Submitted 22 May 2019Accepted 19 August 2019Published 17 September 2019

Corresponding authorsShuaibin Lian,shuai_lian@xynu.edu.cn,shuai_lian@qq.comLei Wang, wangleibio@126.com

Academic editorGiulia Piaggio

Additional Information andDeclarations can be found onpage 17

DOI 10.7717/peerj.7696

Copyright2019 Lian et al.

Distributed underCreative Commons CC-BY 4.0

OPEN ACCESS

The co-expression networks ofdifferentially expressed RBPs with TFsand LncRNAs related to clinical TNMstages of cancersShuaibin Lian1,*, Liansheng Li2,*, Yongjie Zhou1, Zixiao Liu1 and Lei Wang2

1College of Physics and Electronic Engineering, XinYang Normal University, Xinyang, HeNan, China2College of Life Sciences, XinYang Normal University, Xinyang, HeNan, China*These authors contributed equally to this work.

ABSTRACTBackground. RNA-binding proteins (RBPs) play important roles in cellular homeosta-sis by regulating the expression of thousands of transcripts, which have been reportedto be involved in human tumorigenesis. Despite previous reports of the dysregulationof RBPs in cancers, the degree of dysregulation of RBPs in cancers and the intrinsicrelevance between dysregulatedRBPs and clinical TNM information remains unknown.Furthermore, the co-expressed networks of dysregulated RBPs with transcriptionalfactors and lncRNAs also require further investigation.Results. Here, we firstly analyzed the deviations of expression levels of 1,542 RBPs from20 cancer types and found that (1) RBPs are dysregulated in almost all 20 cancer types,especially in BLCA, COAD, READ, STAD, LUAD, LUSC and GBM with proportionof deviation larger than 300% compared with non-RBPs in normal tissues. (2) Up-and down-regulated RBPs also show opposed patterns of differential expression incancers and normal tissues. In addition, down-regulated RBPs show a greater degree ofdysregulated expression than up-regulated RBPs do. Secondly, we analyzed the intrinsicrelevance between dysregulated RBPs and clinical TNM information and found that(3) Clinical TNM information for two cancer types—CHOL and KICH—is shownto be closely related to patterns of differentially expressed RBPs (DE RBPs) by co-expression cluster analysis. Thirdly, we identified ten key RBPs (seven down-regulatedand three up-regulated) in CHOL and seven key RBPs (five down-regulated and twoup-regulated) in KICH by analyzing co-expression correlation networks. Fourthly, weconstructed the co-expression networks of key RBPs between 1,570 TFs and 4,147lncRNAs for CHOL and KICH, respectively.Conclusions. These results may provide an insight into the understanding of thefunctions of RBPs in human carcinogenesis. Furthermore, key RBPs and the co-expressed networks offer useful information for potential prognostic biomarkers andtherapeutic targets for patients with cancers at the N andM stages in two cancer typesCHOL and KICH.

Subjects Bioinformatics, GenomicsKeywords RNA-binding proteins (RBPs), Co-expression networks, Clinical TNM system,Differential expression, Transcriptional factors, lncRNAs

How to cite this article Lian S, Li L, Zhou Y, Liu Z, Wang L. 2019. The co-expression networks of differentially expressed RBPs with TFsand LncRNAs related to clinical TNM stages of cancers. PeerJ 7:e7696 http://doi.org/10.7717/peerj.7696

INTRODUCTIONRecent research has highlighted the importance of changes in RNA metabolism in themechanisms of carcinogenesis, including long non-coding RNAs (lncRNAs). RNAtranscription, maturation, transportation, stabilization, degradation, and translationare molecular processes that regulate the cell cycle, as well as cell survival. The keysto regulating RNA metabolism are a group of proteins called RNA-binding proteins(RBPs), which participate in many steps at the post-transcriptional regulation level, andthereby determine the fate and function of each transcriptional transcript in the cell (Fu& Ares, 2014; Stefanie, Markus & Thomas, 2014; Moore & Proudfoot, 2009). Furthermore,dysregulated expression of some RBPs can lead to disease, including neurological disordersand cancers (Wang et al., 2018; Kechavarzi & Janga, 2014). For instance, PAIP1 is proposedas a novel prognostic biomarker by affecting breast cancer cell growth (Piao et al., 2018).TRNAU1AP has been confirmed to play an important role in the regulation of cellproliferation and migration via the PI3K/Akt signaling pathway (Hu et al., 2018). SRPRis reported to regulate keratinocyte proliferation by affecting cell cycle progression andtend to show high expression in epidermal keratinocytes (Kim et al., 2016). RBMS3 hasbeen found to inhibit breast cancer cell proliferation and tumorigenesis by inactivating theWnt/β-catenin signaling pathway (Yang, Quan & Ling, 2018). In addition, overexpressionof RPL34 is suggested to promote malignant proliferation of non-small cell lung cancer(NSCLC) (Yang et al., 2016). Silencing RPL34 plays a blocking role in cell proliferationand metastasis, but promoting cell apoptosis of oral squamous cell carcinomas (OSCCs)(Dai & Wei, 2017; Liu et al., 2015). Moreover, the splicing regulator PTBP2 is suggestedto control a network of genes involved in germ cell adhesion, migration, and polarityand is also very essential for neuronal maturation (Qin et al., 2014; Molly, Leah & Donny,2017; Leah, Sarah & Thomas, 2015). However, some RBPs can act as tumor suppressors.For example, ZNFX1-AS1 is reported to suppress HCC progression via regulating themethylation of miR-9 (Wang et al., 2016). In addition, the silencing of SRSF7 affects theexpression of osteopontin splice variants and decreases the proliferation rate of renal cancercells (Boguslawska et al., 2016). Finally, RPL22/eL22, as a cancer-mutated RBP, tend to beanti-cancer via regulation of the MDM2-p53 feedback loop (Cao et al., 2017a; Cao et al.,2017b).

Consequently, improving our understanding of the characteristics of RBPs and non-RNA-binding proteins (non-RBPs) is an essential step for understanding their rolesin tumorigenesis. Even though recent studies have shown that RBPs are predominantlydysregulated in cancers relative to normal tissues (Wang et al., 2018; Bobak & Sarath, 2014).But, the intensity of dysregulation of RBPs in different cancers is still need to be investigated.Furthermore, cancer is a complex genetic disease. The different developmental stages ofcancer indicate different degrees of severity. The early detection of cancer is linked toimproved survivorship. The tumor-node-metastases (TNM) system, formed by the Unionfor International Cancer Control (UICC) and the American Joint Committee on Cancer(AJCC), is the most widely used cancer staging system (Sobin, Gospodarowicz & Wittekind,1992; American Joint Committee on Cancer, 2010). The TNM system provides information

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about the prognosis of the disease of patient based on pathologist evaluations of resectedspecimens. This information is also generally used to plan cancer treatment regimens. Dueto the dysregulation of RBPs in many cancers, it is also interesting to determine whetherthere is an intrinsic relevance between RBP dysregulation and the developmental stages ofcancers.

