Aim: The high mortality rate of melanoma is due to partially the lack of good diagnostic markers and treatment strategies. Over the past several years, several microRNA (miRNA) profiling studies have been performed on melanoma tissues, but with extremely inconsistency, the diagnostic value of miRNA candidates in melanoma remains under debate. Thus, this study aims to systematically evaluate the consistency of miRNAs tissue in multiple independent studies in melanoma. Method: Eligible studies were screened and selected from the PubMed, EMBASE, and Web of Science. A systematic analysis of published miRNA expression studies that compared the miRNA expression profiles between melanoma tissues and normal skin tissue was conducted. A vote-counting strategy was followed with the collection of information. Real time PCRs were employed to validate miRNA candidates with high consistency. Targets of consistent miRNAs were predicted by online programs (like miRTarBase, microRNA.org and TargetScanHuman 6.2). Enrichment analyses for gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways were carried out with Database for Annotation, Visualization, and Integrated Discovery (DAVID). Results: A total of 303 differentially expressed miRNAs were reported in the 10 miRNA-profiling studies during comparison of melanoma tissues with normal tissues; 132 were up-regulated in melanoma, and 171 were down-regulated. However, in the group of consistently reported miRNAs (cutoff > 3 times), only moderate numbers of consistent and differentially expressed miRNAs were selected. miRNA-21 was found increased in 5 different studies, miRNA-146b, miRNA-17 and miRNA-18a were reported up-regulated in 4 profiling studies. Meanwhile, miRNA-204 and miRNA-125b were found down-regulated in 5 studies, miRNA-141, miRNA-149, miRNA-224, miRNA-200b, miRNA-200c were consistently decreased in just 4 out of 10 profiling studies in total. The directions of differential expression of these miRNA candidates were confirmed by real time PCRs. Enrichment analyses demonstrated that programmed cell death and transcription regulation played very important roles in the involvement of miRNAs in tumorigenesis of melanoma. Conclusion: This systematic study of melanoma miRNA profiling studies would provide rich information on miRNAs with potential role as the biomarkers and therapeutic agents with high consistency in melanoma.
Cutaneous melanoma, as currently the sixth most common cancer in white men and women in the United States, is the most aggressive form of skin cancer characterized by poor prognosis [
Since discovered in 1993, microRNAs (miRNAs), as small, evolutionarily conserved, single-stranded and non-coding RNA molecules, have been found to be important to regulate the expression of up to 30% encoding genes by binding to specific mRNA targets and promoting their degradation and/or translational inhibition [
We conducted this systematic analysis to identify the most important differentially expressed miRNAs that had been consistently reported in a series of independent miRNA expression profiling studies in melanoma patients. Moreover, we further validated some of the miRNAs that were most up- or down-regulated using real- time PCR in 6 pairs of melanoma and matched adjacent non-tumor tissue samples.
Potential studies published in English were collected from PubMed, EMBASE, Web of Science published from June, 2006 to Feb 2015 (last accessed on April 15, 2015) using the following medical subject headings terms: “miRNA” OR “microRNA” OR “miR”, “skin cancer” OR “melanoma”, “profiling” OR “microarray”.
For a study to be included in this systematic review, several criteria had to be met: 1) studies had to be miRNA profiling studies in melanoma patients; 2) studies had to use melanoma tissues and their corresponding adjacent non-tumor tissues for comparison; 3) miRNA microarray and sequencing methods were comprised; they reported cut-off criteria of differentially expressed miRNAs, and 4) they reported the validation method set. Therefore, miRNA profiling studies that used serum samples of melanoma patients or skin relevant cell lines, those that compared melanoma biopsies from tumors with different stages of disease, or those using different miRNA techniques were excluded. Review articles were also excluded.
Two investigators (X.W. and Y.C.) independently evaluated and extracted the data using standard protocols, and all discrepancies were resolved by the corresponding investigator (Y.Y.). From the full text and corresponding supplementary information, the following eligibility items were collected and recorded for each study: author, journal and year of publication, location of study, methods and characteristics of animal modeling, platform of miRNA expression profiling, author defined cut-off criteria of statistically significant differentially expressed miRNAs.
