REVIEW article

Front. Cell Dev. Biol., 06 December 2021

Sec. Epigenomics and Epigenetics

Volume 9 - 2021 | https://doi.org/10.3389/fcell.2021.772542

The Relationship Between the Network of Non-coding RNAs-Molecular Targets and N6-Methyladenosine Modification in Colorectal Cancer

  • 1. Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China

  • 2. Liaoning Key Laboratory of Molecular Targeted Anti-tumor Drug Development and Evaluation, Liaoning Cancer Immune Peptide Drug Engineering Technology Research Center, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, China

  • 3. Department of Medical Oncology, Cancer Hospital of China Medical University, Shenyang, China

  • 4. Shenyang Kangwei Medical Laboratory Analysis Co. Ltd., Liaoning, China

  • 5. Center of Reproductive Medicine, Shengjing Hospital of China Medical University, Shenyang, China

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Abstract

Recent accumulating researches implicate that non-coding RNAs (ncRNAs) including microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNAs) play crucial roles in colorectal cancer (CRC) initiation and development. Notably, N6-methyladenosine (m6A) methylation, the critical posttranscriptional modulators, exerts various functions in ncRNA metabolism such as stability and degradation. However, the interaction regulation network among ncRNAs and the interplay with m6A-related regulators has not been well documented, particularly in CRC. Here, we summarize the interaction networks and sub-networks of ncRNAs in CRC based on a data-driven approach from the publications (IF > 6) in the last quinquennium (2016–2021). Further, we extend the regulatory pattern between the core m6A regulators and m6A-related ncRNAs in the context of CRC metastasis and progression. Thus, our review will highlight the clinical potential of ncRNAs and m6A modifiers as promising biomarkers and therapeutic targets for improving the diagnostic precision and treatment of CRC.

Introduction

Background

Colorectal cancer (CRC) remains the third most common tumor worldwide with increasing incidence and mortality rates annually. The etiology of CRC is complicated and involves a variety of risk factors such as environmental exposure, genetic alterations as well as a variety of epigenetic modifications based on global molecular biomarkers such as mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), circular RNA (circRNA), etc. Genomic studies show that human ncRNA transcripts that do not encode for proteins account for approximate 98% of the total human transcripts, which consist mainly of lncRNA, miRNA and circRNA, etc. Among them, lncRNAs are non-coding RNAs longer than 200 nt, which play critical roles in regulating gene expression and chromatin dynamics (Bhan and Mandal, 2015). MiRNAs are ncRNAs with a length of 17–25 nt, which usually recognize the 3′UTR of mRNA and inhibit gene expression (Lee and Dutta, 2009). CircRNAs are single-stranded ncRNAs with a covalent closed loop structure, which play important biological functions by acting as miRNA inhibitors, protein “bait” or by encoding small peptides (Li et al., 2020). Notably, accumulating evidence shows that the dysregulated ncRNAs (such as lncRNAs, microRNAs, circRNAs, etc.) are involved in the pathological process of a variety of tumors such as prostate cancer, breast cancer, hepatic cancer, and CRC. Although several studies show that ncRNAs play critical regulatory roles in CRC by targeting different protein-coding transcripts or other ncRNAs to activate various signal pathways. However, the specific mechanism underlying the functions of ncRNAs in CRC remain unclear.

Accumulating researches show that ncRNAs are abnormally expressed in tissues, cells, exosomes, and blood of CRC patients (Barbagallo et al., 2018). These are identified as oncogenes or tumor suppressors that mediate CRC occurrence, metastasis, and resistance to radiotherapy and chemotherapy (Wang et al., 2017a; Luan et al., 2020; Meng et al., 2020). Although the regulatory mechanism of the biogenesis and function of ncRNAs remain unclear, existing studies show that ncRNAs play essential roles during tumorigenesis and progression through diverse mechanisms including action as miRNA sponges or baits, interaction with RNA binding proteins, translation to functional peptides as well as epigenetic modification mediated mechanisms (Ren et al., 2018a; Ni et al., 2019; Long et al., 2021). Notably, epigenetic modification of ncRNAs is a significant factor in the occurrence and development of CRC. Meanwhile, ncRNAs can also rely on epigenetic modification to regulate the expression of mRNA or ncRNAs and ultimately promote the progression of CRC. Among all epigenetic modifications, m6A, as a research hotspot in recent years, exerted its critical functions in the progression and development of CRC. Specifically, the m6A writers (METTL3, METTL14, WTAP, and other writers such as RBM15, VIRMA, CBLL1, ZC3H13) are responsible for “writing” m6A modification. The m6A erasers (FTO and ALKBH5) are responsible for “erasing” m6A modification. Meanwhile, m6A readers (YTHDC1-2, YTHDF1-3, IGF2BP1-3, HNRNPC and HNRNPA2B1) are responsible for “reading” m6A modification. The writers, erasers and readers of m6A modification can collaborate and directly participate in the progression of various types of tumors. In CRC, m6A modification promotes CRC angiogenesis, metastasis, and chemical resistance by regulating lncRNA stability and degradation, miRNA biogenesis, and circRNA reverse splicing and translation (Dang et al., 2021; Liu et al., 2021; Xu et al., 2021). Recently, publications focused on that m6A associated was supposed to regulate the expression of ncRNAs (Wu et al., 2019a; Chen et al., 2020a; Yang et al., 2020a). Yang et al. illustrated that knockdown of METTL14 enhanced the expression of long non-coding RNA XIST through YTHDF2 pathway (Yang et al., 2020a). Wu et al. clarified that m6A-induced lncRNA RP11 triggered the metastasis of CRC cells through the post-translational up-regulation of Zeb1 (Wu et al., 2019a). Similarly, Peng et al. demonstrated that METTL14 promoted the expression of miR-375 in an m6A-dependent pathway to promote the progression of CRC (Chen et al., 2020a). Furthermore, not only m6A can regulate the expression of ncRNAs, ncRNAs are capable to regulate the m6A level of RNA as well. For example, miR-96 downregulated AMPKα2, thereby blocking its m6A modification and leading to increased FTO expression and subsequent upregulation of MYC expression (Yue et al., 2020); LNC00460 directly interacted with IGF2BP2 and DHX9 to bind to the 3′UTR of HMGA1 mRNA, thereby increasing the stability of HMGA1 mRNA (Hou et al., 2021); m6A modified circNSUN2 stabilized HMGA2 mRNA and ultimately promoted liver metastasis of CRC by forming a circNSUN2/IGF2BP2/HMGA2 RNA-protein ternary complex. Thus, linking ncRNAs and m6A modifications is essential for advancing future diagnostic and therapeutic inventions (Chen et al., 2019a). The correlation between ncRNAs and m6A modification is shown in Figure 1.

FIGURE 1

FIGURE 1

The correlation between ncRNAs and m6A modification. The figure shows how m6A regulates ncRNAs expression and how ncRNAs rely on m6A to regulate mRNA expression.

The current researches on ncRNAs in CRC are limited on the selection of one or more representative ncRNAs in clinical genomics. In these studies, the CRC transcriptome is analyzed in cohort retrospectively and usually lacks a holistic approach. The current research is primarily based on miRNA as the representative of ncRNAs, which usually regulate biological functions and promote or inhibit the occurrence of tumors by affecting the expression of multiple direct or indirect targets in common biological networks. For each ncRNA, hundreds of mRNAs or other ncRNAs are generally enriched as direct or indirect targets, and the coordination of many of these can be regulated to produce a series of biological consequences. Using this functional feature to our advantage, we took a data-driven approach and collected all the articles on CRC-related ncRNAs and miRNAs from PubMed in the last 5 years, and set IF > as the threshold. Next, we combined text mining and network statistical analysis, and set all ncRNAs and their target genes that appeared more than twice in the collected literature as nodes, and finally obtained a ncRNA regulatory network as presented this review (All steps of our approach are represented in Figure 2 and the ncRNA regulatory network thus obtained is shown in Figure 3). Next, based on the number of ncRNA targets and the citations of related ncRNAs, we speculated its potential importance in the gene regulatory network for cancer, determined the final priority. Thereafter, we examined the interaction between the star ncRNAs targets, and the potential biological functions of ncRNAs in CRC. Detailed information on the network composition is shown in Table 1. The filtered nodes, which represent the un-replicated findings, are shown in Table 2. Through this review, we aimed to investigate the role of ncRNA regulatory network in the initiation and progression of CRC. Our review may have implications in future research strategies using ncRNAs in the treatment of CRC and tackling multi-drug resistance.

FIGURE 2

FIGURE 2

Synthesis of data-approach used to build the network ncRNAs-target. Flow chart of RNA network construction.

