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.     

NDRG2 and TLR7 as novel DNA methylation prognostic signatures for acute myelocytic leukemia

Acute myelocytic leukemia (AML) is an aggressive malignant tumor and typically fatal without treatment. Identification and development of novel biomarkers could be beneficial for the diagnosis and prognosis of AML patients. Here, we aimed to identify the accurate DNA methylation prognostic signatures for AML patients. The DNA methylation data of AML patients and corresponding clinical information were retrieved from The Cancer Genome Atlas database. CPG sites that correlates closely with the survival of the AML patients were identified and further combined into CPG sites pairs to screen the survival-related pairs. The prognostic signatures were identified by the C-index and forward search algorithms and validated by the verification group. Finally, the functional enrichment analysis was performed on these CPG sites. As a result, a total of 498 CPG sites associated with the overall survival of AML patients was obtained. A prognostic signature composed of 10 CPG sites pairs was obtained and validated. The functional enrichment analysis showed prognostic genes were mainly enriched in tumor protein processing, cell differentiation, blood leukocyte immunity, and platelet growth factor pathways. In summary, we identified two accurate prognostic methylation signatures (NDRG2 and TLR7), which would be served as a novel therapy target for AML.     

Tamoxifen therapy benefit predictive signature combining with prognostic signature in surgical-only ER-positive breast cancer

Tamoxifen treatment is important assistant for estrogen-receptor-positive breast cancer (BRCA) after resection. This study aimed to identify signatures for predicting the prognosis of patients with BRCA after tamoxifen treatment. Data of gene-specific DNA methylation (DM), as well as the corresponding clinical data for the patients with BRCA, were obtained from The Cancer Genome Atlas and followed by systematic bioinformatics analyses. After mapping these DM CPG sites onto genes, we finally obtained 352 relapse-free survival (RFS) associated DM genes, with which 61,776 gene pairs were combined, including 1,614 gene pairs related to RFS. An 11 gene-pair signature was identified to cluster the 189 patients with BRCA into the surgical low-risk group (136 patients) and high-risk group (53 patients). Then, we further identified a tamoxifen-predictive signature that could classify surgical high-risk patients with significant differences on RFS. Combining surgical-only prognostic signature and tamoxifen-predictive signature, patients were clustered into surgical-only low-risk group, tamoxifen nonbenefit group, and tamoxifen benefit group. In conclusion, we identified that the gene pair PDHA2–APRT could serve as a potential prognostic biomarker for patients with BRCA after tamoxifen treatment.     

Prognostic value of differentially methylated gene profiles in bladder cancer

DNA methylation can regulate gene expression and is pivotal in the occurrence and development of bladder cancer. In this study, we analyzed whole-genome DNA methylation on the basis of data from The Cancer Genome Atlas to select epigenetic biomarkers predictive of survival and further understand the molecular mechanisms underlying methylation patterns in bladder cancer. We identified 540 differentially methylated genes between tumor and normal tissues, including a number of independent prognostic factors based on univariate analysis. Genes (MIR6732, SOWAHC, SERPINI1, OR10W1, OR7G3, AIM1, and ZFAND5) were integrated to establish a risk model for prognostic assessment based on multivariate Cox analysis. The methylation of SOWAHC was negatively correlated with its messenger RNA expression, and together these were significantly correlated with prognosis. This study took advantage of high-throughput data mining to provide new bioinformatics evidence and ideas for further study into the pathogenesis and prognosis of bladder cancer.     

Biomarkers for detection and prognosis of breast cancer identified by a functional hypermethylome screen

Breast cancer (BC) is a disease with diverse tumor heterogeneity, which challenges conventional approaches to develop biomarkers for early detection and prognosis. To identify effective biomarkers, we performed a genome-wide screen for functional methylation changes in BC, i.e., genes silenced by promoter hypermethylation, using a functionally proven gene expression approach. A subset of candidate hypermethylated genes were validated in primary BCs and tested as markers for detection and prognosis prediction of BC. We identified 33 cancer specific methylated genes and, among these, two categories of genes: (1) highly frequent methylated genes that detect early stages of BC. Within that category, we have identified the combination of NDRG2 and HOXD1 as the most sensitive (94%) and specific (90%) gene combination for detection of BC; (2) genes that show stage dependent methylation frequency pattern, which are candidates to help delineate BC prognostic signatures. For this category, we found that methylation of CDO1, CKM, CRIP1, KL and TAC1 correlated with clinical prognostic variables and was a significant prognosticator for poor overall survival in BC patients. CKM [Hazard ratio (HR) = 2.68] and TAC1 (HR = 7.73) were the strongest single markers and the combination of both (TAC1 and CKM) was associated with poor overall survival independent of age and stage in our training (HR = 1.92) and validation cohort (HR = 2.87). Our study demonstrates an efficient method to utilize functional methylation changes in BC for the development of effective biomarkers for detection and prognosis prediction of BC.     

