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Open Access Full Text Article ORIGINAL RESEARCH Expression Profiles of tRNA-Derived Fragments and Their Potential Roles in Multiple Myeloma

Cong Xu1 Background: Multiple myeloma is an incurable hematologic malignancy. The discovery of Yunfeng Fu2 mechanisms may help to find new therapeutic targets and prolong survival. tRNA-related fragments (tRF) and tRNA halves (tiRNA) are small RNA derived from tRNAs and 1Department of Hematology, The Third Xiangya Hospital of Central South implicated in a wide variety of pathological processes, including cancer initiation and University, Changsha, 410000, People’s progression. However, there are almost no research reporting the role of these tRNA derived 2 Republic of China; Department of Blood fragments in myeloma as far as we know. Transfusion, The Third Xiangya Hospital of Central South University, Changsha, Methods: In this study, we proposed the expression profiles of tRFs/tiRNAs in myeloma 410000, People’s Republic of China through RNA-sequencing in new diagnosed myeloma and healthy donors. The expression of selected tRFs/tiRNAs was further validated in clinical specimens by qPCR. Bioinformatic analysis was performed to predict their potential roles in multiple myeloma. Results: A total of 33 upregulated tRFs/tiRNAs and 22 downregulated tRFs/tiRNAs were identified. GO enrichment and KEGG pathway analysis were performed to analyze the functions of one significantly up-regulated and one significantly down-regulated tRNA- derived fragments. tRFs/tiRNAs may be involved in MM initiation and progression. Conclusion: We concluded that tRFs/tiRNAs were dysregulated and could be potential biomarkers for multiple myeloma. Keywords: multiple myeloma, tRNA related fragments, tRNA halves, cancer initiation

Introduction Multiple myeloma (MM) is the second most common hematological malignancy and is characterized by the accumulation of malignant plasma cells in the bone marrow.1 With the advent of new drugs, such as proteasome inhibitors and immu­ nomodulatory agents, the outcomes of patients with MM have steadily improved. Despite improvements in response and survival rates, MM remains a costly and incurable disease.2,3 Elucidating the mechanisms governing MM development is pivotal to optimizing the clinical efficacy and prolonging survival. Myeloma cells are dependent on multiple factors at all stages of tumor development and progres­ sion. Among these factors, noncoding RNAs (ncRNAs) play critical roles.4,5 The largest portion of the mammalian genome is transcribed into RNA species with little or no coding potential, which are known as ncRNAs. An explosion of interest in RNA biology has identified a variety of regulatory functions for ncRNAs.6,7 With the rapid development of high-throughput sequencing in recent years, a new class of small ncRNAs derived from tRNAs has attracted increasing attention from researchers. Correspondence: Yunfeng Fu According to the cleavage site, these tRNA derived fragments can be broadly Tel/Fax + 86-73188618511 Email [email protected] classified into two main groups: tRNA related fragments (tRFs) generated from

