Development and validation of a novel 15‐CpG‐based signature for predicting prognosis in triple‐negative breast cancer

DNA methylation is an important biological regulatory mechanism that changes gene expression without altering the DNA sequence. Increasing studies have revealed that DNA methylation data play a vital role in the field of oncology. However, the methylation site signature in triple‐negative breast cancer (TNBC) remains unknown. In our research, we analysed 158 TNBC samples and 98 noncancerous samples from The Cancer Genome Atlas (TCGA) in three phases. In the discovery phase, 86 CpGs were identified by univariate Cox proportional hazards regression (CPHR) analyses to be significantly correlated with overall survival (P < 0.01). In the training phase, these candidate CpGs were further narrowed down to a 15‐CpG‐based signature by conducting least absolute shrinkage and selector operator (LASSO) Cox regression in the training set. In the validation phase, the 15‐CpG‐based signature was verified using two different internal sets and one external validation set. Furthermore, a nomogram comprising the CpG‐based signature and TNM stage was generated to predict the 1‐, 3‐ and 5‐year overall survival in the primary set, and it showed excellent performance in the three validation sets (concordance indexes: 0.924, 0.974 and 0.637). This study showed that our nomogram has a precise predictive effect on the prognosis of TNBC and can potentially be implemented for clinical treatment and diagnosis.     

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.     

Profiles of immune infiltration in lung adenocarcinoma and their clinical significant: A gene-expression–based retrospective study

Lung cancer is one of the fatal tumors. The tumor microenvironment plays a key role in regulating tumor progression. To figure out the role of tumor microenvironment in lung adenocarcinoma (LUAD), ESTIMATE algorithm was used to evaluate the immune scores in LUAD. Patients with low immune scores had a worse overall survival (OS) compared with high immune scores. Using RNA-Seq data of 489 patients in The Cancer Genome Atlas (TCGA), differentially expressed genes (DEGs) were identified between high- and low-immune score groups. Based on the DEGs, nine-gene signature was constructed by the least absolute shrinkage and selection operator Cox regression model in TCGA set. The signature demonstrated significant prognostic value in both TCGA and Gene Expression Omnibus database. Multivariate Cox regression analyses indicated that nine-genes signature was an independent prognostic factor. Subgroup analysis also revealed a robust prognostic ability of nine-gene signature. A nomogram with a C-index of 0.722 had a favorable power for predicting 3-, 5-, and 10-year survival for clinical use by integrating nine-gene signature and other clinical features. Co-expression and functional enrichment analysis showed that nine-gene signature was significantly associated with immune response and provided potential profound molecules for revealing the mechanism of tumor initiation and progression. In conclusion, we revealed the significance of immune infiltration and built a novel nine-gene signature as a reliable prognostic factor for patients with LUAD.     

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.     

Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox''s proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.     

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.     

A 16-mRNA signature optimizes recurrence-free survival prediction of Stages II and III gastric cancer

High-throughput messenger RNA (mRNA) analysis has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. Here, we constructed a signature to predict the recurrence risk of Stages II and III gastric cancer (GC) patients. A least absolute shrinkage and selection operator method Cox regression model was utilized to construct the signature. Using this method, a 16-mRNA signature was identified to be associated with the relapse-free survival of Stages II and III GCs in training dataset GSE62254 (n = 194). Then this signature was validated in an independent Gene Expression Omnibus cohort GSE26253 (n = 297) and a dataset of The Cancer Genome Atlas (TCGA; n = 235). This classifier could successfully screen out the high-risk Stages II and III GCs in the training cohort (hazard ratio [HR] = 40.91; 95% confidence interval [CI] = 5.58–299.7; p < .0001). Analysis in two independent validation cohorts yielded consistent results (GSE26253: HR = 1.69, 95% CI = 1.17–2.43,; p = .0045; TCGA: HR = 2.01, 95% CI = 1.13–3.56, p = .0146). Cox regression analyses revealed that the risk score derived from this signature was an independent risk factor in Stages II and III GCs. Besides, a nomogram was constructed to serve clinical practice. Through gene set variation analysis, we found several gene sets associated with chemotherapeutic drug resistance and tumor metastasis significantly enriched in high-risk patients. In summary, this 16-mRNA signature can be used as a powerful tool for prognostic evaluation and help clinicians identify high-risk patients.     

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.     

Genome‐wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.     

Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients’ prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509–3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601–4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113–8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243–4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.     

