J Genomics 2018; 6:103-112. doi:10.7150/jgen.20581

Research Paper

Expression and network analysis of YBX1 interactors for identification of new drug targets in lung adenocarcinoma

Suriya Narayanan Murugesan1*, Birendra Singh Yadav1*, Pramod Kumar Maurya1*, Amit Chaudhary1*, Swati Singh2*, Ashutosh Mani1* Corresponding address

1. Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, India-211004.
2. Center of Bioinformatics, University of Allahabad, India-211002.
*All authors contributed equally.

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How to cite this article:
Murugesan SN, Yadav BS, Maurya PK, Chaudhary A, Singh S, Mani A. Expression and network analysis of YBX1 interactors for identification of new drug targets in lung adenocarcinoma. J Genomics 2018; 6:103-112. doi:10.7150/jgen.20581. Available from http://www.jgenomics.com/v06p0103.htm

Abstract

Y-Box Binding protein 1 (YBX-1) is known to be involved in various types of cancers. It's interactors also play major role in various cellular functions. Present work aimed to study the expression profile of the YBX-1 interactors during lung adenocarcinoma (LUAD). The differential expression analysis involved 57 genes from 95 lung adenocarcinoma samples, construction of gene network and topology analysis. A Total of 43 genes were found to be differentially expressed from which 17 genes were found to be down regulated and 26 genes were up-regulated. We observed that Polyadenylate-binding protein 1 (PABPC1), a protein involved in YBX1 translation, is highly correlated with YBX1. The interaction network analysis for a differentially expressed non-coding RNA Growth Arrest Specific 5 (GAS5) suggests that two proteins namely, Growth Arrest Specific 2 (GAS2) and Peripheral myelin protein 22 (PMP22) are potentially involved in LUAD progression. The network analysis and differential expression suggests that Collagen type 1 alpha 2 (COL1A2) can be potential biomarker and target for LUAD.

Keywords: YBX1, regulatory network, lung adenocarcinoma, RNA-Seq.

Introduction

Lung Adenocarcinoma, a subtype of non-small cell lung cancer is the most pervasive among lung cancers leading to the death of millions of people each year [1, 2]. Though the recent therapy for the lung adenocarcinoma with mutated EGFR and rearranged ALK have been significant [3, 4], but the other driving force for the progress of lung adenocarcinoma have not been deciphered much. The type of interaction among the gene and its environment decides their role in disease progression [5, 6]. Understanding the gene interactions and its importance in the regulation mechanism is necessary to identify a potential target for therapeutic application.

In this study, we analyzed the expression of Y- box binding protein 1 (YBX1) and its interactors in lung adenocarcinoma (LUAD). YBX1 belongs to the Y-box binding protein family with a highly conserved cold shock domain and known to be involved in various eukaryotic cellular mechanisms [7-9]. YBX1 is usually involved in RNA splicing and translational mechanism in cytoplasm but, it translocates to nucleus and gets involved in transcriptional regulation during stress condition [10-13]. It is associated with drug resistance, cell proliferation and cell death [14]. The transcription factor of YBX1 binds to the E-box for regulating it [15].

YBX1 is over expressed in various cancers and it has been suggested as biomarker in prognosis of various cancer types [16, 17]. Multi drug resistance and metastasis are major reason for it's over expression [18, 19]. YBX1 interactors are known to be involved in various cellular mechanisms ranging from cell signaling, DNA and protein repair mechanism to transcriptional regulation [20-26]. Genes which are known to be involved in regulation of YBX1 as well as those regulated by YBX1 has been listed in (Table 1).

In this study we investigated the role of YBX1 and its interactors in Lung adenocarcinoma by looking at their expression profile and constructed gene regulatory network in order to decipher their importance in network formation by a system biological approach.

 Table 1 

Genes regulates and regulated by YBX1

Gene- IdGeneReference
Genes Up- Regulates YBX1 | 4904
TP73 | 7161Tumor Protein 73[17]
MYC | 4609v-myc avian myelocytomatosis viral oncogene homolog
MAX | 4149MYC Associated Factor X
TWIST1 | 7291Twist basic helix-loop-helix transcription factor 1[14]
PABPC1 | 26986Poly(A) binding protein, cytoplasmic 1[18]
GATA1 | 2623GATA binding protein 1[19]
GATA2 | 2624GATA binding protein 2
PTGER1 | 5731Prostaglandin E receptor 1 (subtype EP1)[20]
SHH | 6469Sonic Hedgehog[21]
Genes Down- Regulates YBX1 | 4094
FOXO3 | 2309Forkhead Box 03[15]
ILK | 3611Integrin linked kinase[16]
TGFB1 | 7040Transforming growth factor, beta 1[22]
C1QBP | 708Complement component 1, q subcomponent binding protein[23]
GAS5 | 60674Long non coding RNA growth arrest specific transcript 5[24]
KAT2B | 8850K (lysine) acetyltransferase 2B[25]
Genes Activated by YBX1 | 4904
CCL5 | 6352Chemokine (C-C motif) ligand 5[26,27]
CD44 | 960CD44 molecule[28]
ITGA6 | 3655Integrin, alpha 6
MMP2 | 4313Matrix metallopeptidase 2[29,30]
POLA1 | 5422Polymerase (DNA directed), alpha 1, catalytic subunit[31]
EGFR | 1956Epidermal growth factor receptor[32]
ERBB2 | 2064V-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2
MET | 4233Met proto-oncogene[33]
ABCB1 | 5243ATP-binding cassette, sub-family B (MDR/TAP), member 1[34,35]
MVP | 9961Major vault protein[36]
PDGFB | 5155Platelet-derived growth factor beta polypeptide[37]
PTPN1 | 5770Protein tyrosine phosphatase, non-receptor type 1[38]
SMAD7 | 4092SMAD family member 7[39]
CCNA1 | 8900Cyclin A1[40]
CCNA2 | 890Cyclin A2
CCNB1 | 891Cyclin B1
PIK3CA | 5290Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha[41]
Genes Repressed by YBX1 | 4904
ACTA1 | 58Actin, alpha 1, skeletal muscle[42,43]
COL1A1 | 1277Collagen, type I, alpha 1[44,45]
COL1A2 | 1278Collagen, type I, alpha 2[46]
CPS1 | 1373Carbamoyl-phosphate synthase 1, mitochondrial[47]
FAS | 355Fas cell surface death receptor[5]
CSF2 | 1437Colony stimulating factor 2 (granulocyte-macrophage)[48,49]
HSPA5 | 3309Heat shock 70kda protein 5[50]
MMP12 | 4321Matrix metallopeptidase 12[51]
MMP13 | 4322Matrix metallopeptidase 13[52]
HLA-A | 3105Major histocompatibility complex, class I, A[53]
HLA-B | 3106Major histocompatibility complex, class I, B
HLA-C | 3107Major histocompatibility complex, class I, C
HLA-E | 3133Major histocompatibility complex, class I, E
HLA-F | 3134Major histocompatibility complex, class I, F
HLA-G | 3135Major histocompatibility complex, class I, G
HLA-DRA | 3122Major histocompatibility complex, class II, DR alpha[54-56]
B2M | 567Beta-2-microglobulin
HLA-DQB1 | 3119Major histocompatibility complex, class II, DQ beta 1
ABCC2 | 1244ATP-binding cassette, sub-family C (CFTR/MRP), member 2[57]
CDKN1A | 1026Cyclin-dependent kinase inhibitor 1A (p21, Cip1)[58]
TP53 | 7157Tumor protein p53[59]
TSHR | 7253Thyroid stimulating hormone receptor[60]
VEGFA | 7422Vascular endothelial growth factor A[61,62]
SOX2 | 6657SRY (sex determining region Y)-box 2[63]

Materials and Methods

RNA Seq data and Sample Quality Analysis

The Cancer Genome Atlas (TCGA) RNA-Seq level 3 data for YBX1 and its interactors belonging to 116 normal and normal matched tumor samples in LUAD were downloaded from Broad Genome Data Analysis Center (GDAC) Firehose site (https://gdac.broadinstitute.org/). RNA Sequence by Expectation Maximization (RSEM) [69] counts for 57 genes of our interest including YBX-1 were used for differential expression analysis.