FIGURE 3

FIGURE 3

The network of non-coding RNAs and its targets in colorectal cancer. The figure shows ncRNAs reported in at least two different literature sources (the squares represent long non-coding RNAs, the purple ellipses represent circular RNAs, the circles represent microRNAs, the triangles represent snoRNAs, the pentagon represents m6A modification and yellow indicates core genes). The target mRNA of ncRNA is represented by a hexagon, and each target is reported in PUBMED. When the interaction is described in multiple articles, multiple line segments are drawn between the two nodes. The edges are directed (i.e., from the non-coding RNA to its target which could either be coding or non-coding). In the figure, red regular arrows indicate active links, and black flat arrows indicate inhibited links.

TABLE 1

ncRNADirect targetType of interactionPMIDReferences
CCAT2miR-145neg28964256Yu et al. (2017a)
CCAT2BOP1pos32805281Chen et al. (2020c)
ciRS-7miR-7pos32917870Kristensen et al. (2020)
ciRS-7miR-7sponge28174233Weng et al. (2017)
CRNDEEZH2Pos28796262Ding et al. (2017a)
CRNDEmiR-181a-5pNeg28086904Han et al. (2017)
CRNDEhnRNPUL2Shuttling neg28594403Jiang et al. (2017)
GAS5YAP31619268Ni et al. (2019)
GAS5miR-222—3pSponge31400607Liu et al. (2019a)
H19miR-141Sponge30083271Ren et al. (2018a)
H19miR-194—5pSponge30451820Wang et al. (2018a)
HOTAIRmiR-218Neg28918035Li et al. (2017b)
HOTAIRmiR-93Sponge32144238Liu et al. (2020a)
HOTAIRmiR-126Sponge31974341Jiang et al. (2020)
LINC00152miR-193a-3pSponge27633443Yue et al. (2016)
LINC00152miR-139—5pSponge29180678Bian et al. (2017)
LINC00460miR-149—5pSponge30092404Lian et al. (2018)
LINC00460miR-149—5pSponge33251049Meng et al. (2020)
LINC00460miR-150—5pSponge33251049Meng et al. (2020)
LINC00460KLF2Neg30092404Lian et al. (2018)
MALAT1miR-15Sponge31097689Ji et al. (2019)
MALAT1miR-126—5pSponge30531836Sun et al. (2019a)
MALAT1miR-663aNeg30154407Tian et al. (2018)
miR-100DKK1Neg29035371Lu et al. (2017)
miR-100DKK3Neg29035371Lu et al. (2017)
miR-100DKK1Neg29094721Thomas, (2017)
miR-100DKK3Neg29094721Thomas, (2017)
miR-101OGTNeg30093632Jiang et al. (2019)
miR-101EZH2Neg30093632Jiang et al. (2019)
miR-125bZNRF3Neg29035371Lu et al. (2017)
miR-125bRNF43Neg29035371Lu et al. (2017)
miR-125bAPC2Neg29035371Lu et al. (2017)
miR-125bRNF43neg29094721Thomas, (2017)
miR-125bAPC2neg29094721Thomas, (2017)
miR-1273g-3pMELKneg31358735Zhao et al. (2019)
miR-1273g-3pMAGEA3/6neg30056111Wu et al. (2018)
miR-145MYCneg29475734Zhu et al. (2018a)
miR-145KLF4neg29475734Zhu et al. (2018a)
miR-145Nanogneg29475734Zhu et al. (2018a)
miR-149—5pCUL4Aneg30092404Lian et al. (2018)
miR-149—5pP53neg33251049Meng et al. (2020)
miR-150—5pP53neg33251049Meng et al. (2020)
miR-150—5pVEGFAneg30250022Chen et al. (2018)
miR-17—5pTRIM8neg28327152Mastropasqua et al. (2017)
miR-17—5pp21neg28327152Mastropasqua et al. (2017)
miR-17—5pBLNKneg30555542Mai et al. (2018)
miR-181dPDGFRBneg28363996Jiang and Hermeking, (2017)
miR-181dFBXL3neg28749470Guo et al. (2017)
miR-181dPEAK1neg29449544Huang et al. (2018b)
miR-18aulk1neg28753429Yu et al. (2017b)
miR-18aPIAS3neg29896300Ma et al. (2018)
miR-18aHIF1αneg27080303Ma et al. (2016a)
miR-193a-3pIL17RDneg28600480Pekow et al. (2017)
miR-193a-3pERBB4neg27633443Yue et al. (2016)
miR-193a-3pIL17RDneg28600480Pekow et al. (2017)
miR-193a-3pERBB4neg27633443Yue et al. (2016)
miR-194—5pMALAT1neg31311811Wu et al. (2019b)
miR-194—5pSIRT1neg30451820Wang et al. (2018a)
miR-195—5pYAPneg28356122Sun et al. (2017)
miR-195—5pNOTCH2neg30808369Lin et al. (2019a)
miR-19aTGFBR2neg27080303Ma et al. (2016a)
miR-19aTIA1neg28257633Liu et al. (2017a)
miR-19aBimneg32591507Guo et al. (2020a)
miR-19aTNFAIP3pos27991929Wang et al. (2017a)
miR-200ZEB1neg26455323Barbáchano et al. (2016)
miR-200b-3pZEB1neg28837144Chen et al. (2017a)
miR-200c-3pZEB1neg28535802Rigoutsos et al. (2017)
miR-20aTGFBR2neg27080303Ma et al. (2016a)
miR-20aVEGFAneg27080303Ma et al. (2016a)
miR-20aWTXneg30631060Zhu et al. (2019)
miR-21RASA1neg27876571Yang et al. (2017)
miR-21IL-6pos25994220Shi et al. (2016)
miR-21TNF-αpos25994220Shi et al. (2016)
miR-21IL-17Apos25994220Shi et al. (2016)
miR-21IL-21pos25994220Shi et al. (2016)
miR-21PTENneg31918721Liang et al. (2020a)
miR-21hMSH2neg31918721Liang et al. (2020a)
miR-214EZH2neg30626446Xu et al. (2019a)
miR-214ANLNneg30195762Barbagallo et al. (2018)
miR-214F23neg30195762Barbagallo et al. (2018)
miR-214KIF2Aneg30195762Barbagallo et al. (2018)
miR-214IPO7neg30195762Barbagallo et al. (2018)
miR-214BIRC5neg30195762Barbagallo et al. (2018)
miR-215NID1neg30831320Rokavec et al. (2019)
miR-215ZEB2neg29187907Chen et al. (2017b)
miR-215LGR5neg30790680Ullmann et al. (2019)
miR-221PTENneg28986522Antoniali et al. (2017)
miR-221QKI-5neg31416845Mukohyama et al. (2019)
miR-222PTENneg28986522Antoniali et al. (2017)
miR-24Snora75neg28500171Michael et al. (2017)
miR-24mt-Nd2neg28500171Michael et al. (2017)
miR-24VHLneg30393198Jin et al. (2019)
miR-24PDHBneg30393198Jin et al. (2019)
miR-24PDHA1neg30393198Jin et al. (2019)
miR-24DLDneg30393198Jin et al. (2019)
miR-24IDH3Aneg30393198Jin et al. (2019)
miR-25—3pKLF2neg30568162Zeng et al. (2018a)
miR-25—3pKLF4neg30568162Zeng et al. (2018a)
miR-25—3pPTENneg31931030Wang et al. (2020a)
miR-26aFUT4neg28640257Li et al. (2017c)
miR-26aEZH2neg30626446Xu et al. (2019a)
miR-26bFUT4neg28640257Li et al. (2017c)
miR-26bEZH2neg30626446Xu et al. (2019a)
miR-27aACLYneg30393198Jin et al. (2019)
miR-27aMDH1Bneg30393198Jin et al. (2019)
miR-27aSDHAneg30393198Jin et al. (2019)
miR-27acalreticulinneg26913599Colangelo et al. (2016a)
miR-27acalreticulinneg26913609Colangelo et al. (2016b)
miR-31Axin1neg28870287Tian et al. (2017)
miR-31Gsk3bneg28870287Tian et al. (2017)
miR-31Tgfbr2neg28870287Tian et al. (2017)
miR-31Bmpr1aneg28870287Tian et al. (2017)
miR-31Smad4neg28870287Tian et al. (2017)
miR-31Smad3neg28870287Tian et al. (2017)
miR-31Dkk1neg28870287Tian et al. (2017)
miR-31Il7Rneg30779922Tian et al. (2019)
miR-31Il17RAneg30779922Tian et al. (2019)
miR-31GP130neg30779922Tian et al. (2019)
miR-34aIL6Rneg30099074Öner et al. (2018)
miR-34aPAI1neg30099074Öner et al. (2018)
miR-34aPPP1R11neg28435028Li et al. (2017a)
miR-34aSNHG7neg29970122Li et al. (2018a)
miR-34aN-MYCneg28327152Mastropasqua et al. (2017)
miR-34aPdgfraneg28363996Jiang and Hermeking, (2017)
miR-34aAxlneg28363996Jiang and Hermeking, (2017)
miR-34aCOL4A2neg28363996Jiang and Hermeking, (2017)
miR-34aWASF1neg28363996Jiang and Hermeking, (2017)
miR-34aSTC1neg28363996Jiang and Hermeking, (2017)
miR-34aPDGFRBneg28363996Jiang and Hermeking, (2017)
miR-34aSIRT1neg30312725Luo et al. (2019)
miR-34aSIRT1neg28943452Fang et al. (2017)
miR-34aLMTK3neg26739063Jacob et al. (2016)
miR-34bPdgfraneg28363996Jiang and Hermeking, (2017)
miR-34bAxlneg28363996Jiang and Hermeking, (2017)
miR-34bCOL4A2neg28363996Jiang and Hermeking, (2017)
miR-34bWASF1neg28363996Jiang and Hermeking, (2017)
miR-34bSTC1neg28363996Jiang and Hermeking, (2017)
miR-34bPDGFRBneg28363996Jiang and Hermeking, (2017)
miR-34cLMTK3neg26739063Jacob et al. (2016)
miR-34cPdgfraneg28363996Jiang and Hermeking, (2017)
miR-34cAxlneg28363996Jiang and Hermeking, (2017)
miR-34cCOL4A2neg28363996Jiang and Hermeking, (2017)
miR-34cWASF1neg28363996Jiang and Hermeking, (2017)
miR-34cSTC1neg28363996Jiang and Hermeking, (2017)
miR-451aEMSYneg28742699Kelley et al. (2017)
miR-451aCAB39neg28742699Kelley et al. (2017)
miR-451aBAP31neg30770794Xu et al. (2019b)
miR-590—5pYAPneg29912317Yu et al. (2018a)
miR-590—5pYAPneg29429755Ou et al. (2018)
miR-7EGFRneg28174233Weng et al. (2017)
miR-7RAF1neg28174233Weng et al. (2017)
miR-7FAKneg29549306Zeng et al. (2018b)
miR-7IGF1Rneg29549306Zeng et al. (2018b)
miR-7EGFRneg29549306Zeng et al. (2018b)
miR-7YY1neg29549306Zeng et al. (2018b)
NEAT1ALDH1pos33168814Zhu et al. (2020a)
NEAT1MYCpos33168814Zhu et al. (2020a)
NEAT1miR-34asponge30312725Luo et al. (2019)
NEAT1DDX5pos30185232Zhang et al. (2018a)
PVT1MYCneg33148262Shigeyasu et al. (2020)
PVT1miR-16—5pneg32276209Wu et al. (2020a)
PVT1Lin28pos30076414He et al. (2019)
PVT1miR-128sponge30076414He et al. (2019)
SATB2-AS1SATB2neg30858153Wang et al. (2019a)
SATB2-AS1SATB2pos31492160Xu et al. (2019c)
UCA1miR-143sponge31955010Luan et al. (2020)
UCA1miR-135asponge30195762Barbagallo et al. (2018)
UCA1miR-143sponge30195762Barbagallo et al. (2018)
UCA1miR-214sponge30195762Barbagallo et al. (2018)
UCA1miR-1271sponge30195762Barbagallo et al. (2018)
ZFAS1NOP58pos32443980Wu et al. (2020b)
ZFAS1miR-150—5psponge30250022Chen et al. (2018)

