Identification of lncRNA-miRNA-mRNA ceRNA axes and KEGG pathways related to uveal melanoma metastasis

Abstract

Background

Uveal melanoma (UM) is a common intraocular malignancy in adults frequently with metastasis and poor survival.

Objective

This study aimed to identify genes, pathways and the ceRNA axes related to the metastasis of UM.

Methods

The GSE73652 dataset was downloaded and 1719 differentially expressed RNAs (DE-RNAs), including 13 lncRNAs, 5 miRNAs, and 1701 genes, were identified in metastatic UM samples compared with non-metastatic ones.

Results

A total of 11 lncRNA-miRNA pairs were identified by interviewing the DIANA-LncBase database. In addition, 49 UM-related KEGG pathways were filtered in CTD with the search term “uveal melanoma”. KEGG pathways involving the differentially expressed genes (DEGs) among the miRNA targets were found and overlapped with UM-related pathways. Accordingly, two crucial overlapped pathways (Wnt and Chemokine signaling pathway) in UM metastasis were mediated by axes consisting of 6 lncRNAs (such as H19, PVT1 and SNGHG1), 3 miRNAs (including hsa-miR-1228, hsa-miR-106b and hsa-miR-6836) and 12 mRNAs (including CTNNB1, MAP3K7, WNT7B, MAPK10 and PLCB4).

Conclusion

The results showed that the involvement of UM-related Wnt/β-catenin and Chemokine signaling pathways and the ceRNA regulatory axes showed noteworthy interest in UM metastasis.

Keywords

uveal melanoma microarray competing endogenous RNA metastasis

1 Introduction

Uveal melanoma (UM) is a common intraocular malignancy in adults. The primary reasons for UM are the vulnerability, high proliferation activity, and metastasis tendency features of uveal melanocytes.^1,2 The incidence of UM is about 5 cases per million,³ and the 5-year survival rate of patients with primary UM is approximately lower than 80%.^4,5 The UM-specific survival rate is affected by metastasis. The survival time of over 80% of UM patients with metastatic tumors was less than one year, and most patients died in the first year from the time of metastasis diagnosis.^4,6 The identification of metastatic biomarkers for UM might be of great significance.

The gene expression profiles of more than a dozen genes have been used as metastatic provisional indicators for UM in clinics, such as the 12-gene classifier or 15-gene classifier [including E-cadherin encoding gene LDH1, Extracellular matrix protein 1 (ECM1) encoding gene, 5-hydroxytryptamine (serotonin) receptor 2B (HTR2B) gene, LIM, and cysteine-rich domains 1 (LMCD1) encoding gene, Eukaryotic translation initiation factor 1B (E1F1B) encoding gene, and SATB homeobox 1 (SATB1) encoding gene].⁷ The patients who are at increased UM metastatic risk may be diagnosed and treated timely. More accurate and specific metastatic indicators may improve the effectiveness of diagnosing UM with metastatic risk.

There are increasing molecular biomarkers identified as diagnostic indicators for metastasis in UM, including preferentially expressed antigen in melanoma (PRAME).⁸ UM patients at low metastatic risk (Class I) with PRAME-negative tumors showed non-metastasis during a five year follow-up, while Class I patients with PRAME-positive tumors showed a high risk of metastasis.^8–10 The expression of PRAME mRNA was correlated with tumor volume.¹¹ It has been reported that nestin expression correlated with a poor prognosis in patients with both primary and metastatic UM.¹² In addition, the abnormal expression of non-coding RNAs, including microRNAs (miRNAs) and lncRNAs, is of great interest nowadays. It has been reported that miR-204 and lncRNA SAMMSON are associated with the progression and metastasis in UM.^13,14 LncRNAs regulate the expression of miRNAs by sponging miRNAs as competing endogenous RNAs (ceRNAs) thus suppressing or blocking the interaction of miRNA-mRNA. SAMMSON lncRNA expressed in UM samples broadly and the inhibition of it impaired the growth and viability of UM cell lines.¹³ Lu et al. showed that HOXA11-AS-mediated UM cell proliferation and invasion via miR-124.¹⁵ The more profound information on ceRNA in UM, the more insights in understanding the molecular mechanism in UM metastasis.