Transcription factors (TFs) perform the first step in interpreting the genome byrecognizing specific DNA sequences to control transcription and gene expression (Lambertet al., 2018). As the unique gene class, TFs represent the proteins whose binding sites areaffected by various regulatory variants in DNA. An accumulating genome-wide associationstudy (GWAS) shows that the mutations of TFs or TF-binding sites are closely related tomany human cancers (reviewed in Deplancke, Alpern & Gardeux, 2016), such as gastriccancer (Yin et al., 2017), liver cancer (Cao et al., 2017a; Cao et al., 2017b), prostate cancer,colorectal cancer (Saijo et al., 2016) and breast (Humphries et al., 2017) cancer. Severalstudies have emerged to identified the regulatory mechanisms and interactions (Drissiet al., 2015; Zhang, Shen & Cui, 2019). Yet, in most cases, we still do not know how tointerpret the regulatory interactions between RBPs and TFs. Furthermore, long non-codingRNAs (lncRNAs)-transcripts of greater than 200 nucleotides are of vital importance intranscriptional and post-transcriptional levels (Ishizuka et al., 2014). Several evidences havedemonstrated that the expression of lncRNAs is closely related to human diseases (Yuanet al., 2017; Vergara et al., 2012), such as viral infections, neurological disease and cancers(Gibb, Brown & Lam, 2011). In particular, the expression levels of lncRNAs in tumortissues are significantly different compared with the normal tissues (Gloss & Dinger, 2015).Moreover, lncRNAs have merged as the novel biomarkers in several diseases diagnosis andtargets for therapeutics (Zhou et al., 2013). It is well established that transcription factorsand long non-coding RNA have played a central role in the genetics of human diseases(St. Laurent, Wahlestedt & Kapranov, 2015). Yet, up to now, we are far from being able toknow the regulatory interactions and mechanisms of RNA-binding proteins (RBPs) withTFs and lncRNAs. For example, whether there is an interaction between RBPs and TFs inthe process of carcinomatosis and what types of TFs affect the regulatory interaction most?In which stage of cancers do RBPs affect the expression of lncRNA the most?

To comprehensively characterize intensity of dysregulated RBPs from many humancancers (Wang et al., 2018) and construct their interactive networks with TFs and lncRNAsrelated to TNM stages, we first assessed the deviations of gene expression levels of RBPs andnon-RBPs in 20 types of cancerous and normal (control) tissues, respectively (Chang etal., 2013) and analyzed the biological and molecular functions of dysregulated RBPs.Second, we analyzed the relationship between RBP dysregulation and TNM systemclinical data of 5,093 patients across 13 types of cancers. We found out two types ofcancers—cholangiocarcinoma (CHOL) and kidney chromophobe (KICH)—show asignificant relationship between RBP dysregulation and TNM stage information. Third,we constructed the interaction networks for dysregulated RBPs related to TNM stageinformation and TFs of CHOL and KICH, respectively. Fourth, we constructed theinteraction networks for key RBPs related to metastases (M) stage and lncRNAs for CHOLand KICH, respectively (see Fig. 1, which outlines the computational workflow, and

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TCGA HTSeq-Counts

RBPs

Cancer-related Risk Modules

RBP information

WGCNA

GO Functional Annotation

TCGA HTSeq-Clinical

TCGA HTSeq-FPKM

Network heatmap plot, all module genes

TCGA TNM information

Networks of key RBPs and lncRNAs

Networks of key RBPs and TFs

DESeq2 and edgeR, LogFC and P_value

DEG=DESeq2∩edgeR

RBP expression heatmap

nonRBPs

Networks of RBPs and nonRBPs

Figure 1 Workflow chart showing the different steps presented in this study. The flow chart shows theacquisition and preparation of data (Pink box), differentially expressed genes analysis and module detec-tion (light yellow box), networks construction and function analysis (pale green box). RBP, RNA-bindingprotein; TCGA, The Cancer Genome Atlas; TF, transcription factor; lncRNA, long non-coding RNA.

Full-size DOI: 10.7717/peerj.7696/fig-1

Materials and Methods). This enabled us to identify the key regulatory RBPs for bothCHOL and KICH cancers.

MATERIALS & METHODSDifferential expression analysisWe downloaded the original data including count matrix, expression FPKM values andclinical informationof 20 cancer types andpairednormal tissues fromTCGAdata base usingSangerBox tool (SangerBox, http://sangerbox.com/). Then, we applied edgeR (Robinson,Mccarthy & Smyth, 2010) and DESeq2 (Anders, 2009) to select differentially expressedgenes (DEGs) from count matrix for each cancer type with parameter padj < 0.05, and|log2FC|> 1. Thirdly, according 1,542RBP genes information (Stefanie, Markus & Thomas,2014), we divided the DEGs into two types for each cancer types: RBPs and non-RBPs. Theexpression FPKM values were used to compute the degree of dysregulation of DEGs andconstruct the co-expression networks. Clinical TNM information for 5,093 patients wereused to co-expressed gene module detection. Our datasets contained 1,542 RBPs, 1,570 TFsand 4,147 lncRNAs; data acquisition information was presented in the Data Availabilitysection. The ‘pheatmap’ package in R was used to generate a heatmap of differentiallyexpressed RBPs shared by 16 cancer types in six systems based on log2-fold change values.

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The Pearson coefficient (R) was used to judge the similarity of RBPs genes in differentcancers occurring at related tissues have similar expression patterns. The gene cluster withR > 0.5 was considered as having the similar expression pattern. The gene ontologycategorization analysis tool DAVID (Huang, Sherman & Lempicki, 2009a; Huang, Sherman& Lempicki, 2009b) was used to determine biological processes and molecular functions ofDE RBPs.

Standard deviation and proportion of dysregulationIn order to investigate the degree of dysregulation of RBPs, we computed the standarddeviation of differentially expressed genes listed in Table 1 using the ‘std’ function in‘MATLAB’ version R2015b, respectively. In detail, we firstly computed the standarddeviation of differentially expressed RBPs in 20 cancers and paired normal tissues (Fig. 2A).Similarly, we next computed the standard deviation of differentially expressed non-RBPsin 20 cancers and paired normal tissues (Fig. 2B).

The definition of the standard deviation is as follows:

σ =1N

√√√√ N∑i=1

(xi−µ)2.

Here, N is the number of genes, xi isthe expression FPKM value of the ith gene, µ isthe mean FPKM value of all N genes. Larger σ values represent gene expression valuesthat deviate from the mean value to a greater degree, which is indicative of greater genedysregulation. The standard deviation values for RBP expression data is presented inTable S1.

In order to more clearly see the expression differences of RBPs and non-RBPs in cancersand normal tissues, we further computed their relative deviations. In detail, we firstcomputed the relative deviation of RBPs in cancers and paired normal tissues, and then wecomputed the relative deviation of non-RBPs in cancers and normal tissues (Fig. 2C). Therelative proportion of deviation is computed using the following function : p= σc

σn. Here,

σc and σn are the standard deviations of the gene expressions in cancers and normal tissuesrespectively.

Similarly, in order to investigate the expression differences of up- and down-regulatedRBPs, we also computed the standard deviation of them in cancers and normal tissues,respectively (Figs. 2D, 2E). Finally, the mean expression values of up- and down-regulatedRBPs in 20 cancers and paired normal tissues were computed and presented in Figs. 2F &2G.

