BioMed Research International

BioMed Research International / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 7947208 | https://doi.org/10.1155/2020/7947208

Jianjun Xiao, Xuemei Wang, Haitao Bai, "Clinical Features and Prognostic Impact of Coexpression Modules Constructed by WGCNA for Diffuse Large B-Cell Lymphoma", BioMed Research International, vol. 2020, Article ID 7947208, 14 pages, 2020. https://doi.org/10.1155/2020/7947208

Clinical Features and Prognostic Impact of Coexpression Modules Constructed by WGCNA for Diffuse Large B-Cell Lymphoma

Academic Editor: Gerald J. Wyckoff
Received01 May 2019
Revised06 Feb 2020
Accepted04 May 2020
Published08 Jun 2020

Abstract

Objective. Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive malignant tumor, accounting for 30-40% of non-Hodgkin’s lymphoma. Our aim was to construct novel prognostic models of candidate genes based on clinical features. Methods. RNA-seq and clinical data of DLBCL were retrieved from TCGA database. Coexpression modules were constructed by WGCNA. Then, we investigated the interactions between modules and clinical features. By overall survival analysis, prognostic candidate genes from modules of interest were identified. A coexpression network of prognostic candidate genes was then constructed through WGCNA. GEPIA was used to analyze the expression of a candidate gene between DLBCL and normal samples. Results. 19 coexpression modules were constructed by 12813 genes from 52 DLBCL samples. The number of genes in modules ranged from 34 to 5457. We found that the purple module was significantly related with histological type ( value = 1-04). Overall survival analysis revealed that MAFA-AS1, hsa-mir-338, and hsa-mir-891a were related with prognosis of DLBCL ( value = 0.027, 0.039, and 0.022, respectively). A coexpression network was constructed for the three prognostic genes. MAFA-AS1 was interacted with 36 genes, hsa-mir-891a was interacted with 11 genes, while no gene showed interaction with hsa-mir-338. Using GEPIA, we found that MAFA-AS1 showed low expression in DLBCL samples (). Conclusion. We constructed a coexpression module related with histological type and identified three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) that possessed potential value as prognostic biomarkers and therapeutic targets of DLBCL.

1. Introduction

DLBCL is a highly aggressive malignant tumor originating from mature B-cells, accounting for 30-40% of non-Hodgkin’s lymphoma [1, 2]. Patients with DLBCL usually have a poor prognosis due to ineffective primary and second-line therapy or recurrence after stem cell transplantation [3]. Therefore, easily applicable prognostic parameters are necessary for clinicians, especially since new molecular markers have not yet entered clinical routines [4]. The International Prognostic Index (IPI) is the most common tool for risk stratification in DLBCL. However, due to improved treatment options, pathobiology, and life expectancy of patients with DLBCL, IPI has been challenged [5]. Therefore, it is necessary to propose novel prognostic models based on clinical features and prognostic biomarkers.

As a molecular heterogeneous disease composed of different histopathologic and genetic subtypes, genetics of DLBCL has clinical implications for patient risk prediction and treatment [6]. Molecular traits are increasingly being used to guide DLBCL drug development, predict patients’ clinical outcomes, and make treatment plans [7]. Therefore, the collection and evaluation of molecular features in clinical samples are critical to improve the prognosis of patients with DLBCL. Genomic studies have revealed a large number of mutant genes in DLBCL; their clinical significance remains unclear.

As a powerful method for transcriptomics analysis, RNA sequencing (RNA-seq) has been widely used to explore gene function and biological patterns, as well as to find candidate drug targets and to identify biomarkers for predicting disease risk and prognosis [8, 9]. The Cancer Genome Atlas (TCGA) has produced RNA-seq data, which provides an unprecedented opportunity for cancer biology. Weighted gene coexpression network analysis (WGCNA) is a systematic biological method, which is widely used to generate gene coexpression networks [10]. Instead of linking thousands of genes to physiological characteristics, it focuses more on the relationship between several modules and features [11]. It provides a specific measure for clinical prediction of DLBCL diagnosis and developing new treatment strategies [12]. Therefore, WGCNA can explore hidden biological patterns. The method has been used to analyze many kinds of diseases, such as breast cancer, uveal melanoma, gastric cancer, and colon cancer [1316]. However, there is no study on clinical modules of DLBCL using the WGCNA method.

In the present study, coexpression modules were constructed by WGCNA. After investigating the correlations between modules and clinical features, a module of interest was identified for functional enrichment analysis. We found three candidate genes (MAFA-AS1, hsa-mir-891a, and hsa-mir-338) that were related with prognosis of DLBCL by overall survival analysis. And MAFA-AS1 had low expression in DLBCL by GEPIA.

2. Materials and Methods

2.1. Data Processing

RNA-seq and clinical data of lymphoid neoplasm diffuse large B-cell lymphoma patients were downloaded from TCGA data repository (https://cancergenome.nih.gov/). The gene expression level was normalized as fragments per kilobase of transcript per million mapped reads (FPKM) using Robust Multiarray Average (RMA) algorithm. The miRNA expression level was measured as RPM. Clinical information included clinical TNM stage, histologic grade, age, gender, and survival information. As genes with little variation in expression usually represent noise, the most variant genes were filtered. Gene variables were measured by median absolute deviation (MAD).

2.2. Construction of Gene Coexpression Network

Gene coexpression network was constructed using WGCNA package in R [17]. Power values were screened out by the WGCNA algorithm. Firstly, gene expression similarity matrix was constructed via calculating the absolute value of Pearson correlation coefficient between two genes. The formula was listed as below: where and were vectors of expression value for gene and and represented the Pearson correlation coefficient of gene and gene .

Next, gene expression similarity matrix was transformed into adjacency matrix . The formula was . To further identify functional modules in the coexpression network, the adjacent matrix was transformed into a topological overlap matrix (TOM), and the corresponding dissimilarity (1-TOM) was calculated. where was the weighted adjacency matrix given by and β =5 was the soft threshold power. where expressed the degree of dissimilarity gene expression in different samples.

Scale independence and average connectivity analysis of modules with different power values was performed by gradient test (power value ranging from 1 to 20). Appropriate power value was determined when the scale independence value was equal to 0.9. The WGCNA algorithm was then used to construct the coexpression network and extract the gene information in the most relevant module. The candidate network was selected according to the coexpression .

2.3. Correlations between MEs and External Clinical Data

The correlations between modules and clinical features were analyzed using WGCNA. Module eigengene (ME) can summarize the gene expression profiles, and the formula was as follows: where represents the th module.

We calculated the correlations between MEs and external clinical data as the module membership (MM). was statistically significant. The genes in the most relevant module were chosen as candidate genes, as follows: where means the identification of the th gene in the th module.

We calculated the correlations between MEs and external clinical data. A value < 0.05 was statistically significant. The genes in the most relevant module were chosen as candidate genes.

2.4. Functional Enrichment Analysis

To explore the potential biological themes and pathways of genes from modules of interest, the clusterProfiler package in R was used to annotate and visualize Gene Ontology (GO) terms (including biological processes, molecular functions, and cellular components) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [18]. A value < 0.05 was considered significant pathways.

2.5. Prognostic Analysis of Candidate Module Genes

Then, we further assessed the prognostic value of candidate genes by overall survival analysis. Log-rank tests were used to select prognosis-related genes from candidate genes. Survival package was used to carry out log-rank tests and survminer package was used to plot Kaplan–Meier survival curves.

2.6. Candidate Gene Coexpression Network Construction

WGCNA can evaluate coexpression information of genes. Through survival analysis, we obtained the core genes related with prognosis. Moreover, we constructed a coexpression network of prognosis-related genes through WGCNA.

2.7. Candidate Gene Risk Assessment

The online database Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html) was used to analyze the gene expression between cancer and normal samples [19]. As an interactive web, GEPIA can analyze the RNA-seq expression including TCGA datasets and Genotype-Tissue Expression (GTEx) datasets [20]. Using GEPIA, we analyzed candidate genes related with prognosis between cancer and normal samples from TCGA.

2.8. Reverse-Transcription Quantitative PCR (RT-qPCR)

Total RNA was extracted from plasma of 11 patients with DLBCL and 11 healthy participates using Trizol. To examine hsa-miR-338-3p, hsa-mir-338-5p, and hsa-miR-891a-5p expression, cDNA was synthesized with the miScript Reverse Transcription Kit (Qiagen, Hilden, Germany). RT-qPCR was performed using the miScript SYBR Green PCR Kit (Qiagen). β-Actin served as an internal control. The relative expression levels of miRNAs were calculated with the 2-ΔΔCt method. The specific primers for hsa-miR-338-3p, hsa-mir-338-5p, and hsa-miR-891a-5p were as follows: hsa-miR-338-3p, 5-AACCGGTCCAGCATCAGTGATT-3 (forward), 5-GTGCAGGGTCCGAGGT-3 (reverse); hsa-mir-338-5p, 5-CAATATCCTGGTGCTGAGTG-3 (forward), 5-GTGCAGGGTCCGAGGT-3 (reverse); and hsa-miR-891a-5p, 5-GTGCTCGCTTCGGCAGCACATA-3 (forward), 5-GTGCAGGGTCCGAGGT-3 (reverse).

3. Results

3.1. Gene Coexpression Network of DLBCL

Clinical and level-3 RNA-seq data of 51 DLBCL samples were retrieved from TCGA. The clinical features of the DLBCL samples are listed in Table 1. After module detection, 12813 most variant genes were selected for further analysis according to MAD value when the value of soft thresholding power was 12, and the connectivity between genes met a scale-free network distribution (Figure 1). 19 modules were identified by hierarchical clustering and the dynamic branch cutting (Figure 2). Each module was assigned a unique color as an identifier. The number of genes in modules ranged from 34 to 5457. The grey module represented a gene set that was not assigned to any of the modules.


Clinical featuresTotal ()Alive ()Dead ()

Age, mean (range)56.3 (23-82)56.8 (23-82)54.1 (27-73)
Gender
 Male22175
 Female25214
Stage
 I761
 II17152
 III541
 IV1293
 -642
Race
 Asian18144
 White28244
 Black or African American101

3.2. Identification of the Purple Module as the Module Most Relevant to Clinical Traits

To explore the molecular mechanisms behind the trait, we identified genes associated with a certain clinical trait. In the present study, the clinical parameters of DLBCL patients, including age, clinical stage, gender, recrudescence stage, and histological type were involved in the module–trait relationship analysis. As shown in Figure 3, the purple module was closely related with histological type ( value = 1-04) and the cyan module was associated with age ( value = 0.02). Therefore, the purple module was selected as module of interest for subsequent analysis.

3.3. Functional Enrichment Analysis of Genes in the Purple Module

To explore potential pathways of the purple module, GO and KEGG enrichment analyses were performed on the genes from the purple module. 52 GO-enriched terms were shown Table 2. The top ten GO terms included regulation of neuron projection development, gliogenesis, regulation of synaptic plasticity, myelin sheath, glial cell differentiation, glial cell development, calcium-dependent protein binding, extracellular matrix assembly, axon ensheathment in central nervous system, and central nervous system myelination (Figure 4(a)). In the KEGG analysis, 10 pathways were enriched by genes in the purple module, including synaptic vesicle cycle, Ras signaling pathway, GABAergic synapse, morphine addiction, cell adhesion molecules (CAMs), PI3K-Akt signaling pathway, retrograde endocannabinoid signaling, gastric cancer, histidine metabolism, and renin-angiotensin system (Table 3, Figure 4(b)).


Term IDOntologyDescription.adjustCount

GO:0048167BPRegulation of synaptic plasticity0.005737
GO:0021782BPGlial cell development0.012695
GO:0022010BPCentral nervous system myelination0.012693
GO:0032291BPAxon ensheathment in central nervous system0.012693
GO:0042063BPGliogenesis0.017767
GO:0010975BPRegulation of neuron projection development0.017769
GO:0010001BPGlial cell differentiation0.019026
GO:0085029BPExtracellular matrix assembly0.021013
GO:0048708BPAstrocyte differentiation0.022144
GO:0050804BPModulation of chemical synaptic transmission0.022998
GO:0099177BPRegulation of transsynaptic signaling0.022998
GO:0060291BPLong-term synaptic potentiation0.025424
GO:0014002BPAstrocyte development0.027363
GO:0110110BPPositive regulation of animal organ morphogenesis0.027764
GO:0001505BPRegulation of neurotransmitter levels0.031317
GO:0014003BPOligodendrocyte development0.038863
GO:0001657BPUreteric bud development0.038864
GO:0072163BPMesonephric epithelium development0.038864
GO:0072164BPMesonephric tubule development0.038864
GO:0001504BPNeurotransmitter uptake0.040083
GO:0001823BPMesonephros development0.040974
GO:0050768BPNegative regulation of neurogenesis0.042826
GO:0006953BPAcute-phase response0.046053
GO:0016264BPGap junction assembly0.046052
GO:0071281BPCellular response to iron ion0.046052
GO:0014013BPRegulation of gliogenesis0.048234
GO:0051961BPNegative regulation of nervous system development0.048236
GO:0042552BPMyelination0.048234
GO:0007272BPEnsheathment of neurons0.048234
GO:0008366BPAxon ensheathment0.048234
GO:0043209CCMyelin sheath0.000327
GO:0014069CCPostsynaptic density0.007777
GO:0032279CCAsymmetric synapse0.007777
GO:0099572CCPostsynaptic specialization0.007777
GO:0098984CCNeuron to neuron synapse0.007777
GO:0097449CCAstrocyte projection0.024592
GO:0043083CCSynaptic cleft0.028922
GO:0098793CCPresynapse0.036027
GO:0031012CCExtracellular matrix0.036027
GO:0033267CCAxon part0.038716
GO:0030426CCGrowth cone0.042084
GO:0030427CCSite of polarized growth0.042084
GO:0097386CCGlial cell projection0.042082
GO:0150034CCDistal axon0.042085
GO:0034364CCHigh-density lipoprotein particle0.046892
GO:0048306MFCalcium-dependent protein binding0.011344
GO:0005539MFGlycosaminoglycan binding0.011346
GO:0048018MFReceptor ligand activity0.011348
GO:0008201MFHeparin binding0.011345
GO:0004857MFEnzyme inhibitor activity0.011347
GO:0005381MFIron ion transmembrane transporter activity0.021162
GO:1901681MFSulfur compound binding0.042495

BP: biological processes; CC: cellular components; MF: molecular functions.

IDDescription.adjustCount

hsa04721Synaptic vesicle cycle0.011712
hsa04014Ras signaling pathway0.012223
hsa04727GABAergic synapse0.015072
hsa05032Morphine addiction0.015722
hsa04514Cell adhesion molecules (CAMs)0.037002
hsa04151PI3K-Akt signaling pathway0.037183
hsa04723Retrograde endocannabinoid signaling0.038902
hsa05226Gastric cancer0.039382
hsa00340Histidine metabolism0.048111
hsa04614Renin-angiotensin system0.048111

3.4. MAFA-AS1, hsa-mir-338, and hsa-Mir-891a as Candidate Genes Related with Prognosis of DLBCL

The purple module was closely related with histological type. Therefore, genes in the module were of great significance to evaluate the potential value. Among the 100 genes, 3 genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) were associated with prognosis of DLBCL (Table 4). As shown in Figure 5, highly expressed MAFA-AS1 and hsa-mir-338 had shorter survival time than their low expression ( value = 0.027 and 0.039, respectively). And low expression of hsa-mir-891a had poorer clinical outcomes than its high expression ( value = 0.022).


GeneHazard ratio95% CI value

MAFA-AS13.7940.96-14.9950.026604
hsa-mir-3384.1851.127-15.540.039354
hsa-mir-891a0.2050.055-0.7720.021503

3.5. Candidate Gene Coexpression Network Construction

We constructed a coexpression network for genes with prognostic significance by WGCNA. As shown in Figure 6, MAFA-AS1 was interacted with 36 genes, hsa-mir-891a was interacted with 11 genes, while no gene showed interaction with hsa-mir-338.

3.6. Candidate Gene Risk Assessment

Using GEPIA datasets, we found that MAFA-AS1 showed low expression in DLBCL samples (, Figure 7).

3.7. Validation of Candidate Prognostic miRNAs

Consistent with our bioinformatics results, RT-qPCR results showed that hsa-miR-338-3p () and hsa-miR-891a-5p () were upregulated in DLBCL (Figure 8). Furthermore, we further validated the candidate prognostic miRNAs in independent datasets. Similarly, the results showed that hsa-miR-338 (Figure 9(a)) and hsa-miR-891a (Figure 9(b)) were highly expressed in DLBCL. High expression of hsa-miR-338 (Figure 9(c); ) and hsa-miR-891a (Figure 9(d); ) was significantly associated with worse prognosis of DLBCL patients.

4. Discussion

As a heterogeneous disease, DLBCL can be classified into activated B-cell, germinal center B-cell, and primary mediastinal B-cell subtypes based on gene expression profiling [21]. Though about 70% of DLBCL patients have survival time longer than five years when treated with immunochemotherapy involving rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) [22], however, the remaining patients are still dying of this malignant tumor. In addition, the short- and long-term toxicity of chemotherapy, including secondary malignancies and leukemia, adversely affects the long-term prognosis of patients. Therefore, it is necessary to study new therapeutic targets in DLBCL.

RNA-seq is a next-generation sequencing technology for genome-wide quantitative gene expression, which has some advantages over microarrays in characterizing transcriptomes [23, 24]. However, few studies have investigated the network characteristics of coexpression networks based on RNA-seq. In the present study, RNA-seq and clinical data of DLBCL were retrieved from TCGA. 19 coexpression modules were built by the 12813 most variant genes from 51 DLBCL samples using WGCNA. WGCNA has been proved to be an effective method for detecting coexpression modules and hub genes in many aspects [25]. The interaction between genes in different coexpression modules can be found. Although a large number of biomarkers have been identified and validated, few studies have considered correlations between genes. Genes with similar expression patterns can encode proteins with similar functional properties that can form complexes or can function in similar pathways. Therefore, we made full use of the WGCNA algorithm to detect key genes associated with sample traits in coexpressed gene networks [26]. The purple module was closely related with histology type ( value = 1-04). And the cyan module was closely related with age. The incidence of DLBCL is positively correlated with age, about two-thirds of DLBCL patients are over 65 years old worldwide [27]. Research has found that age at diagnosis has a relationship with the molecular features of DLBCL [28].