We performed this study to identify UM metastasis-related ceRNA network involving lncRNAs, miRNAs, genes, and UM-related pathways. The GSE73652 dataset (originated from metastatic and primary UM samples) was downloaded and processed for screening differentially expressed RNAs (DE-RNAs). Bioinformatics analyses including Gene Ontology (GO) and pathway enrichment, identification of disease-related pathways, and network analysis were performed for screening of the UM-related network. This study would provide systemic bioinformatics results in understanding the metastasis in UM.

2 Materials and methods

2.1 Microarray data and processing

The GSE73652 dataset (GPL10558 platform, Illumina HumanHT-12 V4.0 expression beadchip) was downloaded from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/). GSE73652, uploaded by Field et al. in 2016, consisted of 13 series from patients with UM (5 with metastatic and 8 with primary UM cancers). Field et al. classified the UM patients into high or low metastasis according to the 12-gene classification (Table 1).^7,16 GEL files of all samples were extracted, log₂-transformed, and quantile normalized using Limma (v3.34.0; https://bioconductor.org/packages/release/bioc/html/limma.html) in R language. The lncRNA, miRNA and mRNA in the dataset were called with mapping to the HUGO Gene Nomenclature Committee (HGNC) database (http://www.genenames.org/) which contains 3982 lncRNAs, 1932 miRNAs, and 19199 protein-coding genes. The flow diagram of the work is shown in Figure 1.

Figure 1.

The flow diagram of the work showing the analysis process in this study. DE-lncRNAs, differentially expressed lncRNAs. DE-miRNAs, differentially expressed miRNAs. DEGs, differentially expressed genes. CTD, the Comparative Toxicogenomics Database.

Table 1.

The 12-gene classification of uveal melanoma metastasis.

Gene name	Change (high vs. low metastatic risk)	Symbol
E-cadherin	Up	CDH1
Extracellular matrix protein 1	Up	ECM1
5-hydroxytryptamine (serotonin) receptor 2B	Up	HTR2B
RAB31, member RAS oncogene family	Up	RAB31
Inhibitor of DNA binding 2	Down	ID2
LIM and cysteine-rich domains 1	Down	LMCD1
Leukotriene A4 hydrolase	Down	LTA4H
Microtubule-associated tumor suppressor 1	Down	MTUS1
Eukaryotic translation initiation factor 1B	Down	E1F1B
Fragile X mental retardation, autosomal homolog 1	Down	FXR1
Roundabout, axon guidance receptor, 1	Down	ROBO1
SATB homeobox 1	Down	SATB1

2.2 Identification of De-RNAs

The list of DE-RNAs, including differentially expressed lncRNAs (DE-lncRNAs), miRNAs (DE-miRNAs), and genes (DEGs), were generated with the selection criteria of |log₂(Fold change, FC) |>1 and false discovery rate (FDR) < 0.05. Hierarchical clustering of DE-RNAs was conducted using pheatmap (v1.0.8, https://cran.r-project.org/package=pheatmap) with an Euclidean distance-based classifier in R language.

2.3 Enrichment analysis for DEGs

The Gene Ontology (GO) biological processes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways involved in the aforementioned DEGs were analyzed to investigate the molecular features of these genes. All DEGs were subjected to online DAVID (Database for Annotation, Visualization, and Integrated Discovery; v6.8; https://david.ncifcrf.gov/), and functional terms with p < 0.05 were defined as significantly enriched terms.

2.4 Construction of ceRNA regulatory network

The lncRNA-miRNA pairs in the DIANA-LncBase database (version 2) were downloaded and overlapped with the aforementioned DE-lncRNAs and DE-miRNAs. The overlapped pairs of DE-lncRNAs and DE-miRNAs with connection scores ≥ 0.6 were selected for constructing the lncRNA-miRNA regulatory network (Net 1). Next, the target mRNAs of DE-miRNAs in Net 1 were predicted in the starBase Version 2.0 database (http://starbase.sysu.edu.cn/). The overlaps of miRNA targets and DEGs were selected and the negative target of DE-miRNAs were left for further analysis. The miRNA-mRNA regulatory network (Net 2) was constructed accordingly. At last, the regulatory pairs in both Net 1 and Net 2 were used for the construction of the ceRNA network (Net 3), that was the lncRNA-miRNA-mRNA network. All the networks were visualized using Cytoscape (v3.6.1; http://www.cytoscape.org/). DEGs in the ceRNA network (Net 3) were used to identify enriched functional items again (KEGG pathways). The significant threshold was set at p < 0.05.

