tRNA‑derived fragments as novel potential biomarkers for relapsed/refractory multiple myelomaCong Xu1, Ting Liang2, Fangrong Zhang1, Jing Liu1* and Yunfeng Fu3*

IntroductionMultiple myeloma (MM) is an incurable neoplastic plasma cell disorder, with a high ten-dency of relapse [1]. Although the advent of new drugs like proteasome inhibitor and CD38 monoclonal antibody have dramatically improved the prognoses, relapse and resistance to therapy frequently occur. The discovery of mechanisms of relapse and resistance is pivotal to optimizing the clinical efficacy and prolonging survival. Multiple factors have been proposed to be associated with relapsed/refractory MM (R/RMM). Of these, in vitro experiments have shown that Non-coding RNAs (ncRNAs) play impor-tant roles, but their clinical application value still need further exploration [2, 3].

NcRNAs are usually classified into long non-coding RNAs (lncRNAs) and small non-coding RNAs (sncRNAs) according to their length. Over past few decades, research has identified roles for ncRNAs in a variety of biological processes[4, 5]. With

Abstract

Background: tRNA-derived fragments have been reported to be key regulatory fac-tors in human tumors. However, their roles in the progression of multiple myeloma remain unknown.

Results: This study employed RNA-sequencing to explore the expression profiles of tRFs/tiRNAs in new diagnosed MM and relapsed/refractory MM samples. The expres-sion of selected tRFs/tiRNAs were further validated in clinical specimens and myeloma cell lines by qPCR. Bioinformatic analysis was performed to predict their roles in mul-tiple myeloma progression.We identified 10 upregulated tRFs/tiRNAs and 16 down-regulated tRFs/tiRNAs. GO enrichment and KEGG pathway analysis were performed to analyse the functions of 1 significantly up-regulated and 1 significantly down-reg-ulated tRNA-derived fragments. tRFs/tiRNAs may be involved in MM progression and drug-resistance.

Conclusion: tRFs/tiRNAs were dysregulated and could be potential biomarkers for relapsed/refractory MM.

Keywords: Multiple myeloma, TRNA related fragments, TRNA halves, Relapsed/refractory, Biomarkers

Open Access

© The Author(s), 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate-rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

RESEARCH

Xu et al. BMC Bioinformatics (2021) 22:238 https://doi.org/10.1186/s12859‑021‑04167‑8

*Correspondence: lj0jingliu@163.com; fuyunfeng@csu.edu.cn 1 Department of Hematology, The Third Xiangya Hospital of Central South University, Changsha 410000, China3 Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha 410000, ChinaFull list of author information is available at the end of the article

Page 2 of 12Xu et al. BMC Bioinformatics (2021) 22:238

rapidly advances in high-throughput sequencing technology and bioinformatics analy-sis in recent years, a new class of sncRNAs derived from tRNAs are gaining increasing attention.

This new class of tRNA derived fragments can be broadly classified into tRNA related fragments (tRFs) and tRNA halves (tiRNAs). tRFs are generated from mature or precur-sor tRNA and tiRNAs are generated by specific cleavage in the anticodon loops of mature tRNA [6]. According to their mapped positions on the precursor or mature tRNA tran-script, tRFs/tiRNAs are subdivided into five types: tRF-5, tRF-3, tRF-1, tRF-2 and tiRNA. tRFs/tiRNAs are involved in diverse molecular processes such as gene silencing, pro-tein translation, cell stress, and cell differentiation [7, 8]. Though the complex biological functions of tRFs/tiRNAs require further elucidation, they can be summarized as three categories: translation regulation, epigenetic regulation and RNA silencing. These three categories have also been a key focus in cancer research of tRFs/tiRNAs in recent years.

Increasing evidence shows that tRFs/tiRNAs contribute to cancer development and progression. For example, tDR-0009 and tDR-7336, which were significantly upregu-lated in hypoxia conditions, have been found to induce doxorubicin resistance in triple-negative breast cancer [9]. In ovarian cancer, tRF-03357 was reported to promote cell proliferation, migration, and invasion by downregulating HMBOX1 [10]. For hemato-logical malignancies, limited data showed that tRFs/tiRNAs may have cancer-associated functions in leukemia and lymphoma [11, 12]. And so far, there are no reports on the role of tRFs/tiRNAs in the mechanism of recurrence and drug resistance of MM to our knowledge.

In this study, we explored the expression profiles of tRFs/tiRNAs in new diagnosed MM(NDMM) and R/RMM samples by RNA-sequencing, with Quantitative Real-time PCR(qPCR)validation. Then, we analyzed their biological functions to uncover their roles in the mechanisms of relapse and drug resistance of MM. This study may provide potential biomarkers and therapeutic targets for R/RMM.

