Sage Journals: Discover world-class research

Abstract

Breast cancer (BC) is the most common cancer among women and a leading cause of cancer-related deaths worldwide. The diagnosis of early patients and the prognosis of advanced patients have not improved over the past several decades. The purpose of the present study was to identify the lncRNA-related genes based on ceRNA network and construct a credible model for prognosis in BC. Based on The Cancer Genome Atlas (TCGA) database, prognosis-related differently expressed genes (DEGs) and a lncRNA-associated ceRNA regulatory network were obtained in BC. The patients were randomly divided into a training group and a testing group. A ceRNA-related prognostic model as well as a nomogram was constructed for further study. A total of 844 DElncRNAs, 206 DEmiRNAs and 3295 DEmRNAs were extracted in BC, and 12 RNAs (HOTAIR, AC055854.1, ST8SIA6-AS1, AC105999.2, hsa-miR-1258, hsa-miR-7705, hsa-miR-3662, hsa-miR-4501, CCNB1, UHRF1, SPC24 and SHCBP1) among them were recognized for the construction of a prognostic risk model. Patients were then assigned to high-risk and low-risk groups according to the risk score. The Kaplan-Meier (K-M) analysis demonstrated that the high-risk group was closely associated with poor prognosis. The predictive nomogram combined with clinical features showed performance in clinical practice. In a nutshell, our ceRNA-related gene model and the nomogram graph are accurate and reliable tools for predicting prognostic outcomes of BC patients, and may make great contributions to modern precise medicine.

Keywords

Breast cancer ceRNA prognosis risk model nomogram

Abbreviations

BC	breast cancer
OS	overall survival
DEGs	differentially expressed genes
GO	gene ontology
TCGA	The Cancer Genome Atlas
K-M	Kaplan-Meier analysis

lncRNAs	Long non-coding RNA
ncRNA	non-coding RNA
ceRNA	competitive endogenous RNA

1. Introduction

Breast cancer (BC) is the most common cancer among women and a leading cause of cancer-related deaths worldwide [1, 2]. Its morbidity rate is increasing year by year, and the mortality rate ranks second among female malignant tumors [3]. BC is a heterogeneous disease, and the recurrence rate varies greatly [4, 5]. The international guidelines include several polygenic prognostic tests to help determine the risk of recurrence and better inform treatment decisions [6]. Screening and diagnosis of early breast cancer are crucial for reducing morbidity and mortality [7]. Although clinical, pathological and molecular markers are usually used to predict prognosis, the underlying pathogenesis of BC remain poorly understood, and biomarkers for prognosis of BC are important in breast cancer research [8, 9, 10]. Consequently, there is a need for new prognostic factors, and assessment and improvement of the prognosis of patients remains a challenging task.

Long non-coding RNA (lncRNA) is a kind of non-coding RNA (ncRNA) without protein coding ability, and its transcripts are more than 200 nucleotides in length [11]. Recent researches have shown that lncRNAs play significant regulatory roles in various cellular processes, especially in cancers [12, 13, 14]. The hypothesis of competitive endogenous RNA (ceRNA), first proposed in 2011, shows that lncRNAs can bind to miRNA response elements (MREs) bound on targeted miRNAs as ceRNAs, affecting the regulation of microRNAs (miRNAs) on targeted mRNAs, leading to different types of human diseases processes, including cancer [15]. Based on ceRNA theory, Huang et al. [16] demonstrated that lncRNA DANCR may serve as a ceRNA by sponging miR-1225-3p in lung cancer. Kong et al. [17] revealed that lncRNA-CDC6 acted as ceRNA for miR-215 to regulate CDC6 expression, prompting breast cancer progression. Therefore, lncRNAs as a function of ceRNA play an important role in the molecular mechanism of cancer prognosis.

Due to the development of high-throughput RNA sequencing (RNA-seq), more and more lncRNAs, miRNAs and mRNAs have been found [18]. As an open source sequencing database, the Cancer Genome Atlas (TCGA) platform contains clinicopathological information and corresponding bioinformatics data of more than 30 human cancers, which is helpful to comprehensively analyze the ceRNA network [19]. Recently, some studies based on the development of bioinformatics analysis have explored lncRNA-related biomarkers to predict the prognosis of breast cancer. As reported by Zhu et al. [20], a panel of 8-lncRNA signature correlated with prognosis of BC was identified. Zhao et al. [21] developed an hypoxia-related 4-lncRNA model for prediction of breast cancer. However, due to the lack of comprehensive analysis of lncRNAs, miRNAs and mRNAs, these prediction models can not well predict the prognosis [22]. The prognostic value of the nomogram and combined lncRNA-miRNA-mRNA model based on gene expression data confirmed by ceRNA regulatory network in BC is still unclear.

