Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Yao, Yan; Zhang, Tingting; Qi, Ling‐Yu; Zhou, Chao; Wei, Junyu; Feng, Fubin; Liu, Ruijuan; Sun, Changgang

doi:10.1111/jcmm.14721

Cited by 53 publications

(50 citation statements)

References 48 publications

(89 reference statements)

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“…High-throughput biological technologies have provided a platform for exploring the molecular characteristics of different tumors [ 45 ]. Many lncRNAs have been used to predict BC prognosis [ 44 , 46 , 47 ], though few IRL signatures have been identified as improving BC survival prediction. Consequently, it is necessary to single out prognostic IRLs integrating TNM stage to predict 5-year RFS of BC patients.…”

Section: Discussionmentioning

confidence: 99%

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Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

et al. 2020

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Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA). Results A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001). Conclusions A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

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Section: Discussionmentioning

confidence: 99%

“…To date, several prognostic tools have been established to predict survival in BC patients [ 44 , 46 – 55 ]. Yao et al identified five lncRNAs as vital prognostic factors to evaluate BC prognosis [ 46 ]. However, these models ignored the prognostic value of IRLs for BC patients.…”

Section: Discussionmentioning

confidence: 99%

Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

et al. 2020

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“…UCA1 (another lncRNA) expression is elevated in gastric cancer, and UCA1 can inhibit miR-214 expression and upregulate PDL1 expression, thus contributing to immune escape in gastric cancer cells ( Wang C. J. et al, 2019 ). In addition, another study found that lncRNAs could be used as prognostic markers for tumor ( Yao et al, 2019 ). In colon cancer, a five-lncRNA signature was reported to predict the survival of patients after patients were divided into high and low risk score groups, and high risk scores were associated with a poor prognosis ( Lv et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Six-lncRNA Immune Prognostic Signature for Cervical Cancer

Chen

Huang

et al. 2020

Front. Genet.

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Background This study searched for immune-related long noncoding RNAs (lncRNAs) to predict the prognosis of patients with cervical cancer. Method We obtained immunologically relevant lncRNA expression profiles and clinical follow-up data from cervical cancer patients from The Cancer Genome Atlas database and the Molecular Signatures Database. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The immune prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator Cox regression, prognosis was analyzed by Kaplan–Meier curves between different groups, and the accuracy of the prognostic model was assessed by receiver operating characteristic-area under the curve (ROC-AUC) analysis. Results A six-lncRNA immune prognostic signature (LIPS) was constructed to predict the prognosis of cervical cancer. The six lncRNAs are as follows: AC009065.8, LINC01871, MIR210HG, GEMIN7-AS1, GAS5-AS1, and DLEU1. A ROC-AUC analysis indicated that the model could predict the prognosis of cervical cancer patients in different subgroups. A Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis; these results were equally meaningful in the subgroup analyses. Risk scores differed depending on the clinical pathology and tumor grade and were independent risk factors for cervical cancer prognosis. Gene set enrichment analysis revealed an association between the LIPS and the immune response, Wnt signaling pathway, and TGF beta signaling pathway. Conclusion Our study shows that the six-LIPS can predict the prognosis of cervical cancer and contribute to decisions regarding the immunotherapeutic strategy.

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“…The regulatory mechanism between lncrnas and miRNAs is extraordinarily complex. Among them, lncrna can be used as the combination of ceRNA and miRNA to compete and share mRNA, thus forming a complex lncRNA-miRNA-mRNA network [30]. Wnt/β-catenin signalling is critical in numerous cellular processes such as proliferation, invasion, and migration [31].…”

Section: Discussionmentioning

confidence: 99%

ceRNA Network Development and Tumour-infiltrating Immune Cell Analysis in Metastatic Breast Cancer of Bone

Liu

Song

Zhou

et al. 2020

Preprint

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Background: Advanced breast cancer commonly metastasises to the bone and the molecular mechanism explaining the bone affinity of breast cancer cells is unclear. Thus, we developed nomograms based on a competing endogenous RNA (ceRNA) network and analysed tumour-infiltrating immunecells to elucidate the molecular pathways that may predict the prognosis of breast cancer patients.Methods: We obtained the RNA expression profile of 1091 primary breast cancer samples from The Cancer Genome Atlas database, 58 of which had bone metastasis. We analysed differential RNA expression patterns between breast cancer with and without bone metastasis and developed a ceRNA network. Cibersort was employed to differentiate between immune cell types based on tumour transcripts. Nomograms were then established using the ceRNA network and immune cell analysis. The value of prognostic factors was evaluated by Kaplan-Meier survival analysis and Cox proportional risk model.Results: There were significant differences in lncRNAs, 18 miRNAs, and 20mRNAs between breast cancer with and without bone metastasis, which were used to construct a ceRNA network. We found that the protein-coding genes gjb3, cammv, ptprz1,and fbn3 were significant in our Kaplan-Meier analysis. We also observed significant differences in plasma cell and follicular helper T cell populations between the two groups. In addition, the proportions of mast cells, gamma delta T cells, and plasma cells differed depending on disease location and stage. Our analysis revealed that a high proportion of follicular helper T cells and a low proportion of eosinophils promoted survival and that dlx6-as1, wnt6,and gabbr2expression may be related to bone metastasis of breast cancer.Conclusions: We provided a bioinformatic basis for exploring the molecular mechanism of bone metastasis in breast cancer patients and identified factors that may predict this.

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Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Cited by 53 publications

References 48 publications

Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Six-lncRNA Immune Prognostic Signature for Cervical Cancer

ceRNA Network Development and Tumour-infiltrating Immune Cell Analysis in Metastatic Breast Cancer of Bone

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