Materials and methodsClinical specimens

Bone marrow specimens were obtained from patients with MM for research according to a protocol approved by ethics committee of the third Xiangya hospital. All patients provided their written informed consent to participate in this study.20 new diagnosed MM (NDMM) and 22 R/RMM patients were enrolled, respectively. All NDMM patients met the criteria for symptomatic multiple myeloma according to diagnostic crite-ria defined by National Comprehensive Cancer Network (NCCN). The diagnosis of relapsed/refractory disease was based on clinical symptoms, biochemical parameters and marrow evaluation. All R/RMM patients were in first relapse.

Cell culture

MM cell lines U266 and RPMI-8226 were kindly provided by the basic laboratory of Central South University Xiangya School of Medicine. Drug-resistant cell lines U266/BTZ, and RPMI-8226/BTZ were induced through stepwise increase of drug concentra-tions. Cells were cultured in RPMI1640 (HyClone, Logan, UT, USA) supplemented with

Page 3 of 12Xu et al. BMC Bioinformatics (2021) 22:238

10% FBS (ExCell Biology,Shanghai, China) and 1% penicillin–streptomycin (HyClone). All cells were incubated at 37 °C in 5% carbon dioxide.

RNA Extraction and Quantitative RT‑PCR

Firstly, anti-CD138 MicroBeads (Miltenyi, Germany) were used to enrich plasma cells from bone marrow samples by magnetic activated cell sorting (MACS). The purity of separated plasma cells was identified by flow cytometry and was all above 90%. Total RNA was then extracted from enriched plasma cells and myeloma cells using TRIzol (Invitrogen, USA) according to the instruction manual. Prepared RNA was stored at − 80 °C. RNA samples were qualified by agarose gel electrophoresis and quantified by NanoDrop ND-1000 (NanoDrop, USA). RNA integrity number (RIN) was evaluated by Agilent BioAnalyzer 2100 and was all above 7. RNA concentration and purity were also assessed (Additional file  1: Table  1). qRT-PCR was performed using ViiA 7 Real-time PCR System (Applied Biosystems) and 2 × PCR Master Mix. The expression levels of each tRFs/tiRNAs were calculated and normalized by U6 small nuclear RNA (snRNA). The primers used are listed in Table 1.

Library preparation and sequencing

Before library preparation, total RNA samples from 5 NDMM and 5 R/RMM patients were pretreated to remove RNA modifications that interfere with small RNA-seq library construction. Then, cDNA was synthesized and amplified using Illumina’s proprietary RT primers and amplification primers. Subsequently, ~ 134 to 160  bp PCR amplified fragments were extracted and purified from the PAGE gel. Finally, the prepared librar-ies were quantified by Agilent BioAnalyzer 2100 and sequenced using Illumina NextSeq 500. Image analysis and base calling were performed by Solexa pipeline v1.8 (Off-Line Base Caller soft-ware, v1.8).

Sequencing quality were examined by FastQC and trimmed reads were aligned allow-ing for 1 mismatch only to the mature tRNA sequences. The abundance of tRFs/tiRNAs were evaluated using their sequencing counts and normalized as counts per million of total aligned reads (CPM). The differentially expressed tRFs/tiRNAs were calculated

Table 1 Primers for qRT-PCR of candidate tRFs/tiRNAs

Primer

tRF-60:77-Thr-TGT-1 F: 5′-AGC CAT CCC AGT AGA GCC TC-3′R: 5′-TAT CCA GTG CAG GGT CCG AG-3′

tRF-1:22-Lys-TTT-1-M3 F: 5′-AGC CAG CCT GGA TAG CTC AGT-3′R: 5′-AGG GTC CGA GGT ATT CGC A-3′

tRF- + 1:T17-Pro-TGG-3–2 F: 5′-TGG GTC GTG GCT ACT GTT TT-3′R:5′-AGT GCA GGG TCC GAG GTA T-3′

tRF-57:75-Gly-TCC-1-M3 F: 5′-TTA TTC CCG GCC AAC GCA -3′R: 5′-CAG TGC AGG GTC CGA GGT AT-3′

tRF-1:31-Lys-CTT-1-M2 F: 5′-CTT GCC CGG CTA GCT CAG T-3′R: 5′-CAG TGC AGG GTC CGA GGT AT-3′

tRF-1:32-Lys-CTT-1-M2 F: 5′-ATT GCC CGG CTA GCT CAG T-3′R: 5′-CGC AGG GTC CGA GGT ATT C-3′

U6 F: 5′-GCT TCG GCA GCA CAT ATA CTA AAA T-3′R: 5′-CGC TTC ACG AAT TTG CGT GTCAT-3′

Page 4 of 12Xu et al. BMC Bioinformatics (2021) 22:238

based on the count value with R package edgeR. Pie plots, Venn plots, Hierarchical clus-tering, Scatter plots and Volcano plots were constructed using R or Perl.