Identification of DNA methylation signature to predict prognosis in gastric adenocarcinoma

Gastric adenocarcinoma is an important death-related cancer. To find factors related to survival and prognosis, and thus improve recovery prospects, a powerful signature is needed. DNA methylation plays an important role in gastric adenocarcinoma processes and development, and here we report on the search for a significant DNA methylation gene to aid with the earlier diagnosis of gastric adenocarcinoma patients. A Cox proportional risk regression analysis and random survival forest algorithm were used to analyze gastric adenocarcinoma patients’ DNA methylation data from The Cancer Genome Atlas, a public database. DNA methylation gene signature consisting of five genes (SERPINA3, AP000357.4, GZMA, AC004702.2, and GREB1L) were selected. As the most accurate predictor, the area under the curve in the training and test group were 0.72 and 0.61, respectively. The signature was able to sort patients into high- and low-risk groups with meaningful overall survival rates (median: 18.36 vs 72.23 months, log-rank test, P < 0.001) in the training group, which predictive ability was validated in a test data set (median: 25.56 vs 58.80 months, log-rank test, P < 0.016). A multivariate Cox regression analysis showed the significant DNA methylation was an independent prediction prognostic factor for gastric adenocarcinoma patients. Functional analysis suggests that these signature genes may be related to pathways and biological processes associated with tumorigenesis. The significant DNA methylation gene could be a novel prediction and prognostic biomarker that both aids in the treatment and predicts the overall survival likelihoods of gastric adenocarcinoma patients.     

The landscape of DNA methylation in hepatocellular carcinoma

Better understanding of the relationship between changes in the overall methylation status of hepatocellular carcinoma (HCC) and disease progression will help us find good strategies for the early detection and treatment of HCC patients. The purpose of the study was to study the relations between the methylation status changes in HCC patients and progression of the disease to enable early detection and treatment of HCC patients. First, the DNA methylation data of 50 HCC samples and the surrounding normal samples were extracted and the change pattern of methylation status in the DNA promoter region of HCC samples against that of normal samples was studied. Then, some DNA methylation genes that could accurately identify cancer and cancer-adjacent tissues were identified using the k-top scoring pair method. Also, a prognostic signature that could predict the survival of HCC patients was constructed based on the overall survival time and death information of the early HCC patients. Finally, the obtained prognostic signature was verified. In conclusion, this study described the changes in the methylation spectrum during the development of HCC and identified genes associated with HCC progression and prognosis, which may offer new opportunities for the diagnosis and treatment of HCC.     

Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.     

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.     

A five-mRNA signature associated with post-translational modifications can better predict recurrence and survival in cervical cancer

High mortality of patients with cervical cancer (CC) stresses the imperative of prognostic biomarkers for CC patients. Additionally, the vital status of post-translational modifications (PTMs) in the progression of cancers has been reported by numerous researches. Therefore, the purpose of this research was to dig a prognostic signature correlated with PTMs for CC. We built a five-mRNA (GALNTL6, ARSE, DPAGT1, GANAB and FURIN) prognostic signature associated with PTMs to predict both disease-free survival (DFS) (hazard ratio [HR] = 3.967, 95% CI = 1.985-7.927; P < .001) and overall survival (HR = 2.092, 95% CI = 1.138-3.847; P = .018) for CC using data from The Cancer Genome Atlas database. Then, the robustness of the signature was validated using GSE44001 and the Human Protein Atlas (HPA) database. CIBERSORT algorithm analysis displayed that activated CD4 memory T cell was also an independent indicator for DFS (HR = 0.426, 95% CI = 0.186-0.978; P = .044) which could add additional prognostic value to the signature. Collectively, the PTM-related signature and activated CD4 memory T cell can provide new avenues for the prognostic predication of CC. These findings give further insights into effective treatment strategies for CC, providing opportunities for further experimental and clinical validations.     

Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer

Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC.Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes.Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues.Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.     

RAB25 expression is epigenetically downregulated in oral and oropharyngeal squamous cell carcinoma with lymph node metastasis

Oral and oropharyngeal squamous cell carcinoma (OOSCC) have a low survival rate, mainly due to metastasis to the regional lymph nodes. For optimal treatment of these metastases, a neck dissection is required; however, inaccurate detection methods results in under- and over-treatment. New DNA prognostic methylation biomarkers might improve lymph node metastases detection. To identify epigenetically regulated genes associated with lymph node metastases, genome-wide methylation analysis was performed on 6 OOSCC with (pN+) and 6 OOSCC without (pN0) lymph node metastases and combined with a gene expression signature predictive for pN+ status in OOSCC. Selected genes were validated using an independent OOSCC cohort by immunohistochemistry and pyrosequencing, and on data retrieved from The Cancer Genome Atlas. A two-step statistical selection of differentially methylated sequences revealed 14 genes with increased methylation status and mRNA downregulation in pN+ OOSCC. RAB25, a known tumor suppressor gene, was the highest-ranking gene in the discovery set. In the validation sets, both RAB25 mRNA (P = 0.015) and protein levels (P = 0.012) were lower in pN+ OOSCC. RAB25 mRNA levels were negatively correlated with RAB25 methylation levels (P < 0.001) but RAB25 protein expression was not. Our data revealed that promoter methylation is a mechanism resulting in downregulation of RAB25 expression in pN+ OOSCC and decreased expression is associated with lymph node metastasis. Detection of RAB25 methylation might contribute to lymph node metastasis diagnosis and serve as a potential new therapeutic target in OOSCC.     

An 11-gene-based prognostic signature for uveal melanoma metastasis based on gene expression and DNA methylation profile

Uveal melanoma (UM) is the most common intraocular tumor worldwide. We proposed to identify a vital gene signature that has prognostic value for UM metastasis. For this purpose, we obtained a published DNA methylation and gene expression data set associated with UM from the Gene Expression Omnibus. The genes whose aberrant expression significantly associated with UM patients’ metastasis-free survival (MFS) were identified by applying a univariate Cox proportional hazards model to the gene expression data set followed by a robust likelihood-based survival analysis to screen the optimal prognostic gene signatures (PGS). A formula for calculating the risk score that represents UM metastasis risk was constructed by including the PGSs’ expression values weighted by their regression coefficients, which were obtained by a multivariate Cox regression analysis. As a result, aberrant expression of 2884 genes were found to be significantly associated with UM patients’ MFS, which were referred to as MFSGs, and 11 out of those MFSGs, GJC1, TCEA1, MFSD3, FAF2, TLCD1, GPAA1, CYC1, ASAP1, JPH1, LDB3, and KDELR3, were identified as PGSs through which we could accurately separate UM samples with shorter MFS from those with longer MFS. By combining the DNA methylation data set and MFSGs, we further identified 265 MFSGs, which contained CpG sites that significantly hyper- or hypo-methylated in UM samples compared with control samples. Functional enrichment analysis and pathway crosstalk analysis of those genes indicated significant enrichment of cancer-related pathways. In conclusion, we identified an 11-gene-based prognostic signature and several gene biomarkers for UM metastasis, which should be helpful for selecting an appropriate treatment method for specific patients with UM.     

An Accurate Prostate Cancer Prognosticator Using a Seven-Gene Signature Plus Gleason Score and Taking Cell Type Heterogeneity into Account

One of the major challenges in the development of prostate cancer prognostic biomarkers is the cellular heterogeneity in tissue samples. We developed an objective Cluster-Correlation (CC) analysis to identify gene expression changes in various cell types that are associated with progression. In the Cluster step, samples were clustered (unsupervised) based on the expression values of each gene through a mixture model combined with a multiple linear regression model in which cell-type percent data were used for decomposition. In the Correlation step, a Chi-square test was used to select potential prognostic genes. With CC analysis, we identified 324 significantly expressed genes (68 tumor and 256 stroma cell expressed genes) which were strongly associated with the observed biochemical relapse status. Significance Analysis of Microarray (SAM) was then utilized to develop a seven-gene classifier. The Classifier has been validated using two independent Data Sets. The overall prediction accuracy and sensitivity is 71% and 76%, respectively. The inclusion of the Gleason sum to the seven-gene classifier raised the prediction accuracy and sensitivity to 83% and 76% respectively based on independent testing. These results indicated that our prognostic model that includes cell type adjustments and using Gleason score and the seven-gene signature has some utility for predicting outcomes for prostate cancer for individual patients at the time of prognosis. The strategy could have applications for improving marker performance in other cancers and other diseases.     

