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Comprehensive characterization of driver genes in diffuse large B cell lymphoma.

ABSTRACT: Diffuse large B cell lymphoma (DLBCL) is the most common hematological malignancy and is one of the most frequent non-Hodgkin lymphomas. Large-scale genomic studies have defined genetic drivers of DLBCL and their association with functional and clinical outcomes. However, the lymphomagenesis of DLBCL is yet to be fully understood. In the present study, four computational tools OncodriveFM, OncodriveCLUST, integrated Cancer Genome Score and Driver Genes and Pathways were used to detect driver genes and driver pathways involved in DLBCL. The aforementioned tools were also used to perform an integrative investigation of driver genes, including co-expression network, protein-protein interaction, copy number variation and survival analyses. The present study identified 208 driver genes and 31 driver pathways in DLBCL. IGLL5, MLL2, BTG2, B2M, PIM1, CARD11 were the top five frequently mutated genes in DLBCL. NOTCH3, LAMC1, COL4A1, PDGFRB and KDR were the 5 hub genes in the blue module that were associated with patient age. TP53, MYC, EGFR, PTEN, IL6, STAT3, MAPK8, TNF and CDH1 were at the core of the protein-protein interaction network. PRDM1, CDKN2A, CDKN2B, TNFAIP3, RSPO3 were the top five frequently deleted driver genes in DLBCL, while ACTB, BTG2, PLET1, CARD11, DIXDC1 were the top five frequently amplified driver genes in DLBCL. High EIF3B, MLH1, PPP1CA and RECQL4 expression was associated with decreased overall survival rate of patients with DLBCL. High XPO1 and LYN expression were associated with increased overall survival rate of patients with DLBCL. The present study improves the understanding of the biological processes and pathways involved in lymphomagenesis. The driver genes, EIF3B, MLH1, PPP1CA, RECQL4, XPO1 and LYN, pave the way for developing prognostic biomarkers and new therapeutic strategies for DLBCL.

SUBMITTER: Fan Z

PROVIDER: S-EPMC7285964 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: The diffuse large B-cell lymphoma (DLBCL) is a heterogeneous lymphoma with a dismal outcome, due to approximately 40% patients will be relapsed or refractory to the standard therapy of rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP). Therefore, we need urgently to explore the approach to classify the risk of DLBCL patients accurately and accurately targeting therapy. The ribosome is a vital cellular organelle that is mainly responsible for translation mRNA into protein, moreover, more and more reports revealed that ribosome was associated with cellular proliferation and tumorigenesis. Therefore, our study aimed to construct a prognostic model of DLBCL patients using ribosome-related genes (RibGs). Method: We screened differentially expressed RibGs between healthy donors' B cells and DLBCL patients' malignant B cells in GSE56315 dataset. Next, we performed analyses of univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses to establish the prognostic model consisting of 15 RibGs in GSE10846 training set. Then, we validated the model by a range of analyses including Cox regression, Kaplan-Meier survival, ROC curve, and nomogram in training and validation cohorts. Results: The RibGs model showed a reliably predictive capability. We found the upregulated pathways in high-risk group most associated with innate immune reaction such as interferon response, complement and inflammatory responses. In addition, a nomogram including age, gender, IPI score and risk score was constructed to help explain the prognostic model. We also discovered the high-risk patients were more sensitive to some certain drugs. Finally, knocking out the NLE1 could inhibit the proliferation of DLBCL cell lines. Conclusion: As far as we know, it is the first time to predict the prognosis of DLBCL using the RibGs and give a new sight for DLBCL treatment. Importantly, the RibGs model could be acted as a supplementary to the IPI in classifying the risk of DLBCL patients.

Project description:BackgroundCachexia is defined as an involuntary decrease in body weight, which can increase the risk of death in cancer patients and reduce the quality of life. Cachexia-inducing factors (CIFs) have been reported in colorectal cancer and pancreatic adenocarcinoma, but their value in diffuse large B-cell lymphoma (DLBCL) requires further genetic research.MethodsWe used gene expression data from Gene Expression Omnibus to evaluate the expression landscape of 25 known CIFs in DLBCL patients and compared them with normal lymphoma tissues from two cohorts [GSE56315 (n = 88) and GSE12195 (n = 136)]. The mutational status of CIFs were also evaluated in The Cancer Genome Atlas database. Based on the expression profiles of 25 CIFs, a single exploratory dataset which was merged by the datasets of GSE10846 (n = 420) and GSE31312 (n = 498) were divided into two molecular subtypes by using the method of consensus clustering. Immune microenvironment between different subtypes were assessed via single-sample gene set enrichment analysis and the CIBERSORT algorithm. The treatment response of commonly used chemotherapeutic drugs was predicted and gene set variation analysis was utilized to reveal the divergence in activated pathways for distinct subtypes. A risk signature was derived by univariate Cox regression and LASSO regression in the merged dataset (n = 882), and two independent cohorts [GSE87371 (n = 221) and GSE32918 (n = 244)] were used for validation, respectively.ResultsClustering analysis with CIFs further divided the cases into two molecular subtypes (cluster A and cluster B) associated with distinct prognosis, immunological landscape, chemosensitivity, and biological process. A risk-prognostic signature based on CCL2, CSF2, IL15, IL17A, IL4, TGFA, and TNFSF10 for DLBCL was developed, and significant differences in overall survival analysis were found between the low- and high-risk groups in the training dataset and another two independent validation datasets. Multivariate regression showed that the risk signature was an independently prognostic factor in contrast to other clinical characteristics.ConclusionThis study demonstrated that CIFs further contribute to the observed heterogeneity of DLBCL, and molecular classification and a risk signature based on CIFs are both promising tools for prognostic stratification, which may provide important clues for precision medicine and tumor-targeted therapy.

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Comprehensive characterization of driver genes in diffuse large B cell lymphoma.

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