In present study based on TCGA database, we comprehensively analyzed the differentially expressed lncRNAs, miRNAs and mRNAs between breast cancer and paired normal breast samples. By co-expression analysis and establishing ceRNA regulatory network, we screened independent prognosis-related lncRNAs, miRNAs and mRNAs, and constructed risk score system and nomogram survival model. Our study presented a novel prognostic model for BC, and provided a preliminary bioinformatic evidence for understanding the complex mechanism of BC progression.

2. Methods

2.1 Sample selection and data processing

RNA-seq expression profile, miRNA expression data, and clinical phenotype information of all the samples were downloaded from the TCGA (https://portal.gdc.cancer.gov/). In addition, the patients with a follow-up time of $\geqslant$ 3 months were randomly separated into two groups (training and testing sets) at a ratio of 1:1 and data in the testing set were applied to verify the validity of the model. The clinical signatures of patients in the two cohorts were similar.

2.2 Screening of differentially expressed lncRNAs (DElncRNAs), miRNA (DEmiRNAs) and mRNAs (DEmRNAs)

The RNA-seq expression profile was separated into mRNA and lncRNA expression data respectively. After normalization and filtering using the “edgeR” package in the R language, the differentially expressed lncRNAs, miRNAs and mRNAs were selected with $P$ -value $\leqslant$ 0.005 and fold change $\geqslant$ 2. The DElncRNAs, DEmiRNAs and DEmRNAs meeting the criteria were displayed in the form of heating map and volcano plots.

2.3 Functional enrichment analysis

To further explore the function of the DEmRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted based on the clusterProfiler package. An enriched GO term or pathway with a $P$ -value $\leqslant$ 0.05 was considered to be statistically significant.

2.4 Construction of the co-expression network

To elucidate the potential relationships between DElncRNAs and DEmRNAs, the lncRNA-mRNA co-expression network was constructed. For each DElncRNA, we calculated the Pearson correlation between its expression value and the expression value of each DEmRNA. When the $P$ -value of the correlation coefficient between DElncRNAs and DEmRNAs expression value was not greater than 0.05 and the absolute correlation value was not less than 0.7, it was considered to be relevant. A total of 259 lncRNAs and mRNAs were included to generate the network map.

2.5 Construction of the ceRNA network

Based on the co-expression network, the ceRNA network was constructed. The miRcode database was used to predict lncRNA-miRNA interactions. The miRDB, miRTarBase, and TargetScan were applied to identify the corresponding coding genes. We only chose miRNA-targeted mRNAs present in all three databases to enhance the validity of this ceRNA network. Furthermore, cytoscape software (version 3.6.1) was used for network construction.

2.6 Cox regression analysis of RNA

The RNA in the ceRNA network was used as candidate RNA. The univariable Cox regression analysis in R survival package (version 2.44-1.1) was performed to analyze the regression coefficient and $P$ -value of each candidate RNA in relation to survival time and status. The RNA with $P$ -value $<$ 0.05 was initially considered to be prognosis related RNA. Multivariate Cox proportional hazards regression model was applied to calculate the risk score based on ceRNA network and overall survival (OS) information. The $P$ -values were two-sided. The hazard ratio (HR) and 95% confidence intervals (95% CI) were calculated.

2.7 Protein-protein interaction (PPI) network construction

The PPI network could display many interactions that connect with hub genes directly or indirectly. To explore the interactions between the prognosis-related DEmRNAs from the ceRNA network, the online Search Tool for the Retrieval of Interacting Genes (STRING, http://string-db.org) was used to get the interactive relationships of mRNA. Confidence score $\geqslant$ 0.7 was considered as significant. Based on the PPI network, 5 core mRNAs were selected for model construction.

2.8 Construction of the ceRNA related prognostic model

To further screen the prognostic RNA, the calculation model of risk score was defined as follows: Risk score $=$ $\beta$ gene 1*expression (gene 1) $+$ $\beta$ gene 2*expression (gene 2) $+\ldots+$ $\beta$ gene n*expression (gene n). The $\beta$ was the prognostic correlation coefficient beta estimated by Cox analysis which equals to log (Hazard Ratio), and the expression represented the expression value of corresponding gene. The univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival. According to the risk scoring formula, the median risk score was used as the cut-off point, and we divided the patients into low-risk group and high-risk group. Survival differences between the two groups were assessed by Kaplan-Meier analysis using the “survival” and “survminer” packages in the R language Following that, calibration curves were plotted to evaluate the concordance between actual and predicted survival. The AUC of the survival ROC curve was calculated via the survival ROC R software package to validate the performance of the prognostic signature. The value of 0.5 to 1.0 represented a random chance and an excellent capacity for predicting survival with the model, respectively. Principal component analysis (PCA) was performed to distinguish the low-risk group and high-risk group based on the “scatterplot3d” package.

