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

ABSTRACT: 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.

SUBMITTER: Ding W

PROVIDER: S-EPMC6380428 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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

Ding Wubin W Chen Geng G Shi Tieliu T

Epigenetics 20190129 1

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 coul ...[more]

PMID: 30696380

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Project description:Detection of DNA hypermethylation has emerged as a novel molecular biomarker for the evaluation of prostate cancer diagnosis and prognosis, but the use of single gene hypermethylation could produce unspecific results. Defining the specific gene hypermethylation profile for prostate cancer could provide groups of genes that specifically discriminate the patients with indolent and aggressive tumors. To compare the methylation profile of normal and malignant prostate we performed Genome-wide methylation analysis on 83 tumor samples, that included all clinical stages of the disease, 10 normal prostate samples and LNCaP, DU145 and PC3 prostate cancer cell lines using the GoldenGate Methylation Cancer Panel I (Illumina, Inc.) that interrogate for 1505 CpGs. We found 41 genes significantly hypermethylated in more than 20% of the tumors analyzed (p<0.01). Of these we identified GSTM2 and PENK as novel hypermethylated genes in prostate cancer that were simultaneously methylated in 40.9% of the tumors analyzed. We also identified panels of genes more frequently methylated in tumor samples with clinico-pathological indicators of poor prognosis: high Gleason score, elevated Ki-67 or advanced disease. Of these, we found that simultaneous hypermethylation of CFTR and HTR1B is common in patients with high Gleason score and high ki-67 levels and might signal the population at higher risk of therapeutic failure. DNA hypermethylation profile was associated with cancer-specific mortality (log-rank, p=0.007) and biochemical recurrence-free survival (log-rank, p=0.0008). In conclusion, our data strongly indicate that epigenetic silencing of GSTM2 and PENK is a common event in prostate cancer that could be used as a molecular marker for prostate cancer diagnosis. In addition, simultaneous HTR1B and CFTR hypermethylation could help discriminate aggressive from indolent prostate tumors.A DNA hypermethylation profile associated with cancer-specific mortality and biochemical recurrence in patients receiving radical prostatectomy is presented. GST2 and PENK are new hipermethylated genes identified. Simultaneous hypermethylation of CFTR and HTR1B could help discriminate aggressive disease.

Project description:Simple Summary Due to their central role in many biological processes, studies on protein kinases are an area of active research. In the last few decades, several inhibitors that target kinases have been approved by U.S. Food and Drug Administration (FDA). However, a large proportion of kinases remain uncharacterized, with very little information on their functionality. Variations in genome-wide DNA methylation and gene expression pattern have been extensively studied in many cancers. However, an extensive kinome-centered pan-cancer methylation and expression analysis are still lacking. This study aims to identify prognostic and diagnostic biomarkers, focusing on uncharacterized (dark) kinases to further encourage their research as therapeutic targets. Abstract Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome’s DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.

Dataset Information

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

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

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