We found that the genes in the purple module were involved in multiple pathways such as Ras signaling pathway, PI3K/AKT signaling pathway, and cell adhesion molecules. The Ras signaling pathway plays an important role in cancer biology, which regulates cell growth and proliferation. Activating mutations in Ras can result in abnormal activation of its downstream target MEK1/2 [29]. MEK has become a potential target for the treatment of DLBCL. The PI3K/AKT signaling pathway is activated in DLBCL, which plays a key role in controlling the proliferation and survival of DLBCL cells [30]. Activation of this pathway in DLBCL can cause gene mutations, loss of PTEN, or constitutive activation of upstream regulatory pathways [31]. Cell adhesion molecules function in complex biological processes like cancer progression, inflammation, angiogenesis, and metastasis [3234].

miRNAs are involved in several biological processes by regulating gene expression at the posttranscriptional level, such as cell proliferation and apoptosis [35, 36]. For example, downregulated microRNA-155 promotes cell cycle arrest and apoptosis in DLBCL [37]. And miR-10a suppresses cell proliferation and promotes cell apoptosis via targeting BCL6 in DLBCL [38]. The main reason for the poor results of DLBCL chemotherapy is that DLBCL cells are resistant to chemotherapeutic drugs [39]. It has been confirmed that miRNAs are closely associated with cancer chemosensitivity. To investigate the prognosis value of the genes in the purple module, we made overall survival analysis and results revealed that three miRNAs were closely related with survival. Highly expressed MAFA-AS1 and hsa-mir-338 had shorter survival time than their low expression ( value = 0.027 and 0.039, respectively). And low expression of hsa-mir-891a had poorer clinical outcomes than its high expression ( value = 0.022). So far, there is no study concerning MAFA-AS1 and hsa-mir-891a. As for hsa-mir-338, it has been reported that hsa-mir-338-3p inhibits invasion and migration of colorectal cancer cells by suppressing smooth expression [40]. Aberrantly expressed hsa-mir-338-3p increases the risk of esophageal cancer [41]. In addition, hsa-mir-338 can be a prognostic biomarker for oral squamous cell carcinoma [42]. Then, we constructed a coexpression network for the three genes by WGCNA. MAFA-AS1 was interacted with 36 genes, hsa-mir-891a was interacted with 11 genes, while no gene showed interaction with hsa-mir-338. Using GEPIA datasets, we found that MAFA-AS1 showed low expression in DLBCL samples ().

Taken together, three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) from the purple module could be considered prognostic biomarkers for DLBCL. Compared to IPI, the new prognostic model had some advantages. For example, the three candidate genes could be detected using immunohistochemistry, which is convenient to screen patients with DLBCL and predict their prognosis. Moreover, based on clinical features and candidate genes, it can help improve treatment options, pathobiology, and life expectancy of patients with DLBCL. However, several limitations of our study need to be pointed out. The heterogeneity of the treatment protocols is inevitable, which can bias outcomes. Furthermore, the number of samples is limited; therefore, the prognostic role of the three candidate genes requires to be verified in a larger sample of DLBCL.

5. Conclusion

In our study, RNA-seq and clinical data of DLBCL were retrieved from TCGA. 19 coexpression modules were built by WGCNA. And we found that the purple module was closely associated with histological type. Further analysis suggested that three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) were significantly related with clinical outcomes. Therefore, our findings revealed a coexpression module related with histological type and identified three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) that possessed potential value as prognostic biomarkers or potential therapeutic targets of DLBCL.

Abbreviations

DLBCL:Diffuse large B-cell lymphoma
IPI:Prognostic index
RNA-seq:RNA sequencing
TCGA:The Cancer Genome Atlas
WGCNA:Weighted gene coexpression network analysis
FPKM:Fragments per kilobase of transcript per million mapped reads
MAD:Median absolute deviation
ME:Module eigengene
GO:Gene Ontology
KEGG:Kyoto Encyclopedia of Genes and Genomes
GEPIA:Gene Expression Profiling Interactive Analysis
BP:Biological processes
CC:Cellular components
MF:Molecular functions.

Data Availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Authors’ Contributions

Jianjun Xiao and Xuemei Wang contributed equally to this work.

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Copyright © 2020 Jianjun Xiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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