2.9 Validation analysis

To further confirm the classification reliability of the model’s impact on the prognosis of the patients with BC. We also attempted to validate the expression patterns of the model in the testing set. The expression levels of the RNA which contained in the model were extracted from the testing set for further research. The K-M plot was also used to discriminate the difference of OS between the low- and high-risk population in testing cohort.

2.10 Construction of a prognostic nomogram

To provide the clinician with a quantitative method by which to predict a patient’s probability of OS the contribution of each covariate was quantified and visualized in a prognostic nomogram with internal validation via 1000-times bootstrapping. The consistency of the resulting model was assessed by the calibration assay. The scatter plots were applied for visualization of the consistency of each model. A 1000-time bootstrapping was employed as indicated.

2.11 Statistical analysis

All statistical analyses were performed using R software, and Pearson correlation coefficient test was applied to evaluate the correlation between different variables. Survival data were analyzed by Kaplan-Meier curve and log-rank test. The univariate Cox regression analysis was performed for survival analysis. The multivariate Cox regression analysis was applied to determine independent prognostic factors, and the ROC curve was drawn to evaluate the accuracy of prognostic prediction model. Differences between groups were evaluated using unpaired Student’s $t$ -test and one-way ANOVA. $P<$ 0.05 was considered statistically significant.

3. Results

3.1 Identification of differentially expressed RNAs in BC

To identify the differentially expressed RNAs involved in BC we devoted the lncRNA, miRNA and mRNA expression profiles to differential expression analysis between breast cancer and paired normal samples. The screening thresholds for lncRNA, miRNA and mRNA were adjusted $P$ value $<$ 0.05 and $|$ log ${}_{2}$ FC $|>$ 1. A total of 844 DElncRNAs were screened from the TCGA datasets, including 553 up-regulated and 291 down-regulated lncRNAs. A total of 206 DEmiRNAs were found, including 128 up-regulated miRNAs and 78 down-regulated miRNAs. A total of 3295 DEmRNAs were found, including 1836 up-regulated and 1459 down-regulated mRNAs. To display the results more clearly, the heatmaps and volcano maps were used to visualize the DERNAs. Red dots represented the up-regulated RNAs and green dots represented the down-regulated RNAs. As a result, the differentially expressed state of lncRNA, miRNA and mRNA was shown in Fig. 1A–F respectively.

Figure 1.

Identification of differentially expressed RNAs between tumor and normal samples. (A, B) The heatmap and volcano plot of the DElncRNAs in BC compared to normal tissue. (C, D) The heatmap and volcano plot showed the expression change of DEmiRNAs in BC when compared with normal tissue. (E, F) The heatmap and volcano plot of the DEmRNAs in BC compared to normal tissue.

Figure 2.

Enrichment analysis of DEmRNAs, co-expression and ceRNA network in BC. (A) GO analysis presented the top 5 enriched GO terms in the biological process, cellular components and molecular functions. (B) KEGG pathway analysis exhibited the top 5 most enrichment pathways. (C) The co-expression network exhibited the potential correlation of DElncRNAs and DEmRNAs. (D) The ceRNA network of the DElncRNAs, DEmiRNAs and DEmRNAs based on the co-expression network.

3.2 GO and pathway enrichment analyses of DEmRNAs in BC

For a better understanding of the molecular underpinnings of the DEmRNAs, these mRNAs were applied to GO and KEGG enrichment analysis. Functional enrichment analysis revealed that these genes were actively associated with functions of mesenchyme development, muscle tissue development, muscle organ development, regulation of lipid metabolic process and epithelial cell proliferation in GO Terms (Fig. 2A), and the genes associated with abnormal transcriptional regulation in cancer, cell cycle, prostate cancer, proteoglycans in cancer and melanoma in KEGG Terms were shown in Fig. 2B.

3.3 Construction of DElncRNA-DEmRNA co-expression and ceRNA network

To explore the potential relationships between lncRNAs and mRNAs, the DElnRNAs and DEmRNAs were selected for the co-expression analysis. The co-expression network implied a complex relationship that one lncRNA could correlate with multiple mRNAs and one mRNA could regulate multiple lncRNAs in different ways. As shown in Fig. 2C, the network contained 239 lncRNA-mRNA pairs, in which 26 DElncRNAs and 191 DEmRNAs were included. The co-expression network might indicate the potential regulatory mechanisms between lncRNAs and mRNAs.