Each included profiling/microarray study [
To validate the profiling results, 6 fresh melanoma tissues and their paired non-tumor skin tissues were obtained from the Second Hospital of Hangzhou City, affiliated to Hangzhou Normal University School of Medicine. Total RNA was extracted from 6 pairs of matched human melanoma specimens (including cancer and adjacent noncancerous tissues) using TRIzol reagent (Life technology, Casbad, CA, USA) according to the manufacturer’s instructions. The differentially expressed amount of the miRNAs was validated in triplicate by real time PCR. Briefly, Reverse transcription from 3 ug RNA was done using SuperScript III First-Strand Synthesis System (Life technology, Casbad, CA, USA) according to the manufacturer’s protocol. Real-time PCR was performed using iQ SYBR Green Supermix kit (Bio-Rad, Hercules, CA) with the iCycler sequence detection system (Bio-Rad) with specific primers (
miRTarBase (mirtarbase.mbc.nctu.edu.tw), microRNA.org and TargetScanHuman 6.2 were used to explore all the target genes of miRNAs, and highlighted the genes that are experimentally validated by luciferase reporter assay, western blot, or microarray experiments with over expression or knock- down of miRNAs.
Primers | Sequence | |
---|---|---|
miR-204 | For 5’ GGCTACAGTCTTTCTTCATG 3’ | Rev 5’ GCCAGTGATGACAATTGAACGTC 3’ |
miR-141 | For 5’ CCATCTTCCAGTACAGTGTTGG 3’ | Rev 5’ GCCATCTTTACCAGACAGTGTTAG 3’ |
miR-125b | For 5’ TGCGCTCCTCTCAGTCCCTGAG 3’ | Rev 5’ AGCACGACTCGCAGCTCCCA 3’ |
miR-149 | For 5’ GAGCTCTGGCTCCGTGTCT 3’ | Rev 5’ TCCAGCTGCCCCAGCACAG 3’ |
miR-224 | For 5’ GCTTTCAAGTCACTAGTGGTTC 3’ | Rev 5’ GCTTTGTAGTCACTAGGGCACCA 3’ |
miR-200c | For 5’ CTCGTCTTACCCAGCAGTG 3’ | Rev 5’ CCTCCATCATTACCCGGCAG 3’ |
miR-200b | For 5’ CCAGCTCGGGCAGCCGTGG 3’ | Rev 5’ CGTGCAGGGCTCCGCCGTCATC 3’ |
miR-17 | For 5’ GTCAGAATAATGTCAAAGT 3’ | Rev 5’ GTCACCATAATGCTACAAGTG 3’ |
miR-21 | For 5’ TGTCGGGTAGCTTATCAGAC 3’ | Rev 5’ TGTCAGACAGCCCATCGACTG 3’ |
miR-146b | For 5’ CCTGGCACTGAGAACTGAA 3’ | Rev 5’ CACCAGAACTGAGTCCACAGGGC 3’ |
miR-18a | For 5’ GTTCTAAGGTGCATCTAGTG 3’ | Rev 5’ GCCAGAAGGAGCACTTAGGGC 3’ |
U6 | For 5’ GCGCGTCGTGAAGCGTTC 3’ | Rev 5’ GTGCAGGGTCCGAGGT 3’ |
Enrichment analyses for gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways were carried out with Database for Annotation, Visualization, and Integrated Discovery (DAVID) [
Student’s t-test was used to compare values between two independent groups.