List of ncRNA-target and the type of interaction present in the network.

TABLE 2

ncRNATargetType of interactionPMIDReferences
ADAMTS9-AS2miR-143—3psponge30217729Xie et al. (2018)
AK000053miR-508sponge29374066Yan et al. (2018)
AK036396Ficolin Bneg32102837Tian et al. (2020)
ASBELATF3neg27791078Taniue et al. (2016a)
BC032913TIMP3pos28918047Lin et al. (2017)
BFAL1miR-155—5p, miR-200a-3psponge31515468Bao et al. (2019)
BLACAT1EZH2, p15Cooperate, neg28277544Su et al. (2017)
CALIChnRNP-L, AXL Cooperate, pos31353791Kawasaki et al. (2019)
CASC11hnRNP-Kpos27012187Zhang et al. (2016a)
CCALAP-2αneg25994219Ma et al. (2016b)
circ101555miR-597—5psponge31300733Chen et al. (2019b)
circ5615miR-149—5psponge32393760Ma et al. (2020)
circACC1AMPKpos31155494Li et al. (2019)
circCTNNA1miR-149—5psponge32699205Chen et al. (2020b)
circHIPK3miR-7neg29549306Zeng et al. (2018b)
CYTORβ-cateninpos29606502Yue et al. (2018)
FARSA-AS1miR-18b-5psponge33318478Zhou et al. (2020)
FEZF1-AS1PKM2pos29914894Bian et al. (2018)
FLANCpSTAT3pos31988194Pichler et al. (2020)
FOXC2-AS1FOXC2pos32513911Pan and Xie, (2020)
GLCC1 c-Myc pos31375671Tang et al. (2019a)
GSECDHX36neg27797375Matsumura et al. (2017)
HITTHIF-1αneg31784651Wang et al. (2020b)
HNF1A-AS1miRNA-34asponge28943452Fang et al. (2017)
HOXA-AS2p21, KLF2, EZH2neg, neg, cooperate28112720Ding et al. (2017b)
HOXD-AS1HOXD3neg30823921Yang et al. (2019)
ITHI4-AS1JAK1/2, FUSpos31557619Liang et al. (2019a)
KRT7-ASKRT7pos31910722Chen et al. (2020d)
LDLRAD4-AS1LDLRAD4neg32111819Mo et al. (2020)
LINC00265 ZMIZ2pos31527801Zhu et al. (2020b)
LINC00659 PI3Kpos29523145Tsai et al. (2018)
LINC00858miR-4766—5psponge31902050Zhan et al. (2020)
LINC01106miR-449b-5psponge33067422Guo et al. (2020b)
LINC01133SRSF6sponge27443606Kong et al. (2016)
LINC01234miR-642a-5psponge30755591Lin et al. (2019b)
LINC01413hnRNPK, ZEB1cooperate, pos31927328Ji et al. (2020)
LINC01578NFKBIBneg33040438Liu et al. (2020b)
LINC02023PTENpos30849479Wang et al. (2019b)
LINC02418miR-1273g-3psponge31358735Zhao et al. (2019)
LINC-UFC1β-cateninpos27195675Yu et al. (2016)
LINRISIGF2BP2pos31791342Wang et al. (2019c)
LNC34amiR-34aneg27077950Wang et al. (2016a)
LNC-C/EBPβArg1, CYBB, NOS2, ptgs2neg30171135Gao et al. (2018)
LNC-CMPK2FUBP3pos32203166Gao et al. (2020)
LNC-CRCMSLHMGB2shuttling30575817Han et al. (2019)
LNC-FAM84B-4hnRNPK, DUSP1cooperate, neg32866608Peng et al. (2020)
LNC-Gata6Lgr4, Lgr5pos30224759Zhu et al. (2018b)
LNC-RImiR-4727—5psponge32279126Liu et al. (2020c)
LncRNA-APC1Rab5bpos30511962Wang et al. (2019d)
LNRRIL6IL-6pos31246342Wang et al. (2019e)
LUCAT1NCLbinding33097685Wu et al. (2020c)
miR-342FOXM1, FOXQ1neg27162244Weng et al. (2016)
miR-101cTet1neg28249902Tie et al. (2017)
miR-105RAP2Cneg29238068Shen et al. (2017a)
miR-106aWTXneg30631060Zhu et al. (2019)
miR-106b-5pTRIM8, p21 neg28327152Mastropasqua et al. (2017)
miR-10aACTG1, MMP14neg28383561Liu et al. (2017b)
miR-124iASPPneg29022915Liu et al. (2017c)
miR-1249HMGA2, VEGFAneg30755600Chen et al. (2019c)
miR-125a-3pFUT5, FUT6neg28771224Liang et al. (2017)
miR-126SCELneg31974341Jiang et al. (2020)
miR-126—5pVEGFA, TWIST, SLUGneg30531836Sun et al. (2019a)
miR-1271ANLN, BIRC5, IPO7, KIF2A, F23neg30195762Barbagallo et al. (2018)
miR-128Lin28neg30076414He et al. (2019)
miR-128—3pBmi1, MRP5neg30890168Liu et al. (2019b)
miR-130b-3pPTENneg31931030Wang et al. (2020a)
miR-135aANLN, BIRC5, IPO7, KIF2A, F23neg30195762Barbagallo et al. (2018)
miR-137GLS1neg29730197Li et al. (2018b)
miR137HGmir-137pos29730197Li et al. (2018b)
miR-139—5pPDE4Dneg27383270Cao et al. (2016)
miR-141β-cateninneg30083271Ren et al. (2018a)
miR-141—3pZEB1neg28535802Rigoutsos et al. (2017)
miR-143ANLN, BIRC5, IPO7, KIF2A, F23neg30195762Barbagallo et al. (2018)
miR-143—3pITGA6neg30217729Xie et al. (2018)
miR-144EZH2neg30770796Shi et al. (2019)
miR-146ac-metneg29133238Bleau et al. (2018)
miR-148a GP130, IL1R1, IKKα, IKKβ, TNFR2neg28960206Zhu et al. (2017)
miR-149CDK4/6, XIAP, BCLXL, cyclin Dneg29061672Lulla et al. (2017)
miR-149—3pPDK2neg31597953Liang et al. (2020b)
miR-15LRP6neg31097689Ji et al. (2019)
miR-150ZEB1neg26455323Barbáchano et al. (2016)
miR-153IDO1neg29685162Huang et al. (2018c)
miR-15bDCLK1Neg30449704Ji et al. (2018)
miR-16—5pVEGFR1Pos32276209Wu et al. (2020a)
miR17HGmiR-375Sponge31409641Xu et al. (2019d)
miR-181aSRCIN1Neg29739921Sun et al. (2018)
miR-181a-5pβ-catenin, TCF4Neg28086904Han et al. (2017)
miR-181bPDCD4Neg27647131Liu et al. (2016)
miR-182LMTK3Neg26739063Jacob et al. (2016)