2.5 Identification of Um-related ceRNA network

The UM-related KEGG pathways were identified from the Comparative Toxicogenomics Database (CTD, http://ctd.mdibl.org/; 2024 updated) directly with the search term “uveal melanoma”. The reduplicated KEGG pathways in CTD and that of DEGs in Net 3 were identified as key pathways related to UM. The regulatory network, consisting of lncRNA, miRNA, mRNA, and KEGG pathways, was constructed using Cytoscape.

3 Results

3.1 Identification of De-RNAs

A total of 19893 RNAs were called from the GSE73652 dataset with mapping to the HGNC database, including 1053 lncRNAs, 520 miRNAs, and 18320 mRNAs. According to the criteria of significance, we identified 1719 DE-RNAs (Figure 2A), including 13 DE-lncRNA, 5 DE-miRNAs, and 1701 DEGs. Figure 2B shows the Log₂FC Kernel density distribution. There were 867 downregulated (50.44%) and 852 upregulated DE-RNAs (49.56%), respectively. Figure 2C shows the hierarchical clustering of DE-RNAs of the 1719 DE-RNAs in UM samples. Samples with metastatic UM were distinct from UM samples without metastasis, suggesting the identified DE-RNAs were correlated with the clinical traits of UM metastasis.

Figure 2.

Summary of differentially expressed RNAs (DE-RNAs) in GSE73652 dataset. A, the volcano figure of DE-RNAs. Red and blue, up- and down-regulation, respectively. B, Log₂FC Kernel density distribution. C, hierarchical clustering of DE-RNAs. FC, fold change (color online).

3.2 Primary enrichment for DEGs

All 1701 DEGs were subjected to the DAVID database and 14 GO biological processes and 7 KEGG pathways (p < 0.05) were called (Table S1). These DEGs were associated with GO biological processes including GO:0006333∼chromatin assembly or disassembly, GO:0006334∼nucleosome assembly, GO:0051276∼chromosome organization, GO:0022402∼cell cycle process and GO:0000087∼M phase of mitotic cell cycle; and KEGG pathways including hsa04110:Cell cycle, hsa04115:p53 signaling pathway, hsa04310:Wnt signaling pathway and hsa04360:Axon guidance.

3.3 Construction of ceRNA network

Based on the comparison in the DIANA-LncBase version 2 database, we identified 11 lncRNA-miRNA pairs among DE-lncRNAs and DE-miRNAs with scores ≥ 0.6. Accordingly, the Net 1 included 11 lncRNA-miRNA pairs (lines), 4 miRNAs (upregulated), and 7 lncRNAs (upregulated, Figure 3A). Table 2 shows the list of DE-RNAs in Net 1.

Next, the miRNA-mRNA regulatory Net 2 of the upregulated miRNAs in Net 1 was constructed. Net 2 was comprised of 431 nodes, including 4 miRNAs (up) and 427 DEGs (down, Figure 3A) and 585 lines (miRNA-mRNA pairs). Finally, we constructed the lncRNA-miRNA-mRNA regulatory Net 3 (ceRNA network, Figure 4). The ceRNA network contained 438 nodes (including 6 lncRNAs, 4 miRNAs, and 428 mRNAs) and 596 lines (including 11 lncRNA-miRNA pairs in Net 1 and 585 miRNA-mRNA pairs in Net 2).

Figure 3.

The regulatory networks of lncRNAs, miRNAs and mRNAs. A, lncRNA-miRNA regulatory network. Boxes and triangles note lncRNAs and miRNAs, respectively. B, miRNA-mRNA regulatory network. Triangles and circles represent miRNA and mRNA, respectively. The higher the log2FC value, the deeper the color. Green and red indicates down- and up-regulation, respectively. FC, fold change (color online).

Table 2.

The list of differentially expressed RNAs in lncRNA-miRNA network.

Mirna_Name	lncRNA_Name	miTG-score
hsa-miR-106b	H19	0.7875
hsa-miR-106b	PVT1	0.6011
hsa-miR-1228	H19	0.8896
hsa-miR-1228	LINC02138	0.7302
hsa-miR-1228	SNHG1	0.6519
hsa-miR-1228	SNHG1	0.7458
hsa-miR-4647	H19	0.9333
hsa-miR-4647	LINC00622	0.8446
hsa-miR-4647	SNHG1	0.7941
hsa-miR-6836	ADIRF-AS1	0.7618
hsa-miR-6836	FAM225A	0.6519

Figure 4.

The ceRNA regulatory network of lncRNA, miRNA and mRNAs. Boxes, triangles, and circles note lncRNAs, miRNAs and mRNAs, respectively. The higher the log2FC value, the deeper the color. Green and red indicates down- and up-regulation, respectively. FC, fold change (color online).

3.4 Final enrichment of DEGs in ceRNA regulatory network

The 428 DEGs (mRNAs) in Net 3 were enriched to 8 KEGG pathways, including hsa04012:ErbB signaling pathway, hsa04360:Axon guidance, hsa04620:Toll-like receptor signaling pathway, hsa04310:Wnt signaling pathway, and hsa04062:Chemokine signaling pathway (Table 3).

Table 3.
The KEGG pathways invloving mRNAs in ceRNA network in Figure 4.

Term P Value Genes

hsa04012:ErbB signaling pathway 0.0052 PAK6, CBL, SHC1, MAPK10, NRG1, PRKCB

hsa04360:Axon guidance 0.0081 EPHA5, ABLIM1, PAK6, NRP1, FYN, SRGAP1, SLIT3

hsa04620:Toll-like receptor signaling pathway 0.0087 MAP3K7, TLR3, MAPK10, CD40, TLR7, TLR8

hsa04514:Cell adhesion molecules (CAMs) 0.0088 CADM3, CLDN18, OCLN, CD8A, NRXN2, CD40, JAM3

hsa04530:Tight junction 0.0093 CLDN18, OCLN, CNKSR3, TJP2, JAM3, PRKCB, CTNNB1

hsa04510:Focal adhesion 0.0098 PAK6, VEGFC, PDPK1, FYN, PDGFC, SHC1, MAPK10, PRKCB, CTNNB1

hsa04310:Wnt signaling pathway 0.0141 MAP3K7, WNT7B, PLCB4, PSEN1, MAPK10, PRKCB, CTNNB1

hsa04062:Chemokine signaling pathway 0.0273 ADCY3, PLCB4, CXCL16, GNB5, GRK4, SHC1, PRKCB

Term	P Value	Genes
hsa04012:ErbB signaling pathway	0.0052	PAK6, CBL, SHC1, MAPK10, NRG1, PRKCB
hsa04360:Axon guidance	0.0081	EPHA5, ABLIM1, PAK6, NRP1, FYN, SRGAP1, SLIT3
hsa04620:Toll-like receptor signaling pathway	0.0087	MAP3K7, TLR3, MAPK10, CD40, TLR7, TLR8
hsa04514:Cell adhesion molecules (CAMs)	0.0088	CADM3, CLDN18, OCLN, CD8A, NRXN2, CD40, JAM3
hsa04530:Tight junction	0.0093	CLDN18, OCLN, CNKSR3, TJP2, JAM3, PRKCB, CTNNB1
hsa04510:Focal adhesion	0.0098	PAK6, VEGFC, PDPK1, FYN, PDGFC, SHC1, MAPK10, PRKCB, CTNNB1
hsa04310:Wnt signaling pathway	0.0141	MAP3K7, WNT7B, PLCB4, PSEN1, MAPK10, PRKCB, CTNNB1
hsa04062:Chemokine signaling pathway	0.0273	ADCY3, PLCB4, CXCL16, GNB5, GRK4, SHC1, PRKCB

3.5 UM-related KEGG pathways

After interviewing CTD with a keyword “uveal melanoma” (Disease ID: MESH:C536494), a total of 49 UM-related KEGG pathways were filtered (Table S2). KEGG pathways including NOD-like receptor signaling pathway (hsa04621), Wnt signaling pathway (hsa04310), Vascular smooth muscle contraction (hsa04270), Chemokine signaling pathway (hsa04062), and Estrogen signaling pathway (hsa04915) have been reported to be associated with UM. Accordingly, we found there were only two overlapped KEGG pathways in Table 3 and Table S2,which were the Wnt signaling pathway and the chemokine signaling pathway.

3.6 Construction of Um-related ceRNA network

Then, the UM-related ceRNA network involving lncRNA, miRNA, mRNAs and two KEGG pathways was constructed. In addition to the two pathways (hsa04310 and hsa04062), the network included 21 nodes, including 6 lncRNAs (PVT1, H19, SNHG1, LINC02138, ADIRF-AS1, and FAM225A), 3 miRNAs (hsa-miR-106b, hsa-miR-1228, and hsa-miR-6836), and 12 mRNA (MAP3K7, WNT7B, PLCB4, PSEN1, MAPK10, PRKCB, CTNNB1, ADCY3, CXCL16, GNB5, GRK4, and SHC1, Figure 5). The regulatory network involved DE-lncRNA, DE-miRNA, DEGs, and UM-related KEGG pathways as well as the regulatory pairs and ceRNA interaction was constructed.

Figure 5.

The ceRNA regulatory network of lncRNA, miRNA and mRNAs involving UM-related KEGG pathways. Boxes, triangles, and circles note lncRNAs, miRNAs and mRNAs, respectively. The higher the log2FC value, the deeper the color. Green and red indicates down- and up-regulation, respectively. FC, fold change (color online).

4 Discussion

UM is widely known as a highly aggressive cancer. Most UM-related deaths are from metastasis. Over dozens of genes, miRNAs and lncRNAs have been identified as provisional indicators of metastasis in UM.^17–19 Although that, the metastatic probability of UM and metastatic mortality are still high. Our current study identified 6 lncRNAs (including PVT1, H19, SNHG1, LINCO2138, ADIRF-AS1, and FAM22A), 3 miRNAs (including hsa-miR-106b, hsa-miR-1228, and hsa-miR-6836), and 12 mRNA (including MAP3K7, WNT7B, PLCB4, PSEN1, MAPK10, PRKCB, CTNNB1, ADCY3, CXCL16, GNB5, GRK4, and SHC1) were tightly associated with UM metastasis through Wnt and Chemokine signaling pathways.

LncRNA PVT1 is widely known as an oncogenic RNA in a variety of human cancers, including UM.²⁰ Xu et al. reported that UM patients with relatively high PVT1 expression showed shorter overall survival times than patients with low PVT1 expression.²¹ LncRNA H19 and SNHG1 had been identified as oncogenic genes in breast cancer,²² and gliomas,²³ and non-small cell lung cancer (NSCLC).²⁴ Also, anti-H19 might be a new therapeutic agent for colorectal cancer.²⁵ SNHG1 contributed to NSCLC progression via sponging miR-330-5p and activating DCLK1.²⁴ H19 contributes to breast cancer tumorigenesis and metastasis by binding to ILF2.²² A new-found lncRNA ADIRF-AS1 was found to regulated miR-214-3p in intervertebral disc degeneration²⁶ and PBAF in renal clear cell tumorigenesis.²⁷ In our present study, we identified that all the aforementioned lncRNAs were upregulated in metastatic UM samples compared with non-metastatic samples. In addition, we identified two novel upregulated lncRNAs (LINCO2138 and FAM22A) in metastatic UM samples compared with control (non-metastatic samples), with a similar regulatory network to the other 4 lncRNAs. These results demonstrated that these 6 lncRNAs expression might be favorable for UM metastasis, and the experiments exploring the potential of using them as diagnostic indicators might be of great interest.

