Open Access

Prognostic genes of melanoma identified by weighted gene co‑expression network analysis and drug repositioning using a network‑based method

  • Authors:
    • Lu Wang
    • Chuan‑Yuan Wei
    • Yuan‑Yuan Xu
    • Xin‑Yi Deng
    • Qiang Wang
    • Jiang‑Hui Ying
    • Si‑Min Zhang
    • Xin Yuan
    • Tian‑Fan Xuan
    • Yu‑Yan Pan
    • Jian‑Ying Gu
  • View Affiliations

  • Published online on: October 4, 2019     https://doi.org/10.3892/ol.2019.10961
  • Pages: 6066-6078
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that target these genes. Weighted gene co‑expression network analysis was used to construct a gene co‑expression network, explore the associations between genes and clinical characteristics and identify potential biomarkers. Gene expression profiles of the GSE65904 dataset were obtained from the Gene Expression Omnibus database. RNA‑sequencing data and clinical information associated with melanoma obtained from The Cancer Genome Atlas were used for biomarker validation. A total of 15 modules were identified through average linkage hierarchical clustering. In the two significant modules, three network hub genes associated with melanoma prognosis were identified: C‑X‑C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol‑4,5‑bisphosphate 3‑kinase catalytic subunit γ (PIK3CG). The receiver operating characteristic curve indicated that the mRNA levels of these genes exhibited excellent prognostic efficiency for primary and metastatic tumor tissues. In addition, the proximity between candidate genes associated with melanoma progression and drug targets obtained from DrugBank was calculated in the protein interaction network, and the top 15 drugs that may be suitable for treating melanoma were identified. In summary, co‑expression network analysis led to the selection of CXCR4, IL7R and PIK3CG for further basic and clinical research on melanoma. Utilizing a network‑based method, 15 drugs that exhibited potential for the treatment of melanoma were identified.

Introduction

Melanoma is a tumor that originates in melanocytes of the skin or other parts of the body (1). The main function of melanocytes is to produce melanin via melanogenesis, a multistep biochemical process regulated by L-tyrosine, L-DOPA and other hormones (2,3). Melanogenesis leads to the upregulation of hypoxia-inducible factor 1, which modulates the cellular metabolism of melanoma (4). A previous study has demonstrated that pigmentation level is associated with the overall and disease-free survival time of patients with stage III and IV melanoma (5). In the United States, >91,000 individuals were diagnosed with cutaneous melanoma in 2018, and >9,000 patients succumbed to the disease in the same period (6). Since melanoma tends to spread lymphogenously and hematogenously, patients with inoperable metastatic melanoma exhibit median survival times between 8 and 12 months (7). Therefore, melanoma poses a serious threat to life.

Gene mutations in melanoma may activate multiple signaling pathways that regulate proliferation, epithelial-mesenchymal transition, invasion and metastasis in an abnormal manner (8). For example, BRAF mutations, predominantly V600E, occur in 40–50% of all melanomas, whereas NRAS proto-oncogene, GTPase and neurofibromin 1 mutations occur in ~20 and 15% of melanomas, respectively (9). Targeted therapy and immunotherapy have been demonstrated to be effective treatment methods (10,11). BRAF/mitogen-activated protein kinase kinase inhibitors, as well as antibodies against cytotoxic T-lymphocyte-associated protein 4 and programmed cell death protein 1 have been used for treatment of metastatic melanoma, with patient response rates ranging between 20 and 70% (12). Although these breakthrough treatments have prolonged progression-free survival to a certain extent, drug resistance still limits their effectiveness (13). For example, immune-based therapy is subject to limitations, such as the prevention of the generation of an immunosuppressive environment (14). Therefore, there remains a need for novel markers of prognosis and novel therapeutic drugs for melanoma.

Weighted gene co-expression network analysis (WGCNA) is widely used to analyze genetic expression data, locate modules of highly correlated genes and identify potential biomarkers, as well as therapeutic targets. Thus, the present study aimed to utilize WGCNA to identify novel biomarkers associated with melanoma prognosis. Additionally, the present study aimed to determine the proximity between disease-associated proteins and drug targets in the human protein-protein interactome in order to identify potential drugs for the treatment of melanoma.

Materials and methods

Data processing

Melanoma transcriptome dataset GSE65904 (15) was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo). GSE65904 comprised 214 samples from patients with melanoma, no non-tumor tissue samples or healthy subjects were included. Illumina HumanHT-12V4.0 expression beadchip was used as the sequencing platform. Clinical information of patients, including sex, age, tumor stage, distant metastasis and survival state, was collected. The GEO query package in R v2.52.0 (https://git.bioconductor.org/packages/GEOquery) was used to process the data. If the expression of a gene was not significant compared with the background value (standard probe) in >25% of all samples (P>0.05), the probe was removed from further analysis. A total of 10,566 genes were obtained.

Weighted co-expression network construction

The top 50% most differentially expressed genes (5,283 genes) were selected for WGCNA analysis following analysis of variance using R 3.3.2 (https://www.r-project.org/) (16). These genes were used for screening and cluster analysis of all samples, as well as to identify outliers, following which one patient was removed from the study (Fig. 1). The gene expression data of the patients was used to construct the co-expression network, and the WGCNA algorithm was utilized for analysis (16). To ensure that the nodes of the constructed co-expression network conformed to the power rate distribution, appropriately soft threshold was selected (β=3), which enabled the deletion of low mutual correlation relationships. The distribution of network nodes conformed to the power rate distribution at β=3. Further investigation of the distribution of node degrees in the co-expression network revealed that the degree of nodes conformed to the power law distribution. This indicated that the constructed co-expression network was a scale-free network, conforming to the characteristics of common biological networks. The average linkage hierarchical clustering method (17) was used to cluster all genes.

Identification of clinically significant modules

To obtain the gene modules that were associated with clinical phenotypes, the correlation between modules and clinical phenotypes was determined. Module eigengenes (MEs) were considered as characteristics of all genes in a certain module. The association between MEs and clinical characteristics was analyzed to determine a clinically significant module for further use.

Gene Ontology (GO) and pathway enrichment analysis

The ClusterProfiler package (https://github.com/GuangchuangYu/clusterProfiler) in R v3.12.0 was used to determine the functions of the enriched genes from the two modules in Fig. 3 (black and turquoise modules) in GO (18) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (19) pathway analysis, respectively. Genes in the clinically significant module were categorized into three functional groups: Biological process (BP), cellular component (CC) and molecular function (MF).

Identification and validation of hub genes

To identify genes associated with melanoma prognosis, the association between each gene and clinical characteristics was evaluated, as well as the association between each gene and core modules, such as module membership (MM) and gene significance (GS). MM is defined by the correlation between the gene expression profile and MEs, whereas GS is defined by the association between a gene and external traits. Genes with |MM+GS|=5% in the aforementioned modules (black and turquoise modules) in Fig. 3 were selected as potentially prognostic genes; all other genes were removed. To further analyze the association between these genes, the remaining candidate genes were input into STRING (https://string-db.org/) to construct a protein-protein interaction (PPI) network using Cytoscape v3.2 (20).

To verify whether the identified genes were associated with tumor progression and prognosis, the association between each gene and survival was determined using the R survival package v2.41-3 (https://cran.r-project.org/web/packages/survminer/index.html). Clinical and RNA-sequencing data from 417 patients with melanoma were downloaded from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) using the TCGA biolinks package in R v2.12.3 (https://git.bioconductor.org/packages/TCGAbiolinks). Overall survival was analyzed using the log-rank test. In addition, the ggpubr package v0.2.1 (http://cran.r-project.org/web/packages/ggpubr/index.html) was used to demonstrate the mRNA expression of hub genes in primary and metastatic tumor, and the two groups were compared by Student's t-test. Receiver operating characteristic (ROC) curve and area under the curve (AUC) values were obtained using the pROC package v1.15.0 (http://cran.r-project.org/web/packages/ROCR) to evaluate the efficiency of the genes in distinguishing metastatic and non-metastatic tumors.

Screening candidates for treatment

Drug-target information of Food and Drug Administration (FDA)-approved drugs was obtained from DrugBank (https://www.drugbank.ca/). The exclusion of drugs that had no known targets in the interactome resulted in a total of 1,269 unique drugs and 1,185 targets selected for further analysis. Notably, only pharmacological targets (‘Targets’ section in DrugBank), excluding enzymes, carriers and transporters typically shared among different drugs, were considered. The protein interaction information was obtained from a previously published study, which contained data from 15 databases (21). Among these, 15,969 nodes and 217,160 mutual relationships were identified in the PPI networks. The prognostic genes of melanoma were mapped to the PPI network. The Igraph package v1.2.4.1 (https://igraph.org/) was used to estimate the shortest distance between each target and a particular prognostic gene for each FDA-approved drug (21). Standardization-based approximation indicated that lower values were associated with an increased likelihood that the drug may act on melanoma and prevent its progression.

Results

Weighted co-expression network construction and key module identification

Following a cluster analysis of all samples, one sample in GSE65904 was removed from subsequent analysis due to bias (Fig. 1; Table I). To ensure a scale-free network, it must satisfy R2> 0.8 (Fig. 2A), and the mean connectivity should be conserved as much as possible (Fig. 2B). Furthermore, the degree distribution of nodes in the co-expression network was investigated further and the degree of nodes conforms to power law distribution (Fig. 2C and D). The WGCNA package in R was used to place genes with similar expression patterns into modules through average linkage clustering; a total of 15 modules were identified (Fig. 3A). The black module exhibited the strongest association with tumor metastasis-free survival and disease-specific death survival (Fig. 3), whereas the turquoise module exhibited the strongest association with tumor stage. Therefore, these two modules were considered to be clinically significant and were selected for further analysis.

Table I.

Summary of number and corresponding GSM in GSE65904.

Table I.

Summary of number and corresponding GSM in GSE65904.

NumberGSM
  1GSM1608593
  2GSM1608594
  3GSM1608595
  4GSM1608596
  5GSM1608597
  6GSM1608598
  7GSM1608599
  8GSM1608600
  9GSM1608601
10GSM1608602
11GSM1608603
12GSM1608604
13GSM1608605
14GSM1608606
15GSM1608607
16GSM1608608
17GSM1608609
18GSM1608610
19GSM1608611
20GSM1608612
21GSM1608613
22GSM1608614
23GSM1608615
24GSM1608616
25GSM1608617
26GSM1608618
27GSM1608619
28GSM1608620
29GSM1608621
30GSM1608622
31GSM1608623
32GSM1608624
33GSM1608625
34GSM1608626
35GSM1608627
36GSM1608628
37GSM1608629
38GSM1608630
39GSM1608631
40GSM1608632
41GSM1608633
42GSM1608634
43GSM1608635
44GSM1608636
45GSM1608637
46GSM1608638
47GSM1608639
48GSM1608640
49GSM1608641
50GSM1608642
51GSM1608643
52GSM1608644
53GSM1608645
54GSM1608646
55GSM1608647
56GSM1608648
57GSM1608649
58GSM1608650
59GSM1608651
60GSM1608652
61GSM1608653
62GSM1608654
63GSM1608655
64GSM1608656
65GSM1608657
66GSM1608658
67GSM1608659
68GSM1608660
69GSM1608661
70GSM1608662
71GSM1608663
72GSM1608664
73GSM1608665
74GSM1608666
75GSM1608667
76GSM1608668
77GSM1608669
78GSM1608670
79GSM1608671
80GSM1608672
81GSM1608673
82GSM1608674
83GSM1608675
84GSM1608676
85GSM1608677
86GSM1608678
87GSM1608679
88GSM1608680
89GSM1608681
90GSM1608682
91GSM1608683
92GSM1608684
93GSM1608685
94GSM1608686
95GSM1608687
96GSM1608688
97GSM1608689
98GSM1608690
99GSM1608691
100GSM1608692
101GSM1608693
102GSM1608694
103GSM1608695
104GSM1608696
105GSM1608697
106GSM1608698
107GSM1608699
108GSM1608700
109GSM1608701
110GSM1608702
111GSM1608703
112GSM1608704
113GSM1608705
114GSM1608706
115GSM1608707
116GSM1608708
117GSM1608709
118GSM1608710
119GSM1608711
120GSM1608712
121GSM1608713
122GSM1608714
123GSM1608715
124GSM1608716
125GSM1608717
126GSM1608718
127GSM1608719
128GSM1608720
129GSM1608721
130GSM1608722
131GSM1608723
132GSM1608724
133GSM1608725
134GSM1608726
135GSM1608727
136GSM1608728
137GSM1608729
138GSM1608730
139GSM1608731
140GSM1608732
141GSM1608733
142GSM1608734
143GSM1608735
144GSM1608736
145GSM1608737
146GSM1608738
147GSM1608739
148GSM1608740
149GSM1608741
150GSM1608742
151GSM1608743
152GSM1608744
153GSM1608745
154GSM1608746
155GSM1608747
156GSM1608748
157GSM1608749
158GSM1608750
159GSM1608751
160GSM1608752
161GSM1608753
162GSM1608754
163GSM1608755
164GSM1608756
165GSM1608757
166GSM1608758
167GSM1608759
168GSM1608760
169GSM1608761
170GSM1608762
171GSM1608763
172GSM1608764
173GSM1608765
174GSM1608766
175GSM1608767
176GSM1608768
177GSM1608769
178GSM1608770
179GSM1608771
180GSM1608772
181GSM1608773
182GSM1608774
183GSM1608775
184GSM1608776
185GSM1608777
186GSM1608778
187GSM1608779
188GSM1608780
189GSM1608781
190GSM1608782
191GSM1608783
192GSM1608784
193GSM1608785
194GSM1608786
195GSM1608787
196GSM1608788
197GSM1608789
198GSM1608790
199GSM1608791
200GSM1608792
201GSM1608793
202GSM1608794
203GSM1608795
204GSM1608796
205GSM1608797
206GSM1608798
207GSM1608799
208GSM1608800
209GSM1608801
210GSM1608802
211GSM1608803
212GSM1608804
213GSM1608805
214GSM1608806
GO and KEGG pathway enrichment analysis

The genes in the clinically significant modules were categorized into functional groups: BP, CC and MF. The genes in the black module were mainly enriched in ‘antigen processing and presentation’, ‘antigen processing and presentation of peptide antigen’ and ‘antigen processing and presentation of exogenous peptide antigen’ in the BP group, ‘endocytic vesicle membrane’, ‘Golgi-associated vesicle’ and ‘COPII-coated ER to Golgi transport vesicle’ in the CC group, and ‘amide binding’, ‘peptide binding’ and ‘antigen binding’ in the MF group (Fig. 4A). The results of the KEGG pathway analysis demonstrated that genes in the black module were mainly involved in ‘antigen processing and presentation’, ‘viral myocarditis’, ‘cell adhesion molecules cams’ and ‘allograft rejection’, among others (Fig. 5A).

The genes in the turquoise module were mainly enriched in ‘leukocyte differentiation’, ‘T cell activation’ and ‘regulation of lymphocyte activation’ in the BP group, ‘cell leading edge’, ‘lamellipodium’ and ‘cytoplasmic side of plasma membrane’ in the CC group and ‘nucleoside-triphosphatase regulator activity’, ‘GTPase regulator activity’ and ‘phospholipid binding’ in the MF group (Fig. 4B). The results of the KEGG pathway analysis demonstrated that the genes in the turquoise module were mainly involved in ‘chemokine signaling pathway’, ‘B cell receptor signaling pathway’ and ‘T cell receptor signaling pathway’, among others (Fig. 5B).

