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.
Highlights
Breast cancer (BRCA) is the most commonly diagnosed cancer and the leading cause of cancer death among women worldwide [1]
The training set was first analyzed to identify possible prognostic Long noncoding RNA (lncRNA); the test and entire sets were used for validation
32 of the 521 differentially expressed lncRNA were found to be associated with survival time (p < 0:05) by performing univariate Cox regression analysis (Table 2)
Summary
Breast cancer (BRCA) is the most commonly diagnosed cancer and the leading cause of cancer death among women worldwide [1]. BRCA death is declining due to early detection and improved treatment, significant variability in patient outcomes remains. Many clinical prediction models for predicting patient prognosis and disease-free survival have been proposed, mainly focusing on age at diagnosis; post-menopausal status; ER, HER-2, and ki-67 status; tumor size; lymph node involvement; metastasis; and therapeutic strategy [4,5,6,7]. These models are difficult to implement in clinical practice due to incomplete diagnostic characteristics and model limitations
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