To better discuss the ceRNA mechanism of BC, a ceRNA network of lncRNA-miRNA-mRNA was constructed based on the DElncRNA-DEmRNA co-expression network. Our data showed that a total of 16 DElncRNAs were found to be combined with 23 binding miRNAs. In addition, 63 mRNAs were the target genes of these miRNAs (Fig. 2D).

3.4 Identification of prognostic-related RNAs

In order to identify the RNAs that were related to patient survival in the ceRNA network, we combined the survival time and survival status of each BC patient with gene expression data. The univariate Cox regression analysis were conducted to figure out the correlation of lncRNA, miRNA and mRNA expression with survival (Fig. 3A, C, E). Then, the Multivariate Cox regression analysis was performed to identify independent prognostic risk factors (Fig. 3B, D, F). After filtering via PPI analysis (Fig. 3G), a prognostic model based on 12 genes (HOTAIR, AC055854.1, ST8SIA6-AS1, AC105999.2, hsa-miR-1258, hsa-miR-7705, hsa-miR-3662, hsa-miR-4501, CCNB1, UHRF1, SPC24 and SHCBP1) was obtained. The expression of these 12 genes in breast cancer and paired normal samples was shown in Fig. 3H.

Figure 3.

Identification of ceRNA-related DERNAs related to prognosis of BC by Cox regression analysis. (A, B) Forest map of ceRNA-related DElncRNAs related to survival in BC analyzed by univariate and multivariate Cox regression. (C, D) Forest map of ceRNA-related DEmiRNAs related to survival in BC analyzed by univariate and multivariate Cox regression. (E, F) Forest map of ceRNA-related DEmRNAs related to survival in BC analyzed by univariate and multivariate Cox regression. (G) PPI network analysis of the prognosis-related DEmRNAs. (H) The gene expression of the 12 biomarkers associated with BC survival.

3.5 Establishment of a prognostic model and Validation of the predictive performance of the model

To verify the predictive performance of the model, 970 patients were randomly divided into two subgroups at a rate of 1:1: training set (dataset 1: $n=$ 485) and testing set 2 (dataset 2: $n=$ 485). According to the median risk score in the risk model, the patients in training set were divided into low-risk group ( $n=$ 242) and high-risk group ( $n=$ 243). Through the log rank test, we plotted the survival curve and found that patients in the high-risk group had worse OS than those in the low-risk group (Fig. 4A) similar result was obtained in testing set (Fig. 4B). Then, the ROC curve was constructed to verify the accuracy of the model. The AUC values in the two subgroups were 0.680 and 0.569 at 5 years (Fig. 4C and D). The AUC values demonstrated that the established prognostic model was effective in predicting OS in patients with BC.

Figure 4.

Survival analysis and ROC curves for the prognostic model based on the 12 prognostic signatures. (A, B) Kaplan-Meier survival curves of high- and low-risk patients in the training and testing cohorts. (C, D) ROC curves with calculated AUCs for OS prediction in the training and testing cohort.

Figure 5.

Characteristics of prognostic signatures in the training and testing test. (A, D) Distribution of the BC patients based on risk scores in the training and testing cohorts. (B, E) Distribution of survival time and status of BC patients in the training and testing cohorts. The black dotted line is the optimal cut-off value for dividing patients into low-risk and high-risk groups. (C, F) Heat map of prognostic ceRNA-related gene expression levels in the training and testing sets.

Figure 6.

The correlation between clinicopathological features and the 12 biomarkers. (A) The association between clinico-pathological features and gene expression levels of the 12 prognostic genes in BC patients. (B) PCA analysis showed the differences between low-risk and high-risk groups according to the risk model of 12 RNAs.

Figure 7.

Expression levels of the biomarkers in clinicopathological features. (A) The expression levels of AC055854.1 between M0 and M1 samples. (B) The expression levels of AC105999.2 between T1-2 and T3-4 patients. (C) The expression levels of hsa-miR-3662 between M0 and M1 samples. (D) The expression levels of hsa-miR-3662 between T1-2 and T3-4 patients. (E) The expression levels of hsa-miR-7705 between N0 and N1-3 patients. (F) The expression levels of SHCBP1 between N0 and N1-3 patients.

Figure 8.

Construction and validation of a nomogram for overall survival prediction in BC patients. (A) Univariate Cox analysis of the clinical features. (B) Multivariate Cox regression analysis of the clinical features. (C) The nomogram consisting of age, T stage and risk score predicting 1-, 3-, and 5-year survival for BC patients. (D) Calibration curves of the nomogram in terms of agreement between predicted and observed 1-, 3-, 5-year outcomes.

According to the risk score of patients, we ranked all patients in training set and analyzed their distribution (Fig. 5A). After classifying the patients according to the risk score, we observed that the number of deaths in the low-risk group was less than that in the high-risk group. With the increase of risk score, an increasing number of patients died. In addition, patients in the low-risk group survived longer than those in the high-risk group (Fig. 5B). The heat map revealed the expression of the 12 prognostic genes in the low-risk and high-risk group (Fig. 5C). Similar results were obtained in the testing set (Fig. 5D–F). These results suggested that the predictive performance of the prognostic model was reliable in BC patients.

3.6 Clinical utility of prognostic signature

To explore the relationship between 12 RNAs in the risk model and clinicopathological features, we performed correlation analysis (Fig. 6A). The results suggested that the expressions of these 12 RNAs were correlated with clinicopathological parameters. PCA analysis was conducted to compare the differences between low-risk and high-risk groups according to the risk model of 12 RNAs (Fig. 6B). The results showed that the high-risk group and the low-risk group were distributed in different directions, indicating that the risk model could divide BC patients into two parts, and the ceRNA status of the high-risk group was different from that of the low-risk group.

In order to further identify the correlation of the RNAs in the risk model with clinicopathological parameters, we analyzed the correlation between the expression of RNAs and clinical pathological features. The Student’s $t$ -test was applied to obtain the p-values relating to gene expression. The results showed that the expression of AC055854.1 ( $P=$ 0.004) and hsa-miR-3662 ( $P=$ 0.021) was significantly higher in M0 patients than in M1 patients (Fig. 7A and C). The expression of AC105999.2 ( $P=$ 0.023) and hsa-miR-3662 ( $P=$ 0.045) was significantly higher for patients with lower T stage (T1-2) (Fig. 7B and D). In addition, the expression of hsa-miR-7705 ( $P=$ 0.008) and SHCBP1 ( $P=$ 0.004) was significantly higher for patients with higher lymph node status (N1-3) (Fig. 7E and F).

3.7 Construction of a nomogram

To further explore the comprehensive value of the risk model combined with other factors, we performed the univariate Cox regression analysis to determine the clinical characteristics related to prognosis. The results showed that age ( $P<$ 0.001), stage ( $P<$ 0.001), T ( $P=$ 0.41), N ( $P<$ 0.001), M ( $P<$ 0.001) and risk score ( $P<$ 0.001) were significantly associated with prognosis (Fig. 8A). Then, the multivariate Cox analysis were applied to analyze the independent prognostic factors. The results suggested that age ( $P<$ 0.001), stage ( $P=$ 0.001) and risk score ( $P<$ 0.001) were independent prognostic factors in predicting overall survival (Fig. 8B). In order to better predict the prognosis of breast cancer patients in different years after diagnosis, we constructed a new nomogram based on clinical features and gene expression. In the nomogram, the higher the total points, the worse the OS (Fig. 8C). The calibration curve showed the accuracy of the nomogram (Fig. 8D).

4. Discussion

Breast cancer is the most common cancer in woman worldwide [23]. Currently, several therapeutic strategies such as surgery, radiation therapy and chemotherapy are employed in clinical treatment [24]. Owing to the development of treatment strategies for this malignancy, the survival rates of breast cancer patients have improved to some extent [25]. However, the diagnosis of early patients and the prognosis of advanced patients have not improved over the past several decades [26]. In recent, biomarkers have played important roles in the diagnosis and prognosis of cancer patients, including breast cancer patients [27]. For patients with early-stage cancer, biomarkers could serve as a detection tool to identify susceptibility [28]. For patients with advanced cancer, biomarkers could serve as therapeutic targets to improve patient survival [29, 30]. Owing to local recurrence and distant metastasis, BC is still a major challenge for clinicians [31]. Therefore, it is urgent to explore novel and effective prognostic indicators in BC.

