognosis

Renal cell carcinoma (RCC) metastasis portends a poor prognosis and cannot be reliably predicted. Early determination of the metastatic potential of RCC may help guide proper treatment. We analyzed microRNA (miRNA) expression in clear cell RCC (ccRCC) for the purpose of developing a miRNA expression signature to determine the risk of metastasis and prognosis. We used the microarray technology to profile miRNA expression of 78 benign kidney and ccRCC samples. Using 28 localized and metastatic ccRCC specimens as the training cohort and the univariate logistic regression and risk score methods, we developed a miRNA signature model in which the expression levels of miR-10b, miR-139-5p, miR-130b and miR-199b-5p were used to determine the status of ccRCC metastasis. We validated the signature in an independent 40-sample testing cohort of different stages of primary ccRCCs using the microarray data. Within the testing cohort patients who had at least 5 years follow-up if no metastasis developed, the signature showed a high sensitivity and specificity. The risk status was proven to be associated with the cancer-specific survival. Using the most stably expressed miRNA among benign and tumorous kidney tissue as the internal reference for normalization, we successfully converted his signature to be a quantitative PCR (qPCR)-based assay, which showed the same high sensitivity and specificity. The 4-miRNA is associated with ccRCC metastasis and prognosis. The signature is ready for and will benefit from further large clinical cohort validation and has the potential for clinical application.     

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。     

Identification of microenvironment-related genes with prognostic value in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor. Previous studies have shown that the interaction between tumor cells and microenvironment has an important impact on prognosis. Immune and stromal cells are two vital components of the tumor microenvironment. Our study aimed to better understand and explore the genes involved in immune/stromal cells on prognosis. We used the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm to calculate immune/stromal scores. According to the scores, we divided ccRCC patients from The Cancer Genome Atlas database into low and high groups and identified the genes which were differentially expressed and significantly associated with prognosis. The result of functional enrichment analysis and protein-protein interaction networks indicated that these genes mainly were involved in extracellular matrix and regulation of cellular activities. Then another independent cohort from the International Cancer Genome Consortium database was used to validate these genes. Finally, we acquired a list of microenvironment-related genes that can predict prognosis for ccRCC patients.     

Diagnosis and prognosis potential of four gene promoter hypermethylation in prostate cancer

The current prostate special antigen (PSA) test causes the overtreatment of indolent prostate cancer (PCa). It also increases the risk of delayed treatment of aggressive PCa. DNA methylation aberrations are important events for gene expression dysregulation during tumorigenesis and have been suggested as novel candidate biomarkers for PCa. This may improve the diagnosis and prognosis of PCa. This study assessed the differential methylation and messenger RNA (mRNA) expression between normal and PCa samples. Correlation between promoter methylation and mRNA expression was estimated using Pearson's correlation coefficients. Moreover, the diagnostic potential of candidate methylation markers was estimated by the receiver operating characteristic (ROC) curve using continuous beta values. Survival and Cox analysis was performed to evaluate the prognostic potential of the candidate methylation markers. A total of 359 hypermethylated sites 3435 hypomethylation sites, 483 upregulated genes, and 1341 downregulated genes were identified from The Cancer Genome Atlas database. Furthermore, 17 hypermethylated sites (covering 13 genes), including known genes associated with hypermethylation in PCa (e.g., AOX1 and C1orf114), showed high discrimination between adjacent normal tissues and PCa samples with the area under the ROC curve from 0.88 to 0.94. Notably, ANXA2, FGFR2, HAAO, and KCNE3 were identified as valuable prognostic markers of PCa through the Kaplan–Meier analysis. Using gene methylation as a continuous variable, four promoter hypermethylation was significantly associated with disease-free survival in univariate Cox regression and multivariate Cox regression. This study identified four novel diagnostic and prognostic markers for PCa. The markers provide important strategies for improving the timely diagnosis and prognosis of PCa.     

The prediction of tumor and normal tissues based on the DNA methylation values of ten key sites

Abnormal DNA methylation can alter the gene expression to promote or inhibit tumorigenesis in colon adenocarcinoma (COAD). However, the finding important genes and key sites of abnormal DNA methylation which result in the occurrence of COAD is still an eventful task. Here, we studied the effects of DNA methylation in the 12 types of genomic features on the changes of gene expression in COAD, the 10 important COAD-related genes and the key abnormal DNA methylation sites were identified. The effects of important genes on the prognosis were verified by survival analysis. Moreover, it was shown that the important genes were participated in cancer pathways and were hub genes in a co-expression network. Based on the DNA methylation levels in the ten sites, the least diversity increment algorithm for predicting tumor tissues and normal tissues in seventeen cancer types are proposed. The better results are obtained in jackknife test. For example, the predictive accuracies are 94.17 %, 91.28 %, 89.04 % and 88.89 %, respectively, for COAD, rectum adenocarcinoma, pancreatic adenocarcinoma and cholangiocarcinoma. Finally, by computing enrichment score of infiltrating immunocytes and the activity of immune pathways, we found that the genes are highly correlated with immune microenvironment.