Weighted co-expression network analysisWeighted gene co-expression network analysis (WGCNA) (Peter & Steve, 2008) is acomprehensive R package that summarizes and standardizes methods and functionsfor co-expression network analysis. Module detection function of WGCNA was usedto detect the correlations between co-expression gene modules and the clinical TNMinformation for 13 types of cancers with default settings one by one. Then, the thresholdof correlation coefficient R> 0.5 and statistical significance P < 0.05 was used to select

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Table 1 Differentially expressed RBPs and non-RBPs across 20 types of cancers in seven systems.

System Cancer Cancer type TotalDEG genes

Totalnon-RBPs

TotalRBPs

Up-regulatedRBPs

Down-regulatedRBPs

Landular system PAAD Pancreatic adenocarcinoma 260 259 1 1 0THCA Thyroid carcinoma 4,804 4,765 39 14 25PRAD Prostate adenocarcinoma 4,777 4,722 55 34 21HNSC Head and neck squamous cell carcinoma 7,421 7,335 86 32 54

Respiratory system LUSC Lung squamous cell carcinoma 12,235 11,972 263 54 209LUAD Lung adenocarcinoma 9,004 8,859 145 35 110

Alimentary system READ Rectum adenocarcinoma 8,620 8,430 190 60 130COAD Colon adenocarcinoma 8,954 8,773 181 44 137STAD Stomach adenocarcinoma 9,037 8,932 105 39 66

Urinary system KICH Kidney chromophobe 9,926 9,758 168 76 92KIRC Kidney renal clear cell carcinoma 11,478 11,370 108 37 71KIRP Kidney renal papillary cell carcinoma 8,293 8,189 104 33 71BLCA Bladder urothelial carcinoma 7,146 7,025 121 53 68

Reproductive system CESC Cervical squamous cell carcinoma and endocervicaladenocarcinoma

4,302 4,154 148 57 91

UCEC Uterine corpus endometrial carcinoma 9,046 8,869 177 64 113BRCA Breast invasive carcinoma 7,426 7,298 128 36 92

Nervous system GBM Glioblastoma multiforme 11,946 11,628 318 118 200PCPG Pheochromocytoma and paraganglioma 4,717 4,560 157 101 56

Liver and gall system LIHC Liver hepatocellular carcinoma 6,677 6,576 101 25 76CHOL Cholangiocarcinoma 10,219 10,030 189 80 109

Lianetal.(2019),PeerJ,D

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(A).

CancerNormal

Valu

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RBP

non-RBP

0

5

10

0

10

20

30

0

10

20

30

down_RBPup_RBP

BLC

A

CE

SC

GB

M

HN

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KIR

CK

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LUA

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BLCA

CESC

GBM

HNSC

KIRC

KIRP

LUAD

LUSC

PRAD

STAD

THCA

CHOLLIHC

BRCA

COAD

KICH

READUCEC

PCPGPAAD

RBPnon-RBP

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p=0.0011

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p=0.0962

p=0.0179

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(B).

(C).

(E). (G).

Figure 2 The different expression patterns of RBPs and non-RBPs in cancers and normal tissues. (A)The standard expression deviation of RBPs in 20 types of cancers and normal tissues. (B) The standard ex-pression deviation of non-RBPs in 20 types of cancers and normal tissues. (C) The relative expression de-viation of RBPs and non-RBPs. (D) The standard expression deviation of up- regulated RBPs in cancersand normal tissues. (E) The standard expression deviation of down- regulated RBPs in cancers and normaltissues. (F) The expression FPKM values of up- regulated RBPs in cancers and normal tissues. (G) The ex-pression FPKM values of down- regulated RBPs in cancers and normal tissues. Significance values calcu-lated from the Mann–Whitney U test are shown.

Full-size DOI: 10.7717/peerj.7696/fig-2

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the cancer type whose TNM information related to co-expressed gene module. Genes withsame expression pattern were clustered into one module and marked with same color. Andthen, the network construction module was used to construct co-expression networks forRBPs with TFs and lncRNAs in the CHOL andKICH cancer types, respectively. The detailedsteps of constructing networks are as follows. Firstly, WGCAN network construction toolwas used to generate the nodes and edges of genes by computing correlations of expressionvalues. The nodes corresponded to genes, and the edges were determined by the pairwisecorrelations between the expression levels of genes. The corresponding called function inthe R package was ‘blockwiseModules’ and the parameters were set as follows: ‘powers =10, minModuleSize = 30, mergeCutHeight = 0.25’, other parameters we set to the defaultsetting. Secondly, nodes with correlation r < 0.5 and edges with weighted threshold <0.3were removed. Finally, the Cytoscape (https://cytoscape.org/) tool was used to plot theinteractions using the nodes and edges of conserved genes.

Clinical TNM information processingClinical TNM information for 5,093 patients exhibiting 13 types of cancers downloadedfrom the TCGA database presented in Table S4. Generally, in the TNM system, ‘T ’ refersto a primary tumor, ‘T1∼T4’ represents the severity of primary cancer according tothe increase in tumor volume and the extent of involvement of adjacent tissues, and‘T0’ indicates no primary tumor. ‘N’ represents the tumor spreading to regional lymphnodes. ‘N1∼N3’ represents the degree of spreading according to the extent of lymphnode involvement. ‘M ’ refers to tumor metastasis. No distant metastasis is expressed by‘M0’, and distant metastasis is expressed by ‘M1’. To investigate the correlation betweenTNM information and gene expression values using WGCNA, we converted the TNMinformation into a weighted matrix. For example, if TNM information for a patient was‘T1-N3-M1’, the corresponding weighted array is [1 3 1].

Statistical methodsWe used the Mann–Whitney U -test (function ‘ranksum’ in software‘MATLAB’ versionR2015b) to examine whether there is statistical significance between given two samples,the default significance level is 0.05 (Lian et al., 2018).

RESULTSThe deviations of expression levels of RBPs between cancerous andnormal tissuesTo investigate the degrees of dysregulation of RBPs in many human cancers, we computedthe standard deviations of gene expression levels of RBPs and non-RBPs in 20 types ofcancerous and normal (control) tissues, respectively. These results indicate that, relativeto normal tissue, RBPs show greater variation in expression in almost all types of cancer(P < 0.05, Mann–Whitney U test, Fig. 2A). In contrast, non-RBPs show considerably lessvariation in expression in cancers relative to normal tissues (P < 0.05, Mann–WhitneyU test, Fig. 2B). These results indicate that RBPs show a greater degree of dysregulatedexpression than non-RBPs in almost 20 types of cancers. Furthermore, the degrees of

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dysregulation of RBPs in different cancer types are significantly different. In particular,RBPs show severely dysregulated expression in eight types of cancers, including BLCA,LUAD, STAD, READ, LUSC, GBM and COAD. For these cancers, the correspondingrelative proportions of dysregulation relative to normal tissues are 365.5%, 415.8%, 446.4%,456.3%, 494.5%, 540.9% and 759.1%, respectively (Fig. 2C, Table S1). Interestingly, up-regulated RBPs show a smaller standard deviation in expression in cancer tissue thanin normal tissue (P < 0.05, Mann–Whitney U test, Fig. 2D); However, down-regulatedRBPs show considerable differences in the standard deviation of their expression in cancertissue relative to normal tissues, especially in KICH, KIRC, KIRP, COAD, LUSC, CESC,UCEC, and GBM (P < 0.05, Mann–Whitney U test, Fig. 2E). This may indicate thatdown-regulated RBPs are more dysregulated in cancers than are up-regulated RPBs.Finally, biological process enrichment analyzing indicates both up- and down-regulatedRBPs were highly enriched in the processes, such as rRNAmetabolism, nuclear-transcribedmRNA catabolism, and ncRNA processing (Fig. S1). In addition, up-regulated RBPs werealso enriched in mitochondrial gene expression and in the regulation of mRNA metabolicprocesses, while down-regulated RBPs were enriched in ribosome biogenesis and rRNAprocessing.