With the development of high-throughput sequencing technology, more and more sequencing data were used in the research of cancer diagnosis, treatment and prognosis [32, 33]. In the present study, we downloaded RNA-seq data and related clinical data related to patients with breast cancer from TCGA database. We analyzed and constructed a ceRNA network containing lncRNAs, miRNAs and mRNAs, and a clinical risk prognostic model. Firstly, the expression profiles of 1222 patients were obtained based on TCGA data. The differential expression of 844 lncRNAs, 206 miRNAs and 3295 mRNAs were identified. GO and KEGG enrichment analyses were applied to further explore the main biological processes and regulatory pathways involved in these DEmRNAs. GO enrichment analysis showed that the DEmRNAs were mainly related to mesenchyme development, muscle tissue development, muscle organ development, regulation of lipid metabolic process and epithelial cell proliferation, and KEGG enrichment analysis revealed that the DEmRNAs were mainly related to abnormal transcriptional regulation in cancer, cell cycle, prostate cancer, proteoglycans in cancer and melanoma. Then, we conducted the co-expression analysis of DElncRNAs and DEmRNAs to identify the potential relationship between lncRNAs and mRNAs. The co-expression network exhibited that 26 DElncRNAs and 191 DEmRNAs were correlated, including 239 lncRNA-mRNA pairs. The results indicated that these DElncRNAs and DEmRNAs may interact with each other through some mechanism.

Previous researches demonstrated that lncRNA can act as ceRNAs, affecting the regulation of miRNAs on targeted mRNAs, leading to different types of human cancer processes [34, 35]. Based on the co-expression network and the miRNA prediction website [36], negatively correlated DElncRNA-DEmiRNA and DEmiRNA-DEmRNA relationship pairs were analyzed and a ceRNA network was constructed. In the ceRNA network, we found that 16 lncRNAs could regulate the expression of 63 mRNAs by competitively binding 23 miRNAs. Considering the important role of ceRNA in breast cancer, we explored the correlation between these ceRNA molecules and the prognosis of breast cancer patients. Through integrating these ceRNA molecules with data of matched clinical survival time of BC patients and PPI network, we presented the interactin among proteins and obtained 12 candidate survival-associated genes, which were HOTAIR, AC055854.1, ST8SIA6-AS1, AC105999.2, hsa-miR-1258, hsa-miR-7705, hsa-miR-3662, hsa-miR-4501, CCNB1, UHRF1, SPC24 and SHCBP1. Previous study demonstrated that silencing of CCNB1 had effects on cell cycle, senescence and apoptosis through the p53 signaling pathway in pancreatic cancer [37]. Elia et al. revealed that UHRF1 could epigenetically orchestrate smooth muscle cell plasticity in arterial disease [38]. A study of Zhou et al. suggested that SPC24 was an important component of the mitotic checkpoint machinery and regulated breast cancer progression by PI3K/AKT signaling pathway [39]. Mo et al. indicated that SHCBP1 could activate STAT3/c-Myc signaling pathway to promote tumor progression in penile cancer [40]. These findings suggested that the genes in our prognostic model played important roles in breast cancer.

To identify the impact of combination of the 12 candidate genes on survival prognosis, we constructed a risk prediction model. The risk score of each patient was calculated according to the estimated regression coefficient and expression level of the genes Based on the median risk score, the patients were divided into high-risk group and low-risk group. Kaplan-Meier curve analysis showed that the prognosis of high-risk group was worse than that of low-risk group. The AUC value confirmed the efficiency of the prognostic model in predicting prognosis for BC patients. The accuracy of the model was tested in the testing group Similar results were observed in the testing group. These results suggested that the prognostic model functioned in BC patients.

Nomogram is widely used as prognostic tool in cancer patients. By including clinical prognostic variables and generating survival probability, nomogram can help clinicians make better treatment decisions [41]. Therefore, to better predict the prognosis of BC patients at 1, 3, and 5 years after operation a concise nomogram was developed by incorporating the gene characteristics based on the ceRNA network and other relevant clinical characteristics into the stepwise Cox model. The calibration curve suggested its perfect calibrations. However, our research has some deficiencies. First, we used data from a public database, and the results need to be further verified by prospective clinical trials. In addition, the mechanism of the genes from risk model affecting BC progression needs further experimental research in vitro and vivo.

In conclusion, our research constructed a prognostic risk model of the ceRNA network and nomogram. The two models can potentially predict the prognosis of patients with BC. These results provide new insights for predicting the prognosis of BC patients, and may be of great value in exploring new cancer treatment strategies and prognostic methods.

Author contributions

Conception: Mengni Wu, Yan Chen and Zhihua Xu.

Interpretation or analysis of data: Mengni Wu, Linlin Lu, Yadi Li and Aoshuang Li.

Preparation of the manuscript: Mengni Wu, Tiantian Dai and Yue Yu.

Revision for important intellectual content: Yan Chen and Zhihua Xu.

Supervision: Yan Chen and Zhihua Xu.

Funding

This work was supported by The Natural Science Foundation of Jiangsu Province (BK20211076); Scientific Development Program of Suzhou City (SS202052); Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX22_1507).

