Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.     

GHSR DNA hypermethylation is a new epigenetic biomarker for gastric adenocarcinoma and beyond

Aberrations of DNA methylation are early events in the development of tumors. In this study, we investigated the DNA methylation status of growth hormone secretagogue receptor (GHSR), a promising pan-cancer biomarker, in gastric cancer (GC). Initially, data sets from DNA methylation and gene expression studies available at Gene Expression Omnibus (GEO) were analyzed. Confirmation was done on primary tumor specimens and adjacent normal stomach tissue samples. Both analyses showed significant hypermethylation of GHSR. For further validation, The Cancer Genome Atlas data on stomach cancer was used. A receiver operating characteristic curve analysis yielded an area under the curve value of 0.85, corroborating its usefulness as a diagnostic marker. A genome-wide comethylation analysis revealed several correlated genes. CREB1 was found to act as an upstream regulator of this gene network. Furthermore, GHSR methylation was found to be a biomarker in several other tumor entities, namely cancers of the bladder, endometrium, esophagus, head and neck, liver, thyroid, kidney, and ovary. Our findings along with previous reports on other types of cancer suggest a high potential of GHSR gene methylation as a pan-cancer biomarker, which could be considered for liquid biopsy applications.     

Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA,DNA Methylation,microRNA, and mRNA across Five Human Cancers

IntroductionAdvances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored.ResultsUsing the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71–0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables.ConclusionOur study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.     

癌症DNA甲基化调控位点的识别

DNA甲基化是一种重要的表观遗传学修饰,在基因的转录调控方面具有重要的作用。异常的DNA甲基化可以导致癌症等复杂疾病发生,癌基因相关的DNA甲基化调控位点的识别对于解析癌症的发生发展机制及识别新的癌症标记具有重要意义。本研究通过整合The Cancer Genome Atlas(TCGA)的泛癌症基因组的高通量甲基化谱和基因表达谱,识别癌基因相关的DNA甲基化调控位点。对于每种癌症分批次计算Cp G位点甲基化与相关基因表达之间的相关性,并筛选调控下游基因的Cp G位点(包括强调控位点、弱调控位点和不调控位点),结果表明仅有一半的Cp G位点对下游基因具有调控作用;对癌症间共享的调控位点的分析发现不同癌症间共享的调控位点不尽相同,表明癌症特异的甲基化调控位点的存在。进一步地,对差异甲基化和差异表达基因的功能富集分析揭示了受甲基化调控的基因确实参与了癌症发生发展相关的功能。本研究的结果是对当前甲基化调控位点集的重要补充,也是识别癌症新型分子标记特征的重要资源。     

DNA methylation analysis using bisulfite treatment and PCR-single-strand conformation polymorphism in colorectal cancer showing microsatellite instability.

The combination of bisulfite treatment and PCR-single-strand DNA conformation polymorphism (SSCP) analysis is proposed for quantitative methylation assay. We applied this procedure to the methylation analysis of the hMLH1 promoter region in colorectal cancer. An analysis of mixtures of known amounts of methylated and unmethylated DNA revealed a linear relation. Using a calibration curve, proportions of methylated DNA were calculated. The hMLH1 promoter region was highly methylated in about 80% of microsatellite instability (MSI) (+) colorectal cancers, but in none of the MSI(-) colorectal cancers. A significant correlation existed between hypermethylation of the hMLH1 promoter and MSI, as in previous reports. In conclusion, bisulfite-PCR-SSCP (BiPS) analysis could be applied to the rapid identification of methylation status in multiple samples, quantification of methylation differences, and detection of methylation heterogeneity in amplified DNA fragments.     

Liquid biopsies: DNA methylation analyses in circulating cell-free DNA

Analysis of patient's materials like cells or nucleic acids obtained in a minimally invasive or noninvasive manner through the sampling of blood or other body fluids serves as liquid biopsies, which has huge potential for numerous diagnostic applications. Circulating cell-free DNA (cfDNA) is explored as a prognostic or predictive marker of liquid biopsies with the improvements in genomic and molecular methods. DNA methylation is an important epigenetic marker known to affect gene expression. cfDNA methylation detection is a very promising approach as abnormal distribution of DNA methylation is one of the hallmarks of many cancers and methylation changes occur early during carcinogenesis. This review summarizes the various investigational applications of cfDNA methylation and its oxidized derivatives as biomarkers for cancer diagnosis, prenatal diagnosis and organ transplantation monitoring. The review also provides a brief overview of the technologies for cfDNA methylation analysis based on next generation sequencing.     

Pan-Cancer analyses of Necroptosis-Related genes as a potential target to predict immunotherapeutic outcome

Necroptosis is a unique programmed death mechanism of necrotic cells. However, its role and specific mechanism in cancer remain unclear, and a systematic pan-cancer analysis of necroptosis is yet to be conducted. Thus, we performed a specific pan-cancer analysis using The Cancer Genome Atlas and Genotype-Tissue Expression databases to analyse necroptosis expression in terms of cancer prognosis, DNA methylation status, tumour mutative burden, microsatellite instability, immune cell infiltration in different types of cancer and molecular mechanisms. For the first time, we explored the correlation between necroptosis and immunotherapy prognosis. Thus, our study provides a relatively comprehensive understanding of the carcinogenicity of necroptosis in different types of cancer. It is suggested that necroptosis can be used to evaluate the sensitivity of different patients to immunotherapy and may become a potential target for tumour immunotherapy.     

DNA methylation and gene expression profiles characterize epigenetic regulation of lncRNAs in colon adenocarcinoma

The long noncoding RNAs (lncRNAs) are associated with tumorigenesis and progression of cancer. While DNA methylation is a common epigenetic regulator of gene expression, the methylation of lncRNAs was rarely studied. To address this gap, we integrated DNA methylation and RNA-seq data to characterize the landscape of lncRNA methylation in colon adenocarcinoma (COAD). We collected and analyzed the lncRNA expression and methylation data from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia to identify the epigenetically regulated lncRNAs. We further investigated the biological and clinical relevance of the identified lncRNAs via bioinformatics analysis. We identified 20 epigenetically upregulated lncRNAs in COAD, including several well-studied lncRNAs whose methylation regulation were poorly investigated, such as PVT1 and UCA1. We also revealed several novel tumor-associated lncRNAs in COAD, including GATA2-As1 and CYTOR. Next, we explored their biology function using gene set enrichment analysis and competitive endogenous RNA analysis. We characterized the methylation landscape of lncRNA in COAD and identified 20 epigenetically upregulated lncRNAs. Our findings will shed new light on the epigenetic regulation of lncRNA expression by DNA methylation.     

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88–0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11–0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman’s ρ from a range of 0.43–0.62 to 0.30–0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.     

Robust prediction of gene regulation in colorectal cancer tissues from DNA methylation profiles

DNA methylation is recognized as one of several epigenetic regulators of gene expression and as potential driver of carcinogenesis through gene-silencing of tumor suppressors and activation of oncogenes. However, abnormal methylation, even of promoter regions, does not necessarily alter gene expression levels, especially if the gene is already silenced, leaving the exact mechanisms of methylation unanswered. Using a large cohort of matching DNA methylation and gene expression samples of colorectal cancer (CRC; n = 77) and normal adjacent mucosa tissues (n = 108), we investigated the regulatory role of methylation on gene expression. We show that on a subset of genes enriched in common cancer pathways, methylation is significantly associated with gene regulation through gene-specific mechanisms. We built two classification models to infer gene regulation in CRC from methylation differences of tumor and normal tissues, taking into account both gene-silencing and gene-activation effects through hyper- and hypo-methylation of CpGs. The classification models result in high prediction performances in both training and independent CRC testing cohorts (0.92<AUC<0.97) as well as in individual patient data (average AUC = 0.82), suggesting a robust interplay between methylation and gene regulation. Validation analysis in other cancerous tissues resulted in lower prediction performances (0.69<AUC<0.90); however, it identified genes that share robust dependencies across cancerous tissues. In conclusion, we present a robust classification approach that predicts the gene-specific regulation through DNA methylation in CRC tissues with possible transition to different cancer entities. Furthermore, we present HMGA1 as consistently associated with methylation across cancers, suggesting a potential candidate for DNA methylation targeting cancer therapy.     

不同亚细胞结构中的长非编码RNA

长非编码RNA (long non-coding RNA,lncRNA)是长度大于200 nt的非编码RNA,最初被认为是不具有生物学功能的转录"垃圾".随着研究的深入,发现lncRNA参与了许多生物学调控过程,例如染色体沉默、染色质修饰、转录激活与干扰等.这些生物学调控过程与lncRNA的结构及时空特异性表达密切相关...     

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.     

Current and emerging applications of liquid biopsy in pan-cancer

Cancer morbidity and mortality are growing rapidly worldwide and it is urgent to develop a convenient and effective method that can identify cancer patients at an early stage and predict treatment outcomes. As a minimally invasive and reproducible tool, liquid biopsy (LB) offers the opportunity to detect, analyze and monitor cancer in any body fluids including blood, complementing the limitations of tissue biopsy. In liquid biopsy, circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are the two most common biomarkers, displaying great potential in the clinical application of pan-cancer. In this review, we expound the samples, targets, and newest techniques in liquid biopsy and summarize current clinical applications in several specific cancers. Besides, we put forward a bright prospect for further exploring the emerging application of liquid biopsy in the field of pan-cancer precision medicine.     

Exploring effects of DNA methylation and gene expression on pan-cancer drug response by mathematical models

Since genetic alteration only accounts for 20%–30% in the drug effect-related factors, the role of epigenetic regulation mechanisms in drug response is gradually being valued. However, how epigenetic changes and abnormal gene expression affect the chemotherapy response remains unclear. Therefore, we constructed a variety of mathematical models based on the integrated DNA methylation, gene expression, and anticancer drug response data of cancer cell lines from pan-cancer levels to identify genes whose DNA methylation is associated with drug response and then to assess the impact of epigenetic regulation of gene expression on the sensitivity of anticancer drugs. The innovation of the mathematical models lies in: Linear regression model is followed by logistic regression model, which greatly shortens the calculation time and ensures the reliability of results by considering the covariates. Second, reconstruction of prediction models based on multiple dataset partition methods not only evaluates the model stability but also optimizes the drug-gene pairs. For 368,520 drug-gene pairs with P < 0.05 in linear models, 999 candidate pairs with both AUC ≥ 0.8 and P < 0.05 were obtained by logistic regression models between drug response and DNA methylation. Then 931 drug-gene pairs with 45 drugs and 491 genes were optimized by model stability assessment. Integrating both DNA methylation and gene expression markedly increased predictive power for 732 drug-gene pairs where 598 drug-gene pairs including 44 drugs and 359 genes were prioritized. Several drug target genes were enriched in the modules of the drug-gene-weighted interaction network. Besides, for cancer driver genes such as EGFR, MET, and TET2, synergistic effects of DNA methylation and gene expression can predict certain anticancer drugs’ responses. In summary, we identified potential drug sensitivity-related markers from pan-cancer levels and concluded that synergistic regulation of DNA methylation and gene expression affect anticancer drug response.