Dataset Information

A gene expression signature-based nomogram model in prediction of breast cancer bone metastases.

ABSTRACT: Breast cancer is prone to form bone metastases and subsequent skeletal-related events (SREs) dramatically decrease patients' quality of life and survival. Prediction and early management of bone lesions are valuable; however, proper prognostic models are inadequate. In the current study, we reviewed a total of 572 breast cancer patients in three microarray data sets including 191 bone metastases and 381 metastases-free. Gene set enrichment analysis (GSEA) indicated less aggressive and low-grade features of patients with bone metastases compared with metastases-free ones, while luminal subtypes are more prone to form bone metastases. Five bone metastases-related genes (KRT23, REEP1, SPIB, ALDH3B2, and GLDC) were identified and subjected to construct a gene expression signature-based nomogram (GESBN) model. The model performed well in both training and testing sets for evaluation of breast cancer bone metastases (BCBM). Clinically, the model may help in prediction of early bone metastases, prevention and management of SREs, and even help to prolong survivals for patients with BCBM. The five-gene GESBN model showed some implications as molecular diagnostic markers and therapeutic targets. Furthermore, our study also provided a way for analysis of tumor organ-specific metastases. To the best of our knowledge, this is the first published model focused on tumor organ-specific metastases.

SUBMITTER: Zhao C

PROVIDER: S-EPMC6346244 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A gene expression signature-based nomogram model in prediction of breast cancer bone metastases.

Zhao Chenglong C Lou Yan Y Wang Yao Y Wang Dongsheng D Tang Liang L Gao Xin X Zhang Kun K Xu Wei W Liu Tielong T Xiao Jianru J Xiao Jianru J

Cancer medicine 20181221 1

Breast cancer is prone to form bone metastases and subsequent skeletal-related events (SREs) dramatically decrease patients' quality of life and survival. Prediction and early management of bone lesions are valuable; however, proper prognostic models are inadequate. In the current study, we reviewed a total of 572 breast cancer patients in three microarray data sets including 191 bone metastases and 381 metastases-free. Gene set enrichment analysis (GSEA) indicated less aggressive and low-grade ...[more]

PMID: 30575323

Similar Datasets

Project description:Metastatic cancer remains the leading cause of death for patients with breast cancer. To understand the mechanisms underlying the development of distant metastases to specific sites is therefore important and of potential clinical value. From 157 primary breast tumours of the patients with known metastatic disease, gene expression profiling data were generated and correlated to metastatic behaviour including site-specific metastasis, metastasis pattern and survival outcomes. We analysed gene expression signatures specifically associated with the development of bone metastases. As a validation cohort, we used a published dataset of 376 breast carcinomas for which gene expression data and site-specific metastasis information were available. 80.5 % of luminal-type tumours developed bone metastasis as opposed to 41.7 % of basal and 55.6 % of HER2-like tumours. A novel 15-gene signature identified 82.4 % of the tumours with bone metastasis, 85.2 % of the tumours which had bone metastasis as first site of metastasis and 100 % of the ones with bone metastasis only (p 9.99e-09), in the training set. In the independent dataset, 81.2 % of the positive tested tumours had known metastatic disease to the bone (p 4.28e-10). This 15-gene signature showed much better correlation with the development of bone metastases than previously identified signatures and was predictive in both ER-positive as well as in ER-negative tumours. Multivariate analyses revealed that together with the molecular subtype, our 15-gene expression signature was significantly correlated to bone metastasis status (p <0.001, 95 % CI 3.86-48.02 in the training set; p 0.001, 95 % CI 1.54-5.00 in the independent set). The 15 genes, APOPEC3B, ATL2, BBS1, C6orf61, C6orf167, MMS22L, KCNS1, MFAP3L, NIP7, NUP155, PALM2, PH-4, PGD5, SFT2D2 and STEAP3, encoded mainly membrane-bound molecules with molecular function of protein binding. The expression levels of the up-regulated genes (NAT1, BBS1 and PH-4) were also found to be correlated to epithelial to mesenchymal transition status of the tumour. We have identified a novel 15-gene expression signature associated with the development of bone metastases in breast cancer patients. This bone metastasis signature is the first to be identified using a supervised classification approach in a large series of patients and will help forward research in this area towards clinical applications.

Project description:BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodsStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.ResultsLow stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

Project description:Background: Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years. Methods: For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals' one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell's concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature. Results: A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan-Meier (K-M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG. Conclusion: In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.

Dataset Information

A gene expression signature-based nomogram model in prediction of breast cancer bone metastases.

Publications

A gene expression signature-based nomogram model in prediction of breast cancer bone metastases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure