Dataset Information

Improving the overall survival prognosis prediction accuracy: A 9-gene signature in CRC patients.

ABSTRACT: Colorectal cancer (CRC) is a malignant tumor and morbidity rates are among the highest in the world. The variation in CRC patients' prognosis prompts an urgent need for new molecular biomarkers to improve the accuracy for predicting the CRC patients' prognosis or as a complement to the traditional TNM staging for clinical practice. CRC patients' gene expression data of HTSeq-FPKM and matching clinical information were downloaded from The Cancer Genome Atlas (TCGA) datasets. Patients were randomly divided into a training dataset and a test dataset. By univariate and multivariate Cox regression survival analyses and Lasso regression analysis, a prediction model which divided each patient into high-or low-risk group was constructed. The differences in survival time between the two groups were compared by the Kaplan-Meier method and the log-rank test. The weighted gene co-expression network analysis (WGCNA) was used to explore the relationship between all the survival-related genes. The survival outcomes of patients whose overall survival (OS) time were significantly lower in the high-risk group than that in the low-risk group both in the training and test datasets. Areas under the ROC curves which termed AUC values of our 9-gene signature achieved 0.823 in the training dataset and 0.806 in the test dataset. A nomogram was constructed for clinical practice when we combined the 9-gene signature with TNM stage and age to evaluate the survival time of patients with CRC, and the C-index increased from 0.739 to 0.794. In conclusion, we identified nine novel biomarkers that not only are independent prognostic indexes for CRC patients but also can serve as a good supplement to traditional clinicopathological factors to more accurately evaluate the survival of CRC patients.

SUBMITTER: Zheng W

PROVIDER: S-EPMC8419765 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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.

Project description:BackgroundBrain glioblastoma multiforme (GBM) is the most common primary malignant intracranial tumor. The prognosis of this disease is extremely poor. While the introduction of β-interferon (IFN-β) regimen in the treatment of gliomas has significantly improved the outcome of patients; The mechanism by which IFN-β induces increased TMZ sensitivity has not been described. Therefore, the main objective of the study was to elucidate the molecular mechanisms responsible for the beneficial effect of IFNβ in GBM.MethodsMessenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) GBM and GSE83300 dataset from the Gene Expression Omnibus. Univariate Cox regression analysis and lasso Cox regression model established a novel 4-gene IFN-β signature (peroxiredoxin 1, Sec61 subunit beta, X-ray repair cross-complementing 5, and Bcl-2-like protein 2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction experiments. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the long non-coding RNAs (lncRNAs) and IFN-β-associated genes. An lncRNA with a correlation coefficient |R2|>0.3 and P<0.05 was considered to be an IFN-β-associated lncRNA.ResultsPatients in the high-risk group had significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust 4-gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction for GBM patients.ConclusionsIn the present study, we established a novel IFN-β-associated gene signature to predict the overall survival of GBM patients, which may help in clinical decision making for individual treatment.

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.

Dataset Information

Improving the overall survival prognosis prediction accuracy: A 9-gene signature in CRC patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets