Abstract
Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are
diagnosed at an advanced stage. Although there are a few therapeutic
strategies for DLBCL, most of them are more effective in limited-stage
cancer patients. The prognosis of patients with advanced-stage DLBCL is
usually poor with frequent recurrence and metastasis. In this study, we
aimed to identify gene expression and network differences between
limited- and advanced-stage DLBCL patients, with the goal of
identifying potential agents that could be used to relieve the severity
of DLBCL. Specifically, RNA sequencing data of DLBCL patients at
different clinical stages were collected from the cancer genome atlas
(TCGA). Differentially expressed genes were identified using DESeq2,
and then, weighted gene correlation network analysis (WGCNA) and
differential module analysis were performed to find variations between
different stages. In addition, important genes were extracted by key
driver analysis, and potential agents for DLBCL were identified
according to gene-expression perturbations and the Crowd Extracted
Expression of Differential Signatures (CREEDS) drug signature database.
As a result, 20 up-regulated and 73 down-regulated genes were
identified and 79 gene co-expression modules were found using WGCNA,
among which, the thistle1 module was highly related to the clinical
stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the
thistle1 module indicated that DLBCL progression was mainly related to
the NOD-like receptor signaling pathway, neutrophil activation,
secretory granule membrane, and carboxylic acid binding. A total of 47
key drivers were identified through key driver analysis with 11
up-regulated key driver genes and 36 down-regulated key diver genes in
advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21
and MOCOS) appeared as hub genes, being closely related to the
occurrence and development of DLBCL. Finally, both differentially
expressed genes and key driver genes were subjected to CREEDS analysis,
and 10 potential agents were predicted to have the potential for
application in advanced-stage DLBCL patients. In conclusion, we propose
a novel pipeline to utilize perturbed gene-expression signatures during
DLBCL progression for identifying agents, and we successfully utilized
this approach to generate a list of promising compounds.
Keywords: diffuse large B-cell lymphoma, drug repurposing,
differentially expressed genes, differential module analysis, key
driver analysis
Introduction
Diffuse large B-cell lymphoma (DLBCL) is the most commonly diagnosed
non-Hodgkin lymphoma (NHL), representing approximately 25% of new NHL
cases each year in the United States ([46]Liu and Barta, 2019). In
practice, about one half of DLBCL patients presented with
advanced-stage disease ([47]Prakash et al., 2012), featuring bulky
tumor burden and poor patient response to treatment. According to
published data, advanced-stage DLBCL (stage I/II and stage III/IV) may
be both biologically and clinically different from limited-stage DLBCL
cases (stage I and II). For example, advanced-stage DLBCL patients were
more likely to express higher levels of CD30 ([48]Rodrigues-Fernandes
et al., 2021) and CD25 ([49]Oka et al., 2020), both of which are
biomarkers of B-cell activation. In addition, advanced-stage DLBCL was
also shown to be associated with a higher immune-inflammation index
([50]Wang et al., 2021) and an increased level of lymphopenia at
diagnosis ([51]Shin et al., 2020), highlighting its deteriorating
immune regulation. Green and Johnson et al. reported there were a few
biological factors known to adversely impact the prognosis of DLBCL
patients, including the cell-of-origin, co-expression of MYC/BCL2 and
co-occurrence of MYC and BCL2/BCL6 rearrangements failed to predict
poorer prognosis in limited stage DLBCL([52]Green et al., 2012;
[53]Johnson et al., 2012). Ajay, Major et al reported that stage I and
II DLBCL cases had a slightly increased risk of secondary primary
malignancies after DLBCL treatment in long-term follow-up (>20 years)
([54]Major et al., 2020). Comparing with limited stage DLBCL,
advanced-stage DLBCL patients were more likely to benefit from
intensified radiotherapy ([55]Hoiland et al., 2020; [56]Freeman et al.,
2021). Also, the pattern of late disease relapses observed in advanced
stage DLBCL cases was different from that of limited-stage cases,
further corroborating that limited and advanced stage DLBCL were
biologically heterogeneous ([57]Hoiland et al., 2020). All of these
observations prompted us to treat advanced- and limited-stage DLBCL
with different strategies, better tailoring for their specific
biological and clinical characteristics.
However, there is limited knowledge regarding the genomic and
transcriptomic differences between limited- and advanced-stage DLBCL.
Two previous large analyses exploring the genetic landscape of DLBCL
were not intended to compare the limited and advanced stages of the
disease ([58]Reddy et al., 2017; [59]Schmitz et al., 2018). Moreover,
at the single gene or single locus level, advanced- and limited-stage
DLBCL may also be different in terms of their altered gene regulation
and regulatory/co-expression networks, which was confirmed in other
clinical comparisons such as cancer vs normal tissue ([60]Zhang et al.,
2018; [61]Xu et al., 2019) and young vs old ([62]Yang et al., 2015;
[63]Yang et al., 2016b).
Although frontline chemoimmunotherapies have been shown to cure up to
60% of patients with advanced-stage disease, with a clear plateau in
progression-free survival (PFS) and rare relapses beyond 5 years
([64]Coiffier et al., 2010), there still is a fraction of patients who
are subject to relapse and have tumors that are refractory to treatment
([65]Coiffier et al., 2010), highlighting the heterogeneity of advanced
DLBCL. Thus, it is critical to develop new drugs for improving the
treatment of advanced-stage DLBCL, so that it might be effectively
treated by using existing treatment strategies as limited-stage DLBCL
patients are. However, the development of a novel drug is usually
costly and time-consuming ([66]Liu et al., 2020; [67]Yang et al., 2020)
and highlights the need for effective drug repositioning strategies.
There are many computer-based drug repositioning methods that have been
used for cancers ([68]Xu et al., 2019; [69]Liu et al., 2020) and other
diseases, such as Coronavirus disease 2019 (COVID-19) ([70]Tang et al.,
2020; [71]Li et al., 2021).
In this study, we propose a new strategy for identifying new agents
that have the potential to specifically target advanced-stage DLBCL. In
general, we retrieved advanced-stage DLBCL-specific expressed genes by
comparing the transcriptome of advanced-stage disease with that of
limited-stage DLBCL. These differentially expressed genes (DEGs) were
then subjected to weighted gene correlation network analysis (WGCNA) to
discover the co-expression modules that may contribute to the
progression of this disease. Finally, potential personal agents were
obtained from the Crowd Extracted Expression of Differential Signatures
(CREEDS) based on the down-regulation and up-regulation of genes (see
Materials and methods for details). We aimed to specifically reveal the
transcriptomic scenario occurring in advanced-stage DLBCL and to
elucidate the genes that were most likely contributing to disease
progression. Based on this knowledge, we then identified some potential
agents for the treatment of advanced-stage DLBCL in future clinical
practice.
Materials and Methods
Data Collection
RNA sequencing data from patients with DLBCL cancer were collected from
the cancer genome atlas (TCGA). Based on the imaging results, including
computed tomography (CT) scans, magnetic resonance imaging (MRI) or
positron emission tomography (PET) scanning, patients were divided into
four stages (I–IV) according to the Ann Arbor system ([72]Heidelberg,
2020).
Differential Gene Expression Analysis Between Samples at Different Stages
An expression matrix of 42 patients and their group information (stage
I/II or III/IV) were used as the input for DEG discovery. DEGs between
samples at stage I/II and stage III/IV were obtained using DESeq2
([73]Love et al., 2014) using log[2] |fold change| ≧ 1 and a p value ≦
0.05.
Survival Analysis
After identifying DEGs, we performed survival analysis on these genes
for all of the patients. Next, Kaplan-Meier ([74]Bland and Altman,
1998) survival estimation was used for all differentially expressed
genes to identify genes correlated with overall survival. Kaplan- Meier
arranged the survival time in descending order, at each death node, it
estimated the proportion of the observed values that survived for a
certain period of time under the same circumstances, which could
intuitively show the survival and mortality rates of two or more
groups. The R packages survival and survminer were used for survival
analysis and curve plotting, respectively.
Weighted Gene Correlation Network Analysis
The WGCNA package in R ([75]Peter and Horvath, 2008) was used to
construct a co-expression network. For this step, we randomly picked
400 genes from the stage III/IV patients to generate a topological
overlap matrix since the gene number was too large to perform this
analysis using all of the genes. For the constructed gene network to
conform to a scale-free distribution, a soft threshold was used to
select the appropriate
[MATH: β :MATH]
after removing outliers. Finally, the soft threshold was set to 10.
Then, genes were clustered by hierarchical clustering, and the tree was
cut into different modules using a dynamic cutting algorithm, in which
genes were highly correlated. Furthermore, we calculated the Pearson
correlation coefficient between different modules and clinical stage
and used this Pearson correlation coefficient to judge the relationship
between the module and clinical stage. Finally, significant modules
closely related to the occurrence and development of DLBCL were
identified for follow-up analysis.
Functional and Pathway Enrichment Analyses
KEGG pathway ([76]Ogata et al., 1999) analysis and Gene Ontology (GO)
analysis ([77]Botstein et al., 2000), including biological process
(BP), cellular composition (CC) and molecular function (MF), were
performed on the genes in the module identified by WGCNA to understand
the biological significance of the progression of DLBCL. The R package
clusterProfiler ([78]Yu et al., 2012) was used in the process of
enrichment analysis to analyze the functions of the genes from these
modules.
Key Driver Analysis
For key driver analysis, we used up- or down-regulated genes separately
as inputs to identify key drivers. Key driver analysis ([79]Yang et
al., 2016a) (KDA) was used to identify hub genes, and protein actions
v11.0 was used as a reference protein–protein interaction network
([80]Szklarczyk et al., 2021). Parameters were set as follows:
nlayerExpansion was set to 1, nlayerSearch was set to 6 and
enrichedNodesPercentCut was set to −1. A p value_whole ≦ 0.05 was used
to filter out key drivers. The hub genes were of great significance in
terms of the occurrence and development of DLBCL.
Drug Discovery
CREEDS includes single gene perturbation signatures, as well as disease
and drug perturbation signatures, and it can be used to identify the
relationship between gene, disease and drug ([81]Gillies et al., 2016).
CREEDS is composed of single-drug perturbation-induced gene expression
signatures. Utilizing this database, agents that can reverse the
behavior of up/down-regulated genes can be discovered, and the best
matched agents are reported. We used this tool for drug discovery for
advanced-stage DLBCL. In this work, we combined differentially
expressed genes and key driver genes as a new gene set to discover new
agents related to advanced-stage DLBCL.
Results
A Brief Study Design of Drug Repurposing
For the purpose of specifically developing new agents that could be
utilized in combination with R- CHOP backbones to treat advanced stage
DLBCL patients, we proposed a new method of drug repurposing based on
gene expression and network perturbation ([82]Figure 1). In order to
identify key factors for DLBCL progression, WGCNA and DEG, differential
module (DM) and key driver (KD) analyses were performed. Then, the key
factors of DLBCL progression and drug perturbation signature were used
to predict potential agents for the treatment of advanced stage DLBCL.
Finally, some previous studies were reviewed to demonstrate the
effectiveness of the newly identified agents.
FIGURE 1.
FIGURE 1
[83]Open in a new tab
A brief study design for drug repurposing, including these major steps:
1) Download and organize the RNA-seq data and clinical information of
DLBCL from TCGA; 2) Got key factors of DLBCL progression through DEG
analysis, key driver analysis and WGCNA analysis; 3) Potential drug
prediction through CREEDs; 4) Literature confirmation.
Patient Characteristics
The clinical characteristics of DLBCL cancer patients collected from
TCGA are presented in [84]Table 1, including 25 patients at clinical
stage I/II and 17 patients at clinical stage III/IV. It was more likely
to occur in elder patients and involve extranodal sites or organs.
Patients of advanced stage disease also tended to have B symptoms. No
gender preference was observed in this group of patients and all
patients received no treatment before resection of tumors.
TABLE 1.
Summary of general clinical information of DLBCL cases in TCGA.
Limited stage Advanced stage
[MATH: χ2 :MATH]
P
Gender Male 9 10 0.006 0.938
Female 16 7
Age ≥60 6 10 5.203 0.023
<60 19 7
Extranodal disease Yes 8 11 4.369 0.037
No 17 6
B symptoms Yes 1 9 13.36 0.000
No 24 8
[85]Open in a new tab
Identification of DEGs and Survival Analysis
After collecting data from TCGA, DEGs were obtained using DESeq2, by
comparing the transcriptome of advanced stage DLBCL with limited stage
DLBCL. Of the 93 DEGs that were identified with a log[2] |fold change|
≧ 1 and a p value ≦ 0.05, 20 genes were up-regulated and 73 genes were
down-regulated in advanced DLBCL. The top 10 genes that were
differently expressed between advanced and limited stage DLBCL are
shown in [86]Figure 2A.
FIGURE 2.
[87]FIGURE 2
[88]Open in a new tab
Analysis of differentially expressed genes. (A) Heat map of the top 10
differentially expressed genes. The x-axis represents different samples
from TCGA, blue indicates samples at limited stage (stage I/II) and red
indicates samples at advanced stage (stage III/IV). The y-axis
represents differentially expressed genes. (B) Survival curve of the
association between the expression levels of DAB1 and survival time
after diagnosis with DLBCL.
We aimed to evaluate whether this set of differentially expressed genes
could define a group of patients with poorer prognosis. We dichotomized
42 DLBCL cases into either the high expression group or the low
expression group as per the mean expression level of each DEG. In
addition, the Kaplan-Meier survival estimation method was used to
evaluate all DEGs to study the relationship between gene expression and
overall survival. Through this Kaplan-Meier survival estimation
analysis, we found that DAB1 was negatively correlated with overall
survival, while other DEGs were not correlated with overall survival.
Weighted Gene Correlation Network Analysis and Differential Model Analysis
WGCNA, based on a scale-free network to analyze genes according to
their expression patterns, was used to cluster highly related genes
into one module. As can be seen from [89]Figure 3A, the soft threshold
value was set at 10 to build this scale-free network. Next, 79 gene
modules were identified by hierarchical clustering and dynamic branch
cutting, and each module was assigned a unique color identifier
([90]Supplementary Figure S4). We then selected a portion of these
genes to construct a topological overlapping heat map, shown in
[91]Figure 3B. Through differential module analysis, we found that the
thistle1 module was most relevant to advance stage of DLBCL in this
dataset ([92]Figure 3C).
FIGURE 3.
[93]FIGURE 3
[94]Open in a new tab
Weighted co-expression and key module identification associated with
clinical DLBCL stage. (A) Determination of soft threshold in WGCNA. (B)
Topological overlapping WGCNA heat map. (C) The relationship between
modules and clinical traits. Pearson correlation coefficient was used
to calculate the correlation degree between each module and trait.
Functional and Pathway Enrichment Analysis of the thistle1 Module
In order to understand the causes of DLBCL deterioration from the
biological level, we analysed the genes in the thistle1 module using
KEGG pathway and GO enrichment analysis. KEGG pathway analysis results
indicated that the development of DLBCL was very strongly correlated to
the NOD-like receptor signalling pathway, osteoclast differentiation,
leishmaniasis, Staphylococcus aureus infection and viral protein
interaction with cytokine and cytokine receptor ([95]Figure 4A).
Furthermore, GO enrichment was performed based on three aspects: BP, CC
and MF. In the BP analysis, we found that the genes in the thistle1
module were mainly related to neutrophil activation, positive
regulation of response to external stimulus and response to
interferon−gamma ([96]Figure 4B). In addition, in the CC analysis, the
genes in the thistle1 module were related to secretory granule
membrane, endocytic vesicle and apical part of cell ([97]Figure 4C).
Moreover, the genes in the thistle1 module were mainly enriched in
7 MFs, including carboxylic acid binding, organic acid binding,
cysteine−type endopeptidase activity, manganese ion binding,
ligand−gated cation channel activity, immunoglobulin G (IgG) binding
and immunoglobulin binding ([98]Figure 4D).
FIGURE 4.
[99]FIGURE 4
[100]Open in a new tab
Pathway and functional enrichment analysis of genes in the thistle1
module. (A) KEGG pathway analysis. (B) GO enrichment for biological
process. (C) GO enrichment for cellular composition. (D) GO enrichment
for molecular function. The x-axes are the ratio of genes, and the
y-axes are the GO terms.
Hub Genes Identified Through Key Driver Analysis
A total of 47 key drivers were identified through key driver analysis,
with 11 up-regulated key driver genes and 36 down-regulated key diver
genes being diagnostic of advanced-stage DLBCL relative to
limited-stage DLBCL. Then, five hub genes were identified from key
drivers as shown in [101]Figure 5, which were most related to the
occurrence and development of DLBCL. MMP1 ([102]Rosas et al., 2008),
also known as matrix metalloproteinase-1, encodes a protein of 469
amino acid residues and is a kind of photolytic enzyme closely related
to tumor genesis, invasion and metastasis. Rab6c ([103]Young et al.,
2010) is a member of the RAS family. Its mutation can affect the
balance of Ras-GTP and cause malignant transformation of cells. Gene
ontology annotations for 1-Aminocyclopropane-1-Carboxylate Synthase
Homolog (Inactive) Like (ACCSL) ([104]Chen and Karampinos, 2020)
include pyridoxal phosphate binding. Dysregulation of gene levels of
molybdenum cofactor sulfurase (MOCOS) ([105]Kurzawski et al., 2012) can
lead to cell disorders. Studies have demonstrated that this gene can be
used as a key detection gene for kidney genetic diseases. RGS21
([106]Von Buchholtz et al., 2004), a new member of the regulator of G
protein signaling (RGS) protein family. It can inhibit signal
transduction by increasing GTPase activity.
FIGURE 5.
[107]FIGURE 5
[108]Open in a new tab
Network of key DLBCL drivers and hub genes. Red, key drivers from
up-regulated gene set in advanced-stage samples. Blue, key drivers from
down-regulated gene set in advanced-stage samples. Yellow, hub genes.
Agent Screening
Potential personal agents associated with DLBCL were identified
according to the differences between differential genes and drug
signaling. Approximately 10 potential agents were selected according to
their drug perturbation-induced gene expression signatures, and
detailed information on these agents is presented in [109]Table 2,
including the type, drug/small molecule, possible effect and evidence
for activity. The top five agents could reverse the expression of
down-regulated genes, and the remaining agents could reverse the
expression of up-regulated genes. In other words, after treatment with
these drugs, gene expression levels may return to normal. The top five
agents that may reverse down-regulated gene expression are
formaldehyde, ethanol, dibutyl phthalate, paclitaxel, and prednisolone.
Ethanol (EtOH) is similar to pharmacological mTOR inhibitors and has
been shown to inhibit the mTOR signaling pathway. Mazan et al. studied
the influence of EtOH on the mTOR signaling pathway and explored the
translational group analysis of downstream effects of EtOH in DLBCL,
and the results showed that EtOH partially inhibited mTOR signaling and
protein translation ([110]Mazan-Mamczarz et al., 2015). In a previous
study, newly diagnosed DLBCL patients treated with rituximab,
cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP)
were evaluated for their clinical characteristics, therapeutic efficacy
and patient survival, and DLBCL patients treated with R-CHOP had better
survival than other patients ([111]Hong et al., 2011). Ohe et al. also
reported a case of DLBCL successfully treated with prednisolone
([112]Ohe et al., 2012). The top five agents that may reverse
up-regulated gene expression are oxaliplatin, eribulin, NC1153,
EPZ-6438 and R547. Oxaliplatin selectively inhibits the synthesis of
deoxyribonucleic acid (DNA). Shen et al. studied the efficacy, safety
and feasibility of the combination of rituximab, gemcitabine, and
oxaliplatin (R-GemOx) as a first-line treatment in elderly patients
with DLBCL. They found that R-GemOx might be a therapeutic option for
the management of DLBCL ([113]Shen et al., 2018).
TABLE 2.
Potential DLBCL treatment agents.
Gene type Drug/Small molecule Possible effect Evidence
Down Formaldehyde A metabolite of vitamin A that plays important roles
in cell growth, differentiation and organogenesis acts as an inhibitor
of the transcription factor Nrf2 through the activation of retinoic
acid receptor alpha DOI:10.14423/SMJ.0000000000000545
Down Ethanol Similar to pharmacological mTOR inhibitors, which can
inhibit the mTOR signaling pathway DOI: 10.1186/s12964-015-0091-0
Down Dibutyl phthalate Is expected to cause severe side effects to the
central nervous system of animals and humans DOI:10.1016/S0145-2126
(96)00033-1
Down Paclitaxel A synthetic macrocyclic ketone analog of the marine
sponge natural product halichondrin B, which leads to the inhibition of
microtubule growth in the absence of effects on microtubule shortening
at microtubule plus ends Unknown
Down Prednisolone Belongs to the adrenal corticotropic hormone and
adrenal corticotropic hormone class and has strong anti-inflammatory
effects DOI:10.3109/10428194.2011.588761
DOI:10.5045/kjh. 2012.47.4.293
Up Oxaliplatin It selectively inhibits the synthesis of
deoxyribonucleic acid (DNA). The guanine and cytosine contents
correlate with the degree of oxaliplatin-induced cross-linking DOI:
10.1016/S2352-3026 (18)30054-1
Up Eribulin Is a microtubule inhibitor indicated for the treatment of
patients with metastatic breast cancer who have previously received at
least two chemotherapeutic regimens for the treatment of metastatic
disease. Also being investigated for use in the treatment of advanced
solid tumors DOI: 10.1007/s00280-012-1976-x. Epub 2012 Sep 26
Up NC1153 Specifically inhibits JAK3 via NC1153-induced apoptosis of
certain leukemia/lymphoma cell lines DOI: 10.1016/j.febslet.
2010.02.071
Up EPZ-6438 Selectively inhibits intracellular histone H3 lysine 27
(H3K27) methylation in a concentration- and time-dependent manner in
both EZH2 wild-type and mutant lymphoma cells DOI:
10.1158/1535-7163.MCT-13-0773
Up R547 A potent CDK inhibitor with a potent anti-proliferative effect
at pharmacologically relevant doses DOI: 10.1158/1535-7163.MCT-09-0083
[114]Open in a new tab
Discussion
DLBCL remains a highly heterogenous disease, with the frontline R-CHOP
modality achieving only a 40–60% complete response (CR) rate in
unselected patients. The prognosis of patients with DLBCL with
refractory tumors or relapse remains dismal. As a result, designing
more sophisticated personal treatment modalities has the potential to
improve the outcomes in high-risk DLBCL patients. Although a wealth of
studies has focused on targeted therapies based on the molecular
classification of DLBCL, the clinical stage of DLBCL remains an
important factor for choosing an appropriate treatment regime. DLBCL
patients with advanced- and limited-stage disease have different
responses to standard chemoimmunotherapies, due to the different
genomic profiles of advanced-stage disease relative to limited-stage
disease ([115]Miao et al., 2019). In this study, we propose a new
approach to gain insights into the intrinsic heterogeneity of DLBCL,
which focused on comparing the transcriptomic profile of advanced- and
limited-stage DLBCL and distilling the disease to a few distinctly
expressed genes and hub genes that might contribute to disease
progression. In general, 20 genes were up-regulated and 73 genes were
down-regulated in advanced-stage samples compared to limited-stage
samples. We also found that DAB1 was negatively correlated with overall
survival through survival analysis of all identified DEGs ([116]Figure
2B, p = 0.045). Due to the limitations of differential expression
analysis, it is impossible to group genes with the same function
together. Therefore, we carried out weighted gene co-expression network
analysis and analysis on different modules. During these analyses, 79
similar gene expression modules were found using WGCNA, among which,
the thistle1 module was highly related to disease stage. KEGG pathway
and GO enrichment analyses of the genes in the thistle1 module
indicated that DLBCL progression was mainly related to the NOD-like
receptor signaling pathway, neutrophil activation, secretory granule
membrane and carboxylic acid binding. There is evidence that tumors and
their mesenchymal cells produce many cytokines and chemokines to
stimulate the differentiation of N2 neutrophils ([117]Valerius et al.,
1993; [118]Souto et al., 2014). However, neutrophils can cause DNA
damage through reactive oxygen species and related products of
myeloperoxidase (MPO), and N2 cells secrete VEGF, TNF and other
cytokines to promote tumor angiogenesis and, at the same time,
synthesize and secrete MMP and NE to the tumor stroma to participate in
the tumor reconstruction of the extracellular matrix to promote tumor
growth and metastasis ([119]Zvi et al., 2009; [120]Mishalian et al.,
2013; [121]Zhou et al., 2016). During key driver analysis, 47 key
drivers were identified and five hub genes were extracted from these
key drivers, including MMP1. MMP1 ([122]Rosas et al., 2008) can alter
the microenvironment of cells. When MMP1 is out of balance, it
accelerates the degradation of the matrix barrier and promotes the
formation and growth of tumors by releasing matrix-related growth
factors. Studies have shown that MMP1 is associated with lung squamous
cell carcinoma, colon cancer and adenocarcinoma.
Based on gene expression and network perturbations, 10 potential agents
for the treatment of DLBCL were obtained. For instance, NC1153 can
inhibit JAK3 specifically and induce the apoptosis of certain
leukemia/lymphoma cell lines. Using Affymetrix microarray profiling
following NC1153 treatment, Nagy et al. reported that JAK3-dependent
survival modulating pathways (p53, TGF-beta, TNFR and ER stress) were
altered in Kit225 cells ([123]Nagy et al., 2010). EPZ-6438 selectively
inhibited intracellular H3K27 methylation in a concentration- and
time-dependent manner in both EZH2 wild-type and mutant lymphoma cells.
Inhibition of H3K27 trimethylation (H3K27Me3) leads to selective cell
killing of human lymphoma cell lines bearing EZH2 catalytic domain
point mutations ([124]Knutson et al., 2014).
In summary, we proposed a novel pipeline to utilize perturbed
gene-expression signatures during DLBCL progression for identifying
agents, and we successfully utilized this approach to generate a list
of promising compounds. Whether this can be used clinically needs
further research. We will continue to follow the latest developments of
these agents in the treatment of DLBCL and explore its
pharmaco-mechanisms under the aid of stage-of-art technologies in the
future.
Data Availability Statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found in the article/[125]Supplementary Material.
Author Contributions
HL and CX designed the project, CX, HN, and ZW wrote the manuscript, BJ
and XS collected data, BW carried out data analysis, NL and WW analyzed
experimental results. YG and DM researched literatures. All authors
read and gave their approval for the final version of the manuscript.
Conflict of Interest
Authors BJ, BW and XS were employed by Geneis Beijing Co. Ltd.
The remaining authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as potential conflicts of interest.
The handling Editor declared a past co-authorship/collaboration with
one of the authors BW.
Publisher’s Note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at:
[126]https://www.frontiersin.org/articles/10.3389/fgene.2021.756784/ful
l#supplementary-material
[127]Click here for additional data file.^ (106.9KB, pdf)
References