OncoTargets and Therapy 2021:14 2805–2814 2805 © 2021 Xu and Fu. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Xu and Fu Dovepress mature or precursor tRNA and tRNA halves (tiRNAs) Table 1 Primers for qRT-PCR Validation of Candidate tRFs/ generated by specific cleavage in the anticodon loops of tiRNAs 8 mature tRNA. On the basis of mapped positions on the Primer precursor or mature tRNA transcript, tRFs/tiRNAs are iRNA-1:34-Glu- F: 5ʹAGGCTCCCACATGGTCTAGC3’ subdivided into five types: tRF-5, tRF-3, tRF-1, tRF-2 TTC-2 R: 5ʹCAGTGCAGGGTCCGAGGTAT3’ and tiRNA. tRFs/tiRNAs are involved in various molecu­ tRF-60:76-Arg- F: 5ʹATTATCCTGGCTGGCTCGCC3’ lar processes through translation regulation, epigenetic ACG-1-M2 R: 5ʹCCGAGGTATTCGCACTGGA3’ regulation and RNA silencing.9,10 Accumulating evidence supports the significance of tRF-1:23-Ala-AGC F: 5ʹTAAACCGGGGGTATAGCTCAGT3’ -1-M3 R:5ʹCAGTGCAGGGTCCGAGGTAT3’ tRFs/tiRNAs in multiple human diseases, including cancer. For example, some sex hormone-dependent tRF-1:16-Lys-TTT F: 5ʹCTTTGCCCGGATAGCTCAGT3’ tRNA-derived RNAs were found to enhance cell pro­ -3-M2 R: 5ʹCAGTGCAGGGTCCGAGGTAT3’ liferation in breast cancer cells and to act as oncogenes tRF-1:22-Leu-AAG F: 5ʹATTGGTAGCGTGGCCGAGT3’ in breast cancer.11 tRF-1001, which is present in the -4-M2 R: 5ʹCGCAGGGTCCGAGGTATTC3’ cytoplasm and produced by the tRNA 3ʹ-endonuclease tRF-+1:T14-Gly- F: 5ʹACCCTGCGGTACCACTTTGT3’ ELAC2, is essential for the proliferation of prostate TCC-1 R: 5ʹCGCAGGGTCCGAGGTATTC3’ 12 cancer cells and colorectal cancer cells. Upregulated U6 F: 5ʹGCTTCGGCAGCACATATACTAAAAT3’ or downregulated tRFs/tiRNAs are both involved in the R: 5ʹCGCTTCACGAATTTGCGTGTCAT3’ occurrence and development of solid tumors.13,14 In hematological malignancies, limited data have demon­ strated the potential roles played by tRFs/tiRNAs in National Comprehensive Cancer Network (NCCN). Bone leukemia and lymphoma.15,16 To the best of our knowl­ marrow was obtained from NDMM patients and healthy edge, few studies have described the roles played by donors after informed consent. tRFs/tiRNAs in the mechanism underlying MM. One study showed that smoldering multiple myeloma Table 2 Baseline Characteristics of MM Patients and Healthy patients with lower expression levels of 3′-tRF- Donors LeuAAG/TAG, i-tRF-GluCTC, and i-tRF-ProTGG were at great risk of suffering from MM.17 However, MM Healthy Donors (n=20) (n=18) the expression profile of tRFs/tiRNAs in myeloma patients compared to healthy people has not been Age determined. Median - yr 59 55 Range - yr 38–75 31–68 In this study, we investigated the expression profiles of tRFs/tiRNAs in paired newly diagnosed MM and healthy Sex donor samples through RNA-sequencing and quantitative Male 11(55.0) 11(61.1) Female 9(45.0) 7(38.9) real-time PCR (qPCR) verification. Next, using bioinfor­ matics analysis, we characterized the functions of these ECOG performance RNAs in the initiation and progression of MM. These status 0 7(35.0) – findings may provide potential biomarkers and therapeutic 1 11(55.0) – targets for MM. 2 2(10.0) –

R-ISS stage Materials and Methods I 3(15.0) – Clinical Samples II 6(30.0) – III 11(55.0) – This study was approved by ethics committee of the third Xiangya hospital. Twenty new diagnosed MM(NDMM) Cytogenetics patients and 18 healthy donors were enrolled, respectively. High risk 6(30.0) – Standard risk 12(60.0) – The diagnosis of NDMM was based on the diagnostic Unknown/missing 2(10.0) – criteria of symptomatic multiple myeloma defined by

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RNA Extraction and Quantitative RT-PCR Library Preparation and Sequencing Validation Firstly, total RNA samples were pretreated to remove Firstly, plasma cells were enriched from bone marrow RNA modifications that interfere with small RNA-seq samples using the anti-human CD138 magnetic- library construction. The libraries then were denatured as activated cell sorting (MACS) beads (Miltenyi, single-stranded DNA molecules, captured on Illumina Germany). The purity of separated plasma cells was flow cells, amplified in situ as sequencing clusters and sequenced for 50 cycles on Illumina NextSeq 500 system above 90% (identified by flow cytometry). Total RNA following the manufacturer’s instructions. Image analysis was then extracted from enriched plasma cells using and base calling were conducted by Solexa pipeline v1.8 TRIzol (Invitrogen, USA) following the instruction man­ (Off-Line Base Caller software, v1.8). ual. Prepared RNA was preserved at −80°C. RNA sam­ Sequencing quality was examined by FastQC. The ples were qualified and quantified by agarose gel abundance of tRFs/tiRNAs was evaluated by sequencing electrophoresis and NanoDrop ND-1000 (NanoDrop, counts and normalized as counts per million of total USA). qRT-PCR was implemented using ViiA 7 Real- aligned reads (CPM). R package edgeR was used to cal­ time PCR System (Applied Biosystems) and 2 × PCR culate the differentially expressed tRFs/tiRNAs. Master Mix. U6 small nuclear RNA (snRNA) was used Hierarchical clustering, Volcano plots, Pie plots and Venn as internal reference. The fold changes were calculated plots were constructed by R or Perl. and normalized by U6. The primers used are listed in Table 1. RNA quality is shown in Supplementary Table 1. Bioinformatic Analysis of tRFs/tiRNAs RNA integrity number (RIN) was evaluated by Agilent We used TargetScan algorithms to predict gene targets of BioAnalyzer 2100 and was all above 7. tRFs/tiRNAs.18 Gene Ontology (GO) and Kyoto