Systematic construction and validation of an immune prognostic model for lung adenocarcinoma

Lung adenocarcinoma (LUAD), the most common non‐small‐cell lung cancer, is characterized by a dense lymphocytic infiltrate, which indicates that the immune system plays an active role in the development and growth of this cancer. However, no investigations to date have proposed robust models for predicting survival outcome for patients with LUAD in terms of tumour immunology. A total of 761 LUAD patients were included in this study, in which the database of The Cancer Genome Atlas (TCGA) was utilized for discovery, and the Gene Expression Omnibus (GEO) database was utilized for validation. Bioinformatics analysis and R language tools were utilized to construct an immune prognostic model and annotate biological functions. Lung adenocarcinoma showed a weakened immune phenotype compared with adjacent normal tissues. Immune‐related gene sets were profiled, an immune prognostic model based on 2 immune genes (ANLN and F2) was developed with the TCGA database to distinguish cases as having a low or high risk of unfavourable prognosis, and the model was verified with the GEO database. The model was prognostically significant in stratified cohorts, including stage I‐II, stage III‐IV and epidermal growth factor receptor (EGFR) mutant subsets, and was considered to be an independent prognostic factor for LUAD. Furthermore, the low‐ and high‐risk groups showed marked differences in tumour‐infiltrating leucocytes, tumour mutation burden, aneuploidy and PD‐L1 expression. In conclusion, an immune prognostic model was proposed for LUAD that is capable of independently identifying patients at high risk for poor survival, suggesting a relationship between local immune status and prognosis.     

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 CD28 and PTEN as novel prognostic markers for cervical cancer

Cervical cancer (CC) is the most common malignant tumor with poor clinical outcome among women. Identification of novel biomarkers could be beneficial for the clinical diagnosis and treatment of CC. This study aimed to identify prognostic biomarkers for the prediction of prognostic status of CC patients, and explore the effect of the corresponding methylated genes in the occurrence and development of CC. The methylation microarray data of CC was extracted from The Cancer Genome Atlas (TCGA) dataset. The methylation genes associated with the prognostic status were identified based on the information of the relapse-free survival (RFS) of the CC patients. The prognostic gene pairs were further identified. Then, the prognostic signature was identified by the forward search algorithm based on the C-index method. The results were validated by independent dataset. Finally, the functional analysis was performed on the methylation genes. A total of 276 methylation genes and 2508 gene pairs associated with the prognostic status of the CC were identified. A signature composed of eight methylation gene pairs was obtained to predict the prognostic status of cervical patients. A series of genes that played an important role in the occurrence and development of CC were obtained by the functional enrichment analysis. To summary, a prognostic signature consisting of eight methylation gene pairs was obtained. Of note, the CD28 and PTEN gene pair were found to play important roles in the occurrence and development of CC.     

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

BackgroundThis study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.MethodLGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.ResultsImmunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.ConclusionsThe IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.     

Novel gene signatures for prognosis prediction in ovarian cancer

Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi‐gene signature based on the LASSO Cox regression model in the TCGA database and time‐dependent ROC curves showed good predictive accuracy for 1‐, 3‐ and 5‐year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi‐gene signature stratified patients into high‐risk and low‐risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi‐gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug‐gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.     

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.     

结合自噬相关基因表达谱和临床因素的肺腺癌预后预测模型

对肺腺癌自噬相关基因进行生物信息学分析,结合多基因预后标志和临床参数构建能够预测肺腺癌患者预后的模型。首先,对TCGA肺腺癌数据中的938个自噬相关基因进行差异分析,获得了82个差异自噬相关基因,使用单因素Cox比例风险回归模型从差异自噬相关基因中筛选出候选基因,通过 lasso回归进一步筛选出预后相关基因,分别是ARNTL2、NAPSA、ATG9B、CAPN12、MAP1LC3C和KRT81。通过多因素Cox回归分析以构建风险评分模型,根据最优cutoff值将患者分为高低风险组,生存曲线显示高低风险组之间生存差异显著,ROC曲线显示风险评分的预测能力良好,并在内、外验证集中得到验证。同时对传统的临床因素进行单因素和多因素Cox回归分析,结果显示Stage、复发和风险评分能够独立预测预后,结合这三个独立的预后参数以构建列线图模型,使用一致性指数、校准曲线评估列线图的预测能力,结果显示预测结果与实际结果之间具有良好的一致性。通过与Stage和风险评分的比较发现,列线图的预测能力表现最佳。基于肺腺癌相关的自噬基因和临床参数构建了一个列线图模型来预测肺腺癌患者的预后生存,这可能为临床医生提供了一种可靠的预后评估工具。     

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.     

Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.