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.     

Identification of a novel cell cycle-related gene signature predicting survival in patients with gastric cancer

Gastric cancer (GC) is one of the most fatal cancers in the world. Thousands of biomarkers have been explored that might be related to survival and prognosis via database mining. However, the prediction effect of single gene biomarkers is not specific enough. Increasing evidence suggests that gene signatures are emerging as a possible better alternative. We aimed to develop a novel gene signature to improve the prognosis prediction of GC. Using the messenger RNA (mRNA)-mining approach, we performed mRNA expression profiling in a large GC cohort (n = 375) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and we recovered genes related to the G2/M checkpoint, which we identified with a Cox proportional regression model. We identified a set of five genes (MARCKS, CCNF, MAPK14, INCENP, and CHAF1A), which were significantly associated with overall survival (OS) in the test series. Based on this five-gene signature, the test series patients could be classified into high-risk or low-risk subgroups. Multivariate Cox regression analysis indicated that the prognostic power of this five-gene signature was independent of clinical features. In conclusion, we developed a five-gene signature related to the cell cycle that can predict survival for GC. Our findings provide novel insight that is useful for understanding cell cycle mechanisms and for identifying patients with GC with poor prognoses.     

The underlying pathophysiology association between the Type 2-diabetic and hepatocellular carcinoma

Type 2-diabetic (T2D) disease has been reported to increase the incidence of liver cancer, however, the underlying pathophysiology is still not fully understood. Here, we aimed to reveal the underlying pathophysiology association between the T2D and hepatocellular carcinoma (HCC) and, therefore, to find the possible therapeutic targets in the occurrence and development of HCC. The methylation microarray data of T2D and HCC were extracted from the Gene Expression Omnibus and The Cancer Genome Atlas. A total of 504 differentially methylated genes (DMGs) between T2D samples and the controls were identified, whereas 6269 DMGs were identified between HCC samples and the control groups. There were 336 DMGs coexisting in diabetes and HCC, among which 86 genes were comethylated genes. These genes were mostly enriched in pathways as glycosaminoglycan biosynthesis, fatty acid, and metabolic pathway as glycosaminoglycan degradation and thiamine, fructose and mannose. There were 250 DMGs that had differential methylation direction between T2D DMGs and HCC DMGs, and these genes were enriched in the Sphingolipid metabolism pathway and immune pathways through natural killer cell-mediated cytotoxicity and ak-STAT signaling pathway. Eight genes were found related to the occurrence and development of diabetes and HCC. Moreover, the result of protein-protein interaction network showed that CDKN1A gene was related to the prognosis of HCC. In summary, eight genes were found to be associated with the development of HCC and CDKN1A may serve as the potential prognostic gene for HCC.     

MicroRNA signature predicts survival in papillary thyroid carcinoma

Papillary thyroid cancer (PTC) accounts for the majority of malignant thyroid tumors. Recently, several microRNA (miRNA) expression profiling studies have used bioinformatics to suggest miRNA signatures as potential prognostic biomarkers in various malignancies. However, a prognostic miRNA biomarker has not yet been established for PTC. The aim of the present study was to identify miRNAs with prognostic value for the overall survival (OS) of patients with PTC by analyzing high-throughput miRNA data and their associated clinical characteristics downloaded from The Cancer Genome Atlas database. From our dataset, 150 differentially expressed miRNAs were identified between tumor and nontumor samples; of these miRNAs, 118 were upregulated and 32 were downregulated. Among the 150 differentially expressed miRNAs, a four miRNA signature was identified that reliably predicts OS in patients with PTC. This miRNA signature was able to classify patients into a high-risk group and a low-risk group with a significant difference in OS (P < .01). The prognostic value of the signature was validated in a testing set ( P < .01). The four miRNA signature was an independent prognostic predictor according to the multivariate analysis and demonstrated good performance in predicting 5-year disease survival with an area under the receiver operating characteristic curve area under the curve (AUC) score of 0.886. Thus, this signature may serve as a novel biomarker for predicting the survival of patients with PTC.