DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer

Abstract

Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.

Identification of a Prognostic 3-Gene Risk Prediction Model for Thyroid Cancer

“…Studies have shown that gene modules are much reliable in cancer prognosis than biomarkers. While there are few studies on the cross-talk among the modules, and some important modules might be ignored (13). Therefore, in our study, gene co-expression network was constructed via WGCNA, and was used to identify THCA tissue type-associated gene modules, including blue, brown, pink and turquoise.…”

DNA methylation for cervical cancer screening: A validation trial in China

“…A number of reports have revealed the favorable performance of DNA methylation assessment of endometrial cancer tissues, [42][43][44][45][46][47] ovarian cancer tissues, [48][49][50] or both. [51,52] In our current study, we enrolled more patients with endometrial and ovarian tumors (Table 5).…”

“…No matter whether the patients previously received hrHPV testing or not, hrHPV genotyping was uniformly performed with TaqMan-based technology using an ABI 7500 Real Time Fluorescence Quantitative PCR system (Life Tech, USA) or a Stratagene Mx3000p Fluorescence Quantitative PCR system (Stratagene, USA) with an HPV nucleic acid genotyping diagnostic kit (Real time Fluorescent PCR) (Beijing SinoMDgene Technology Co., Ltd, China). The diagnostic kit detects a pooled result for hrHPV types, including HPV 16,18,31,33,45,52,6,11,35,51,39,59,68,56,58, and 66, with type-speci c probes.…”

Age-related trajectories of DNA methylation network markers: a parenclitic network approach to a family-based cohort of patients with Down Syndrome

“…However, the epigenetic regulation of the genome is a complex and highly integrated system, in which DNA methylation modules are not isolated 7 . Analytical approaches that consider the cross-talk between DNA methylation alterations in different regions of the genome could therefore provide new insights on the epigenetic remodelling occurring in various physiological and pathological conditions, as recently demonstrated [8][9][10][11] . In this framework, parenclitic network analysis has the potential to provide deeper insights into the regulation of DNA methylation profiles, intended as a complex system.…”