Identification and validation of hub genes

Genes with |MM+GS|=5% in the black and turquoise modules were selected as candidate prognostic genes, and all other genes were removed. A PPI network of all genes in the black and turquoise modules was constructed using Cytoscape. The network comprised 222 nodes and 1,416 edges according to the STRING database (Fig. 6). Among those, C-X-C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG) were positively associated with overall survival (Fig. 7D-F). Based on TCGA data, the expression levels of CXCR4, IL7R and PI3KG were upregulated in primary tumors compared with metastatic tumors (Fig. 7A-C). In addition, the ROC curves indicated that CXCR4, IL7R and PI3KG exhibited excellent efficacy for diagnosing primary and metastatic tumor tissues (Fig. 7G-I).

Screening candidates for treatment

Using genes which were identified as hub nodes in the PPI network (degree >30) associated with prognosis (P<0.05) and metastasis (AUC >0.7) as potential targets in the drug-gene interaction analysis (Fig. 8), the top 15 drugs ranked by the proximity of genes and drugs were screened as possible treatments for melanoma. The screened drugs could be divided into several major categories, including tyrosine kinase inhibitors (TKIs), vascular endothelial growth factor receptor (VEGFR) inhibitors, estrogen receptor modulators, proteasome inhibitors, Burton's tyrosine kinase (BTK) inhibitors and Raf kinase inhibitors. The top 15 drugs are: Ponatinib, nintedanib, tamoxifen, framycetin, regorafenib, dasatinib, sunitinib, bosutinib, benzylpenicilloyl polylysine, ibrutinib, pazopanib, methyl aminolevulinate, bortezomib, sorafenib, lenvatinib.

Discussion

Among skin tumors, melanoma is the most malignant (22). High recurrence and metastasis rates affect the efficacy of melanoma treatment (23). The effects of conventional chemotherapy, immunotherapy and targeted therapy remain limited. Thus, identifying novel molecular targets and exploring therapeutic drugs for melanoma is important. In the present study, the GEO database was used to obtain genetic and clinical information from patients with melanoma, construct a co-expression network, select the most significant module and identify three hub genes: CXCR4, IL7R and PIK3CG. TCGA, which was used for further verification, revealed that three aforementioned specific molecules: CXCR4, IL7R and PIK3CG identified in melanoma tissues were associated with prognosis and metastasis. In addition, the top 15 drugs ranked by the proximity of genes and drugs were screened using a network screening method, and a drug-gene network was constructed.

CXCR4, which is a receptor of C-X-C motif chemokine 12 (CXCL12), is located on the surface of >23 human tumors, for example breast cancer, ovarian cancer, glioma, pancreatic cancer and prostate cancer (24). CXCL12 binds to CXCR4, which activates several extra- and intracellular signaling pathways, including the nuclear factor κB, Ca2+-dependent protein tyrosine kinase 2β, PI3K-Akt and mitogen-activated protein kinase signaling pathways (25). In various types of cancer, such as oral (26), esophageal (27), gastric, colon, liver, pancreatic, thyroid and ovarian cancer (28), and leukemia (29), CXCR4 expression is strongly associated with chemotaxis, invasion, angiogenesis and cell proliferation, all of which are involved in tumorigenesis and cancer. However, the results of the present study indicated that, compared with primary tumors, CXCR4 is downregulated in metastatic tumors, and is therefore associated with good prognosis in patients with melanoma. Mitchell et al (30) demonstrated that most of melanoma cases with mitosis, ulceration and regression were CXCR4-negative. Patients with American Joint Committee on Cancer (AJCC) stage (31) I and II melanoma exhibit higher expression of CXCR4 compared with those with AJCC stages III and IV, and a proportion of patients with AJCC stage III–IV melanoma are CXCR4-negative (30). Therefore, the role of CXCR4 as a biomarker warrants further investigation.

IL7R, which is expressed in immune cells, is crucial for the survival, development and homeostasis of the immune system (32). IL-7Rα activates Janus kinases 1 and 3, promoting the function of signal transducer and activator of transcription 5, which leads to the modulation of gene expression, as well as the activation of anti-apoptotic and pro-survival signaling pathways (33). Thus, IL7R is classified as an oncogene associated with several tumors, including esophageal and prostate cancer (34). However, a bioinformatics study has demonstrated that patients with colon cancer lacking IL7R (two cases of mortality out of three cases) had a median survival time of 34 months compared with patients with normal IL7R status, whose survival time was 45 months (35). Studies on the association between IL7R and melanoma, as well as the association between IL7R and metastasis, are lacking.

The PI3K signaling pathway modulates various biological processes, including cell proliferation, survival, motility, death and metabolism (36). Aberrations in these processes are pivotal for the pathogenesis of cancer. Based on structural differences, PI3K can be divided into several subunits, including PIK3CA, PIK3CB, PIK3CD and PIK3CG (37). A previous study has revealed that PIK3CG is expressed at undetectable levels in glioblastoma cells, and that blocking this specific subunit does not cause cytotoxicity (38). Another study has demonstrated that PIK3CG is downregulated in colorectal cancer, whereas 12 other genes in the PI3K-AKT signaling pathway are upregulated (39). However, a bioinformatics-based study reported that PIK3CG is significantly associated with melanoma metastasis to regional lymph nodes, which contradicted the results of the present study, suggesting that further investigation may be required to clarify the role of PIK3CG in the metastasis of melanoma (40).

In the present study, the GEO database, which comprised 214 melanoma samples, and TCGA database, which included 417 patients, were selected to verify the roles of the identified genes. Double validation and a large number of samples contributed to the reliability of the candidate genes. However, a limitation of the present study was a lack of clinical or experimental validation. Further study is required to verify the role of CXCR4, IL7R and PI3K3CG in melanoma.

The analysis of the association between genes and FDA-approved drugs demonstrated that the top 15 drugs were TKIs, VEGFR inhibitors, estrogen receptor modulators, proteasome inhibitors, Bcr-Abl kinase inhibitors, BTK inhibitors, Raf kinase inhibitors, framycetin, benzylpenicilloyl polylysine and methyl aminolevulinate. TKIs that function by blocking the Bcr-Abl tyrosine-kinase included dasatinib, ponatinib and bosutinib, which are used to treat chronic myelogenous leukemia and acute lymphocytic leukemia (41). Other drugs, including nintedanib, regorafen, sunitinib, pazopanib, sorafenib and lenvatinib inhibit several receptor tyrosine kinases, including platelet-derived growth factors, VEGFR, fibroblast growth factor receptors and Raf family kinases, which inhibit tumor angiogenesis and tumor cell proliferation (42). Ibrutinib, a BTK inhibitor, is used to treat chronic lymphocytic leukemia (43). Tamoxifen, a selective estrogen receptor modulator, is used for the treatment and prevention of estrogen receptor-positive breast cancer (44). Bortezomib was the first therapeutic proteasome inhibitor to be tested in humans; it serves a role in cell cycle arrest and apoptosis, and is approved in the United States for the treatment of relapsed multiple myeloma and mantle cell lymphoma (45). Framycetin, which is an antibiotic, is used to treat leg ulcers and other conditions associated with wound healing (46). Benzylpenicilloyl polylysine is used as a skin-testing reagent for individuals with a history of penicillin allergy (47). Methyl aminolevulinate, which is metabolized into phototoxic compounds, such as protopophyrin IX, may represent a candidate for photodynamic therapy, as it can induce oxidative damage to the cell (48).

Angiogenesis is a hallmark of several types of tumor, including melanoma. The process of angiogenesis is crucial for tumor development and metastasis (49). VEGF is one of the most important cytokines responsible for tumor-mediated angiogenesis (50). VEGF is strongly expressed in melanoma and serves a critical role in the progression of the disease (51). In a phase II study of sunitinib in patients with advanced melanoma, 4/31 (13%) patients exhibited a partial response and 8 (26%) had stable disease (52). Pazopanib, a VEGF and platelet-derived growth factor inhibitor, has been used in combination with paclitaxel in a phase II study of patients with metastatic melanoma; the 6-month progression-free survival rate was 68%, and the 1-year overall survival rate was 48% (53). The objective response rate was 37%, comprising one complete and 20 partial responses (54). A phase Ib study using lenvatinib (E7080) in combination with temozolomide for the treatment of advanced melanoma indicated an overall objective response rate of 18.8% (six patients), comprising all partial responses (55). SRC proto-oncogene, non-receptor tyrosine kinase (SRC) is a promising target in the treatment of solid types of cancer, including human melanoma; bosutinib, a SRC inhibitor, which induces cell death via lysosomal membrane permeabilization in melanoma cells, is a promising therapeutic agent for melanoma treatment (56). SRC inhibitor Dasatinib specifically inhibits p53 phosphorylation in melanoma; however, a comprehensive validation is required (57). Ibrutinib, a BTK inhibitor, has been used to treat chronic lymphocytic leukemia/small lymphocytic lymphoma and subsequent melanoma that occurs following leukemia (58). Sorafenib, a Raf inhibitor, is a first-line therapeutic agent used in advanced melanoma (phase I and open-label phase II) trials with an overall response rate of 12% with one complete response and nine partial responses (59). Bortezomib administration reduces the levels of proangiogenic cytokines in plasma (60). A clinical trial has indicated that tamoxifen therapy is not effective for treating melanoma, and that the mode of action of antiestrogens in melanoma is unclear (61). To the best of our knowledge, ponatinib, nintedanib, regorafen, framycetin, benzylpenicilloyl polylysine and methyl aminolevulinate have not been used to treat melanoma.

The present study used WGCNA to construct a gene co-expression network in order to determine the associations between genes and modules and to explore the association between the gene modules and clinical characteristics. Two significant modules (black and turquoise modules) shown in Fig. 3, were identified to be associated with the progression of melanoma. GO and KEGG pathway analyses demonstrated that this module was mostly involved in functions associated with antigen presentation. In addition, three hub genes, CXCR4, IL7R and PI3K3CG, were identified and demonstrated to be associated with the progression and prognosis of melanoma. Analysis of the interaction between genes and drug targets of the top 15 drugs for melanoma enabled the construction of a network of drug-gene interactions. Ponatinib, regorafen, nintedanib, framycetin, benzyl penicilloyl polylysine and methyl aminolevulinate, which were among the 15 drugs not currently used to treat melanoma, may be potential novel therapeutic drugs for this disease.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

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

Authors' contributions

JYG conceived and designed the study. XYD, QW, SMZ and JHY acquired and interpreted the data. LW, CYW, YYX, XY, TFX and YYP conducted the data analyses. LW, CYW and YYX wrote the manuscript. JYG revised the manuscript. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

WGCNA

weighted gene co-expression network analysis

GEO

Gene Expression Omnibus

ROC

receiver operating characteristic

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

BP

biological process

CC

cellular component

MF

molecular function

MEs

module eigengenes

GS

gene significance

MM

module membership

PPI

protein-protein interaction

TKIs

tyrosine kinase inhibitors

BTK

Burton's tyrosine kinase

VEGFR

vascular endothelial growth factor receptor

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December 2019
Volume 18 Issue 6

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Online ISSN:1792-1082

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APA
Wang, L., Wei, C., Xu, Y., Deng, X., Wang, Q., Ying, J. ... Gu, J. (2019). Prognostic genes of melanoma identified by weighted gene co‑expression network analysis and drug repositioning using a network‑based method. Oncology Letters, 18, 6066-6078. https://doi.org/10.3892/ol.2019.10961
MLA
Wang, L., Wei, C., Xu, Y., Deng, X., Wang, Q., Ying, J., Zhang, S., Yuan, X., Xuan, T., Pan, Y., Gu, J."Prognostic genes of melanoma identified by weighted gene co‑expression network analysis and drug repositioning using a network‑based method". Oncology Letters 18.6 (2019): 6066-6078.
Chicago
Wang, L., Wei, C., Xu, Y., Deng, X., Wang, Q., Ying, J., Zhang, S., Yuan, X., Xuan, T., Pan, Y., Gu, J."Prognostic genes of melanoma identified by weighted gene co‑expression network analysis and drug repositioning using a network‑based method". Oncology Letters 18, no. 6 (2019): 6066-6078. https://doi.org/10.3892/ol.2019.10961