Principle Component Analysis (PCA) and hierarchical clustering with log transformed datasets of the samples were performed using R-Bioconductor [70,71] package DESeq2 [72] to estimate sample dispersion and to filter out the outlier.

Differential Expression (DE) Analysis and Correlation between genes

R-Bioconductor package DESeq2 was used to carry out differential expression analysis with nbionom Wald test for calculating logarithmic fold change and Benjamini- Hochberg method [73] for estimating adjusted-p value. Genes with adjusted-p value less than 0.05 were considered as differentially expressed. Correlation between differentially expressed genes was calculated with Pearson Correlation method using R package Hmisc [74] and the plot for the correlation coefficient was constructed using R package Corrplot [75].

Gene Regulatory Network Construction and Analysis

Gene regulatory network was constructed for the 56 genes omitting GAS5 using the GeneMANIA [76] with maximum resultant gene and attributes to enrich were 30 and 215 respectively. And the constructed network was analyzed for its network topology by looking at its parameters like closeness centrality, betweenness centrality and degree with network analysis tool in Cytoscape 3.4 [77]. RAIN (RNA - protein Association and Interaction Network) [78] was used to identify interactors for the long non-coding RNA (lncRNA) GAS5. And Search Tool for Retrieval of Interacting Genes (STRING) database [79] was used to analyze the network and the interactions with confidence level of 0.7 were considered significant. Network with not more than 10 interactors for the GAS5 was constructed.

Gene Set Enrichment Analysis

Functional enrichment for the genes common to both differentially expressed gene sets and hub genes from network was done by using Database for Annotation, Visualization and Integrated Discovery (DAVID) [80]. Gene Ontology (GO) database [81] provides the annotation for the gene set and Reactome pathway [82] illustrate the pathways in which the genes were involved. With DAVID tool we analyzed the gene set in both GO and Reactome pathway with p-value < 0.05 and gene-count > 3 as condition for enrichment analysis.

Results

Assessment of Sample Quality and Filtering

In this study, we initially took 116 LUAD samples with TCGA level-3 data. With Principle Component Analysis (PCA) and hierarchical clustering removed samples whichever showed variation within its group (Figure S1). And we plotted again PCA and hierarchical clustering for the final 95 samples with 57 genes. After filtering, two methods now able to distinguish the samples based on their conditions- normal or tumor (Figure 1).

Differential Expression Analysis and Correlation between Genes

The 43 genes which have adjusted-p value less than 0.05 were selected as differentially expressed with GATA1 having a high negative fold change (-1.91476) and MMP13 having a high positive fold change (5.81790). And correlation matrix between the differentially expressed genes was constructed and the correlation coefficient with p-value less than 0.05 were considered significant (Figure 2). YBX1 showed a maximum positive correlation with C1QBP (0.592) and negative correlation with HLA-E (-0.281) (Table S1).

Gene Network Construction and Analysis

Gene network was constructed and analyzed (Figure 3), it has 86 nodes and 3611 interactions (Table 2). Top 10 genes involved in the network formation were selected based on the topology parameter-degree, betweenness centrality and closeness centrality (Table 3). The attributes from GeneMANIA showed cancer pathways hold 15.34% in network with involvement of 17 genes (Table 4).