Competing interests

The authors declare that they have no competing interests.

Patient consent

Not required.

References

Thompson

Hohl

S.D.

Molina

et al., Breast cancer disparities among women in underserved communities in the USA, Current Breast Cancer Reports 10 (2018), 131–141.

Ashoor

Monti

Pezzella

et al., Fibromatosis, a benign breast disease mimicking carcinoma: A case report, International Journal of Surgery Case Reports 41 (2017), 392–397.

Roumeliotis

G.A.

Dostaler

and Boyd

K.U.

, Complementary and alternative medicines and patients with breast cancer: A case of mortality and systematic review of patterns of use in patients with breast cancer, Plastic surgery (Oakville, Ont.) 25 (2017), 275–283.

Galluzzi

Vitale

Aaronson

S.A.

et al., Molecular mechanisms of cell death: Recommendations of the Nomenclature Committee on Cell Death 2018, Cell Death and Differentiation 25 (2018), 486–541.

Marinko

, Pericardial disease after breast cancer radiotherapy, Radiology and Oncology 53 (2018), 1–5.

Gurrapu

Pupo

Franzolin

et al., Sema4C/PlexinB2 signaling controls breast cancer cell growth, hormonal dependence and tumorigenic potential, Cell Death and Differentiation 25 (2018), 1259–1275.

Xie

Zhang

Jin

et al., Gemcitabine-based chemotherapy as a viable option for treatment of advanced breast cancer patients: A meta-analysis and literature review, Oncotarget 9 (2017), 7148–7161.

et al., Hibernoma in the clavicular fossa: A case report and literature review, Radiology Case Reports 13 (2017), 47–50.

W.W.

Yang

et al., A circular RNA circ-DNMT1 enhances breast cancer progression by activating autophagy, Oncogene 37 (2018), 5829–5842.

10.

Zhang

Liu

et al., Epigenetic regulation of NAMPT by NAMPT-AS drives metastatic progression in triple-negative breast cancer, Cancer Research 79 (2019), 3347–3359.

11.

Fatica

and Bozzoni

, Long non-coding RNAs: New players in cell differentiation and development, Nature Reviews. Genetics 15 (2014), 7–21.

12.

Castro-Oropeza

Melendez-Zajgla

Maldonado

et al., The emerging role of lncRNAs in the regulation of cancer stem cells, Cellular Oncology (Dordrecht) 41 (2018), 585–603.

13.

Yan

Feng

et al., Comprehensive genomic characterization of long non-coding RNAs across human cancers, Cancer Cell 28 (2015), 529–540.

14.

Jing

Huang

Guo

et al., LncRNA CASC15 promotes colon cancer cell proliferation and metastasis by regulating the miR4310/LGR5/Wnt/betacatenin signaling pathway, Molecular Medicine Reports 18 (2018), 2269–2276.

15.

Salmena

Poliseno

Tay

et al., A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language? Cell 146 (2011), 353–358.

16.

Huang

Y.F.

Zhang

and Fu

, Long non-coding RNA DANCR promoted non-small cell lung cancer cells metastasis via modulating of miR-1225-3p/ErbB2 signal, European Review for Medical and Pharmacological Sciences 25 (2021), 758–769.

17.

Kong

Duan

Sang

et al., LncRNA-CDC6 promotes breast cancer progression and function as ceRNA to target CDC6 by sponging microRNA-215, Journal of Cellular Physiology 234 (2019), 9105–9117.

18.

Meng

W.Y.

Jie

et al., LncRNA MALAT1 induces colon cancer development by regulating miR-129-5p/HMGB1 axis, Journal of Cellular Physiology 233 (2018), 6750–6757.

19.

Meng

X.M.

Huang

et al., Long noncoding RNAs: Novel insights into hepatocelluar carcinoma, Cancer Letters 344 (2014), 20–27.

20.

Zhu

L.L.

Cui

Weng

L.L.

et al., A panel of 8-lncRNA predicts prognosis of breast cancer patients and migration of breast cancer cells, Plos One 16 (2021), e0249174.

21.

Zhao

Liu

L.X.

Zhao

J.D.

et al., Construction and Verification of a Hypoxia-Related 4-lncRNA Model for Prediction of Breast Cancer, International Journal of General Medicine (2021), 4605–4617.

22.

Giulietti

Righetti

Principato

et al., LncRNA coexpression network analysis reveals novel biomarkers for pancreatic cancer, Carcinogenesis 39 (2018), 1016–1025.

23.

Buyse

Loi

van’t Veer

et al., Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer, Journal of the National Cancer Institute 98 (2006), 1183–1192.

