Abstract
Background International Prognostic Index (IPI) was widely used to better discriminate prognosis of patients with diffuse large B-cell lymphoma (DLBCL). However, there is a significant need to identify novel valuable biomarkers in the context of targeted therapies, such as immune checkpoint blockade (ICB) therapy.
Methods Gene expression data and clinical information of DLBCL were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. 371 immune-related hub genes in DLBCL patients with different IPI levels were identified by weighted gene co-expression network analysis (WGCNA), and 8 genes were selected to construct an IPI-based immune prognostic model (IPI-IPM). Afterward, the genetic, somatic mutational and molecular profiles of IPI-IPM subgroups were analyzed, as well as the potential clinical response of ICB in different IPI-IPM subgroups.
Results The IPI-IPM was constructed based on the expression of CMBL, TLCD3B, SYNDIG1, ESM1, EPHA3, HUNK, PTX3 and IL12A, where high-risk patients had shorter overall survival (OS) than low-risk patients, consistent with the results in the GEO cohorts. The comprehensive results showed that high IPI-IPM risk scores were correlated with immune-related signaling pathways, high KMT2D and CD79B mutation rates, as well as up-regulation of inhibitory immune checkpoints including PD-L1, BTLA and SIGLEC7, indicating more potential response to ICB therapy.
Conclusion The IPI-IPM has independent prognostic significance for DLBCL patients, which provides an immunological perspective to elucidate the mechanisms on tumor progression, also sheds a light on developing immunotherapy for DLBCL.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Shidai Mu: shidaimu{at}gmail.com
Deyao Shi: shideyao{at}hust.edu.cn
Lisha Ai: ailisha86{at}126.com
Fengjuan Fan: 4291125{at}qq.com
Fei Peng: pengfei0118{at}foxmail.com
Chunyan Sun: suncy0618{at}163.com
Yu Hu: dr_huyu{at}126.com
Abbreviations
- DLBCL
- Diffuse large B-cell lymphoma
- NHL
- non-Hodgkin B-cell lymphoma
- GCB Subtype
- germinal center B-cell-like subtype
- ABC Subtype
- activated B-cell-like subtype
- GEP
- gene expression profiling
- COO
- cell-of-origin
- SCNA
- somatic copy number alterations
- SV
- structural variants
- IPI
- International Prognostic Index
- TME
- tumor microenvironment
- ICB
- immune checkpoint blockade therapy
- GC form
- germinal center-like form
- MS form
- mesenchymal form
- IN form
- inflammatory form
- DP form
- depleted form
- WGCNA
- weighted gene co-expression network analysis
- IPI-IPM
- IPI-based immune prognostic model
- TCGA
- the Cancer Genome Atlas
- VST
- variance stabilizing transformation
- LME
- DLBCL/Lymphoma microenvironment
- DEGs
- differentially expressed genes
- GSEA
- gene set enrichment analysis
- GO
- Gene Ontology
- KEGG
- Kyoto Encyclopedia of Genes and Genomes
- NES
- normalized enrichment score
- OS
- overall survival
- Lasso
- least absolute shrinkage and selection operator
- AIC
- Akaike information criterion
- ROC
- receiver operating characteristic
- C-index
- concordance index
- DCA
- decision curve analysis
- t-SNE
- t-distributed stochastic neighbor embedding
- PCA
- principal component analysis
- GSVA
- gene set variation analysis
- ECM
- extracellular matrix
- TFs
- transcription factors
- Tregs
- regulatory T cells
- M0
- non-activated macrophages
- mDCs
- myeloid dendritic cells
- HSCs
- hematopoietic stem cells
- CAFs
- cancer associated fibroblasts
- FGES
- functional gene expression signatures
- MOA
- mechanisms of action
- FGF
- fibroblast growth factor
- Breg
- regulatory B cells
- IRAK
- IL-1 receptor-associated kinase
- ITAM
- immunoreceptor tyrosine-based activation motif
- PGE2
- prostaglandin E2
- PDTX
- patient-derived tumor xenografts
- DNMT
- DNA methyltransferase
- HAT
- Histone acetyltransferase