Up- and down-regulated RBPs show opposite expression patterns incancer and normal tissueTo investigate cancer-specific differences in RBP expression, we analyzed the standarddeviations and mean expression values of up-regulated and down-regulated RBPs in 20types of cancers and in normal tissues (see methods and materials). Our results for all20 cancer types suggests that, compared to normal tissues, up- and down-regulated RBPsshow opposite patterns of expression in almost all cancers; what’s more, down-regulatedRBPs tend to show the larger expression deviations in cancers than up-regulated RBPs(P < 0.05,Mann–WhitneyU test, Figs. 2D, 2D). In particular, in almost all types of cancers,down-regulated RBPs show larger expression values than up-regulated RBPs. Furthermore,the expression deviations of up-regulated RBPs in cancers are lower than in normal tissues.However, the expression deviations of down-regulated RBPs are considerably greaterin cancer tissue than in normal tissue; this is especially true for BLCA, GBM, HNSC,LUAD, LUSC, STAD, BRCA, KICH, READ, and UCSC (P < 0.05, Mann–Whitney U test,Figs. 2E, 2G). These results suggest that down-regulated RBPs show a severer dysregulationin cancers than up-regulated RBPs. Furthermore, the expression pattern of up- anddown-regulated RBPs in 20 types of cancers are the opposite of those found in normaltissues; what’s more, molecular functions enrichment analyzing indicates that both up-and down-regulated RBPs showed enrichment in: catalytic activity acting on RNA, mRNAand mRNA 3′-UTR binding, nuclease and ribonuclease activity, and single-stranded RNAbinding. In addition, we found enrichment in translation factor activity and RNA bindingfor up-regulated RBPs, and in catalytic activity acting on tRNAs for down-regulated RBPs(Fig. S1). In addition, we also were able to identify which specific biological processes andfunctions were regulated by up- and down-regulated RBPs, which may reveal how thenormal processes of cells can be altered in a way that leads to cell carcinomatosis. These

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PCPG GBM KICH KIRC KIRP BLCACHOL LIHC STAD READ COAD BRCA UCEC CESCLUAD LUSC

Nervous system Urinary systemLiver and gall Alimentary system Reproductive systemRespiratory system

C1

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4

20

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-4

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-0.5

0

0.5

1Pearson

(A) (B) (C) (D) (E) (F)

Figure 3 An expression heat map of 801 differentially expressed RBPs shared by 16 cancer types in sixsystems. (A) Respiratory system. (B) Liver and gall. (C)Nervous system. (D) Alimentary system. (E) Re-productive system. (F) Urinary system. Red and blue represent high and low expression. The right columnof each system is the Pearson coefficient R of the corresponding cluster. Gene cluster with R > 0.5 wasmarked with dark yellow, which represents the similar expression pattern.

Full-size DOI: 10.7717/peerj.7696/fig-3

results provide a new insight into understanding the roles of up- and down-regulated RBPsin the process of cell carcinogenesis.

RBPs of different cancers in same system have a similar expressionprofileTo gain clearer insight into RBP expression in cancerous tissues, we analyzed expressionheatmaps of 16 types of cancers, except four types of glandular cancer (PAAD, THCA,PRAD, and HNSC). Because the number of DE RBPs in these four types of cancers aretoo small (Table 1). We divided DE RBPs into six scale systems according to type (i.e.,organization of canceration) and analyzed the expression profiles of 801 DE RPBs sharedby all 16 cancers by system. In each system, we divided RBPs into different types accordingto their expression values and Pearson coefficient R. Gene clusters with R> 0.5 wereconsidered as having the similar expression pattern, which was shown in heatmaps. Thiswas true for all cancers except two cancers of the nervous system. The corresponding genelists of RBPs relevant for each system are presented in Table S2.

In the respiratory and liver and gall systems (Figs. 3A, 3B), two pairs of cancers in samesystems, LUAD and LUSC, CHOL and LIHC, show very similar RBP expression profiles.Among these two cancer systems, C1 and C4 genes showed the co-expression patterns ofRBP for high expression and low expression respectively (R > 0.5). The proportions ofco-expressed RBPs in the respiratory and liver and gall systems were 88.8% and 76.8%,

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respectively. However, RBP expression in two cancers of the nervous system (PCPGand GBM) showed opposite expression patterns (Fig. 3C). The proportions of highlyexpressed RBPs in PCPG and GBM cancer tissue were 60.5% and 36.1%, respectively.Highly expressed RBPs in PCPG showed a lower degree of expression in GBM, whilehighly expressed RBPs in GBM showed lower expression in PCPG. The proportion ofRBPs showing opposing patterns of expression in PCPG and GBM was 68.2% (R < 0.5). Insystems containing three types of cancers (i.e., the alimentary and reproductive systems)(Figs. 3D, 3E), the proportion of co-expressed RBPs decreased slightly, reaching 70.4%and 71.5% (R > 0.5), respectively. In the urinary system, which was affected by four typesof cancers, the proportion of co-expressed RBPs reached its minimum value of 35.3%(Fig. 3F). Of the cancers of the urinary system, two (KIRC and KIRP) showed the strongestdegree of RBP co-expression reaching 85.6% (R >0.5). Taken together, our results suggestthat RBP expression in different cancers in similar tissues have similar expression profiles.

Co-expressed gene regulatory networks correlate with clinical TNMstageNext, we investigated whether expression patterns are closely related to developmentalstages of different cancers and constructed the co-expression networks for DE RBPs andnon-RBPs using module detection and network construction tools of WGCNA (Peter &Steve, 2008). Results showed that for two types of cancers—CHOL and KICH—clinicalTNM stage information was closely related to patterns of gene expression (Fig. 4). Theresults for the other 11 cancer types did not satisfy the threshold (Methods and Material,Figs. S2–S7).

