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A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma.

ABSTRACT: Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease that can have profound differences in survival outcomes. A variety of powerful prognostic factors and models have been constructed; however, the development of more accurate prognosis prediction and targeted treatment for DLBCL still faces challenges. An explosion of research on super-enhancer (SE)-associated genes provide the possibility to use in prognostication for cancer patients. Here, we aimed to establish a novel effective prognostic model using SE-associated genes from DLBCL. Methods: A total of 1,105 DLBCL patients from the Gene Expression Omnibus database were included in this study and were divided into a training set and a validation set. A total of 11 SE-associated genes (BCL2, SPAG16, PXK, BTG1, LRRC37A2, EXT1, TGFBR2, ANKRD12, MYCBP2, PAX5, and MYC) were initially screened and identified by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis. Finally, a risk score model based on these 11 genes was constructed. Results: Kaplan-Meier (K-M) curves showed that the low-risk group appeared to have better clinical survival outcomes. The excellent performance of the model was determined via time-dependent receiver operating characteristic (ROC) curves. A nomogram based on the polygenic risk score was further established to promote reliable prognostic prediction. This study proposed that the SE-associated-gene risk signature can effectively predict the response to chemotherapy in DLBCL patients. Conclusion: A novel and reliable SE-associated-gene signature that can effectively classify DLBCL patients into high-risk and low-risk groups in terms of overall survival was developed, which may assist clinicians in the treatment of DLBCL.

SUBMITTER: Xu H

PROVIDER: S-EPMC9237400 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThis study targeted at developing a robust, prognostic signature based on super-enhancer-related genes (SERGs) to reveal survival prognosis and immune microenvironment of breast cancer.MethodsRNA-sequencing data of breast cancer were retrieved from The Cancer Genome Atlas (TCGA), 1069 patients of which were randomly assigned into training or testing set in 1:1 ratio. SERGs were downloaded from Super-Enhancer Database (SEdb). After which, a SERGs signature was established based on the training set, with its prognostic value further validated in the testing set. Subsequently, we identified the potential function enrichment and tumor immune infiltration of the model. Moreover, in vitro experiments were completed to further explore the biological functions of ZIC2 gene (one of the risk genes in the prognostic model) in breast cancer.ResultsA risk score system of prognostic value was constructed with 6 SERGs (ZIC2, NFE2, FOXJ1, KLF15, POU3F2 and SPIB) to find patients in high-risk group with significantly worse prognosis in both training and testing sets. In addition, a multivariate regression was established via integrating the 6 genes with age and N stage, indicating well performance by calibration, time-dependent receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Further analysis demonstrated that tumor-associated pathological processes and pathways were significantly enriched in the high-risk group. In general, the novel SERGs signature could be applied to screen breast cancer with immunosuppressive microenvironment for the risk score was negatively correlated with ESTIMATE score, tumor-infiltration lymphocytes (such as CD4 + and CD8 + T cell), immune checkpoints and chemotactic factors. Furthermore, down-regulation of ZIC2 gene expression inhibited the cell viability, cellular migration and cell cycle of breast cancer cells.ConclusionsThe novel SERGs signature could predict the prognosis of breast cancer; and SERGs might serve as potential therapeutic targets for breast cancer.

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A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma.

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