24.

Cardoso

Piccart-Gebhart

Van’t Veer

et al., The MINDACT trial: The frst prospective clinical validation of a genomic tool, Molecular Oncology 1 (2007), 246–251.

25.

Nielsen

T.O.

Parker

J.S.

Leung

et al., A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer, Clinical Cancer Research: An Official Journal of the American Association for Cancer Research 16 (2010), 5222–5232.

26.

Paik

Shak

Tang

et al., A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer, The New England Journal of Medicine 351 (2004), 2817–2826.

27.

Prat

Ellis

M.J.

and Perou

C.M.

, Practical implications of gene expression-based assays for breast oncologists, Nature Reviews. Clinical Oncology 9 (2011), 48–57.

28.

Tang

Shak

Paik

et al., Comparison of the prognostic and predictive utilities of the 21-gene Recurrence Score assay and Adjuvant! for women with node-negative, ER positive breast cancer: Results from NSABP B-14 and NSABPB-20, Breast Cancer Research and Treatment 127 (2011), 133–142.

29.

van’t Veer

L.J.

Dai

H.Y.

van de Vijver

M.J.

et al., Gene expression profling predicts clinical outcome of breast cancer, Nature 415 (2002), 530–536.

30.

McGuire

Brown

J.A.L.

Malone

et al, Effects of age on the detection and management of breast cancer, Cancers 7 (2015), 908–909.

31.

Sestak

and Cuzick

, Markers for the identification of late breast cancer recurrence, Breast Cancer Research: BCR 17 (2015), 10.

32.

Mia

Zhang

et al., Combined eight-long noncoding RNA signature: A new risk score predicting prognosis in elderly non-small cell lung cancer patients, Aging 11 (2019), 467–479.

33.

Giulietti

Righetti

Principato

et al., LncRNA co-expression network analysis reveals novel biomarkers for pancreatic cancer, Carcinogenesis 39 (2018), 1016–1025.

34.

Schmitt

A.M.

and Chang

H.Y.

, Long noncoding RNAs in cancer pathways, Cancer Cell 29 (2016), 452–463.

35.

Slaby

Laga

Sedlacek

et al., Therapeutic targeting of non-coding RNAs in cancer, The Biochemical Journal 474 (2017), 4219–4251.

36.

Zhang

Kong

et al., Long non-coding RNA CASC15 is upregulated in hepatocellular carcinoma and facilitates hepatocarcinogenesis, International Journal of Oncology 51 (2017), 1722–1730.

37.

Zhang

et al., Effect of CCNB1 silencing on cell cycle, senescence, and apoptosis through the p53 signaling pathway in pancreatic cancer, Journal of Cellular Physiology 234 (2018), 619–631.

38.

Leonardo

Paolo

Pierluigi

et al., UHRF1 epigenetically orchestrates smooth muscle cell plasticity in arterial disease, The Journal of Clinical Investigation 128 (2018), 2473–2486.

39.

Zhou

Pei

Y.F.

Chen

et al., SPC24 Regulates breast cancer progression by PI3K/AKT signaling, Gene 675 (2018), 272–277.

40.

Tong

S.Y.

Yin

H.L.

et al., SHCBP1 regulates STAT3/c-Myc signaling activation to promote tumor progression in penile cancer, American Journal of Cancer Research 10 (2020), 3138–3156.

41.

Balachandran

V.P.

Gonen

Smith

J.J.

et al., Nomograms in oncology: More than meets the eye, The Lancet. Oncology 16 (2015), 173–180.

Construction of a lncRNA-mediated ceRNA network and a genomic-clinicopathologic nomogram to predict survival for breast cancer patients

Abstract

Keywords

Abbreviations

1. Introduction

2. Methods

2.1 Sample selection and data processing

2.2 Screening of differentially expressed lncRNAs (DElncRNAs), miRNA (DEmiRNAs) and mRNAs (DEmRNAs)

2.3 Functional enrichment analysis

2.4 Construction of the co-expression network

2.5 Construction of the ceRNA network

2.6 Cox regression analysis of RNA

2.7 Protein-protein interaction (PPI) network construction

2.8 Construction of the ceRNA related prognostic model

2.9 Validation analysis

2.10 Construction of a prognostic nomogram

2.11 Statistical analysis

3. Results

3.1 Identification of differentially expressed RNAs in BC

3.3 Construction of DElncRNA-DEmRNA co-expression and ceRNA network

3.4 Identification of prognostic-related RNAs

3.7 Construction of a nomogram

4. Discussion

Author contributions

Funding

Competing interests

Patient consent

References