miR-187SOX4, PTK6, NT5ENeg26820227Zhang et al. (2016b)
miR-18b-5pFARSANeg33318478Zhou et al. (2020)
miR-193aCaprin1Neg28211508Teng et al. (2017)
miR-194VAPANeg29109785Chang et al. (2017)
miR-195WEE1, CHK1Neg29080751Kim et al. (2018)
miR-196b-5pHOXB7, GALNT5Pos28533224Stiegelbauer et al. (2017)
miR-19bBim Neg32591507Guo et al. (2020a)
miR-200a-3pRHEBNeg31515468Bao et al. (2019)
miR-203BIRC5Neg31091026Okugawa et al. (2019)
miR-205—5pZEB1Neg29352232Gulei et al. (2018)
miR-206MetNeg30250188Xu et al. (2018a)
miR-214—3pMyD88Neg30914411Shang et al. (2019)
miR-215—5pEREG, TYMSNeg31542354Chen et al. (2019d)
miR-216bGALNT1Neg29915311Shan et al. (2018)
miR-218VOPP1Neg28918035Li et al. (2017b)
miR-22HuRNeg29351796Liu et al. (2018a)
miR-222—3pPTENNeg31400607Liu et al. (2019a)
miR-223FBX8Neg27916606Wang et al. (2017b)
miR-224SMAD4Neg25804630Ling et al. (2016)
miR-23aCS, PDHA1, IDH1, DLDNeg30393198Jin et al. (2019)
miR-23bLGR5Neg28487386Viswanathan et al. (2017)
miR-301ABTG1Neg28193514He et al. (2017)
miR-302aNFIB, CD44Neg31754405Sun et al. (2019b)
miR-30aME1Neg28475173Shen et al. (2017b)
miR-30a-5pLDHANeg28461244Li et al. (2017d)
miR-320aPKCγNeg31515469Aljagthmi et al. (2019)
miR-338—5pIL-6Neg31208913Xu et al. (2019e)
miR-372/373SPOP, VDR, SETD7, RELANeg30171794Wang et al. (2018b)
TRERF1, ZNF367, MTUS1
miR-375RELA, MALT1, NFKBIENeg31409641Xu et al. (2019d)
PPP3R1, MAP3K7, CBL
miR-425—5pPTENNeg31931030Wang et al. (2020a)
miR-4260MCC, SMAD4Neg28638476Xiao et al. (2017)
miR-448IDO1Neg31391111Lou et al. (2019)
miR-449b-5pGli4Neg25961913Wang et al. (2016b)
miR-4727—5pLIG4Neg32279126Liu et al. (2020c)
miR-4766—5pPAK2Neg31902050Zhan et al. (2020)
miR-4775Smad7Neg28095858Zhao et al. (2017)
miR-4802ATG7Neg28753429Yu et al. (2017b)
miR-486—5pPLAGL2Neg30305607Liu et al. (2018b)
miR-487b-3pGRM3Neg28114282Yi et al. (2017)
miR-490—3pFRAT1Neg27037061Zheng et al. (2016)
miR-494APCNeg29304823Zhang et al. (2018b)
miR-500a-5pHDAC2Neg30737378Tang et al. (2019b)
miR-508SALL4Neg29374066Yan et al. (2018)
miR-514b-3pFZD4, NTN1Neg29880874Ren et al. (2018b)
miR-514b-5pCDH1, CLDN1Neg29880874Ren et al. (2018b)
miR-532—3pETS1 Neg31570702Gu et al. (2019a)
miR-532—5pTGFBR1Neg29971498Gu et al. (2019b)
miR-550a-3-5pYAPNeg29844307Choe et al. (2018)
miR-550a-5pRNF43neg25961913Wang et al. (2016b)
miR-5582—5pGAB1, CDK2, SHC1neg27475256An et al. (2016)
miR574—5pAPAF1neg32784109Wu et al. (2020d)
miR-597—5pCDK6, RPA3neg31300733Chen et al. (2019b)
miR-625—3pMAP2K6neg27526785Rasmussen et al. (2016)
miR-642a-5pSHMT2neg30755591Lin et al. (2019b)
miR-655—3pTGFBR2, ICKneg28457664Oshima et al. (2017)
miR-663aTGFB1, PIK3CD, P53, JUND, P21neg30154407Tian et al. (2018)
miR-675—5pTP53neg31734182Cen et al. (2020)
miR-6883—5pCDK4/6, XIAP, BCLXL, cyclin Dneg29061672Lulla et al. (2017)
miR-92a-3pFBXW7, MOAP1neg31064356Hu et al. (2019)
miR-93ATG12neg32144238Liu et al. (2020a)
miR-944COP1, MDM2neg30393117Kim et al. (2019b)
MYUmiR-16neg27568568Kawasaki et al. (2016)
N-BLRmiR-141—3p, miR-200c-3pneg28535802Rigoutsos et al. (2017)
OLA1P2STAT3neg26898989Guo et al. (2016)
Olfr29-ps1miR-214—3psponge30914411Shang et al. (2019)
OVAALPTBP1neg30478051Sang et al. (2018)
PiHLRPL11, GRWD1pos31903119Deng et al. (2020)
PINCRMatrin3neg28580901Chaudhary et al. (2017)
piR-1245ATF3, BTG1, DUSP1, NFKBIA,FAS, UPP1, SESN2, TP53INP1,MDX1neg29382334Weng et al. (2018)
pirl -54265p-STAT3, BCL-XL,cleaved-CASP3/7/9Pos, pos, neg30555542Mai et al. (2018)
RAMS11CBX4pos32358485Silva-Fisher et al. (2020)
RBM5-AS1 CMYC, CCND1, YAP1, SGK1pos27520449Di Cecilia et al. (2016)
RPPH1TUBB3pos31685807Liang et al. (2019b)
SNHG1miR-154—5psponge30266084Xu et al. (2018b)
SNHG5SPATS2pos28004750Damas et al. (2016)
SNHG6miR-26a, miR-26b, miR-214sponge30626446Xu et al. (2019a)
SNHG7miR-216bsponge29915311Shan et al. (2018)
SNHG11HIF-1αsponge33060856Xu et al. (2020a)
SNHG14miR-186—5p, EZH2sponge, pos31273190Di et al. (2019)
SNHG15Slugpos29604394Jiang et al. (2018)
SNORA42SMAD2pos32127004Xu et al. (2020b)
SNORD12C/78EIF4A3, LAMC2pos32443980Wu et al. (2020b)
tcon_00012883MMP1pos33135346Yang et al. (2020b)
TUG1TWIST1pos1988275Mosthaf et al. (1991)
u50535CCL20pos29970882Yu et al. (2018b)
UICLMmiR-215sponge29187907Chen et al. (2017b)
UPATUHRF1pos26768845Taniue et al. (2016b)
WiNTRLINC1ASCL2pos27292638Giakountis et al. (2016)
ZNFX1-AS1miR-144sponge30770796Shi et al. (2019)
δNp63αmiR-320apos31515469Aljagthmi et al. (2019)

List of ncRNAs, their targets and the type of interactions, cited by only one scientific article.

