Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Abstract

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p<0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

“…However, multiomic angles have to be focused which primarily included genomics, epigenetics, and transcriptomic profiles. On the basis of the dataset, Shen and colleagues established an lncRNA panel associated with immunological signatures to stably predict the prognosis of breast cancer [16], which is consistent with several studies [17,18], and supported that the phenotypes could be the results of molecular heterogeneity. From the immunologic perspective, we indicated that inherent heterogeneity could lead to divergent prognosis and curated three robust TME subtypes for breast cancer, of which the potential mechanisms leading to this kind of differentiation remained to be explored.…”

Weighted Gene Coexpression Network Analysis Identifies TBC1D10C as a New Prognostic Biomarker for Breast Cancer

“…First, the stepwise multivariate Cox regression analysis was performed to construct the risk score formula, and the R package “survival” was applied (a model was chosen by AIC using a stepwise model selection) [ 3 , 19 , 20 ]. The following four-gene signature formula was constructed based on gene expression levels and coefficients ( β ): …”

“…With advances in multidiscipline therapies including immunotherapy, the prognosis of patients with BRCA has improved dramatically. However, due to the significant variability in tumor heterogeneity, almost 62,667 patients died of BRCA in 2018 [ 3 ].…”

Weighted Gene Co-expression Network Analysis Identifies TBC1D10C as A New Prognostic Biomarker for Breast Cancer

“…First, the stepwise multivariate Cox regression analysis was performed to construct the risk score formula and the R package "survival" was applied (a model was chosen by AIC using a stepwise model selection) (3,19,20). The following four-gene signature formula was constructed based on gene expression levels and coe cients (β):…”

“…With advances in multi-discipline therapies including immunotherapy, the prognosis of patients with BRCA has improved dramatically. However, due to the signi cant variability in tumor heterogeneity, almost 62,667 patients died of BRCA in 2018 (3).…”