Cancer Medicine (Mar 2020)

OSdlbcl: An online consensus survival analysis web server based on gene expression profiles of diffuse large B‐cell lymphoma

  • Huan Dong,
  • Qiang Wang,
  • Guosen Zhang,
  • Ning Li,
  • Mengsi Yang,
  • Yang An,
  • Longxiang Xie,
  • Huimin Li,
  • Lu Zhang,
  • Wan Zhu,
  • Shuchun Zhao,
  • Haiyu Zhang,
  • Xiangqian Guo

DOI
https://doi.org/10.1002/cam4.2829
Journal volume & issue
Vol. 9, no. 5
pp. 1790 – 1797

Abstract

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Abstract Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of non‐Hodgkin lymphoma (NHL) and is a clinical, pathological, and molecular heterogeneous disease with highly variable clinical outcomes. Currently, valid prognostic biomarkers in DLBCL are still lacking. To optimize targeted therapy and improve the prognosis of DLBCL, the performance of proposed biomarkers needs to be evaluated in multiple cohorts, and new biomarkers need to be investigated in large datasets. Here, we developed a consensus Online Survival analysis web server for Diffuse Large B‐Cell Lymphoma, abbreviated OSdlbcl, to assess the prognostic value of individual gene. To build OSdlbcl, we collected 1100 samples with gene expression profiles and clinical follow‐up information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. In addition, DNA mutation data were also collected from the TCGA database. Overall survival (OS), progression‐free survival (PFS), disease‐specific survival (DSS), disease‐free interval (DFI), and progression‐free interval (PFI) are important endpoints to reflect the survival rate in OSdlbcl. Moreover, clinical features were integrated into OSdlbcl to allow data stratifications according to the user's special needs. By inputting an official gene symbol and selecting desired criteria, the survival analysis results can be graphically presented by the Kaplan‐Meier (KM) plot with hazard ratio (HR) and log‐rank p value. As a proof‐of‐concept demonstration, the prognostic value of 23 previously reported survival associated biomarkers, such as transcription factors FOXP1 and BCL2, was evaluated in OSdlbcl and found to be significantly associated with survival as reported (HR = 1.73, P < .01; HR = 1.47, P = .03, respectively). In conclusion, OSdlbcl is a new web server that integrates public gene expression, gene mutation data, and clinical follow‐up information to provide prognosis evaluations for biomarker development for DLBCL. The OSdlbcl web server is available at https://bioinfo.henu.edu.cn/DLBCL/DLBCLList.jsp.

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