In terms of module detection, we identified three modules (orange, green and red)related to the cancer metastasis stage (M -stage), as well as two modules (royal blueand red modules) closely related to the regional lymph node stage (N -stage) for CHOL(Fig. 4A). The redmodule was consistently in both theM andN stages. The gene list for eachcorresponding module is presented in Table S3. Furthermore, we found 10 differentiallyexpressed RBPs in these three modules. These included six RBPs (ACO1, PPARGC1A,PUS7, KHDC1, ELAVL3, and BICC1) in the green module, two RBPs (PANBP17 andHNRNPA1) in the royal blue module, and two RBPs (DCPS and C2orf15) in the redmodule. For KICH, we found that the royal blue module was most closely related tothe M -stage, with a correlation coefficient of 0.96 (P < 4e−15), while the red and greenmodules were closely related to the N -stage (Fig. 4B). The gene list for each correspondingmodule is presented in Table S3. Furthermore, we found seven DE RBPs related to differentdevelopmental stages of cancer. These included four RBPs (TNRC6A,MECP2, ZCCHC14,and POLR2F) in the green module and three RBPs (TDRD1, TDRD9, and CELF4) inthe red module. The regulatory networks of CHOL (Fig. 4C) revealed that (1) in eachsub-network, one RBP interacts with almost all non-RBPs, suggesting that these RBPs arekey regulators for each module; (2) the RBPs in different sub-networks interact with eachother, indicating that they work together to regulate the corresponding developmental stageof the cancer. (3) we found that most key RBPs are down-regulated, the proportion is 80%.In addition, in the red module, two RBPs—including one down-regulated RBP (C2orf15)

Lian et al. (2019), PeerJ, DOI 10.7717/peerj.7696 11/22

0.96 (4e−15)

0.0055 (1)

0.027 (0.9)

−0.087 (0.7)

−0.025 (0.9)

−0.041 (0.8)

−0.028 (0.9)

−0.038 (0.8)

−0.0098 (1)

−0.056 (0.8)

−0.077 (0.7)

0.26 (0.2)

−0.23 (0.2)

0.24 (0.2)

0.22 (0.3)

−0.22 (0.3)

0.27 (0.2)

0.23 (0.2)

0.12 (0.5)

0.27 (0.2)

0.27 (0.2)

0.3 (0.1)

0.22 (0.3)

−0.12 (0.6)

0.39 (0.05)

0.04 (0.8)

0.45 (0.02)

0.23 (0.2)

0.2 (0.3)

0.49 (0.01)

0.22 (0.3)

0.33 (0.1)

0.18 (0.4)

Module−trait relationships

greenoyellowmidnightblue

grey60

purple

redroyalblue

darkredlightgreen

cyan

green

grey

0.61 (0.001)

0.058 (0.8)

0.63 (5e−04)

0.15 (0.5)

0.1 (0.6)

−0.059 (0.8)

0.11 (0.6)

0.014 (0.9)

−0.014 (0.9)

0.32 (0.1)

MNT

0.53 (0.005)

−0.0068 (1)

0.47 (0.01)

0.18 (0.4)

0.52 (0.007)

0.42 (0.03)

0.007 (1)

0.058 (0.8)

−0.0056 (1)

0.45 (0.02)

Module−trait relationships

−1

−0.5

0

0.5

1orangebrown

blackgreen

cyan

redsaddlebrown

royalbluepaleturquoise

salmonblue

0.33 (0.09)

−0.017 (0.9)

−0.022 (0.9)

−0.091 (0.7)

0.31 (0.1)

0.025 (0.9)

0.7 (4e−05)

0.69 (6e−05)

−0.056 (0.8)

−0.05 (0.8)

−0.23 (0.2)

CHOL KICH

−0.045 (0.8) −0.13 (0.5)

MNT

ENSG00000280195 ENSG00000132681

ENSG00000119547

ZCCHC14

MECP2

TDRD9TNRC6A

POLR2FTDRD1

CELF4

ENSG00000238178

ENSG00000109424

ENSG00000180730

ENSG00000182798

ENSG00000231621

ENSG00000164082

ENSG00000070601ENSG00000260519

ENSG00000162891

ENSG00000183396 ENSG00000054938

ENSG00000237973

ENSG00000258551ENSG00000005249

ENSG00000235023

ENSG00000133636

ENSG00000237989

ENSG00000259763

ENSG00000228549

ENSG00000150471

ENSG00000233421

ENSG00000227218

ENSG00000228016

ENSG00000245651

ENSG00000224614

ENSG00000207138

ENSG00000258425

ENSG00000198223

ENSG00000259158

ENSG00000275772

ENSG00000274735

ENSG00000224161

ENSG00000270697

ENSG00000258896

ENSG00000178233

ENSG00000223715

ENSG00000166793

ENSG00000271547

ENSG00000232360

ENSG00000213171

ENSG00000230316

ENSG00000263862ENSG00000119715

ENSG00000164520ENSG00000114854

ENSG00000154252ENSG00000266968

ENSG00000251187ENSG00000253524

ENSG00000247624ENSG00000265944

ENSG00000122691

ENSG00000126010

ENSG00000163485

ENSG00000262884

ENSG00000254042

ENSG00000259518

ENSG00000198846

ENSG00000231794

ENSG00000152104

ENSG00000165309

ENSG00000114349

ENSG00000198626

ENSG00000187720

ENSG00000259417

ENSG00000261273

ENSG00000265046

ENSG00000258603

ENSG00000198756

ENSG00000205312

ENSG00000249274

ENSG00000125430

ENSG00000255750

ENSG00000177994

ENSG00000133640

ACO1

ENSG00000230121

ENSG00000253476

ENSG00000279353

ENSG00000125931

ENSG00000274150

DCPS

ENSG00000176907

ENSG00000253525

ENSG00000172817

ENSG00000273259

ENSG00000094755ENSG00000080709

ENSG00000142609

ENSG00000161249

ENSG00000234215

ENSG00000147509

ENSG00000226926

ENSG00000253671

ENSG00000152592

ENSG00000152527

ENSG00000107518

ENSG00000177138

ENSG00000176595

ENSG00000147614

ENSG00000146166

ENSG00000233532

ENSG00000248757

ENSG00000238283

ENSG00000243137

ENSG00000235897

ENSG00000178568

ENSG00000086570

ENSG00000182389

ENSG00000223774

ENSG00000224739

ENSG00000225362ENSG00000254656 ENSG00000186073

ENSG00000259732

ENSG00000214510ENSG00000137090

ENSG00000223949

ENSG00000253250

ENSG00000253213

ENSG00000246130ENSG00000232480

ENSG00000118997ENSG00000228940

ENSG00000214914ENSG00000214650

PUS7

PPARGC1A

ELAVL3

BICC1 KHDC1

C2orf15

HNRNPA1RANBP17

(A) (B)

(C) (D)

Pearson

Figure 4 The co-expressionmodules detection of CHOL and KICH, respectively. (A) and (B) Co-expression modules of CHOL and KICH correlated with their clinical TNM stage information, respec-tively. Different color represents the gene modules with different expression pattern. The first, middle,and last column are detected gene modules related toM, N, and T stages, respectively. The two num-bers in each module ‘‘a(b)’’ represent coefficients of co-expression and statistical significance, a is the co-expression coefficient and b is the corresponding P-value. The modules with coefficient larger than 0.5and P-value smaller than 0.05 were considered as the related modules. (C) and (D) Co-expression net-works of RBPs and non-RBPs in gene modules (green, red and royal blue) related withM - and N -stagesfor CHOL and KICH, respectively. Inner circle are key RBPs, outer circle are the non-RBPs. Triangularrepresents the up-regulated genes, block represents the down-regulated genes.