As GeneMANIA construct network and interactions for the coding gene, it was not able to predict for GAS5. So RAIN database which identify the interactors for non-coding RNA was used to identify interactors of GAS5. Total 10 interactors were found and the network was constructed between the genes with confidence level of 0.7 using STRING database (Figure 4)

 Figure 1 

Quality of the samples after filtering. (1A) Heatmap shows the unsupervised hierarchical clustering of the normal and tumor samples in LUAD after filtration. The row represents the genes and column represent the samples. Normal and tumor samples are clustered within their group based on their Euclidean distance. (1B) Principle Component Analysis for the filtered samples shows that first principle component (PC1) separates the normal and tumor samples. In both analysis, samples are found to be grouped within their type.

J Genomics Image (Click on the image to enlarge.)
J Genomics Image (Click on the image to enlarge.)
 Figure 2 

Correlation plot between the differentially expressed genes in LUAD. Correlation with the significance p- value (<0.05) are shown. Insignificant correlation are left blank without colors.

J Genomics Image (Click on the image to enlarge.)
 Table 2 

Interaction type and number of interactions in the network constructed by GeneMANIA

Type of InteractionsNumber of Interactions
Co-expression1761
Co-localization116
Genetic Interaction98
Pathways182
Physical Interaction462
Predicted13
Shared Protein Domain979

Gene Set Enrichment Analysis

The annotation of the gene with GO and Reactome pathways using DAVID resulted in a single cluster with genes enriched in GO- cellular components of proteinaceous extracellular matrix and extracellular region. GO- Biological process contained the collagen catabolic process and Reactome pathways included Generic transcriptional regulation. Only 3 genes were involved in the enrichment when the condition for enrichment was made high, namely- COL1A2, MMP12, MMP13. The enriched terms are shown in Table 5.

 Table 3 

Central genes in Gene Regulatory network constructed using GeneMANIA and analyzed by Cytoscape- Network Analysis Tool. Top 10 gene with high Closeness Centrality, Betweenness Centrality and Degree.

GenesCloseness CentralityGenesBetweenness CentralityGenesDegree
MMP131.000HLA-DRA0.018HLA-DRA317
YBX-11.000B2M0.017HLA-DMA274
TSHR1.000HLA-A0.015HLA-A266
ABCC20.800HLA-C0.014HLA-DPB1266
KATA2B0.750CDKN1A0.014HLA-DPA1257
ABCB10.750ILK0.014B2M255
COL1A20.700HLA-B0.010HLA-G252
CDKN1A0.689HLA-DMB0.010HLA-DMB246
MMP 120.667VEGFA0.009HLA-F241
MYC0.667HLA-DRB10.008HLA-DRB1226
 Figure 3 

Gene network constructed and analyzed with Cytoscape. Directed root network built from the gene interaction data obtained from GeneMANIA. Network has 86 nodes with 3611 interactions. Figure 3A, 3B and 3C highlights the top 10 genes with closeness centrality, betweenness centrality and degree respectively. CDKN1A is the common gene between closeness centrality and betweenness centrality. Five genes namely, HLA-DRA, HLA-A, HLA-DRB1, HLA-DMB and B2M showed highest betweenness centrality and degree.

J Genomics Image (Click on the image to enlarge.)
J Genomics Image (Click on the image to enlarge.)
J Genomics Image (Click on the image to enlarge.)
 Figure 4 

Interactors of GAS5 derived from RAIN database. Interaction with confidence level > 0.7 is considered as significant. Among the interactors GAS2 and PMP22 are the proteins involved in interaction with GAS5.

J Genomics Image (Click on the image to enlarge.)
 Table 4 

Genes involved in Cancer Pathways, known from GENEMANIA network attributes.

