Dataset Information

A Novel Overall Survival Prediction Signature Based on Comprehensive Research in Prostate Cancer Bone Metastases.

ABSTRACT:

Background

Prostate adenocarcinoma (PRAD)-related bone metastases are a leading source of morbidity and mortality; however, good diagnostic biomarkers are not known yet. The aim of this study was to identify biomarkers and prognostic indicators for the diagnosis and treatment of PRAD-associated bone metastases.

Methods

By combining the data from The Cancer Genome Atlas(TCGA) and PRAD SU2C 2019, We performed a comprehensive analysis of the expression differences, biological functions, and interactions of genes associated with PRAD bone metastasis. Annotation, visualization, and integrated discovery were accomplished through the use of gene ontology enrichment and gene set enrichment analysis. The protein-protein interaction network was constructed using the STRING database, and the diagnostic value of prognostic genes was validated using receiver-operating-characteristic and Kaplan-Meier curves.

Results

Six genes (DDX47, PRL17, AS3MT, KLRK1, ISLR, and S100A8) associated with PRAD bone metastases were identified; these had prognostic value as well. Among them, enrichment was observed for the biological processes extracellular matrix tissue, extracellular structural tissue, steroid hormone response, and cell oxidative detoxification. KEGG analysis revealed enrichment in interactions with extracellular matrix receptors, diseases including Parkinson's disease and dilated cardiomyopathy, and estrogen signaling pathways. The area under the curve values of 0.8938, 0.9885, and 0.979, obtained from time-dependent receiver-operating-characteristic curve analysis for 1, 3, and 5-year overall survival confirmed the good performance of the model under consideration. S100A8 expression was not detected in the normal prostate tissue but was detected in PRAD.

Conclusions

We identified ISLR as a potential biomarker for PRAD bone metastasis. Moreover, the genes identified to have prognostic value may act as therapeutic targets for PRAD bone metastasis.

SUBMITTER: Hu K

PROVIDER: S-EPMC9243502 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundBreast cancer is the most common malignancy in female patients worldwide. Because of its heterogeneity in terms of prognosis and therapeutic response, biomarkers with the potential to predict survival or assist in making treatment decisions in breast cancer patients are essential for an individualised therapy. Epigenetic alterations in the genome of the cancer cells, such as changes in DNA methylation pattern, could be a novel marker with an important role in the initiation and progression of breast cancer.MethodDNA methylation and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analysed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Applying gene ontology (GO) and single sample gene set enrichment analysis (ssGSEA) an epigenetic signature associated with the survival of breast cancer patients was constructed that yields the best discrimination between tumour and normal breast tissue. A predictive nomogram was built for the optimal strategy to distinguish between high- and low-risk cases.ResultsThe combination of mRNA-expression and of DNA methylation datasets yielded a 13-gene epigenetic signature that identified subset of breast cancer patients with low overall survival. This high-risk group of tumor cases was marked by upregulation of known cancer-related pathways (e.g. mTOR signalling). Subgroup analysis indicated that this epigenetic signature could distinguish high and low-risk patients also in different molecular or histological tumour subtypes (by Her2-, EGFR- or ER expression or different tumour grades). Using Gene Expression Omnibus (GEO) the 13-gene signature was confirmed in four external breast cancer cohorts.ConclusionAn epigenetic signature was discovered that effectively stratifies breast cancer patients into low and high-risk groups. Since its efficiency appears independent of other known classifiers (such as staging, histology, metastasis status, receptor status), it has a high potential to further improve likely individualised therapy in breast cancer.

Project description:IntroductionBreast cancer is the most common malignant tumor in women worldwide. However, advanced multidisciplinary therapy cannot rescue the mortality of high-risk breast cancer metastasis. Ferroptosis is a newly discovered form of regulating cell death that related to cancer treatment, especially in eradicating aggressive malignancies that are resistant to traditional therapies. However, the prognostic value of ferroptosis-related gene in breast cancer remains unknown.Materials and methodsIn this study, a total of 1,057 breast cancer RNA expression data with clinical and follow-up information were downloaded from the TCGA cohort, multivariate Cox regression was used to construct the 11-gene prognostic ferroptosis-related gene signature. The breast cancer patients from the GEO cohort were used for validation. The expression levels of core prognostic genes were also verified in erastin-treated breast cancer cell lines by real-time polymerase chain action (PCR).Results and discussionOur results showed that 78% ferroptosis-related genes were differentially expressed between breast cancer tumor tissue and adjacent non-tumorous tissues, including 29 of them which were significantly correlated with OS in the univariate Cox regression analysis. Patients were divided into high-risk group and low-risk group by the 11-gene signature. Patients with high-risk scores had a higher probability of death earlier than the low-risk group both in the TCGA construction cohort and in the GEO validation cohort (all P < 0.001). Meanwhile, the risk score was proved to be an independent predictor for OS in both univariate and multivariate Cox regression analyses (HR > 1, P < 0.01). The predictive efficacy of the prognostic signature for OS was further verified by the time-dependent ROC curves. Moreover, we also enriched many immune-related biological processes in later functional analysis; the immune status showed a statistical difference between the two risk groups. In addition, the differences in expression levels of 11 core prognostic genes were examined in ferroptosis inducer-treated breast cancer cell lines.ConclusionIn conclusion, a novel ferroptosis-related gene model can be used for prognostic prediction in breast cancer. New ferroptosis-related genes may be used for breast cancer targeting therapy in the future.

Project description:IntroductionGlioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients.MethodsIn this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM.ResultsDepending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups.ConclusionThe 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.