Full-size DOI: 10.7717/peerj.7696/fig-4

and one up-regulated RBP (CDPS) also play such a regulatory role. For the regulatorynetworks of KICH, we also identified three sub-networks that corresponded to the M -and N -stages (Fig. 4D). First, we found that RBPs interacted with almost all non-RBPsin each module, indicating that RBPs are key regulatory factors of the genes in thesemodules. Second, we identified seven key RBPs in the three modules, of which five weredown-regulated and two were up-regulated. In the green module, we identified four keyRBPs—TNRC6A, MECP2, ZCCHC14, and POLR2F—all of which were down-regulated.In the red module, we identified two key up-regulated RBPs (TDRD1 and TDRD9) andone key down-regulated RBP, CELF4. These results suggest that dysregulated RBPs play akey role in the regulation of the development of the CHOL and KICHM -stage, which mayprovide a new perspective for potential prognostic biomarkers and therapeutic targets forpatients with cancers atM stages in two cancer types CHOL and KICH.

Lian et al. (2019), PeerJ, DOI 10.7717/peerj.7696 12/22

TMEM72-AS1

RP11-227H15.5

TTC39A-AS1

CTC-490G23.4

RP11-323F24.4

RP11-64C12.8 CLDN10-AS1

AC004540.5

LINC00323RP11-728F11.4SBF2-AS1

AC007365.3

RP11-540A21.2

ROR1-AS1

RP11-881M11.4

HIF1A-AS1

RP11-315D16.4

RP11-59H7.3

RP11-49I11.4LINC02115

LINC00665NEXN-AS1

RP11-96H17.1

ALDH1L1-AS2

RP11-143N13.2

RP11-247L20.3 RP11-548L20.1

LINC00460

RP11-368L12.1RP11-701H24.3

LINC00113RP11-268J15.5

CTB-178M22.2

RBPMS-AS1

C12orf80RP4-564F22.6

LINC01594

AC006547.13RP11-128A17.2

LINC01973

SERPINB9P1

RP11-92G12.3

GACAT2

RP11-359E10.1

TM4SF19-AS1

RP11-540O11.1

NDUFB2-AS1 RP11-20J15.2

SAPCD1-AS1LINC01730

AC144833.1

RP13-49I15.6

LINC00261

RP11-701H24.4

RASAL2-AS1

RP11-210M15.2

AC092667.2

RP11-148B18.4

LINC01117

LINC00322

LINC00957

LINC01426

SH3RF3-AS1

RP11-521O16.1

AFAP1-AS1

FAM212B-AS1LINC01956

RP5-942I16.1RP11-1081L13.4

U47924.32RP11-59H7.4

LINC02198

RP5-999L4.2

LINC01852

CHRM3-AS2AC002480.3

AC013275.2LINC01320

LINC01127

LINC00346

IFNG-AS1

LINC01697

TTC21B-AS1

RP11-713N11.6

RP11-272L14.2

BX470102.3LINC01444

CADM3-AS1PAX8-AS1

AP002954.3

RP5-965F6.2

DNM3OS

WT1-AS

LINC01353

RP11-66B24.4

CMAHP

LINC02182

RP11-527L4.6

RP11-613D13.8

CTD-3247F14.2

RP11-222K16.1RP11-276H7.2

CTC-498J12.1

SPATA13

AC006129.1

LINC01268 RP11-379B8.1

RP11-326C3.13

RP13-580F15.2

RP1-41C23.4

LINC00511

AP000355.2

CTD-2033D15.2

AC008753.6

RP11-443B7.3

AC007365.3

CTC-455F18.1

RP11-61G19.1

RP5-1050E16.2U62631.5

AC007950.1

LINC00589

AC013472.3

CH17-437K3.1

CTD-2566J3.1RP11-597D13.9

RAPGEF4-AS1

RP11-431J24.2

RP11-289F5.1

LINC00524

CTA-796E4.4

RP11-663P9.1

LINC00445

RP11-65M17.1

CTA-363E6.6

RP11-85G21.2

RP11-520D19.2

LINC01531

PKIA-AS1

AC011298.2

LINC01213

RP11-17M24.3

RP5-1050E16.1

RP11-793A3.1

RP11-737O24.1RP11-17E2.2

RP11-356J5.12

LINC01055

RP11-569G13.2

RP11-348F1.2

USP12-AS2

AC005392.13

RP11-107M16.2

AC016907.3

LINC00652

FLJ27354

RP11-413E1.4LINC00211

AC002401.1RP11-13N12.2

XXbac-BPG308K3.5

CTD-2147F2.1

RP11-575F12.2RP11-48G14.1

LINC01983

XXYLT1-AS2

RP11-284F21.10

RP11-347E10.1

RP1-122P22.4

LINC00443

AC129492.6

RP11-881M11.2

RP11-284F21.9

RP11-317M11.1

RP11-90P13.1

LINC01426

RP11-348F1.3

RP1-10C16.1

TNKS2-AS1

AF064858.10

RP1-78O14.1LINC00857

LINC01451 LINC00310RP11-169K16.4 RP11-131L23.2

RP4-583P15.16

WDR11-AS1

TMEM246-AS1

LINC00908

RP11-492E3.2

RP11-526F3.1

RP11-61E11.2

LIPC-AS1

RP11-213H15.1

LINC01136

HSD52

ZNF503-AS1

RP11-116O18.1

RP11-13N12.1

RP4-598P13.1

RP11-706O15.3

RP6-65G23.5

RNF157-AS1

CTD-2382E5.6

RP11-264E20.2

LINC00265

RP11-547D24.1

RP11-21A7A.4

TNK2-AS1

CRAT37

LINC01679

RP11-396O20.1

AC013264.2

FAM87A

LINC01387

RP11-713M15.2

CTD-2269F5.1

ADORA2A-AS1

RP11-193M21.1

LINC02147RP1-60O19.1

RP11-923I11.6

LINC02188

RP11-173C1.1

AC092614.2LINC00706

CTD-2506J14.1

RP11-311F12.1

AC061992.2RP11-1399P15.1

RP11-54A9.1

CALML3-AS1 C1RL-AS1AC002480.4

RP11-359N11.1

RP11-667K14.3ADAMTS9-AS1

LINC02038RP1-118J21.25

RP5-899B16.1EGOT

LINC01936

RP11-680C21.1

AC114730.2

LINC00840

CTD-3252C9.4

CTC-296K1.4

RP1-150O5.3

SLIT2-IT1

CTD-2377O17.1AC074289.1

RP11-1069G10.1ITPKB-IT1

XXbac-BPG249D20.9

LINC00924

RP11-276H7.3

CTD-2033D15.3

RP11-286H15.1

LINC02091

CTD-3094K11.1

(A)CHOL (B)KICH

C1QTNF1-AS

RP5-1009E24.9

RP11-806O11.1

RP11-563P16.1

cluster 1

cluster 2 cluster 3

cluster 1

cluster 3

cluster 4

cluster 2

C2orf15DCPS

TDRD1

TDRD9

CELF4

Figure 5 The co-expression networks of key RBPs and lncRNAs. (A) The co-expression network of twokey RBPs related toM stage (C2orf15 and DCPS) and lncRNAs for CHOL. (B) The co-expression net-work of three key RBPs (TDRD1, TDRD9 and CELF4) related toM stage and lncRNAs for KICH. Red andgreen represent up- and down-regulated DE genes.