Classification of ncRNA Networks in Colorectal Cancer

Through the ncRNA network of colorectal cancer, we can clearly observe that the entire network graph is mainly divided into three large sub-networks (Including the miR-34a/b/c/miR-194-5p/miR21 sub-network, the CRNDE/EZH2/miR214/UCA1 sub-network and the miR-149/150-5p/LINC00460/miR-19a/20a sub-network) and a series of small networks (Including small networks with LNC00152, YAP, miR-27a, miR-24, miR-31, miR-7 as the core genes). Among these sub-networks, the miR-34a/b/c/miR-194-5p/miR21 sub-network and the LINC00152 network are mainly related to colorectal cancer chemotherapy resistance, which we call colorectal cancer chemotherapy resistance network; the CRNDE/EZH2/miR214/UCA1 sub-network, the YAP network and miR-24 network are mainly related to the metastasis of colorectal cancer, which we call the colorectal cancer metastasis network. These networks act synergistically and promote the progression of CRC (Figure 4; Table 3).

FIGURE 4

FIGURE 4

The relationship between ncRNAs network and the progression of colorectal cancer. Diagram of the relationship between ncRNAs sub-network and colorectal cancer progression.

TABLE 3

ncRNA networkTumorigenesisTumor angiogenesisTumor metastasisImmune escapeDrug resistant
The miR-34a/b/c/miR-194—5p/miR-21 sub-networkmiR-34PMID 24009080
miR-194—5pPMID 30451820
miR-21PMID 31918721
The CRNDE/EZH2/UCA1 sub-networkCRNDEPMID 28086904
EZH2PMID 27638307
UCA1PMID 31955010
The miR-149/150—5p/LINC00460/miR-19a/20a sub-networkmiR-149—5pPMID 30531836PMID 33251049
miR-150—5pPMID 33251049
LINC00460PMID 33251049
miR-19aPMID 27991929
miR-20aPMID 30631060
The YAP sub-networkYAPPMID 28356122
The miR-24 sub-networkmiR-24PMID 28500171
The LINC00152 sub-networkLINC00152PMID 27633443
The miR-27a sub-networkmiR-27aPMID 26913599
The miR-31 sub-networkmiR-31PMID 28870287

The relationship between ncRNAs network and the progression of colorectal cancer.

Chemotherapy Resistance Network of Colorectal Cancer

Chemotherapy resistance is one of the predominant reasons for the recurrence as well as poor prognosis of colorectal cancer (CRC) patients; ncRNAs reduce chemotherapy resistance of tumors by regulating signaling pathways in the initiation and progression of CRC. We integrated a variety of ncRNAs in CRC chemotherapy resistance and speculated that the combination of ncRNA-targeted inhibitors and chemotherapeutic drugs could be potential agents for improving the therapeutic effect of CRC.

The miR-34a/b/c/miR-194-5p/miR21 sub-network is the core chemotherapeutic resistance network in CRC treatment. The miR-34 family played a critical role in this sub-network by connecting multiple target proteins and lncRNAs. Furthermore, a number of reports show a reduced p53-induced miR-34 expression in CRC cells, and miR-34 can inhibit the occurrence and development of intestinal tumors. Moreover, miR-34 loss is related to tumor progression and chemotherapeutic resistance (Siemens et al., 2013). The mRNA induction in miR-34a/b/c-deficient tumors was enriched in miR-34a/b/c seed-matching sites and mRNAs encoding proteins for Wnt signaling in epithelial-mesenchymal transition (EMT) and stemness such as INHBB, AXL, FGFR1 and PDFGRB, etc. This leads to a decrease in immune cell infiltration and down-regulation of barrier proteins, which in turn promote proliferation and inhibit apoptosis (Jiang and Hermeking, 2017). Meanwhile, studies show that miR-34 mimics can be utilized to stimulate target multiple key pathways, thereby preventing the emergence of drug resistance caused by mutations in a single pathway. The deletion of miR-34a also enhances the effects of TP53 deletion in the early or late stages during CRC initiation and progression. Additionally, miR-34a and TP53 can synergistically inhibit tumor initiation, invasion and metastasis in mouse models of CRC by increasing the levels of target proteins IL6R and PAI1 (Öner et al., 2018). PPP1R11 is also a target of miR-34a, and its product inhibits PP1. In p53-deficient CRC cells, PPP1R11 can activate the phosphorylation of STAT3, and simultaneously, high expression of PPP1R11 can induce EMT, invasion, migration and resistance to 5-fluorouracil under hypoxic conditions. Moreover, miR-34a can reduce the activation of STAT3 in p53-deficient CRC cells by decreasing the expression of PPP1R11, and ultimately inhibit EMT and metastasis of CRC cells (Li et al., 2017a). Thus, we speculate that inhibiting the expression of TP53 and miR-34a in CRC or using miR-34a/b/c replacement therapy may be a potential approach for CRC treatment. The antibodies or small molecule inhibitors to repress miR-34a targeting IL6R and PAI1 are potent promising treatment of CRC in the future.

Additionally, in this sub-network, we can find that KLF4 is significant related to miR-25-3p, and miR-25-3p, as an inhibitor of KLF4, has the effect of promoting the metastasis of CRC (Zeng et al., 2018a). A recent study further showed that MeCP2 bound to METTL14 and enhanced the m6A level of KLF4, while m6A-modified KLF4 was supposed to be stabilized by IGF2BP2 to increase the expression of KLF4, thereby inhibiting the metastasis of CRC (Wang et al., 2021). Therefore, the development of drugs that simultaneously target to promote the expression of IGF2BP2 and target to inhibit the expression of miR-25-3p may be also an important approach for the treatment of CRC.

SNHG7 (small nucleolar RNA host gene 7), miR-34a and GALNT7 also play an important role in the progression of CRC through the PI3K/AKT/mTOR pathway. SNHG7 can be used as a competitive endogenous RNA (ceRNAs). Along with the sponge miR-34a, it can regulate the level of GALNT7 in CRC and activate the PI3K/AKT/mTOR pathway to promote proliferation and metastasis (Li et al., 2018a). Mastropasqua et al. report that TRIM8 (tripartite motif containing 8) and its regulatory factors including miR-17-5p and miR-106b-5 participate in a feedback loop that controls cell proliferation in CRC by mutual regulation of p53, miR-34a, and N-Myc. In CRC, TRIM8 is a key target that triggers the sensitivity of CRC cells to chemotherapy. TRIM8 restores the function of the p53 tumor suppressor by inactivating the activity of oncoprotein N-Myc in chemotherapy-resistant tumors. Additionally, the silencing of miR-17-5p and miR-106b-5p restore the levels of TRIM8, and effectively promote the tumor suppressor activity of p53 and the transcription of miR-34a, thereby reducing the carcinogenic potential of miR-34a’s target N-Myc. It restores the sensitivity of cells to chemotherapy (Mastropasqua et al., 2017). In addition, LMTK3 (lemur tyrosine kinase 3), an important node in the network diagram, plays an important role in the progression of a variety of cancers (breast cancer, lung cancer, CRC, etc.) (Xu et al., 2014; Xu et al., 2015). In CRC, nuclear LMTK3 interacts with DDX5 to target and regulate the expression of a group of miRNAs (miR-34a, miR-196a2, and miR-182). The tumor suppressor-like miRNAs, miR-34a and miR-182 directly bind to the 3′UTR of LMTK3 mRNA and inhibit its stability and translation, thereby inhibiting the proliferation, invasion, and migration in CRC (Jacob et al., 2016).

In addition to targeting some encoded proteins, miR-34a could also target long-chain non-coding RNAs, and played an important regulatory role in the progression of CRC. NEAT1 (nuclear paraspeckle assembly transcript 1), a long-chain non-coding RNA, is a known oncogene in CRC. For example, NEAT1 can directly interact with its target DDX5 and stabilizes its protein expression. DDX5, thus, activates the Wnt/β-catenin signaling pathway and promotes the progression of CRC (Zhang et al., 2018a). Additionally, some studies show that NEAT1 is associated with 5-FU resistance in CRC. NEAT1 increases H3K27ac enrichment at ALDH1 and c-Myc promoters by altering chromatin remodeling, thereby up-regulating their expression, enhancing the stemness of CRC cells, and promoting 5-FU resistance (Zhu et al., 2020a). Thus, NEAT1 plays an important role in tumor resistance and tumorigenesis in CRC.