A total of 68 studies were recorded using PubMed, EMBASE and web of science. 38 of which were excluded after screening the titles and abstracts, and 20 studies were excluded after reading the full text based on the inclusion and exclusion criteria, only 10 independent studies were included in this systematic analysis. The detailed workflow used in our analysis was shown in
These 10 studies from 10 different groups with different platforms, different number of samples, and various statistical analyses had been employed for microRNA profiling analysis to compare melanoma tissue with corresponding noncancerous skin tissue. The number of differentially expressed microRNAs ranges from 4 to 53. A total of 303 differentially expressed miRNAs were reported in the 10 profiling studies. 171 microRNAs are down- regulated in melanoma, and 132 microRNAs are up-regulated. Among the 171 down-regulated miRNAs, two of them were reported in 5 microarray studies (miRNA-204 and miRNA-125b), five miRNAs were reported in four profiling studies (miRNA-141, miRNA-149, miRNA-224, miRNA-200b, miRNA-200c). Among the 132 up- regulated miRNAs, there was only one different miRNA in 5 studies (miRNA-21), three studies were reported
Author | Region | Year | Platform | Number of Tissues | Altered miRNAs | Criteria | Up-Regulated miRNAs in HCC | Down-Regulated miRNAs in HCC |
---|---|---|---|---|---|---|---|---|
Qi | China | 2014 | GPL9052 Illumina | 31 (7/24) | 4 | p < 0.05 | 4 | |
Sand | Germany | 2013 | Agilent Microarray | 42 (21/21) | 49 | p < 0.05 | 21 | 28 |
Xu | UK | 2012 | Illumina microarray | 32 (21/21) | 24 | p < 0.05 | 10 | 14 |
Poliseno | USA | 2012 | miRCURY LNA 11.0 array (Exiqon) | 91 (82/9) | 27 | p < 0.05 | 27 | |
Yang | China | 2011 | LuxScan 3.0 | 8 (4/4) | 7 | ? | 5 | 2 |
Caramta | USA | 2010 | Agilent Microarray | 19 (16/3) | 54 | p < 0.01 | 48 | 6 |
Segura | USA | 2010 | Agilent Microarray | 118 (59/59) | 12 | p < 0.05 | 12 | |
Chen | Canada | 2010 | Agilent Microarray | 16 (8/8) | 31 | p < 0.05 | 13 | 18 |
Philippidou | Luxemburg | 2010 | uParaflo microarray | 40 (20/20) | 45 | p < 0.05 | 25 | 20 |
Schultz | Germany | 2008 | ABI 7700 systerm | 20| (10/10) | 72 | p < 0.05 | 6 | 66 |
in 4 profiling studies (miRNA-146b, miRNA-17 and miRNA-18a). Here, consistently expressed miRNAs, the corresponding microarray study and the total number of tissue samples were shown in
To validate the consistency and direction of the eleven most consistently reported miRNAs (miRNA-204, miRNA- 125b, miRNA-141, miRNA-149, miRNA-224, miRNA-200b, miRNA-200c, miRNA-17, miRNA-21, miRNA- 146b and miRNA-18a), the expression of these miRNAs in melanoma biopsies and adjacent noncancerous tissues
miRNAs | Frequence (references) | Number of tissue samples (melanoma/healthy) |
---|---|---|
Up-regulated miRNAs | ||
miR-21 | 5 (16; 17; 19; 20; 24) | 100 (52/48) |
miR-17 | 4 (16; 20; 21; 22) | 188 (81/107) |
miR-146b | 4 (16; 17; 21; 22) | 136 (68/68) |
miR-18a | 4 (16; 17; 19; 22) | 136 (68/68) |
Down-regulated miRNAs | ||
miR-204 | 5 (15; 16; 18; 20; 22) | 90 (47/43) |
miR-125b | 5 (15; 16; 18; 19; 22) | 118 (59/59) |
miR-141 | 4 (15; 16; 21; 22) | 108 (54/54) |
miR-149 | 4 (15; 16; 21; 22) | 108 (54/54) |
miR-224 | 4 (15; 19; 21; 22) | 132 (54/78) |
miR-200c | 4 (15; 16; 21; 22) | 108 (54/54) |
miR-200b | 4 (15; 16; 21; 22) | 108 (54/54) |
miR-23b | 4 (15; 16; 22) | 78 (39/39) |
miR-455 | 4 (21; 22; 23) | 190 (95/95) |
miRNAs | N# of in up-regulated miRNAs | N# of in down-regulated miRNAs |
---|---|---|
miR-9 | 1 | 1 |
miR-17 | 1 | 5 |
miR-21 | 1 | 5 |
miR-28 | 2 | 1 |
miR-96 | 1 | 1 |
miR-107 | 1 | 1 |
miR-132 | 1 | 1 |
miR-140 | 1 | 1 |
miR-143 | 1 | 1 |
miR-146 | 1 | 1 |
miR-155 | 1 | 1 |
miR-185 | 1 | 2 |
miR-186 | 1 | 2 |
miR-194 | 1 | 1 |
miR-211 | 3 | 2 |
miR-219 | 1 | 1 |
miR-323 | 1 | 2 |
miR-342 | 1 | 1 |
miR-494 | 1 | 1 |
miR-768 | 1 | 1 |
miR-106a | 1 | 1 |
miR-199a | 1 | 1 |
miR-26a | 1 | 1 |
miR-27a | 1 | 1 |
miR-29c | 1 | 1 |
were compared in 6melanoma patients using real-time PCR with specific primers (
The identified and selected miRNAs all play specific roles in skin homeostasis, or pathogenesis of melanoma. Particularly, all these miRNAs are either oncogenic miRNAs or tumor suppressors. Specifically, miRNA-21, miRNA-146b and miRNA-18a are oncogenic miRNAs (oncomir), and miRNA-204, miRNA-125b, miRNA-141, miRNA-200b and miRNA-200care tumor suppressors. We need to point out that these selected oncomires and tumor suppressors are differentially expressed only in moderate number of profiling studies (less than 5 or 4 times), implying that miRNA plays marginal roles in the tumorigeneisis and development of melanoma.
After we identified the most consistently differentially expressed microRNAs that were either down-regulated
or up-regulated in melanoma, we screened the their potential target genes with programs such as miRTarBase (mirtarbase.mbc.nctu.edu.tw), microRNA.org and TargetScanHuman 6.2, focusing on the genes which were confirmed either by real time PCR, western blot, microarray or luciferase assay. As a result, we identified 211 and 318 target genes (519 in total, some target genes are shared by two different kinds of miRNAs, reasons are to be defined) corresponding to those down-regulated and up-regulated miRNAs in melanoma (
We used DAVID program to build up the molecular networks of down-regulated and up-regulated microRNAs target genes. The top GO terms (18 for up-regulated miRNAs and 17 for down-regulated miRNAs) and KEGG pathways showing significant association with target genes were listed with GO terms, KEGG pathway, number of genes in the terms and number of genes in the pathways (Figures 4-9). The important GO terms were critically involved in the regulation of regulation of transcription, metabolic process for down-regulated miRNAs (