Full-size DOI: 10.7717/peerj.7696/fig-5

The networks of key RBPs and lncRNAs for CHOL and KICHTo infer the potential regulatory mechanisms of lncRNAs with key RBPs related to Mstages, we constructed the co-expression networks of key RBPs and differentially expressed(DE) lncRNAs for CHOL and KICH respectively and performed Gene Ontology andfunctional enrichment analyses.

Ten key RBPs and 2,943 DE lncRNAs were used to construct the co-expression networkfor CHOL. The resulting co-expression network consisted of two key RBPs (down-regulation C2orf15 and up-regulation DCPS) and 75 lncRNAs, which were grouped intothree clusters (Fig. 5A). There were 63 up-regulated lncRNAs, and the proportion ofup-regulated lncRNAs in three clusters was 86%, 75%, 83.3%, which probably suggest thatup-regulated lncRNAs have a greater interaction with RBPs in the process of metastasisof CHOL cells. Functional enrichment analyzing demonstrated that cluster 1 and cluster3 had similar functions and mainly enriched in functional categories involved in genesilencing and negative regulation of translation, such as post transcriptional gene silencing,negative regulation of translation and cellular amide metabolic process, cellular responseto dsRNA, miRNA metabolic process. Cluster 2 had some special functions and mainlyenriched in such as positive regulation of mRNA catabolic process, cellular response tointerleukin-1 and calcium ion, RNA destabilization.

Seven key RBPs and 1,204 DE lncRNAs were used to construct the co-expressionnetwork for KICH. The resulting co-expression network consisting of 3 key RBPs (up-regulated TDRD1 and TDRD9, down-regulated CELF4) and 227 lncRNAs. There are 177down-regulated lncRNAs in the network and were grouped into four clusters (Fig. 5B).The corresponding proportion of down-regulated lncRNAs was 46.7%, 65.3%, 75% and95%, which probably suggest that down-regulated lncRNAs play more important roles in

Lian et al. (2019), PeerJ, DOI 10.7717/peerj.7696 13/22

interacting with key RBPs TDRD1, TDRD9 andCELF4 in the process of metastasis of KICHcells. Functional enrichment analysis demonstrated that cluster 1 shared by three key RBPsmainly enriched in dsRNA fragmentation and production of miRNAs involved in genesilencing by miRNA. Cluster 2 regulated by TDRD9 mainly enriched in endoribonucleaseand exon-exon junction complex, cluster 3 regulated by TDRD1mainly enriched in mRNAcatabolic process and regulation of mRNAmetabolic process, cluster 4 regulated by CELF4mainly enriched in transporting of RNA, mRNA and nucleic acids. Besides, cluster 1 andcluster 2 have some similar functions, such as telomere maintenance, histone mRNA andmiRNAmetabolic process, dosage compensation. Cluster 3 and cluster 4 have some similarfunctions, such as regulation of RNA stability, RNA localization and regulation of mRNAcatabolic process.

These results provide a new insight into the understanding of the interactions of keyRBPs with lncRNAs in the metastasis stage (M stage) of cancer cells.

The co-expression networks of DEG RBPs and TFs for CHOL andKICHTo investigate the interactions of RBPs and TFs, we constructed the co-expression networksof DEG RBPs and TFs for CHOL and KICH, respectively. The key regulatory RBPs werethose (ten for CHOL and seven for KICH) detected in above section.

The co-expression network of CHOL revealed two important insights. First, we found thefive largest transcription factor families, they areC2H2-ZF,Homeodomain,Nuclear receptor,bHLH and bZIP, and the corresponding proportion is 37%, 17%, 9%, 9% and 9% (Figs. 6A,6B), which interacted with almost all differentially expressed RBPs. This result indicatesthat these transcription factors tend to show a co-expression pattern with DEG RBPs,which further suggest that they play a major regulatory role in RBP post-regulatory levelsfor CHOL. Second,we also identified several special TFs related to up- or down-regulatedRBPs for CHOL. For example, Grainyhead, MADF, HMG-Sox and SAND are specifictranscription factors associated with down-regulated RBPs and GTF2I-like, Myb-SANT,MADS box and CENPB are specific transcription factors associated with up-regulated RBPsfor CHOL. The co-expression network for KICH also revealed the following insights. First,we found that the four largest transcription factor families, they are bHLH, bZIP, C2H2-ZFand Nuclear receptor, the proportion is 54%, 9%, 7% and 7% (Figs. 6C, 6D). Notably,transcription factor bHLH interact with all RBPs and it accounts for more than half of allinteracted transcription factors, which indicate that bHLH transcription factor probablyinvolved in regulation of all differentially expressed RBPs for KICH. Second, the proportionof up- and down-regulated RBPs co-expressed with TFs is 57% and 43% respectively. But,the up-regulated RBPs tend to show more interactions with TFs (Fig. 6C). These resultsprovide insights into understanding the mechanism of interaction between transcriptionfactors and RBPs.

DISCUSSIONRNA-binding proteins have been shown to be the key units to regulating RNA metabolism(Fu & Ares, 2014; Stefanie, Markus & Thomas, 2014; Moore & Proudfoot, 2009) and

Lian et al. (2019), PeerJ, DOI 10.7717/peerj.7696 14/22

bHLH

Myb-SANTT-boxSANDP53Nuclear recepotor

POUMADS boxMADF

Homeodomain

HMG-SoxGTF2I-likeGrainyhead

Forkhead

DM

CUT

CENPB

C2H2 ZF

bZIP

AT hook

ARID-BRIGHI

AP-2

Prospero

Homeodomain

Nuclear receptor

CUT

C2H2 ZF

bZIP

BED ZF

bHLH

AT hook

Ets

54%

9%

7%7% 5%

5%4%

4%

37%

17%

9%

9% 9% 2% 2%

(C) KICH-TFs

(A) CHOL-TFs

APOBEC2

SRRM4

APOBEC3G

ISG20

PURG

HMG-Sox

Grainyhead

POU

C2orf15

PUS7 KHDC1ELAVL3

BICC1MADF

SAND

SMG9

CELF4

TERT

CELF3

ENOX1

DM

PTGES3L-AARSD1POP1

IGF2BP3GTPBP2

PIWIL2

PAIP2B

Myb-SANT

LARS2

MADS box

TIPARP

CPEB4

CENPB

GTF2I-like

ZGPAT

PSTK

CPEB3

KHDRBS3

AUH

MOV10L1

AZGP1

DQX1DDX26BRBMXL2

RPP14

P53

HNRNPA1DCPS

RANBP17

ACO1

PPARGC1A

HomeodomainC2H2 ZFNuclear receptor

Forkhead

AT hook

EtsbZIP

bHLHCUT

T-box

RBMS3

MAEL

NXF3

HRSP12

DZIP1L

KHDRBS2

ZNF385A

ENOX1 MOV10L1

DDX58

CELF6

PEG10

CELF3

ELAVL4

SPATS2L ZFP36ZC3H12BANGSECISBP2L

Prospero

Homeodomain

CUT

ARID-BRIGHT

BED ZF

YBX2

XPOT

RBPMS2

UTP20

R3HCC1L

GARS

TDRD9

MECP2

CELF4

TNRC6A

ESRP1

ZCCHC14

TDRD1 POLR2F

TRNAU1AP

C2H2 ZF

IFIT1

CSDC2

ZCCHC24

Nuclear receptor

bZIP

bHLHAT hook

AP-2

(B) CHOL-Pie chart

(D) KICH-Pie chart

Figure 6 The co-expression networks of DE RBPs and TFs. (A) and (C) The co-expression networksof DE RBPs and TFs for CHOL and KICH respectively. (B) and (D) The corresponding pie chart of TFsco-expressed with DE RBPs for CHOL and KICH, respectively. Inner circle are key TFs, outer circle areDE RBPs. Red and green in outer circle represent up- and down-regulated RBPs. Block represents the keyRBPs identified by module detection, circle dot represents other DE RBPs.