A B 10 tiRNA-5 10 tiRNA-5 tRF-5c tRF-5c tRF-5b tRF-5b tRF-5a tRF-5a r r e e b b 5 5 tRF-3b

tRF-3b m m u u n n tRF-1 tRF-3a tRF-2 tRF-1 0 0

C A G C C C T T T T C G G G C T G A T T T T T T A C C G C T T C C T T C G A C T T C A A A T T C G G G T A A C C T G T C T T A T C -T G A C T G C T A -G -C - -C -G - - r- - -T -A - - - - -A ------l- - s n u y y s s r l g n ln ly le u u s s r r r r r l a la y l l l l y y e h a r l I e e y y e h h h y a A C G G G G L L S T V A G G G L L L L S T T T T V V C D tiRNA-5 tRF-1 tRF-5c (1) tRF-1(2) (1) tRF-2 tiRNA-5(3) (1) tRF-3b(2) tRF-5b (1) tRF-1 (1) tRF-1 tRF-3b tRF-2 tRF-5a tRF-3a tRF-5b tRF-3b tRF-5a (7) tRF-5a tRF-5c tRF-3a(6) tRF-5b tRF-5a(5) tiRNA-5 tRF-5c tiRNA-5 tRF-5c (13)

tRF-5b (6) tRF-3b(6)

Figure 1 Categories of tRFs/tiRNAs in MM patients and healthy donors. (A) The number of up-regulated tRFs/tiRNAs against tRNA isodecoders. (B) The number of down- regulated tRFs/tiRNAs against tRNA isodecoders. (C) The distribution of up-regulated tRFs/tiRNAs subtypes. (D) The distribution of down-regulated tRFs/tiRNAs subtypes.

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A B

2

1

0

C N N N N N M M M M M o o o o o M M M M M r r r r r m m m m m 2 3 1 4 5 a a a a a l l l l l 3 2 5 1 4

Figure 2 Expression profiles of tRFs/tiRNAs in paired healthy donors and MM patients. (A) Venn diagram based on number of commonly expressed and specifically expressed tRFs/tiRNAs between healthy donors and MM patients. (B) The hierarchical clustering heatmap for significantly differentially expressed tRFs/tiRNAs. (C) The volcano plot of tRFs/tiRNAs (MM vs healthy donors).

Encyclopaedia of Genes and Genomes (KEGG) function Results enrichment analysis were conducted using DAVID General Features of tRFs/tiRNAs 19 Bioinformatics Resources 6.8. Graphing was performed Expression in MM with Cytoscape 3.7.2 and GraphPad Prism 8.0.1. We performed tRFs/tiRNAs sequencing on bone marrow spe­ cimens from five MM patients and five normal donors. Statistical Analysis Clinical characteristics of MM patients and healthy donors Analysis of data was performed with two-tail unpaired are shown in Table 2. After Illumina quality control and 5ʹ, 3ʹ- Student t-test using SPSS 23.0 software. qPCR valida­ adaptor trimmed, reads with length 14nt ~ 40nt were retained. tion was presented as mean ± standard deviation. The tRNA isodecoders share the same anticodon but have differ­ statistical significance level was set at P value < 0.05. ences in their body sequence. All the mature tRNAs and pre-