Gene IdGene
CCNAI | 8900Cyclin A
VEGFA | 7422vascular endothelial growth factor A
MMP2 | 4313Matrix Metallopeptidase 2
MET | 4233Met proto-oncogene
MAX | 4149MYC associated factor X
SHH | 6469Sonic hedgehog
TGFB1 | 7040Transforming Growth Factor, Beta 1
ITGA6 | 3655Integrin, alpha 6
FAS | 355Fas cell surface death receptor
TP53 | 7157Tumor Protein p53
PDGFB | 5155Platelet-derived growth factor beta polypeptide
CDKN1A | 1026Cyclin-dependent kinase inhibitor 1A
ERBB2 | 2064v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2
PIK3CA | 5290Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha
EGFR | 1956Epidermal growth factor receptor
CDKN1B |Cyclin-dependent kinase inhibitor 1B
 Table 5 

Gene Enrichment Analysis- Gene Ontology (GO) and Reactome Pathways (RP) enriched by genes common to differential expression and hub gene in the gene regulatory network.

GroupTermGene Countp-value
GO-BPGO:0030574~collagen catabolic process31.419E-4
GO-CCGO:0005578~proteinaceous extracellular matrix30.002
GO-CCGO:0005576~extracellular region30.065
RPR-HSA-1442490:R-HSA-144249034.829E-4

Discussion

Despite of various studies carried out on YBX1, role of its interactors in lung adenocarcinoma have been less explored. YBX1 have been involved in the regulation of tumor progression in lung adenocarcinoma [83, 84], so understanding its interactions with other gene become a necessary to know their mechanism in disease progression and drug discovery. Our present study finds that YBX1 is up regulated with a low fold change (0.224). GATA 1 and GATA 2 which upregulate YBX1 level in erythroid cells [20] were found to be down regulated that indicates that they don't play major role in regulation of YBX1 in lung adenocarcinoma.

Among the genes which upregulate YBX1, PABPC1 which is involved in the translation of YBX1 mRNA [85] was found to be having high correlation (> 0.4) with YBX1 and it was significantly expressed with a fold change of 1.259. C1QBP which is known to be involved in prostate cancer progression [86] and a highly negative regulator of YBX1 in renal cell carcinoma [87] is highly correlated with YBX1 along with GAS5, a long non-coding RNA which involves in regulating cell death in prostate cancer [88]. Among the GAS5 interactors identified from RAIN database there are two proteins namely Growth Arrest Specific 2 (GAS2) and Peripheral myelin protein 22 (PMP22) which are known to be over expressed in colorectal cancer cell and breast cancer patients [89, 90] respectively. However the evidence regarding their role in LUAD disease progression needs to be explored for being a potential biomarker in LUAD identification and for therapeutic application.

Comparison of the differentially expressed genes and the hub genes in the network formation revealed that MMP13, ABCC2, MMP12, TSHR, COL1A2, PABPC1 and YBX1 are common among the both groups. Except for PABPC1, rest of the genes are usually repressed by YBX1, but in our study they are among the highly up regulated genes making a need for further study to understand their relationship between YBX1 in LUAD. Among the three genes involved in the enrichment, MMP12 and MMP13 are reported to be involved in the lung cancer in earlier studies [90- 94]. Significantly COL1A2 is involved in progress of gastric cancer [95] and in head and neck cancer [96]. It can serve as a potential biomarker in LUAD and its involvement in the disease progress need to be explored.

Conclusion

The study aimed to investigate the role of YBX1 and its interactors in lung adenocarcinoma by looking at their differential expression and network topology study. The study showed PABPC1 can be potential target in regulating YBX1. The lncRNA GAS5, whose role in the LUAD need to be explored and it can be a potential biomarker along with COL1A2.

Supplementary Material

Attachment

Supplementary figures and tables.

Acknowledgements

AM is highly thankful to SERB New Delhi for a project grant. SNM is highly thankful to MNNIT Allahabad for a fellowship.

Competing Interests

The authors have declared that no competing interest exists.

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Corresponding address Corresponding author: amaniac.in


Received 2017-4-15
Accepted 2017-8-31
Published 2018-6-26