Construction and Analysis of the Dysregulated ceRNA Network and Identification of Risk Long Noncoding RNAs in Breast Cancer

Abstract

Breast cancer (BRCA) is the second leading cause of cancer-related mortality in women worldwide. However, the molecular mechanism involved in the development of BRCA is not fully understood. In this study, based on the miRNA-mediated long non-coding RNA (lncRNA)–protein coding gene (PCG) relationship and lncRNA–PCG co-expression information, we constructed and analyzed a specific dysregulated lncRNA–PCG co-expression network in BRCA. Then, we performed the random walk with restart (RWR) method to prioritize BRCA-related lncRNAs through comparing their RWR score and significance. As a result, we identified 30 risk lncRNAs for BRCA, which can distinguish normal and tumor samples. Moreover, through gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, we found that these risk lncRNAs mainly synergistically exerted functions related to cell cycle and DNA separation and replication. At last, we developed a four-lncRNA prognostic signature (including AP000851.1, LINC01977, MAFG-DT, SIAH2-AS1) and assessed the survival accuracy of the signature by performing time-dependent receiver operating characteristic (ROC) analysis. The areas under the ROC curve for 1, 3, 5, and 10 years of survival prediction were 0.68, 0.61, 0.62, and 0.63, respectively. The multivariable Cox regression results verified that the four-lncRNA signature could be used as an independent prognostic biomarker in BRCA. In summary, these results have important reference value for the study of diagnosis, treatment, and prognosis evaluation of BRCA.

Comprehensive analysis of anoikis-related long non-coding RNA immune infiltration in patients with bladder cancer and immunotherapy

“…During the research, we gathered 1019 differentially expressed arlncRNAs, and identified those with a prognostic value. We performed uni-and multi-Cox regression and LASSO regression, whereby seven arlncRNAs were validated for the construction of risk models, including UBE2Q1-AS1 that constituted a prognostic model for gastric cancer (27), `MAFG-DT`which was also a crucial member of the prognostic model for breast cancer (28), and AC112721.2 that regulated breast cancer expression through the TGF-b signaling pathway (29). Further studying these lncRNAs may provide a survival prediction.…”

Recognition of Glycometabolism-Associated lncRNAs as Prognosis Markers for Bladder Cancer by an Innovative Prediction Model

“…The expression of another prognosis-related lncRNA AC011468.1 was repressed in Stage Ⅲ–Ⅳ, which is consistent with the report by Zhao et al (2021) . Also, AL354919.2, Z84484.1, PTOV1-AS2, and MAFG-DT were found to be associated with the prognosis of cancer patients in several studies ( Liu et al, 2021 ; Su et al, 2021 ; Wang et al, 2021 ; Zhao et al, 2021 ). Also, especially, AL354919.2 was obviously upregulated in low-grade BC patients.…”

Prognostic and diagnostic values of non-coding RNAs as biomarkers for breast cancer: An umbrella review and pan-cancer analysis

“…Mammography has a variable rate of false positives ranging from 12% to 65%, which is responsible for the over-diagnosis of 31% of all breast cancer cases ( Nassar et al, 2017 ). Other imaging techniques such as MRI, ultrasound, CT, and PET are also accompanied by some limitations such as low sensitivity and specificity and being expensive ( Alyami, 2021 ; Su et al, 2021 ; Jin et al, 2021 ; Darbeheshti et al, 2022 ; Yousefi et al, 2022 ). As a result, it is deemed necessary to introduce new biomarkers that can overcome these limitations.…”