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A B

Figure 3 qPCR validation of tRFs/tiRNAs expression. (A) Relative expression of three significantly upregulated tRFs/tiRNAs and downregulated tRFs/tiRNAs in paired healthy donors and MM samples. (B) Relative expression of tiRNA-1:34-Glu-TTC-2 and tRF-60:76-Arg-ACG-1-M2 18 healthy donors and 20 MM patients. **P<0.01, ***P<0.001. tRNAs were found to produce tDRs in our profiles. We tiRNA-1:34-Glu-TTC-2 Was Upregulated counted the number of significantly differentially expressed and tRF-60:76-Arg-ACG-1-M2 Was subtype tRFs/tiRNAs against tRNA isodecoders which the CPM of the sample is not less than 20 (Figure 1A and B). Downregulated in MM We performed qPCR to detect the expression of three tRFs/tiRNAs may be produced from different tRNAs by clea­ significantly upregulated or downregulated tRFs/ vage into the fragments with identical sequences. The number tiRNAs in paired healthy donors and MM samples. of tRF-3a and tRF-3b, which mainly occur in the cytoplasm, decreased in myeloma patients. The pie charts depict the The expression of all these six tRFs/tiRNAs was distribution of the number for each subtype tRFs/tiRNAs aligned to the sequencing data, while the most signifi­ (Figure 1C and D). cantly upregulated and downregulated tRFs/tiRNAs were tiRNA-1:34-Glu-TTC-2 and tRF-60:76-Arg-ACG -1-M2, respectively (Figure 3A). Then, we expanded Differentially Expressed tRFs/tiRNAs sample size for further verification of tiRNA-1:34-Glu- Between MM and Normal Groups TTC-2 and tRF-60:76-Arg-ACG-1-M2 expression in 18 Venn diagram shows the commonly expressed and specifically healthy donors and 20 MM patients. At last, we con­ expressed tRFs/tiRNAs (Figure 2A). The “commonly expres­ firmed that tiRNA-1:34-Glu-TTC-2 was upregulated sion” means CPM values ≥ 20 in both two groups, and the and tRF-60:76-Arg-ACG-1-M2 was downregulated in “specificallyexpression” means CPM values ≥ 20 in one group MM (Figure 3B). and <20 in the other group. We identified 354 upregulated tRFs/tiRNAs and 515 downregulated tRFs/tiRNAs in this project in total. After limiting the condition with fold change Target Gene Prediction with ≥1.5 and P < 0.05, there remained 33 up-regulated and 22 Bioinformatics Tool down-regulated tRFs/tiRNAs. The heatmap shows hierarchi­ We used TargetScan algorithms to explore the putative cal clustering of significantly differentially expressed tRFs/ roles of tiRNA-1:34-Glu-TTC-2 and tRF-60:76-Arg- tiRNAs (Figure 2B). We present the overall tRFs/tiRNAs ACG-1-M2 in MM. Through this strategy, we predicted expression variation between the two groups in the volcano 193 conserved targets and 310 conserved targets, plots (Figure 2C). respectively. The target genes were presented in the

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ZNF30 A ZNF343 ZNF271

PHF19 ZNF460 PIGK PCDH17 PATL1 PITPNM1 PAM PLAGL2 ZNF136 ZNF682 ZNF671 NRXN2 ZNF318 PLCB2 AAK1 ZNF10 ZNF501 ABCG4 NMT2 PRCD ZMYND10 ZNF221 hCG_1757335 ARR3 A2BP1 ZNF823 NIPBL ACVR1B TRIM3 ZFP2 PTP4A1 ZNF799 ATP11B ARL3 SH3PXD2A SMARCD2 MYO5B SENP2 TPM3 SNF1LK2 ZNF782 ZNF544 REEP4 ATP1B4 SCD CCDC88A SNRPD1 GNAI3 GCC1 MMP15 ZNF718 TLE3 SAMD12 IKZF2 FOXP1 SPECC1L ATP8B1 CITED2 SURF4 ZDHHC21

MAN2A2 KLF12 AP2B1 FLJ45831 RAP1B ZNF674 SMAD4 ARF3 UBE2D3 STAG2 C12orf53 DCTN2 TESC ZNF557 LGI1 ARL8B SFRS1 FAM129A PUNC ZNF673 SLC6A17 LYNX1 ZNF594 ZNF302

SYT7 CBX1 DDX54 TGFBR2

ZNF763 ZNF80 ZNF781 LIMD1 ARRDC3 tiRNA-1:34-Glu-TTC-2 EVI5 PTPRD ZNF624 SFRS7 LPHN2 WWC3

TEAD3 CCDC86 DIRAS1 TUSC2

ZNF440 ZNF514 LOC728957 AVPI1 ZNF563 ELL2 PTH2R ZNF565 RELT KIAA2022 ZNF584

TET3 CCDC97 ELAVL2 UBL3 BCL9 ZNF181 LPGAT1 CUL3 PLXNA4 ZNF439 ZNF562 RANBP9 IPPK

VASP WIPF2 TEX13B CD164 EPHA7 BTBD9 COG3 PIP3-E ZNF559 CD276 CHRDL1 PPP1R9A IGF2BP1 MED10 VAX1 GLCE PELI3 ZNF630 CLINT1 MOBKL1A ZNF506 PLCB4 PCNX HNRNPH3 ZHX2 MTMR9 NARG2 GRHL2 CLTC ZNF37A ZNF14 PHC3 WDR81 ZNF266 HMGA2 H3F3B ZNF182 ZNF253 ZNF260 DENND4C PHACTR2 HIPK2 ZNF689 GNB5 MTCH2 DIRAS2 JARID1A MLL4 JHDM1D MACROD2 GFI1B EIF2S1 WDR23 GABRA4 FAM122A G3BP2 FLJ12529 THBS2 FLJ20309 FZD5

VAMP7

TMED10 TMED9

Figure 4 Continue. network diagrams constructed by Cytoscape (Figure 4A category, “nucleus” and “intracellular” in cellular compo­ and B). nent (CC) category, “protein binding” and “metal ion binding” in molecular function (MF) category (Figure GO/KEGG Enrichment Analysis 5A). Whereas, the most enriched terms of target genes of We performed GO and KEGG enrichment analysis for tRF-60:76-Arg-ACG-1-M2 were “apoptotic process” and target genes of tiRNA-1:34-Glu-TTC-2 and tRF-60:76- “regulation of transcription from RNA polymerase II pro­ Arg-ACG-1-M2 using DAVID database. GO analysis moter” in BP category, “cytoplasm” and “plasma mem­ showed that 37 and 58 GO terms were enriched (P < brane” in CC category, “protein binding” and “zinc ion 0.05) for target genes of tiRNA-1:34-Glu-TTC-2 and binding” in MF category (Figure 5B). tRF-60:76-Arg-ACG-1-M2, respectively. The most KEGG pathway analysis indicated that tiRNA-1:34- enriched terms of target genes of tiRNA-1:34-Glu-TTC-2 Glu-TTC-2 might participate in cancer-related pathways were “transcription, DNA-templated” and “regulation of such as Pathways in cancer, Wnt signaling pathway and transcription, DNA-templated” in biological process (BP) Hippo signaling pathway (P < 0.05) (Figure 5C). While the

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OTUD5 P2RX1 MTMR12

B PDSS2 MPI CHST3 CENPB CD72 CLDN19 CCPG1 CNOT6 CAPN12 PGLYRP4 MAP3K3 CNTNAP4 CAMK2G SEPT4 RPP30 SERF2 RNF41 COPS7B SGCD RNF24 CACNA1C

SHPK RNF114 PKNOX2 CRY2 KCNG3 C9orf25 MAGED2 KCNJ10 KCNA1 SHROOM4 IL8 RIMS4 KIAA0152 CSNK1G1 IGF2BP1 C22orf9 SIPA1L3 KLHL24 RDH13 RNF165 CRX HNRNPA2B1 RFXAP KLHL25 CUGBP2 CTDSP2 CRTC1 C22orf15 PLEKHA6 SLC30A2 RBCK1 LYPLA3 SCARF1 HIF3A KLHL8 RFX5 MAFK C2CD2 DHCR24 DEDD C6orf89 C1orf144 SLC38A3 LMOD1 HARS2 RASSF5 MBNL1 CREB5 LARP4 C16orf70 SETD1A CASZ1 GDF6 DHDDS SLC6A6 SMG5 ZNRF3 RAP2B C16orf57 POU2F2 PRICKLE2 RALBP1 LPPR2 GLS2 LIMD1 LBH DHX40 FOSL2 MC1R PNPO DLG3 SMC1A BCL11B BAK1 GPRC5B RAP1A C14orf68 C10orf141 CPNE5 EDA BDH1 ATP1B1 LDLR SF3A1 CBX3 GRIN3A

