Dataset Information

Highly robust model of transcription regulator activity predicts breast cancer overall survival.

ABSTRACT:

Background

While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes.

Methods

Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome.

Result

We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients.

Conclusion

Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

SUBMITTER: Dong C

PROVIDER: S-EPMC7118819 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Highly robust model of transcription regulator activity predicts breast cancer overall survival.

Dong Chuanpeng C Liu Jiannan J Chen Steven X SX Dong Tianhan T Jiang Guanglong G Wang Yue Y Wu Huanmei H Reiter Jill L JL Liu Yunlong Y

BMC medical genomics 20200403 Suppl 5

<h4>Background</h4>While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicti ...[more]

PMID: 32241272

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Project description:BackgroundThe fatality and recurrence rates of bladder cancer (BC) have progressively increased. DNA methylation is an influential regulator associated with gene transcription in the pathogenesis of BC. We describe a comprehensive epigenetic study performed to analyse DNA methylation-driven genes in BC.MethodsData related to DNA methylation, the gene transcriptome and survival in BC were downloaded from The Cancer Genome Atlas (TCGA). MethylMix was used to detect BC-specific hyper-/hypo-methylated genes. Metascape was used to carry out gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related methylation-driven genes. Subsequently, we developed a six-gene risk evaluation model and a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the six genes.ResultsIn total, 167 methylation-driven genes were identified. Based on the LASSO Cox regression, six genes, i.e., ARHGDIB, LINC00526, IDH2, ARL14, GSTM2, and LURAP1, were selected for the development of a risk evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better OS (P?=?1.679e-05). The area under the curve (AUC) of this model was 0.698 at 3 years of OS. The verification performed in subgroups demonstrated the validity of the model. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.694. The methylation levels of IDH2 and ARL14 were appreciably related to the survival results. In addition, the methylation and gene expression-matched survival analysis revealed that ARHGDIB and ARL14 could be used as independent prognostic indicators. Among the six genes, 6 methylation sites in ARHGDIB, 3 in GSTM2, 1 in ARL14, 2 in LINC00526 and 2 in LURAP1 were meaningfully associated with BC prognosis. In addition, several abnormal methylated sites were identified as linked to gene expression.ConclusionWe discovered differential methylation in BC patients with better and worse survival and provided a risk evaluation model by merging six gene markers with clinical characteristics.

Project description:BackgroundSurgical therapy of breast cancer and bone metastasis can effectively improve the prognosis of breast cancer. However, after the first operation, the relationship between preoperative indicators and outcomes in patients who underwent metastatic bone surgery remained to be studied. Purpose 1. Recognize clinical and laboratory prognosis factors available to clinical doctors before the operation for bone metastatic breast cancer patients. 2. Develop a risk prediction model for 3-year postoperative survival in patients with breast cancer bone metastasis.MethodsFrom 2014 to 2020, patients who suffered from breast cancer bone metastasis and received therapeutic procedures in our institution were included for analyses (n=145). For patients who underwent both breast cancer radical surgery and bone metastasis surgery, comprehensive datasets of the parameters of interest (clinical features, laboratory factors, and patient prognoses) were collected (n=69). We performed Multivariate Cox regression to identify factors that were associated with postoperative outcome. 3-year survival prediction model and nomograms were established by 100 bootstrapping. Its benefit was evaluated by calibration plot, C-index, and decision curve analysis. The Surveillance, Epidemiology, and End Results database was also used for external validation.ResultsRadiotherapy for primary cancer, pathological type of metastatic breast cancer, lymph node metastasis, elevated serum alkaline phosphatase, lactate dehydrogenase were associated with postoperative prognosis. Pathological types of metastatic breast cancer, multiple bone metastasis, organ metastases, and elevated serum lactate dehydrogenase were associated with 3-year survival. Then those significant variables and serum alkaline phosphatase counts were integrated to construct nomograms for 3-year survival. The C-statistic of the established predictive model was 0.83. The calibration plot presents a graphical representation of calibration. In the decision curve analysis, the benefits are higher than those of the extreme curve. The receiver operating characteristic of the external validation of the model was 0.82, indicating a favored fitting degree of the two models.ConclusionOur study suggests that several clinical features and serological markers can predict the overall survival among the patients who are about to receive bone metastasis surgery after breast cancer surgery. The model can guide the preoperative evaluation and clinical decision-making for patients. Level of evidence Level III, prognostic study.

Dataset Information

Highly robust model of transcription regulator activity predicts breast cancer overall survival.

Background

Methods

Result

Conclusion

Publications

Highly robust model of transcription regulator activity predicts breast cancer overall survival.

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