Up-Regulated miRNAs | Predicted Target |
---|---|
miR-21 | BTG2, CDKAP1, PDCD4, LRRFIP1, CDC25A, PELI1, PDCD4, TP63, PTEN, HNRPK, TGFBR2, TGFBR2, SMARCA4, SPRY1/2, PRARA, TIMP3, RHOB, MSH2/6, ANP32A, BCL2, ANP32A, BMPR2, MARCK5, ANKRD46, RECK |
miR-17 | ZNFX1, CCL1, GPR137B, NABP1, NPAT, YES1, JAK1, PTEN, CDKN1A, PTPRO, PKD2, BCL2L11, E2F1, MAP3K12, BCL2, MEF2D, RUNX1, APP, ICAM1, VEGFA, MAPK9, DNAJC27, FBXO31, TGFBR2, TNFSF12, MUC17, BMPR2, VIM, CCND1, MYC, NCOA3, THBS1, SMAD4, ICAM1, SELE, SOD2, GPX2, TXNRD2, CCND2, E2F3, RB1, RBL1, RBL2, WEE1, TGFBR2, RND3, SMURF1, TCF3, HSPB2, MMP2, HBP1, GALNT7, SIRPA, DYNC1LI2, PLEKHA3, BRMS1L, ZNFX1, MYT1L, EPHA4, ENPP5, SLITRK3, RPS6KA5, SERF1B, CAMTA1, C14ORF28, ANUBL1, RUFY2, SUV420H1, GNPDA2, SCN1A, SERF1A, RPGR, LHX8, PKD2, GPR6, RALYL, GPR137C, ATG7, SACS, VSX1, PCDHA13, PCDHA5, PCDHA4, ANKRD44, MYEF2,TRIP11, ARHGAP12, TANC1, STAT3, NBEA, NR4A2, ZNF367, CAMK2N1, ZNF800, MYNN, PDCD1LG2, PRDM6, ARID4A, PFN2, FTSJD1, AGGF1, PCDHA12 |
miR-146b | CXCR4, CFH, IRAK2, TLR2, FADD, TRAF6, IRAK1, IRCK1, ROCK1, BRCA2, BPCA1, BRCA1, FAF1CCNA2, PA2G4, IL8, NFKB1, CDKN1A, EGFR, MTA2, MMP16, KIT, Card10, Scube2, TRAF6, IRAK1, CD40LG, PDGFB, FAS, CDKN3, KIF22, ERBB4, SMAD4, TLR4, WASF2, BGLAP, SPP1, SLPI, L1CAM, NOVA1, RHOXF2B, RHOXF2, TPM1, TRAF6, TRAF6, IGSF1, PCGF5, PCGF5, ZNF90, TMEM185B, ITCH, LIN52, PHC1, FAM153B, ABCD3, CMAH, LRRTM3, PMS1, TFAP2D, LRCH1, MGC11082, TMEM19, ADRB2, RASGRP1, FBXL3, LCA5L, UBE2J1, RAB7L1, RAK1, UHRF1, SLC16A14, TIMELESS, AGMAT, CCDC150, CADM2, RFTN2, SLC1A1, TRDN, FLJ10489, TXNDC5, C10orf4, RNF148, RHOBTB3, DPY19L2P1, DPY19L2, BLZF1, PPM1K, DPY19L2P4, ARF6, RUNDC2B, RUNDC2C, PAK7, ANKRD11, LOC203274, SLC6A14, ARNT, CAMK2G, GBA2, TCHP, CCNB3, MORF4L2, PTPN12, TSC22D2, SULT2A1, BTBD11, PRKCH, TTC38, CCNE1, FAM160B1, KALRN, ORC4L, C14orf129, STRN, DYNLL1, PIWIL4, TET2, CMTM1, ITM2B, C6orf94, SIGLEC5, HLA-F, CPA6, COPZ1, ACAD8, PARK7, MBNL1, UBE2D2, ABCD3, DECR1, RBM15, OXR1, DNM1L, IL1A, TUBD1, CIRH1A, FAM35B2, HIF3A, CPEB4, TTLL11 |
miR-18a | KRAS, ESR1, PTEN, Runx1, CTGF, NCOA3, CCNL1, CSRNP3, THRA, TNFSF11, Myc, Prmt5, NR3C1, TSC22D3, Prmt5, SMAD4, HSF2, ATM, Smad2, Smad4, NEDD9, CDK19, DICER1, SMAD3, Pten, ERLIN1, OTX2, ENTHD1, GLRB, BEND6, BBX, INADL, CAB39, NEDD9, HIF1A, TXK, DSC1, DIP2C, BTG3, MAP3K1, SCD5, PRPF4B, TMEM2, RIMS2, AKAP7, PRDM6, RAD51L1, HCFC2, ITM2B, CASC2, PFN2, RMST, CDS2, MTCP1, TMEM64, BEX1, NAE1, NCOA1, ZNF367,PHF20L1, SAR1A, C22orf31, C5orf30, FAM3C, MAP7D1, RFC4, PPIP5K2, RPL23AP82, KLHL20, PTPMT1, TRPC4, VPS13A, SH3BP4, RNF145, PASK |
WnT signaling pathway (Wnt, Frizzled and RhoA) (Supplemental data S8). In the context of tumorigenesis, all these pathways work synergistically to evade apoptosis by regulation of transcription of these target genes, which in turn regulates relevant signaling pathways.
In the current study, we established the concept, by conducting a systematic analysis, which only a few of consistently differentially expressed miRNAs may be of significance for tumorigenesis and development of melanoma. The potential targets of these selected miRNAs conduct the signaling pathways critical for programmed cell death and the regulation of transcription. Together, this research shed some light on the potential of certain miRNAs as biomarkers and therapeutic target for melanoma.
Many conventional tumor biomarkers, such as ovarian cancer biomarker CA-125 and melanoma biomarker S-100, have encountered some limitation like poor sensitivity of specificity, for example, only 50% of patients with early stage ovarian cancer have elevated levels of CA125; specificity of S-100 for melanoma is about 75% [
Based on our systematic analysis, these oncomirs and tumor suppressors were consistently and differentially expressed in relative less investigations (no more than 5 profiling studies), suggesting that miRNAs may only
Down-Regulated miRNAs | Predicted Target |
---|---|
miR-204 | MEIS1, HOXA10, BCL2, TGFBR1, TGFBR2, SNAI2, Runx2, MEIS2, SNAI1, Mmp9, Aurkb, Hoxb7, SPDEF, THRB, CDX2, CDX2, BCL2L2, BIRC2, EDEM1, EZR, FARP1, FZD1, IL11, M6PR, RAB22A, RAB40B, SERINC3, SERP1, TCF12, TCF4, FOXC1, MAP1LC3B, CREB5, ELOVL6, RUNX2, SOX4, EFNB2, ALPL,SOST,SLC37A3, BPY2, ACSL4, ZNF638, GRIA2, ZCCHC10, TARDBP, AP1S2, RHOBTB1, HELLS, GRIA4, LRIT3, PRAMEF8, JRKL, ACSM2A, CHEK1, PHOX2B, ACSM2B, C2orf68, C9orf72, ENTPD5, CNOT10, MLLT3, MAPRE2, PRAMEF7, ELOVL6, BRWD1, PRO0628, ATP10B, LOC284294, EGR1, NR4A2, FSIP2, NDRG3, HDAC9, C2orf56, SS18, NUDT13, LOC253724, TRMT11, TMEM57, BHLHE22, ZFP91-CNTF, ZFP91-CNTF, SIX1, GATAD1, GPR6, RAP2C, B3GNT5, KIAA0776, PTGR1 |
miR-125b | BMPR1B, EIF4EBP1, HMGA2, HMGA1, GLI1, NKIRAS2, TP53, SMO, VDR, SGPL1, BAK1, ERBB3, ERBB2, BMF, KLF13, NTRK3, LIN28A, CBFB, AKT1, CYP24A1, RAF1, SMO, PRDM1, IRF4, GRIN2A, CDKN2A, LIN28A, MAP2K7, JUB, KRT7, TNF, TP53INP1, E2F3, TRIM71, IGF2, LIN28B, BAK1, BBC3, BMF, KLF13, TEF, STAT3, BAK1, JUN, JUND, PPP1CA, PPKRA, PRKRA, PPP1CAB, SRF, NKX2-5, PRPF8, BCL2, ETS1, RPS6KA1, TNFAIP3, PIGF, BCL3, TBC1D1, DGAT1, FGFR2, SUV39H1, ARID3B, SMAD4, MCL1, IL6R, STARD13, ABTB1, CBFB, HK2, MMP13, SNAI1, MAPK14, MUC1, NES |
miR-141 | ZEB2, ZEB1, DLX5, BAP1, Dlx5, Cdh11, Zeb2, Zeb1, Stk3, Klf5, KLF5, STK3, TGFB2,SFPQ, CLOCK, BRD3, UBAP1, PTEN, ZFPM2, TRAPPC2P1, EIF4E, CTBP2, CDYL, ACVR2B,MAPK14, PPARA, NR0B2, YWHAG, Elavl4, MAPK9, TFDP2, E2F3, SHC1, VAC14, TCF7L1, ELMO2, RASSF2, KLHL20, RIN2, SEPT7, HOXB5, ERBB2IP, KLF11, PTPRD, WDR37, AP3S1, RANBP6, ARPC5, WTAP, TAC1, NRP1, ALS2, PITX2, MTF2, SELE, ITGA6, ZFR, IRF2BP2, IRF2BP2, PCDH9, LMO3, KCNIP4, SLC18A2, TRAM1, LHFP, IPO5, YY1, UFM1, C11orf54, LEPR, FAM188A, MYH10, LGR4, ZNF248, HS6ST2, ELAVL2, BBS10, SNRPB2, KIDINS220, ANKS1B, NBPF16, NBPF8, PRKD1, ATL1, GLCCI1, CHD9, TSHZ3, MARCH7, LBR, UNC5C, SP4, MYBL1, NBPF11, MAP2K4 |
