Comprehensive Characterization of Cachexia-Inducing Factors in Diffuse Large B-Cell Lymphoma Reveals a Molecular Subtype and a Prognosis-Related Signature
Authors: Rongqiang Liu
Jiannan Tu
Zhixing Kuang
Xun Li
Shaoxing Chen
Journal: Frontiers in Cell and Developmental Biology (2021)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: cachexia-inducing factors; diffuse large B-cell lymphoma; molecular subtype; prognosis-related; signature.
Affiliations: Department of Radiation Oncology, Nanping First Hospital Affiliated to Fujian Medical University, Nanping, China., Department of Oncology, Haikou Hospital Affiliated to Xiangya Medical College, Central South University, Haikou, China., Department of Radiation Oncology, Zhangzhou Hospital Affiliated to Fujian Medical University, Zhangzhou, China., Department of Oncology, Nanping First Hospital Affiliated to Fujian Medical University, Nanping, China.

Abstract:  Background: Cachexia 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. Methods: We 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. Results: Clustering 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. Conclusion: This 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. 

Retracted: No

PubPeer comments:
 The survival analysis in the paper is done bad. Here are some examples: 1) Fig 5A (and overally). In the GSE10846 1 2 of the patients that were previously deposited in GEO datasets GSE11318 and GSE53786 were treated with CHOP but not R-CHOP and should have been excluded or kept separately in any survival comparisons. (for example sample GSM275057) ![file] ![file] Moreover, OS difference in FIG 2C is super low for DLBCL (having ~800 samples). 2) In Fig 5B on the KM curve there censorship flag is mixed up (see comparison below how the curve looks in case of switching). ![file] ![file] 3) In Fig 5C authors utilize GSE32918 dataset. First of all there are a lot of replicates for some patients. Secondly not all the patients were treated with R-chop (some were treated with CHOP). There are should be only 130 unique samples from patients treated with R-CHOP with OS data available. ![file] Taking into account a lot of problems in the OS analysis, there is a big concern in the validity of the results ,