However, the effect of NEAT1 on the Wnt/β-catenin signaling pathway is not completely dependent on DDX5, and NEAT1 can also exert carcinogenic effects through miR-34a (Luo et al., 2019). In CRC, NEAT1 acts as a ceRNA that targets miR-34a and regulates its expression, thereby inhibiting the miR-34a/SIRT1 axis. It activates the Wnt/β-catenin signaling pathway, and inhibits miR-34a/SIRT1 feedback loop, which in turn promotes CRC progression, invasion, and metastasis, etc. The above studies show that NEAT1 can be used as a diagnostic marker and is a potential therapeutic target for CRC. Moreover, traditional chemotherapy combined with drugs targeting tumor stem cells provides a new strategy for the treatment of CRC patients CRC patients with high NEAT1 expression. More importantly, the combined network analysis showed that miR-34 may simultaneously target different genes and multiple core pathways in CRC, inhibit EMT, invasion, migration, and proliferation of cancer cells, and prevent the emergence of drug resistance caused by mutations in a single pathway. Therefore, miR-34 replacement therapy could also be a potential option for the treatment of CRC. In addition, targeting a certain pathway regulated by miR-34 for specific effects could also be a potential direction for further research in the treatment of CRC.

In addition to NEAT1, the network diagram also connects a series of other long non-coding RNAs through different target genes. Indeed, there are several studies confirming the role of these long non-coding RNAs in CRC. Therefore, the interaction of different lncRNAs in CRC and their target proteins in this network require in-depth analysis. For example, some studies show that H19 may be the main marker for predicting 5-FU chemotherapy resistance. H19 acts as a ceRNA to target miR-194-5p, and in turn regulates the SIRT1-dependent autophagy pathway which promotes 5-FU chemotherapy resistance in CRC (Wang et al., 2018a). Autophagy is triggered by the target protein of miR-34a, SIRT1 in this network diagram too. Some studies show that H19 acts as a ceRNA sponge of miR-141, activates the β-catenin pathway and promotes stemness as well as chemotherapy resistance of CRC by transferring H19 from exosomes (Ren et al., 2018a). The above studies suggest that conventional chemotherapy combined with targeted therapy can be a potential treatment for 5-FU resistant patients with elevated expression of H19. From the network diagram, we observe that the sub-network highlights another branch of miR-194-5p, which can directly target the lncRNAs-MALAT1 harboring the rs664589 G allele in the nucleus of CRC cells, thereby regulating the nuclear expression of MALAT1 and exerting a tumor suppressor effect (Ren et al., 2018a). Researchers indicate that in CRC, the rs664589 polymorphism of MALAT1 inhibits its affinity to miR-194-5p, resulting in its increased expression, and thus, promotes the development of CRC. Moreover, MALAT1 is primarily induced by YAP1 in CRC and YAP1 interacts with TCF4 and β-catenin to regulate the expression of MALAT1 (Sun et al., 2019a). MALAT1 also primarily functions as a competitive endogenous lncRNA in CRC, which targets and regulates the sponging of miR-126-5p, miR-663a, miR-15, and other microRNAs to exhibit a tumor suppressor effect. MALAT1 promotes the expression of VEGFA, SLUG, TWIST, and other metastasis-related molecules by regulating the sponge miR-126-5p; it regulates the angiogenesis and EMT of CRC cells and promotes metastasis (Sun et al., 2019a). Additionally, MALAT1 protects the targets of miR-663a from degradation. MiR-663a and MALAT1 may form a negative feedback loop and affect the progression of CRC (Tian et al., 2018). MALAT1 functions as a ceRNA to regulate the miR-15 family. MiR-15 family inhibits the expression of LRP6 and the activation of the downstream β-catenin signaling pathway. MALAT1 regulates the transcription of the proto-oncogene RUNX2 through the miR-15s/LRP6/β-catenin signaling pathway and thus, regulates the progression of CRC (Ji et al., 2019).

Thereafter, we focus on the last lncRNA-PVT1 of the network diagram. PVT1, a previously unknown transcriptional regulator in CRC, shows a significantly high enhancer activity controlled by epigenetic regulation due to abnormal methylation involved in the occurrence and development of CRC. Enhanced expression of PVT1 is associated with the poor survival in CRC patients with clinical stage II or III status. It also exerts its function as a novel epigenetic enhancer of MYC and responsible for regulating the expression of oncogenic MYC gene (Shigeyasu et al., 2020). Furthermore, PVT1 also functions as a ceRNA to regulate the expression of target genes in the cytoplasm. For example, it can promote the proliferation and invasion of CRC cells by stabilizing Lin28 and interacting with miR-128 (He et al., 2019). Another study reported that PVT1 also promoted the specific binding of RNA-binding proteins (Lin28 and Lin28B) to let-7 by the up-regulation of Lin28 for driving carcinogenic activity of CRC; PVT1 stabilizes and post-transcriptionally regulates Lin28, which targets the Lin28/let-7 axis and promotes tumorigenesis. It is also speculated that the low expression of PVT1 in CRC inhibits EMT and angiogenesis. PVT1 promotes the occurrence of CRC by stabilizing miR-16-5p targeting the VEGFA/VEGFR1/AKT axis. Vascular endothelial growth factor A (VEGFA) is the direct downstream target of miR-16-5p. In the absence of PVT1-miR-16-5p/VEGFA/VEGFR1/AKT, signaling pathway is inactive, thereby inhibiting the progression of CRC (Wu et al., 2020a). In sum, targeting PVT1 may be a potential treatment option for CRC patients.

MiR-21 is also observed as an important multi-target miRNA in this network. Fusobacterium activates the toll-like receptor 4 signaling pathway, which leads to the activation of nuclear factor kappa B (NFκb) and up-regulation of miR-21 expression. Up-regulation of miR-21 reduces the level RAS GTPase (RASA1) and promotes the occurrence and development of CRC (Yang et al., 2017). In mice, silencing miR-21 results in a significant decrease in the expression of pro-inflammatory and cancer-promoting factors (IL6, IL-23, IL-17a and IL-21) and inhibition of tumor proliferation. Studies show that the absence of miR-21 leads to the decrease in Ki67 expression and the inhibition of tumor growth in colitis-associated colon cancer (CAC) mouse, an up-regulation of E-cadherin, and the downregulation of β-catenin and SOX9. The deletion of miR-21 increases the expression of its target gene PDCD4, which in turn regulates the activation of NFκb. The deletion of miR-21 also inhibits the activation of STAT3 and Bcl-2 in CAC mice, leading to an increase in tumor cell apoptosis. These studies indicate the regulatory role of miR-21 in the development of CAC caused by colitis (Shi et al., 2016). Moreover, other studies show that miR-21 is correlated with chemotherapeutic resistance of CRC. MiR-21 regulates the expression of downstream targets PTEN and hMSH2, induces tumor cell cycle arrest, inhibits tumor cell proliferation, promotes cell apoptosis, and inhibits migration. MiR-21 targeted therapy can significantly enhance the cytotoxicity of 5-FU in resistant CRC cells and reverse the resistance in CRC just like the exosomal delivery of 5-FU (Liang et al., 2020a).

In the entire chemotherapy resistance network, LINC00152 acting as a ceRNA targets and regulates the expression of miR-193a-3p, antagonizes chemotherapy sensitivity, regulates erb-b2 receptor tyrosine kinase 4 (ERBB4), reduces the phosphorylation of AKT, and thereby reduces resistance to L-OHP (Yue et al., 2016). Similarly, it regulates the expression of NOTCH1 by inhibiting the activity of miR-139-5p, and increasing the resistance of CRC cells to 5-FU (Bian et al., 2017). These studies suggest that the Linc00152/miR-193a-3p/ERBB4/AKT and the LINC00152/miR-139-5p/NOTCH1 signaling axes may provide new insights into CRC resistance mechanisms. In addition, LINC00152 may also be a key tumor suppressor of ulcerative colitis-related CAC. Studies show that miR-193a-3p regulates the expression of IL17RD and controls the downstream EGFR signaling and inhibits the growth of colon cancer (Pekow et al., 2017). Thus, LINC00152 might be a novel potential target in the inflammation-driven CRC patients.

Metastasis Sub-network of Colorectal Cancer

The metastasis of CRC is the main reason for the poor clinical outcomes and high mortality for CRC patients. The CRNDE/EZH2/UCA1 network is the main component of the metastasis network in CRC. The common target of multiple ncRNAs in this network was the oncogene EZH2 (enhancer of zeste2 polycomb repressive complex 2 subunits). The histone lysine N-methyltransferase encoded by the EZH2 gene is an important part of the PRC2/EED-EZH2 complex, which can methylate the “Lys-9” (H3K9me) and “Lys-27” (H3K27me) of histone H3 and inhibits the transcription of its downstream target genes (McCabe et al., 2012; Hübner et al., 2019). Mutation or over-expression of EZH2 is associated with many types of cancers (breast cancer, prostate cancer, melanoma, bladder cancer, etc.) (Bracken et al., 2003). Presently, many EZH2 targets have been identified. For example, the INK4B-ARF-INK4A tumor suppressor gene locus is a well-known target of EZH2 and its inhibition affects cancer growth and embryonic development (Kheradmand Kia et al., 2009); E-cadherin gene is another critical target of EZH2, and its down-regulation is essential for EMT and metastasis (Luo et al., 2016). Mu Xu et al. report that lncRNA SNHG6 acts as a molecular sponge of miR-26a/b and miR-214, and releases EZH2 by isolating the endogenous microRNA of CRC cells, which mainly regulates the expression of EZH2 in CRC (Xu et al., 2019a). Moreover, EZH2 and its targets H3K27me3, P14ARF, P15INK4b, P16INK4a and E-cadherin are involved in the carcinogenic effect of SNHG6 in CRC and regulate the EMT (Xu et al., 2019a).