miR-149 | Aicda, AKT1, E2F1, SP1, FOXM1,FRMD7, PPIP5K1, RSBN1L, ERBB3, KIAA1467, C17orf81, BEYLA, CAV2, DYNLRB1, POLR2L,HINFP, EGLN3, MOGAT2, BIRC5, MRPS22, CACHD1, PPAP2B, CCDC109A, RNF2, YJEFN3, NISCH, RUNDC2C, BRPF3, SPEF2, NUCB2, KCNIP1, HSFYL1, CHEK1, SH3GL3, STRADB, TOP1, HSD17B4, NAP1L1, MAGEB18, ZMYM6, STRA6, RAP1BL, RAP1B, ZNF385D, PRDM2, IL6, RAP1A, KCNMA1, HNRNPA1, PHLPP2, UPF2, SSBP1, CCNI, ODZ3, CCDC45 |
miR-224 | KLK10, CXCR4, CDC4, AP2M1, NIT1, FOSB, NCOA6, API5, EYA4, EDNRA, DIO1, SMAD4, PEBP1 |
miR-200c | TUBB3, BMI1, SIP1, BAP1, ZEB2, ZEB1, Zeb1, Zeb2, FN1, ZFPM2, UBE2I, JAG1, PTPN13, Flt1, RNF2, RCOR3, BRD7, ACVR2B, Mapk14, MSN, NTRK2, ERRFI1, FHOD1, PPM1F, CCNE2, XIAP, BCL2, TIMP2, FBLN5, VEGFA, NCAM1, Vldlr, Reln, IKBKB, FLT1, KLF9, TBK1, PMAIP1, NTF3, LPAR1, EDNRA, RHOA, KLHL20, PTPRD, ELMO2, ERBB2IP, WDR37, VAC14, TCF7L1, RASSF2, HOXB5, RIN2, KLF11, SEPT7, SHC1, CGRRF1, QKI, WIPF1, LCA5, LOX, ERRFI1, RBM46, LEPR, DNA2, PTPN22, CCNJ, FOXF1, TSGA14, NRG1, SEC23A, RECK, VASH2, KIAA1244, NOG, MARCKS, ASF1A, A2BP1, LHFP, RAP1BL, RAP1B, ZNF423, C9orf93, ZNF532, LRP1B, MEX3B, ELAVL2, APOO, ZDHHC17, INTS8, MSN, NOVA1, CDK17, RAP2C, ZNF292, GPR158, LBR, GPM6A, TFAP2A, KCTD8, WNT16, SULF1, HSPA9, WIF1 |
miR-200b | PTPN12, ZEB2, BAP1, ZEB1, RERE, Zeb2, Zeb1, ets1, gata4, fn1, wasf3, zfpm2, matr3, rnf2, bml1, ezf3, vegfa, FLT1, KDR, RND3, CCNE2, BCL2, XIAP, SMAD2, CREB1, KLHL20, ELMO2, PTPRD, ERBB21P, WDR37, TCF7L1, VAC14, HOXB5, RIN2, RASSF2, KLF11, SEPT7, SHC1 |
play marginal roles in the tumorigenesis and development of melanoma, which is concert with the fact that environmental factors like UV plays important roles in the onset of melanoma. Here, we found that three of the selected miRNAs (miRNA-21, miRNA-17, miRNA-146b and miRNA-18a) are enhanced, displaying oncogenic characteristics (oncomir), and seven of screened miRNAs (miRNA-204, miRNA-125b, miRNA-141, miRNA- 200b and miRNA-200c) decreased markedly, functioning as tumor suppressors. As an oncomir, miRNA-21 negatively regulates the expression of many different tumor suppressor genes, such as PTEN [
cancers including melanoma, by suppressing melanoma cell proliferation and metastasis. miR-125b is down- regulated in metastatic melanoma and controls melanoma progression by direct regulation of c-Jun protein expression [
miRNAs function as oncogenes or tumor suppressors by involving multiple molecular mechanisms. By enrichment analysis, we found these oncomirs and tumor suppressors can targets variety of substrate genes, it is of great significance to point out that these targets concentrate on the cancer-related cellular process, mainly including apoptosis, which is in concert with the fact that apoptosis plays checkpoint roles in tumorigenesis of any cancers [
