Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes

نویسندگان

  • Juan Chen
  • Juan Xu
  • Yongsheng Li
  • Jinwen Zhang
  • Hong Chen
  • Jianping Lu
  • Zishan Wang
  • Xueying Zhao
  • Kang Xu
  • Yixue Li
  • Xia Li
  • Yan Zhang
چکیده

Although competing endogenous RNAs (ceRNAs) have been implicated in many solid tumors, their roles in breast cancer subtypes are not well understood. We therefore generated a ceRNA network for each subtype based on the significance of both, positive co-expression and the shared miRNAs, in the corresponding subtype miRNA dys-regulatory network, which was constructed based on negative regulations between differentially expressed miRNAs and targets. All four subtype ceRNA networks exhibited scale-free architecture and showed that the common ceRNAs were at the core of the networks. Furthermore, the common ceRNA hubs had greater connectivity than the subtype-specific hubs. Functional analysis of the common subtype ceRNA hubs highlighted factors involved in proliferation, MAPK signaling pathways and tube morphogenesis. Subtype-specific ceRNA hubs highlighted unique subtype-specific pathways, like the estrogen response and inflammatory pathways in the luminal subtypes or the factors involved in the coagulation process that participates in the basal-like subtype. Ultimately, we identified 29 critical subtype-specific ceRNA hubs that characterized the different breast cancer subtypes. Our study thus provides new insight into the common and specific subtype ceRNA interactions that define the different categories of breast cancer and enhances our understanding of the pathology underlying the different breast cancer subtypes, which can have prognostic and therapeutic implications in the future.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Study of Long Noncoding RNA FER1L4 and RB1, as Its Competing Endogenous RNA Network Target Gene, in Breast Cancer

Introduction: Breast cancer is the second most common cause of cancer-related death among females, which requires an exploration for markers to propose a more specific categorization of this cancer. Long noncoding RNAs (lncRNAs), the main subset of noncoding transcripts, are involved in tumorigenic processes. In this study, we investigated the expression of the fer-­1–­like family member 4 (FER...

متن کامل

MiR-96 induced non-small-cell lung cancer progression through competing endogenous RNA network and affecting EGFR signaling pathway

Objective(s): Non-small cell lung cancer (NSCLC) has become a serious global health problem in the 21st century, and tumor proliferation and metastasis are the leading causes of death in patients  with lung cancer. The present study aimed to verify the function of miR-96 and miR-96 in relation to competing with endogenous RNA regulatory network in NSCLC progression inc...

متن کامل

Identification of Prognostic Genes in Her2-enriched Breast Cancer by Gene Co-Expression Net-work Analysis

Introduction: HER2-enriched subtype of breast cancer has a worse prognosis than luminal subtypes. Recently, the discovery of targeted therapies in other groups of breast cancer has increased patient survival. The aim of this study was to identify genes that affect the overall survival of this group of patients based on a systems biology approach. Methods: Gene expression data and clinical infor...

متن کامل

Exploring Gene Signatures in Different Molecular Subtypes of Gastric Cancer (MSS/ TP53+, MSS/TP53-): A Network-based and Machine Learning Approach

Gastric cancer (GC) is one of the leading causes of cancer mortality, worldwide. Molecular understanding of GC’s different subtypes is still dismal and it is necessary to develop new subtype-specific diagnostic and therapeutic approaches. Therefore developing comprehensive research in this area is demanding to have a deeper insight into molecular processes, underlying these subtypes. In this st...

متن کامل

Diagnosis of Breast Cancer Subtypes using the Selection of Effective Genes from Microarray Data

Introduction: Early diagnosis of breast cancer and the identification of effective genes are important issues in the treatment and survival of the patients. Gene expression data obtained using DNA microarray in combination with machine learning algorithms can provide new and intelligent methods for diagnosis of breast cancer. Methods: Data on the expression of 9216 genes from 84 patients across...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2017