In the sub-network of EZH2, O-glycosylation, is a unique post-translational modification (PTM), which participates in CRC metabolic reprogramming. The level of O-glycosylation increases in metastatic CRC tissues or cells. The expression of miR-101 reduces, while the expression of o-GlcNAc acyltransferase (OGT) and EZH2, which are regulated by miR-101, increases significantly. The down-regulation of miR-101 promotes O-GlcNAcylation, and the increased O-GlcNAcylation further enhances the stability and function of the EZH2 protein. O-GlcNAcylation and EZH2-mediated H3K27me3 modification of the miR-101 promoter region further reduce the expression of miR-101, consequently, miR-101/O-GlcNAcylation/EZH2 signals form a feedback loop that inhibits metastasis and eventually inhibits the invasion of CRC cells and regulate the EMT (Jiang et al., 2019). Thus, EZH2 has extremely high potential as a new target for CRC treatment.

Another important network node in the network diagram was CRNDE. CRNDE is located on human chromosome 16 and is highly expressed in a variety of cancers including CRC. CRNDE binds to EZH2, which in turn, can directly bind to DUSP5 and CDKN1A promoter regions, and induce histone H3 lysine 27 trimethylation (H3K27me3) modification in DLD1 and HCT116 cells (Ding et al., 2017a). This also inhibits dual specific phosphatase 5 (DUSP5) and CDKN1A expression and promotes the development of CRC (Ding et al., 2017a). In addition, CRNDE is also related to microRNAs. Han et al. found that miR-181a-5p could be used as the inhibitory target of CRNDE. β-catenin and TCF4 are inhibitory targets of miR-181a-5p and repress the Wnt/β-catenin signaling pathway. In CRC cell lines, CRNDE promotes CRC cell proliferation and chemotherapy resistance through the Wnt/β-catenin signaling pathway mediated by miR-181a-5p (Han et al., 2017). Thus, it warrants further studies to investigate the regulatory mechanism of CRNDE as a potential target in the therapy strategy and resistance of CRC.

Another ncRNA in the network diagram was the lncRNAs UCA1 (urothelial cancer associated 1). The presence of UCA1 in exosomes is verified, but its role and clinical applicability in CRC remain unclear. Barbagallo reported that UCA1 is upregulated in CRC biopsy (Barbagallo et al., 2018). In serum exosomes, the expression of UCA1 is regulated by an activating MAPK signal. UCA1 isolates miR-135a, miR-143, miR-214, and miR-1271 to protect ANLN, BIRC5, IPO7, KIF2A, KIF23 and other actin and cytoskeleton related proteins from miRNA-induced degradation, and thus, regulates their expression and promotes the progression of CRC and other key biological processes (Barbagallo et al., 2018). Luan et al. also demonstrate that UCA1 is upregulated in the serum exosomes of patients with CRC. UCA1 is packaged into exosomes which are transferred to CRC cells. As a ceRNA, UCA1 regulates the expression of MYO6 through miR-143, enhances cell proliferation and migration, and exerts essential functions in the tumor progression of CRC (Luan et al., 2020). Taken together, these reports suggest that UCA1 may be a potential new clinical biomarker for CRC.

From Figure 4, we observe that in addition to the CRNDE/EZH2/miR214/UCA1 network, the YAP sub-network and the miR-24 sub-network also play indispensable roles during the metastasis of CRC. YAP1 is upregulated through a variety of biological mechanisms and has a carcinogenic effect in a variety of tumors. As the core sub-network of CRC metastasis, YAP connected multiple ncRNAs such as GAS5, miR375, and circ1662, etc. The inactivation of YAP1 is required in cell-cell contact inhibition and act as a transcriptional co-activator to mediate the biological functions of the Hippo pathway (Zhao et al., 2007). It should be noted that lncRNA GAS5 (growth arrest-specific 5), a tumor suppressor in CRC as a ceRNA of miR-222-3p, regulates the expression of Beclin1, LC3B, and PTEN by targeting miR-222-3p/GAS5 phosphatase and PTEN signaling pathways, thereby inhibiting CRC cell migration and invasion, and promotes autophagy (Liu et al., 2019a). Notably, GAS5 as well as its target YAP are intently linked to m6A modification. GAS5 directly interacts with the WW domain of YAP to promote the transfer of endogenous YAP from the nucleus to the cytoplasm, as well as its phosphorylation and its subsequent ubiquitin-mediated degradation which leads to tumor suppression (Ni et al., 2019). Interestingly, YTHDF3 selectively bound to GAS5 which was modified by m6A and promoted the degradation of GAS5 in an m6A-dependent manner. Meanwhile, GAS5 negatively regulated the expression of YAP, and YAP could bind to the promoter region of YTHDF3 to promote the transcription of YTHDF3, in other words, YTHDF3-GAS5-YAP-YTHDF3 formed a positive feedback loop and promoted the metastasis of CRC in an m6A-dependent manner (Ni et al., 2019). Moreover, YAP not only regulated the expression of YTHDF3, m6A modified YAP also directly bound to IGF2BP2, and stabilized YAP promoted the occurrence of CRC by up-regulating the expression of ErbB2 (Cui et al., 2021). In addition to directly regulating the expression of YAP mRNA, m6A modification is supposed to indirectly regulate the expression of YAP1 protein. Chen et al. illustrated that the overexpression of METTL14 increased the m6A level of primiR-375, and the m6A-modified primiR-375 was transformed into premiR-375 under the action of DGCR8, thereby promoting the expression of miR-375 in CRC. Thereafter, elevated miR-375 suppressed the expression of YAP1, and ultimately inhibited the metastasis of CRC (Chen et al., 2020a). Interestingly, the expression of YAP1 protein is not only regulated by m6A-modified miRNA, but also by m6A-modified circRNA. Studies have shown that METTL3 induced the expression of circ1662 by installing m6A modification in the circ1662 flanking reverse complement sequence. The overexpression of circ1662 promoted the transport of YAP1 protein to the nucleus and reduced the level of YAP1 protein in the cytoplasm and ultimately accelerated the metastasis of CRC (59).

MiR-590-5p inhibits the YAP expression by directly targeting its 3′UTR, thereby inhibiting intestinal inflammation and tumorigenesis of CRC cells (60). Ou et al., also validated the existence of the miR-590-5p/YAP axis. MiR-590-5p is a miRNA with density-sensitive property. The high density of CRC cells upregulates the expression of the RNase III endonuclease DICER1, which in turn promotes the biosynthesis of miR-590-5p and ultimately inhibits YAP expression (Ou et al., 2018). This also suggests that the miR-590-5p/YAP axis may be an important specific therapeutic target contributing to the pathogenesis of CRC. Furthermore, miR-590-5p itself may also serve as a therapeutic potential target for CRC patients. miR-590-5p is a hypoxia-sensitive miRNA and inhibits the expression of RECK, which promotes the invasion and metastasis of CRC cells by activating matrix metalloproteinases (MMPs) and filamentous processes in vitro, and consequently promotes tumor cell proliferation (Kim et al., 2019a). Moreover, Nuclear factor 90 (NF90), a direct target of miR-590-5p, is a positive regulator of vascular endothelial growth factor (VEGF) mRNA stability and protein synthesis. The NF90/VEGFA signaling axis can inhibit angiogenesis and metastasis in CRC (Zhou et al., 2016). In contrast, MiR-195, in the YAP sub-network, is an inhibitor of the Hippo-YAP signaling pathway. There are two conserved miR-195-5p homologous sites at the 3′UTR of YAP mRNA. MiR-195-5p inhibits EMT and blocks Hippo signaling, thereby inhibiting the proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) of CRC cells (Sun et al., 2017). In addition, miR-195-5p can also regulate the expression of NOTCH2 in a post-transcriptional manner (Lin et al., 2019a).

