The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients

Abstract

Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.

Keywords: Bioinformatics; Breast cancer; Metabolism-related lncRNAs; Prediction signature; Risk score.

Figure 1

The flowchart of our research.

Figure 2

Exploration of metabolism‐related lncRNAs in BC. (A) lncRNA expressed differently in tumor and normal tissues. Up-regulated lncRNAs were shown in red, while down-regulated lncRNAs were shown in blue. (B) Venn diagram showing lncRNAs met two criteria. 9 lncRNAs were tagged in (A). (C) After further filtering, the metabolism‐related lncRNAs that were substantially linked with prognosis.

Figure 3

Signature test in the training cohort. (A) Risk score and survival status distribution of BC patients in low-risk and high-risk groups. (B) OS survival curves for low-risk and high-risk patients. (C) Risk score ROC Curve for one, three, and five years. (D) PCA visualization of risk categorization.

Figure 4

Signature test in the validation cohort. (A) Risk score and survival status distribution of BC patients in low-risk and high-risk groups. (B) OS survival curves for low-risk and high-risk patients. (C) Risk score ROC Curve for one, three, and five years. (D) PCA visualization of risk categorization.

Figure 5

Co-expressed lncrna mRNA of the prognostic signature. (A) Annotated coefficients for 9 lncRNAs. (B) A metabolic-related lncRNA-mRNA co-expression regulation network. (C) Sankey diagram depicting the relationships between mRNAs, lncRNAs, and risk types.

Figure 6

Functional analysis of lncrnas mRNAs co-expression. (A) GO enrichment analysis. (B) KEGG pathway analysis.

Figure 7

Immune infiltration signature in two groups. (A) 16 immune cells in low and high-risk groups. (B) 13 immune functions in two groups. (C) Known immune checkpoints. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 8

Potential therapeutic targets and drugs for different risk groups. (A) Expression of important known drug targets in breast cancer in different risk subgroups. (B) The sensitivity to Docetaxel, Paclitaxel, and AKT inhibitor VIII of BC patients. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 9

Evaluating risk features and constructing a prognostic nomogram. (A) Univariate and multivariate analysis in BC. (B) ROC curves of risk model score and clinical features. (C) The prognostic nomogram utilized the risk score and clinicopathological characteristics to predict one-, three-, and five-year survival rates. (D) Calibration curves demonstrated the concordance between predicted and observed 1-, 3-, and 5-years survival rates based on the nomogram.

Figure 10

The effects of C6orf99 on BC cell proliferation, migration, and invasion. (A) The expression level of C6orf99 in normal and BC cell lines. (B) Transfection siRNA efficiency was detected in MCF-7. (C) CCK-8 assays were evaluated cell viability in MCF-7. (D) Cell migration and invasion were detected by transwell assays in MCF-7. *P < 0.05, **P < 0.01, ***P < 0.001.