Full-size DOI: 10.7717/peerj.7696/fig-6

dynamically interactwith both coding andnoncodingRNA (Kim et al., 2016). Furthermore,recent studies have shown that RBPs are down-regulated in cancers (Wang et al., 2018),but the study of 16 tissues from 80 healthy individuals indicated that RBPs show thehigher expression than non-RBPs (Bobak & Sarath, 2014). Consequently, we investigatedthe comprehensive expression differences of RBPs and non-RBPs simultaneously incancers and normal tissues. Results indicate that RBPs are significantly dysregulated incancers. In particular, recent studies have confirmed that RBPs show severely dysregulatedexpression in BLCA (Kato et al., 2012), LUAD (Dong et al., 2018), STAD (Hapkova etal., 2013), READ, LUSC (Shi et al., 2017), GBM (Pavlyukov et al., 2018) and COAD(Saki et al., 2016). Furthermore, up- and down-regulated RBPs tend to show oppositepatterns of differential expression in cancers and normal tissues (Figs. 2D, 2E). Up-regulated RBPs show higher expression in normal tissues than down-regulated RBPs,

Lian et al. (2019), PeerJ, DOI 10.7717/peerj.7696 15/22

which is consistent with the results in Bobak & Sarath (2014), but down-regulated RBPsshow the significantly higher expression in cancers than up-regulated RBPs (Figs. 2F, 2G),which is consistent with results in (Wang et al., 2018). These results probably suggest amechanism of RBPs in the process of carcinomatosis by which the up-regulated RBPstend to show lower expression but down-regulated RBPs tend to show higher expression.Carcinogenesis is probably caused by the combined actions of the low expression ofup-regulated RBPs and the high expression of down-regulated RBPs. This mechanism maybe useful for understanding the roles of RPBs and the design of targeted drugs for cancertherapy.

We found 10 key regulated RBPs for CHOL (Seven down-regulated RBPs, HNRNPA1,PANBP17, PUS7, KHDC1, ELAVL3, BICC1 and C2orf15; Three up-regulated RBPs, ACO1PPARGC1A and DCPS) and seven key regulated RBPs for KICH (Six down-regulatedRBPs, TNRC6A, MECP2, ZCCHC14, CELF4 and POLR2F; Two up-regulated RBPs,TDRD1 and TDRD9), respectively. Notably, recent studies have shown the importanceof these key RBPs. For instance, multiple PPARGC1A transcripts are more abundant andCNS-specific in Parkinson’s disease (PD) (Soyal et al., 2019). KHDC1A is highly expressedin oocytes and induces endoplasmic reticulum apoptosis (Cai et al., 2012). Elavl3 is closelyrelated to neurodegenerative diseases and play an important role in maintaining theaxonal homeostasis of neurons (Ogawa et al., 2018).HNRNPA1, regulated by miR-503 andmiR-424, is associated with breast cancer cell proliferation (Otsuka, Yamamoto & Ochiya,2018). DCPS is very essential for acute myeloid leukemia cell survival by interacting withpre-mRNA (Yamauchi et al., 2018). CELF4 plays an important role in brain development,the haploinsufficiency of CELF4 is associated with autism disorders (Barone et al., 2017).POLR2F is significantly high expression in colorectal carcinomas (Antonacopoulou et al.,2008) and potential molecule in carcinogenesis.TDRD1 is over-expressed inmajority of 131primary prostate tumors patients (Xiao et al., 2016). In all, these results have demonstratedthat the key RBPs have played the important roles in other types of cell carcinomatosis andprovide a new perspective for potential prognostic biomarkers and therapeutic targets forpatients with cancers at the N andM stages in two cancer types CHOL and KICH.

CONCLUSIONSIn this study, we analyzed detailed differences in the expression of RBPs and non-RBPsacross 20 types of cancers and constructed the co-expression networks of dysregulated RBPswith TFs and lncRNAs for CHOL and KICH, respectively. Our results indicate that: (1)RBPs are dysregulated in almost all 20 cancer types comparedwith normal tissues, especiallyin BLCA, COAD, READ, STAD, LUAD, LUSC and GBM with proportion of deviationlarger than 300% compared with non-RBPs in normal tissues. (2) Up- and down-regulatedRBPs also show opposed patterns of differential expression in cancers and normal tissues.In addition, down-regulated RBPs show a greater degree of dysregulated expression thanup-regulated RBPs do. (3) Clinical TNM information for two cancer types—CHOL andKICH—is shown to be closely related to patterns of differentially expressed RBPs (DERBPs). (4) We constructed the co-expression networks of key RBPs between 1,570 TFs

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and 4,147 lncRNAs for CHOL and KICH, respectively. By analyzing these networks, weidentified ten key RBPs (of which seven were down-regulated and three up-regulated)in CHOL and seven RBPs (of which five were down-regulated and two up-regulated) inKICH. These key RBPs—and especially down-regulated RBPs—likely play important rolesin cell carcinomatosis. This study lays the foundation for further efforts to understand theroles played by RBPs in human carcinogenesis and provides a new insight into identifyingthe potential prognostic biomarkers and therapeutic targets for patients.

ACKNOWLEDGEMENTSThe authors thank three anonymous reviewers for their comments on the manuscript. Thelinguistic editing and proofreading provided by TopEdit LLC during the preparation ofthis manuscript are acknowledged.

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis work was supported by the National Natural Science Foundation of China (Grant.61501392) and the Nanhu Scholars Program for Young Scholars of XYNU (Xin YangNormal University). The funders had no role in study design, data collection and analysis,decision to publish, or preparation of the manuscript.

Grant DisclosuresThe following grant information was disclosed by the authors:National Natural Science Foundation of China: 61501392.Nanhu Scholars Program for Young Scholars of XYNU (Xin Yang Normal University).

Competing InterestsThe authors declare there are no competing interests.

Author Contributions• Shuaibin Lian conceived and designed the experiments, contributed reagents/material-s/analysis tools, authored or reviewed drafts of the paper, approved the final draft.• Liansheng Li performed the experiments, analyzed the data, contributed reagents/mate-rials/analysis tools, prepared figures and/or tables, approved the final draft.• Yongjie Zhou analyzed the data, contributed reagents/materials/analysis tools, preparedfigures and/or tables, approved the final draft.• Zixiao Liu analyzed the data, contributed reagents/materials/analysis tools, approvedthe final draft.• Lei Wang conceived and designed the experiments, approved the final draft.

Data AvailabilityThe following information was supplied regarding data availability:

Raw data is available in the Supplemental Files.

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Supplemental InformationSupplemental information for this article can be found online at http://dx.doi.org/10.7717/peerj.7696#supplemental-information.

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