Previous publications show that the miRNAs are usually organized in clusters (within 3 kb) in the genome and have the characteristics of a regulatory network that controls tumor metabolism. MiRNA clusters play essential roles in tumor progression by coordinating or inhibiting multiple target genes. The coordinated regulation of miRNA clusters may cause rapid switching of the metabolic signaling networks in CRC cells. Jin et al. report a cluster consisting of miR-23a, miR-27a and miR-24 induced by hypoxia conditions in CRC cells, which promotes glycolysis by regulating the related gene networks. Inhibition of miR-23a, miR-24, and miR-27a under hypoxic conditions weaken the stimulating effect of reduced oxygen on glycolysis-related genes along with the inhibitory effect on tricarboxylic acid cycle-related genes including PDHB, PDHA1, IDH2, DLD, and IDH3A. Moreover, miR-24 promotes the expression of HIF-1α by targeting VHL, forming a double negative feedback loop and exhibits the strongest regulatory effect. Thus, it shows that the miR-23a/27a/24 cluster promotes the progression of CRC through metabolism reprogramming (Jin et al., 2019).

Other Sub-network in Colorectal Cancer

Among other sub-networks, miR-149/150-5p/LINC00460/miR-19a/20a occupies a major position. It covers tumorigenesis, metastasis and chemotherapy resistance of CRC. In the sub-network, LINC00460, acting as a vital ncRNA, linked multiple miRNAs such as miR-149-5p, miR-150-5p, etc. Studies show that LINC00460 has a carcinogenic effect on CRC. It recruits EZH2 (enhancer of zeste homolog 2, EZH2) and H3K27me3 to the tumor suppressor KLF2 promoter in the nucleus. Thereby, it epigenetically inhibits the expression and inactivation of KLF2 (Lian et al., 2018). LINC00460, as a molecular sponge of miR-149-5p, antagonizes its ability to inhibit the translation of cullin4A (CUL4A) protein and regulates the occurrence of CRC. Thereafter, LINC00460 directly interacted with IGF2BP2 and DHX9 and combined with m6A-modified HGMA1 mRNA to enhance the stability of HGMA1 and ultimately promoted the metastasis of CRC (Hou et al., 2021). Notably, LINC00460 may also be a promising therapeutic target involved in chemotherapeutic resistance of CRC. Meng et al. found that LINC00460-miR-149/150-5p-mutant p53 feedback loop is associated with oxaliplatin resistance of CRC. Similarly, LINC00460 promotes oxaliplatin resistance by isolating miR-149-5p/miR-150-5p and upregulating the expression of the target p53 (Meng et al., 2020). In addition to LINC00460, the circCTNNA1 also acts as a ceRNA competitive sponging miR-149-5p to counteract its inhibitory effect on the downstream target FOXM1, thereby promoting the progression of CRC (Chen et al., 2020b). Similarly, circ5615 binds to miR-149-5p, exerting miR-149-5p sponge effect, upregulating TNKS, and subsequently promoting the progression of CRC through the Wnt/β-catenin signaling pathway (Ma et al., 2020). Thus, the carcinogenic functions of LINC00460 or circCTNNA1 as ceRNA in CRC were validated, which suggested that these indicators might be potential and valuable therapeutic targets in CRC treatment and multi-drug resistance.

Besides miR-149-5p, miR-150-5p, miR-19a/20 as critical parts of the sub-network, miR-200 family including miR-200, miR-200b-3p, and miR-200c-3p was found involved in the regulation of ZEB1 and XIST, etc. Interestingly, ZEB1 acted as one of the downstream targets of miR-200b-3p, the combination of XIST and miR-200b-3p disrupts the combination of miR-200b-3p and ZEB1. Meanwhile, XIST can also act as a sponge of miR-200b-3p to promote the expression of ZEB1 and thus promote the progression and metastasis of CRC (71). Importantly, recent report supports that METTL14 can increase the m6A level of XIST and decrease the expression of XIST in a YTHDF2-dependent regulation manner. The decrease of XIST expression promotes the expression of miR-200b-3p by directly binding to miR-200b-3p (Yang et al., 2020a). Thus, these findings indicated that linking m6A-modified XIST with miR-200 and miR-200c-3p might provide novel directions and approach for excavating the potential targets for CRC therapy.

Notably, another lncRNA, ZFAS1 is highly expressed in CRC tissues and cells. Moreover, as a miR-150-5p sponge, it targets and regulates the expression of its downstream VEGFA, and promotes the progression of CRC by promoting miR150-5p-mediated VEGFA/VEGFR2/Akt/mTOR signaling pathway and EMT (Chen et al., 2018). In addition, studies show that ZFAS1 promotes CRC by small nucleolar RNA-mediated 2′-O methylation through NOP58 recruitment and plays essential roles through the ZFAS1-NOP58-SNORD12C/78-EIF4A3/LAMC2 signaling axis (Wu et al., 2020b). Collectively, these researches broaden our spectrum and lay a solid foundation for further excavating the crosstalk functions between epigenetic modification and ncRNAs during the early prediction and therapy of CRC.

Conclusions and Perspectives

During the past few decades, extensive promotions have been made to explore the biological functions of ncRNAs in the involvement of tumorigenesis and progression of various types of tumors including CRC. In this review, we analyzed the regulation network and sub-networks related to ncRNAs involved in the progression, metastasis and chemoresistance of CRC via transcriptional and post-transcriptional epigenetic modification levels. Among the networks, the miR-34a/b/c/miR-194-5p/miR21 sub-network showed a direct relationship with oxaliplatin resistance for CRC therapy. Meanwhile, the CRNDE/EZH2/UCA1 sub-network had a significant association with metastasis and progression of CRC. Furthermore, we analyzed the regulatory manner of the core m6A regulators with m6A-related ncRNAs as exemplified by YTHDF3-GAS5-YAP, IGF2BP2-YAP-ErbB2, METTL14-YTHDF2-XIST, MeCP2/METTL14-KLF4, LINC00460/IGF2BP2/DHX9-HMGA1 signaling axis in CRC progression.

Thus, the crosstalk and regulation network of m6A modifications associated modulators and ncRNAs provide a novel direction for exploring the underlying regulatory mechanisms of gene expression in CRC development.

Until now, multiple ncRNAs associated epigenetic m6A modification modulators has been found acting as potential biomarkers and targets for CRC therapeutic interventions. However, these indicators have not been effectively developed and applied for the CRC therapy, partly due to exceeding targets for each regulator. For example, IGF2BP1, IGF2BP2 and IGF2BP3 has an enrichment of 3747, 3211, and 3914 high confidence downstream targets, respectively (Huang et al., 2018a). These targets and cellular biological pathways were closely connected to form a huge ncRNAs regulatory network. Thus, targeting multiple dysregulated targets in the m6A associated ncRNAs network holds an important potential direction contributing for CRC therapy. Developing highly specific and selective small-molecule inhibitors targeting m6A regulators and associated ncRNAs demand urgently for inter-individual precision therapy of CRC. Overall, the regulatory network provides a foundation for further study of ncRNAs, which also provide critical possibilities for clinical treatment through their associations with m6A epigenetic modifications that warrants further investigations for CRC.

Statements

Author contributions

XD and HW contributed to the conception of this study. XD and SL drafted the manuscript. XD, SL, XH and TS made the diagrams and tables. XD, SL and YW conducted literature screening and data collection. HW, XW, MW and SL revised the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China (81872905, 82073884), Shenyang S&T Projects (20-204-4-22, 19-109-4-09), Liaoning Revitalization Talents Program (XLYC1807155), Science and Technology Innovative Foundation for Young and Middle-aged Scientists of Shenyang City (RC200382), Science and Technology Program of Liaoning Province (2017225036), Shenyang High Level Talent Innovation and Entrepreneurship Team (2019-SYRCCY-B-01), and Major Special S&T Projects in Liaoning Province (2019JH1/10300005).

Conflict of interest

Author MW was employed by Shenyang Kangwei Medical Laboratory Analysis Co. Ltd. The remaining authors declare that the review was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Summary

Keywords

long non-coding RNA, micro RNA, interaction network, colorectal cancer, N6-methyladenosine modification

Citation

Lu S, Ding X, Wang Y, Hu X, Sun T, Wei M, Wang X and Wu H (2021) The Relationship Between the Network of Non-coding RNAs-Molecular Targets and N6-Methyladenosine Modification in Colorectal Cancer. Front. Cell Dev. Biol. 9:772542. doi: 10.3389/fcell.2021.772542

Received

08 September 2021

Accepted

11 November 2021

Published

06 December 2021

Volume

9 - 2021

Edited by

Huilin Huang, Sun Yat-sen University Cancer Center (SYSUCC), China

Reviewed by

Guohui Wan, Sun Yat-sen University, China

Ying Yang, Chinese Academy of Sciences, China

Duo Liu, Harbin Medical University Cancer Hospital, China

Updates

Copyright

© 2021 Lu, Ding, Wang, Hu, Sun, Wei, Wang and Wu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Minjie Wei, ; Xiaobin Wang, ; Huizhe Wu,

†These authors contributed equally to this work.

This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Cell and Developmental Biology

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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