Comprehensive Characterization of Cachexia-Inducing Factors in Diffuse Large B-Cell Lymphoma Reveals a Molecular Subtype and a Prognosis-Related Signature.

Zhixing Kuang,Shaoxing Chen,Rongqiang Liu,Jiannan Tu,Xun Li

doi:10.3389/fcell.2021.648856

Abstract

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.

Highlights

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous B-cell neoplasm morphologically characterized by large lymphoid cells with B-cell markers growing in a rapidly proliferating and diffuse pattern (Caimi et al, 2016)
A total of 1,475 patients with DLBCL and 53 with normal B cells from six independent academic institutions were included in the analysis after excluding samples that lacked clinical metadata; of these, 1,347 DLBCL samples from four datasets with survival time were used for prognosis-related research
Almost all Cachexia-inducing factors (CIFs) were dramatically over-expressed in DLBCL that comprised the dataset GSE56315, which was subsequently validated in another dataset, GSE12195

Summary

Introduction

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous B-cell neoplasm morphologically characterized by large lymphoid cells with B-cell markers growing in a rapidly proliferating and diffuse pattern (Caimi et al, 2016). The risk assessment of DLBCL has mainly concentrated on the international prognostic index (IPI) and cell of origin (COO); the application of COO classification in DLBCL has revealed two subtypes, namely, the germinal center B-cell-like (GCB) and activated B-cell-like (ABC) (Moffitt and Dave, 2017) subtypes Both IPI and COO are widely questioned regarding the risk stratification of a small number of DLBCL and do not accurately predict the outcome for cases (Wight et al, 2018) because the distinction based on COO does not fully account for the heterogeneous outcomes and chemotherapy response of DLBCL. The non-negative matrix factorization consensus clustering algorithm used by Chapuy et al (2018) and the GenClass algorithm were employed by Schmitz et al (2018) to analyze the genetic data of 304 and 574 cases of patients with DLBCL, respectively Their analyses showed the existence of distinct subtypes independent of or within the COO subtypes. 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

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Discussion

Conclusion