Identification of competitive endogenous RNAs network in breast cancer

Abstract

Background MiRNAs can regulate gene expression directly or indirectly, and long noncoding RNAs as competing endogenous RNA (ceRNAs) can bind to miRNAs competitively and affect mRNA expression. The ceRNA network is still unclear in breast cancer. In this study, a ceRNA network was constructed, and new treatment and prognosis targets and biomarkers for breast cancer were explored. Methods A total of 1 096 cancer tissues and 112 adjacent normal tissues to cancer from the TCGA database were used to screen out significant differentially expressed mRNAs (DEMs), lncRNAs (DELs), and miRNAs (DEMis) to construct a ceRNA network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to predict potential functions. Survival analysis was performed to predict which functions were significant for prognosis. Results From the analysis, 2 139 DEMs, 1 059 DELs, and 84 DEMis were obtained. Targeting predictions for DEMis‐DELs and DEMis‐DEMs can yield 26 DEMs, 90 DELs, and 18 DEMis. We performed GO enrichment analysis, and the results showed that the upregulated DEMs were involved in nucleosomes, extracellular regions, and nucleosome assembly, while the downregulated DEMs were mainly involved in Z disk, muscle contraction, and structural constituents of muscle. KEGG pathway analysis was performed on all DEMs, and the pathways were enriched in retinol metabolism, steroid hormone biosynthesis, and tyrosine metabolism. Through survival analysis of the ceRNA network, we identified four DEMs, two DELs, and two DEMis that were significant for poor prognosis. Conclusions This study suggested that constructing a ceRNA network and performing survival analysis on the network could screen out new significant treatment and prognosis targets and biomarkers.

Coexpression Network Analysis of Genes Related to the Characteristics of Tumor Stemness in Triple-Negative Breast Cancer

“…The comparison results of Lv et al showed that all key genes were included in the upregulation group of tumor versus normal, while CDC25A, KIF18B, KIF2C, ORC1, RAD54L, and TPX2 were in the upregulation group of TNBC versus non-TNBC [ 19 ]. R package edgeR [ 18 ] was used to normalize and analyze the differential expression of mRNAs, lncRNAs, and miRNAs of BC in the research of Wang et al and Gao et al [ 20 , 21 ]. The result showed that all seven genes were upregulated in breast cancer tissues, which was consistent with our result.…”

“…Lv et al downloaded RNA-seq data of BC from TCGA database and applied the R package edgeR [ 18 ] for DEG analysis to explore the pathogenesis and prognosis of TNBC [ 19 ]. Moreover, we had collected other DEG consequences on BC versus normal tissues to further verify whether the expression trend of our key genes is correct [ 20 , 21 ].…”

Dysregulation of CircRNA_0001946 Contributes to the Proliferation and Metastasis of Colorectal Cancer Cells by Targeting MicroRNA-135a-5p

“…Therefore, clarifying the underlying mechanism of CRC progression and identifying novel targets are of great importance. Circular RNAs (circRNAs) are characterized by their unique closed loop structure (Wang et al, 2019b;Ye et al, 2019). In recent years, accumulating studies have demonstrated that circRNAs are involved in the progression of various cancers, including ovarian and liver cancer Wu et al, 2019), and emerging evidence has shown that circRNAs can sponge miRNAs and interfere with their biological function and thus participate in carcinogenesis (Meng et al, 2017;Cui et al, 2018;Verduci et al, 2019).…”

Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer

“…Estrogen receptor signaling has also been shown to influence the expression of microtubule-associated protein tau ( MAPT ) such that the ER inhibitor fulvestrant can promote MAPT downregulation and thereby increase breast cancer cell sensitivity to taxane-based chemotherapy [ 41 ]. PP14571 , also referred to as AC110619.1 , is a long noncoding RNA that is capable of competitively binding microRNAs and thereby indirectly altering the expression of important genes associated with breast cancer progression [ 42 ].For in vitro experiments, paclitaxel- and adriamycin-resistant MCF-7/Tax cell lines were established using the chemosensitive MCF-7 parental cell line, and were used as chemoresistant breast cancer cell model systems with basic drug resistance characteristics. We also found that the expression of KDM7A was upregulated in chemoresistant cells (MCF-7/Adr and MCF-7/Tax), while MAPT and PP14571 were downregulated compared with the chemosensitive breast cancer control cell group (MCF-7/Con).…”