ZNF318 PRKAR2A DNAJC14 SNX27 C14orf115 SYT7 APOL2 FBXL11 KLHL6 PLEKHG1 RAF1 ATP1B4 LOC26010 TBC1D20 SUDS3 LMX1B DNAJB12 MCL1 NDST1

DSCAML1 SPARC C15orf27 TCTA SSTR1 AGPAT3 IGFBP5 BHLHB5 CPLX4 GLDN RAB3D ARIH2 SLC24A3 SHE

LOC116236 SHANK3 CCDC120 GSG1L

RAB11FIP1 EIF5 SPTBN2 ADAMTS17 SFXN3 SMARCC1 C20orf134 THSD7B tRF-60:76-Arg-ACG-1-M2 SRPX2 FAM123B KLHL3 PLEK WDR81 RAB10 AQP10 LNPEP

LOC91461 EFNB1 MGAT3 SNCB

SMURF1 SNTB2 ELAVL3 STK35 C22orf24 TMEM184C SRL tcag7.1228 DCTN5 ATRN CNIH3 GARNL4 PTPRJ ANKRD52

LPCAT3 SLC9A8 CD59 HDAC5 TMEM50B SRC CACNB3 hCG_1757335 CTDSP1 RAP1B EXT2 SYP SOX3 KIAA0232 PEAR1 TTYH2 PRF1 ABHD4 LEP

ABCB9 LPHN1 ELMO2 NCOA1 TNS3 XYLT2 CRISPLD1 ARHGDIA TANC2 LPP TUFT1 WAPAL CLLU1 GALNT10 POGZ ABAT CALN1 HN1L SMAD3 CEBPA CLCN5 CBL KIAA0082 CDK5R1 PDPK1 SAMD4B TEX261 MAMLD1 TTC9 PNKD TMEM129 KLHDC7A ACLY CCND1 CCNJL NME3 EPB41L4B ARF3 HOXA3 CLCF1 MAP3K7IP1 KCNJ4 FLJ44838 PITX3 TGM6 CELSR3 ADCY1 LRRC8E HS6ST1 ARC SOLH PDE4D NUFIP2 TNFSF12 MCAM PHF2 FGF11 THY1 CHMP7 TXLNA IQGAP3 FAIM2 CENPP PDAP1 NXF1 CHD1 FLJ33790 MECP2 SOX12 PDS5A TINAGL1 TMEM95 PRICKLE4 FAM160B2 MLL2 FBXO40 PAPOLA SYNPO TBC1D22B FRMD5 TMEM70 FAT2 ZNF704 IPO8 MYD88 NR1I2

TNS1 NARFL NPSR1 ZNF335 NCDN NHP2L1 NFASC NHLRC3 TTC39A ZMIZ1 GALNT6 IHPK1 TTLL2 ZFAND3 UBE3B ZC3H10 WIZ ZBTB38 ZBTB39 GIT1 HTATIP2

GNAO1 HM13 GRAMD2

Figure 4 Target gene prediction of tRFs/tiRNAs. (A) Target genes of tiRNA-1:34-Glu-TTC-2. (B) Target genes of tRF-60:76-Arg-ACG-1-M2. most enriched pathways of tRF-60:76-Arg-ACG-1-M2 Therefore, we performed a preliminary evaluation of the were cAMP signaling pathway, Endocytosis, Oxytocin role of tRFs/tiRNAs in MM. signaling pathway and Rap1 signaling pathway First, we screened the expression profile of tRFs/ (Figure 5D). tiRNAs in MM by RNA-sequencing. All tRFs/tiRNAs were named using the standardized tDR (tRNA-derived Discussion RNA) naming system (tDRnamer) from the Lowe Lab at Although the clinical outcome of MM has been consider­ the University of California Santa Cruz. The four parts ably improved over the last several decades, MM is still an in the name sequentially represent prefix, position, incurable type of hematological malignancy. The patho­ source tRNA and matching tRNA transcripts.20 genesis of MM is complex and multifactorial. Many genes tDRnamer provides a consistent, stable name and gives or ncRNAs have been determined to be involved in the additional annotation for each submitted tDR sequence pathological process. However, whether tRFs/tiRNAs par­ compared to tRFdb ID and MINTbase ID. The biogen­ ticipate in MM initiation has not been elucidated. esis of most of tRF-3s is unknown. The decrease in the

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A C

Hsa05200:Pathways in cancer

− log10(P Value)

Hsa04723:Retrograde endocannabinoid signaling 3.5 3.0 Hsa04611:Platelet activation 2.5

2.0 Hsa04310:Wnt signaling pathway 1.5 Hsa04261:Adrenergic signaling in cardiomyocytes Gene counts

Hsa04390:Hippo signaling pathway 5 6 7 8 Hsa04062:Chemokine signaling pathway

Hsa05016:Huntington's disease

0 2 4 Gene Ratio

B D Hsa04024:cAMP signaling pathway − log10(P Value)

Hsa04144:Endocytosis 3.0 Hsa04921:Oxytocin signaling pathway 2.5

Hsa04015:Rap1 signaling pathway 2.0

Hsa04727:GABAergic synapse Gene counts Hsa04724:Glutamatergic synapse 6 7 Hsa04919:Thyroid hormone signaling pathway 8

Hsa04722:Neurotrophin signaling pathway 9 10 Hsa04261:Adrenergic signaling in cardiomyocytes 11 12

0 1 2 3 4 5 Gene Ratio

Figure 5 Functional analysis of tRFs/tiRNAs. (A) GO enrichment analysis of tiRNA-1:34-Glu-TTC-2. (B) GO enrichment analysis of tRF-60:76-Arg-ACG-1-M2. (C) KEGG pathway analysis of tiRNA-1:34-Glu-TTC-2. (D) KEGG pathway analysis of tRF-60:76-Arg-ACG-1-M2. number of tRF-3 may affect DNA replication, repair and repress global translation by displacing eIF4G and eIF4A damage response.10 Next, the two most differentially (translation eukaryotic initiation factors) from mRNAs and expressed tRFs/tiRNAs were verified by qPCR. To displacing eIF4F from isolated m7G caps.21 Gkatza et al22 explore the possible biological functions of these showed that the loss of NSUN2, a cytosine-5 RNA methyl­ RNAs, we predicted their target genes and performed transferase, could lead to impaired regulation of protein synth­ GO/KEGG analysis. For tiRNA-1:34-Glu-TTC-2, its esis by altering the biogenesis of tiRNAs in response to predicted target genes were primarily localized in the stress.23 However, the mechanisms underlying how tRFs/ nucleus and implicated in transcription regulation and tiRNAs regulate carcinogenesis have not been elucidated. protein binding. However, the target genes of tRF-60:76- To explore whether tiRNA-1:34-Glu-TTC-2 and tRF- Arg-ACG-1-M2 were primarily located in the cytoplasm 60:76-Arg-ACG-1-M2 are involved in the initiation and and participated in apoptotic processes and protein bind­ development of MM, we performed KEGG pathway ana­ ing. This result is consistent with the common molecular lysis. Among the top ten enriched pathways, pathways in functions of tRFs/tiRNAs in a previous report.12 cancer, the Wnt signaling pathway, the Hippo signaling tRFs/tiRNAs may regulate transcription and apoptotic pro­ pathway and the Rap1 signaling pathway are widely cesses through multiple molecular mechanisms. For example, known to be related to carcinogenesis in previous Ivanov et al found that 5′-tiRNAAla and 5′-tiRNACys could reports.23–25 These pathways are also associated with

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MM initiation and development. Harmen et al reported Acknowledgment that aberrant Wnt signaling mediated the proliferation, Authors would like to thank Ms. Ting Liang for her help in migration, and drug resistance of MM cells and MM collecting some of the samples for this study. cells secreted Wnt antagonists that promoted the develop­ ment of osteolytic lesions by impairing osteoblast Author Contributions differentiation.26 The Hippo pathway has also emerged as All authors made a significant contribution to the work an important mediator of MM oncogenesis and is impli­ reported, whether that is in the conception, study cated in the pathogenesis of MM-induced bone design, execution, acquisition of data, analysis and 27,28 disease. There are relatively few studies investigating interpretation, or in all these areas; took part in draft­ the role played by the Rap1 pathway in MM. Taken ing, revising or critically reviewing the article; gave together, tiRNA-1:34-Glu-TTC-2 and tRF-60:76-Arg- final approval of the version to be published; have ACG-1-M2 may be involved in the pathological process agreed on the journal to which the article has been of MM but these hypotheses required further validation. submitted; and agree to be accountable for all aspects Several study limitations warrant consideration. First, of the work. due to the short follow-up time, we have not obtained the efficacy and prognosis data of myeloma patients to date. Funding Second, in this preliminary experiment, we did not collect This study was supported by National Natural Science specimens from cases of different stages of plasma cell Foundation of China (Grant No. 82002452). disease (monoclonal gammopathy of undetermined signif­ icance, smoldering myeloma, multiple myeloma and plasma cell leukemia). The evaluation of the expression Disclosure of tRFs/tiRNAs in these four stages requires further study. The authors declare no conflicts of interest. Third, although tRFs/tiRNAs are also involved in the silencing of microRNAs, there are few related data reports. References The current bioinformatics databases do not provide infor­ 1. Boise LH, Kaufman JL, Bahlis NJ, Lonial S, Lee KP. The Tao of myeloma. Blood. 2014;124(12):1873–1879. doi:10.1182/blood-2014- mation on the network of tRFs/tiRNAs-microRNA- 05-578732 mRNA. We look forward to realize this analysis basing 2. Terpos E, Kleber M, Engelhardt M, et al. European myeloma network guidelines for the management of multiple myeloma-related on the in-depth research on tRFs/tiRNAs in the future. complications. Haematologica. 2015;100(10):1254–1266. doi:10.3324/ In conclusion, we assessed the potential function of haematol.2014.117176 tRFs/tiRNAs in MM and the results establish 3. Gooding S, Olechnowicz SWZ, Morris EV, et al. Transcriptomic profiling of the myeloma bone-lining niche reveals BMP signalling a foundation for continued research. tRFs/tiRNAs may inhibition to improve bone disease. Nat Commun. 2019;10(1):4533. play important roles in the pathogenesis of myeloma and doi:10.1038/s41467-019-12296-1 4. Gao M, Li C, Xiao H, et al. hsa_circ_0007841: a novel potential serve as clinical biomarkers and therapeutic targets in the biomarker and drug resistance for multiple myeloma. Front Oncol. future. Further experiments are warranted to explore how 2019;9:1261. doi:10.3389/fonc.2019.01261 5. Yuan X, Ma R, Yang S, et al. miR-520g and miR-520h overcome tRFs/tiRNAs regulate target genes and miRNAs in MM. bortezomib resistance in multiple myeloma via suppressing We look forward to further research on tRFs/tiRNAs APE1. Cell Cycle. 2019;18(14):1660–1669. doi:10.1080/ in MM. 15384101.2019.1632138 6. Ulitsky I, Bartel DP. lincRNAs: genomics, evolution, and mechanisms. Cell. 2013;154(1):26–46. doi:10.1016/j.cell.2013.06.020 7. Cech TR, Steitz JA. The noncoding RNA revolution-trashing old Ethics Approval and Informed rules to forge new ones. Cell. 2014;157(1):77–94. doi:10.1016/j. cell.2014.03.008 Consent 8. Anderson P, Ivanov P. tRNA fragments in human health and The studies involving human participants were reviewed disease. FEBS Lett. 2014;588(23):4297–4304. doi:10.1016/j. and approved by ethics committee of the third Xiangya febslet.2014.09.001 9. Guzzi N, Bellodi C. Novel insights into the emerging roles of hospital. The approval ID was 2016121. The patients/par­ tRNA-derived fragments in mammalian development. RNA Biol. ticipants provided their written informed consent to parti­ 2020;17(8):1214–1222. doi:10.1080/15476286.2020.1732694 10. Kumar P, Kuscu C, Dutta A. Biogenesis and function of transfer cipate in this study. This study was conducted in RNA-related fragments (tRFs). Trends Biochem Sci. 2016;41 accordance with the Declaration of Helsinki. (8):679–689. doi:10.1016/j.tibs.2016.05.004

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