In our present study, we identified that the expression 3 miRNAs (hsa-miR-106b, hsa-miR-1228, and hsa-miR-6836) were positively correlated with the 6 lncRNAs in UM metastasis. Hsa-miR-106b expression promotes cancer cell growth, metastasis, radioresistance, and poor prognosis,^28,29 and miR-106b as an emerging therapeutic target in cancer.³⁰ Hsa-miR-6836 derived from esctracellular cesicles from tumor cells conferred cisplatin resistance of ovarian cancer cells.³¹ Our current study demonstrated the upregulations of hsa-miR-106b, hsa-miR-1228, and hsa-miR-6836 were associated with the metastasis in UM and were regulated by the 6 upregulated lncRNA as mentioned above. These revealed that these three miRNAs play novel roles in the progression and metastasis of UM.

Circular RNA ITCH suppressed metastasis through miR-106b-5p/PDCD4 axis in clear cell renal cell carcinoma.³² The lncRNA BRE-AS1 -miR-106b ceRNA axis inhibited proliferation, migration and invasion of clear cell renal cell carcinomaZou, 2024 #2901}. Our current study identified 12 genes (including MAP3K7, WNT7B, PLCB4, PSEN1, GNB5, MAPK10, and ADCY3) were specific or common targets of hsa-miR-106b, hsa-miR-1228 and/or hsa-miR-6836.

WNT7B is reported to be essential for epithelial-to-mesenchymal and Wnt/β-catenin signaling-dependent cellular progresses, including pancreatic cell proliferation and pancreatic development.³³ WNT7B repressed epithelial-to-mesenchymal and stem-like properties in bladder urothelial carcinoma.³³ However, higher WNT7B expression and Wnt/β-catenin signaling activation induced cystic epithelial metaplasia in pancreatic epithelium,³⁴ and lymph invasion and poor survival of patients with pancreatic adenocarcinoma.³⁵ Johansson et al. identified PLCB4 mutation correlated with UM tumorigenesis using whole-genome or whole-exome sequencing of UM tumor samples and cell lines.³⁶ Our current study showed that all these 12 genes (including MAP3K7, WNT7B, PLCB4, and MAPK10) in the final ceRNA network were downregulated in metastatic UM compared with non-metastatic samples, suggesting the downregulation of these genes might have been associated with (unfavorable or favorable) UM metastasis.

A limitation exist in this study. Identifying the genes with potential prognostic values in human diseases is of great value in the medical research.^37,38 There are many similar reports on this kind of research at present, based on a variety features, including immune,³⁷ energy metabolism,³⁹ and disulfidoptosis.³⁸ These prognostic signatures, genes, or ceRNA networks associated with clinical diagnosis closely and have strong clinical applicability. However, a common limitation in most of there references is the lack of experimental data to support the hypotheses and conclusions.

5 Conclusions

With the bioinformatics analysis of the GSE73652 dataset, we identified 6 upregulated lncRNAs, 3 upregulated miRNAs, and 12 downregulated genes related to UM metastasis. The involvement of UM-related Wnt/β-catenin and Chemokine signaling pathways together with the aforementioned 21 genetic factors in the final ceRNA regulatory network showed these noteworthy ceRNA regulatory axes in UM metastasis were of great interest.

Supplemental Material

sj-xls-1-thc-10.1177_09287329241291428 - Supplemental material for Identification of lncRNA-miRNA-mRNA ceRNA axes and KEGG pathways related to uveal melanoma metastasis

Supplemental material, sj-xls-1-thc-10.1177_09287329241291428 for Identification of lncRNA-miRNA-mRNA ceRNA axes and KEGG pathways related to uveal melanoma metastasis by Zhiyun Zhan, Huilong Chen, Ting Wang, Tingting Wang and Xionggang Chen in Technology and Health Care

Supplemental Material

sj-xls-2-thc-10.1177_09287329241291428 - Supplemental material for Identification of lncRNA-miRNA-mRNA ceRNA axes and KEGG pathways related to uveal melanoma metastasis

Supplemental material, sj-xls-2-thc-10.1177_09287329241291428 for Identification of lncRNA-miRNA-mRNA ceRNA axes and KEGG pathways related to uveal melanoma metastasis by Zhiyun Zhan, Huilong Chen, Ting Wang, Tingting Wang and Xionggang Chen in Technology and Health Care

Footnotes

Acknowledgments

The authors have no acknowledgments.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of The First Affiliated Hospital of Fujian Medical University.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of data and materials

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

Supplemental material

Supplemental material for this article is available online.

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