Novel lncRNAs with diagnostic or prognostic value screened out from breast cancer via bioinformatics analyses

Abstract

Background: Recent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer.

Materials and methods: The database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, etc.) to investigate the diagnostic and prognostic values of these EV lncRNAs and their effectors.

Results: In this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor.

Conclusion: A total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).

Keywords: Biomarker; Diagnosis; Exosome; Prognosis; lncRNA; Breast cancer.

Figure 1. Expression profiles of lncRNAs and mRNAs in breast cancer and adjacent tissues.

The red dots indicated that were up-regulation and blue dots pointed down-regulation. (A) Volcano plot of differentially expressed lncRNAs; (B) heat maps of differentially expressed lncRNAs; (C) volcano plot of differentially expressed mRNAs; (D) heat maps of differentially expressed mRNAs.

Figure 2. Disease related lncRNAs expression profile in plasma exosomes.

(A) Volcano plot of differential expression of lncRNAs; (B) differential expression of lncRNAs of four kinds of cancer; (C) Venn diagram of differential expression of lncRNAs in breast cancer tissues and plasma EVs. Abbr: CRC, colon cancer; HCC, liver cancer; PAAD, pancreatic cancer; BC, breast cancer.

Figure 3. Construction of weighted correlation network.

(A) Gene module cluster diagram was identified by dynamic pruning. The upper layer was the sample cluster tree, and the lower layer was the co-expression module of gene, and a total of 19 modules were obtained; (B) correlation heat maps between modules and different clinical characteristics. The abscissa is the clinical feature, the left ordinate is the module name, the right ordinate represents the threshold range of Pearson correlation coefficient, the correlation coefficients and p values of modules and traits are shown in the figures.

Figure 4. Function enrichment analysis of important modules.

(A) Enrichment analysis of blue–green module; (B) enrichment analysis of yellow module; (C) enrichment analysis of purple module; (D) enrichment analysis of blue module; (E) enrichment analysis of brown module. The vertical axis represents the items of enrichment analysis, the horizontal axis represents the number of genes, and different colors represent BP, CC, MF, KEGG and other classifications.

Figure 5. Co-expression network of lncRNAs and mRNAs.

The triangle represents lncRNAs, the circle represents mRNA, the thickness of the line represents the strength of the correlation between lncRNA and mRNA, the size of the shape and the depth of the color represents the importance of mRNAs in the network. In addition, the shape is larger, the color is deeper, and the dot is more important in this network.

Figure 6. The ROC curves of key lncRNAs.

The longitudinal axis shows the sensitivity of the biomarker, and the transverse axis shows the specificity of the biomarker. The AUC areas of all curves were >70%.

Figure 7. Survival analysis of key lncRNAs.

The forest map shows the results of multivariate regression analysis of diagnostic age, tumor stage and AL355974.2. HR ⁄ = 1 and p < 0.05 were used as the criteria for screening prognostic factors.

Figure 8. Expression of key lncRNAs in EV verification data.

The vertical axis is the expression of lncRNA, which was expressed by log ₂(TPM+1), and the transverse axis is divided into lncRNA groups: the normal group and the tumor group. The t-test was used for comparing the expression of the two groups.

Figure 9. Expression of key lncRNAs in tissue verification data.

The vertical axis is the expression of lncRNA, which is expressed by log ₂(TPM+1), and the transverse axis is divided into lncRNA groups: the normal group and the tumor group. The t-test was used to compare the expression of the two groups.

Figure 10. The ROC curves of key lncRNAs in tissue verification data.

The longitudinal axis shows the sensitivity of the biomarker, and the transverse axis shows the specificity of the biomarker. The AUC areas of all curves were >70%.