Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

Abstract

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

MAGI2‐AS3 inhibits breast cancer by downregulating DNA methylation of MAGI2

“…MAGI2‐AS3 promotes cancer progression through colorectal cancer through regulating miR‐3163/TMEM106B axis (Ren, Li, Tang, Li, & Lang, 2020), whereas in non‐small–cell lung cancer and acute myeloid leukemia, MAGI2‐AS3 inhibits tumor progression by upregulating the expression of the tumor suppressor genes PTEN or LRIG1 (Chen et al, 2020; Hao & Yang, 2019) Therefore, its role in pancreatic cancer is unclear. However, in breast cancer, transcriptome sequencing studies have shown that MAGI2‐AS3 is one of the core tumor suppressor lncRNAs in breast cancer (Zhang et al, 2019), and related mechanism studies have shown that it can promote apoptosis by activating the Fas/FasL signaling pathway and inhibit breast cancer cell proliferation (Y. Yang et al, 2018), MAGI2‐AS3 has the potential to be a diagnostic or prognostic marker for patients with breast cancer. However, the molecular mechanism by which MAGI2‐AS3 inhibits breast cancer remains to be studied, which may provide a new strategy for the diagnosis and treatment of breast cancer.…”

LncRNA MAGI2-AS3 and ZEB2 Plays a Crucial Role in the Lung Squamous Cell Carcinoma:a Bioinformatics Study

“…Bioinformatics analysis of prognostic biomarkers for human bladder cancer (22) showed that MAGI2-AS3 was downregulated in bladder cancer samples, and it was identi ed as the hub lncRNA in the ceRNA network. The tumor-suppressive role of MAGI2-AS3 has also been con rmed in multiple studies related to breast cancer (23)(24)(25). In a study on hepatocellular carcinoma (HCC) (26), the tumor-suppressive role of MAGI2-AS3 and targeted regulatory relationship between MAGI2-AS3 and miR-374b-5p were also con rmed.…”

Identification of Hub Genes Associated with the Development of Acute Kidney Injury by Weighted Gene Co-Expression Network Analysis

“…Then, the soft threshold is calculated by WGCNA algorithm. The scale-independent value and average connectivity of modules under different soft thresholds were tested by the gradient method in order to determine the appropriate soft threshold [13, 16, 17]. By selecting appropriate soft threshold values, the co-expression network was constructed to ensure the authenticity of the results, and the minimum number of genes in each module was not <30.…”