built up a very rich networks, combined with different pathways and molecule-molecule interactions. Wnt signaling pathway is very important for the integrity of endothelial cells and for melanoma tissue invasion and metastasis, inhibition of mTOR signaling pathway can reduce cellular apoptosis; p53 signaling pathway contributes to inhibit apoptosis; MAPK signaling pathway are crucial for cell proliferation; All these pathways work synergistically to evade apoptosis and increase proliferation. Some other pathways related to block of differentiation, resistance to chemotherapy, failed repair of genes, insensitivity to anti-growth signals are also implied. All these miRNA/target genes related pathways mimic exactly the pathways involved in the tumorigenesis of melanoma, which make these miRNAs even more important as potential biomarkers and therapeutic candidates for clinical application. Extensive studies should focus these miRNAs to establish them as biomarkers for cancer detection and progression.
Several limitations of this research should be considered when interpreting the results due to some unsolvable reasons. Firstly, our literature searching was depended on English databases only, as a result, language bias may present. Secondly, our study did not include all the populations (only Chinese, British, American, German, Canadian and Luxemburg), so the result may not be able to apply to other populations such as Latin American and African.
We performed comprehensive literature search in multiple databases by limiting publication language and date. By systematic analysis, we filtered the most consistently expressed 4 up-regulated and 7 down-regulated miRNAs, and further confirmed by real time PCR, specially, highlight would be given to miRNA-21, miRNA-204 and miR-125b since they consistently differentially expressed the most (5 studies respectively). By DAVID analysis, we showed that the target genes were mainly involved in the regulation of programmed cell death mediated by combined signaling pathways such as cell cycle signaling pathway. In short, these selected miRNAs are hopefully of significance by their potential as biomarkers and/or as therapeutic agents against melanoma.
This work was supported by grants from the Natural Science Foundation of China (81570479), the Climbing Program in Hangzhou Normal University-Phase II (PF14002004021) and the Program for Zhejiang Leading Team of Science and Technology Innovation (2011R50021).
ChengtanLi,XiayuWang,Ya’niChen,XiaohuaTan,WenLi,ShengYan,WeibinCai,XianrongXu,LiangwenXu,LeiYang,YutaoYan, (2015) MicroRNA-21, 204 and 125b Play Potential Roles in Tumorigenesis of Melanoma. Advances in Bioscience and Biotechnology,06,677-692. doi: 10.4236/abb.2015.612071