Functional Analysis of tRFs/tiRNAs

Gene targets of tRFs/tiRNAs were predicted using TargetScan algorithms [13]. DAVID Bioinformatics Resources 6.8 was used for Gene Ontology (GO) and Kyoto Encyclopae-dia of Genes and Genomes (KEGG) function enrichment analysis [14]. All data were graphed using Cytoscape 3.7.2 and GraphPad Prism 8.0.1.

Statistical analysis

SPSS 23.0 software was used for statistical analysis. qPCR value was presented as mean ± standard deviation. KS test and Shapio-Wilk test were used to determine whether the data were normally distributed. For data that obeyed normal distribu-tion, statistical significance was assessed using two-tail unpaired Student’s t test. Oth-erwise, non-parametric tests (Mann–Whitney test) were used for statistical analysis. P value < 0.05 was considered significant.

ResultsCatalogue of tRFs/tiRNAs expression in MM

After Illumina quality control and 5’, 3’-adaptor trimmed, reads with length < 14nt or length > 40nt were discarded. The bar diagrams show the sequence read length distribu-tion (Fig. 1a, b). We also calculated the frequency of subtype tRFs/tiRNAs against the length of the sequence. The stacked bar charts show the length distribution of subtype (Fig. 1c, d). Clinical characteristics of all patients are shown in Table 2.

14 15 16 17 18 19 20 21 22 23 24 28 29 30 31 32 33 34 39 40

Length of the tRFs (nt)

Freq

uenc

y

0.0

0.2

0.4

0.6

0.8

1.0

tRF−1tRF−2tRF−3atRF−3btRF−5atRF−5btRF−5ctiRNA−3tiRNA−5

14 15 16 17 18 19 20 21 22 23 24 25 26 28 29 30 31 32 33 34 39 40

Length of the tRFs (nt)

Freq

uenc

y

0.0

0.2

0.4

0.6

0.8

1.0

tRF−1tRF−2tRF−3atRF−3btRF−5atRF−5btRF−5ctiRNA−3tiRNA−5

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

0.0

0.2

0.4

0.6

0.8

0.15

31

0.16

15

0.15

85

0.17

860.21

51

0.18

67

0.21

07

0.28

14

0.51

70

0.48

57

0.27

57

0.24

15

0.21

75

0.20

06

0.20

15

0.20

55

0.21

57

0.28

88

0.48

30

0.48

99

0.24

24

0.19

70

0.18

79

0.17

20

0.15

83

0.14

28

0.13

11

Tota

lRea

dsC

ount

s(M

)

Adapter trimmed reads length(nt)

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

0.0

0.2

0.4

0.6

0.8

0.10

65

0.11

39

0.10

79

0.11

87

0.13

30

0.13

59

0.16

740.20

68

0.57

53

0.44

33

0.19

46

0.17

57

0.15

93

0.14

98

0.15

42

0.16

31

0.18

68

0.27

70

0.64

250.68

69

0.26

68

0.18

66

0.19

02

0.16

47

0.15

20

0.13

37

0.11

99

Tota

lRea

dsC

ount

s(M

)

Adapter trimmed reads length(nt)

a b

c d

Fig. 1 Catalogue of tRFs/tiRNAs expression profile between NDMM and R/RMM. a The average total read counts against the lengths of the trimmed reads in NDMM. b The average total read counts against the lengths of the trimmed reads in R/RMM. c The frequency of subtype against length of the tRFs/tiRNAs in NDMM. d The frequency of subtype against length of the tRFs/tiRNAs in R/RMM

Page 5 of 12Xu et al. BMC Bioinformatics (2021) 22:238

Differentially expressed tRFs/tiRNAs between NDMM and R/RMM groups

Our high-throughput tRFs/tiRNAs sequencing identified more than 400 commonly expressed tRFs/tiRNAs (CPM ≥ 20). Of these, 298 tRFs/tiRNAs were commonly expressed in both groups, while 68 were specifically presented in R/RMM and 43 were specifically found in NDMM (specifically expressed tRFs/tiRNAs represent the CPM ≥ 20 in one group and < 20 in the other group) (Fig.  2a). Under the condition of fold change ≥ 1.5 and P < 0.05, of which 10 tRFs/tiRNAs were upregulated and 16 were downregulated. The heatmap shows hierarchical clustering of significantly differentially expressed tRFs/tiRNAs (Fig.  2b). The volcano and scatter plots indicate tRFs/tiRNAs expression variation between the two groups (Fig. 2c, d).

Table 2 baseline characteristics

ASCT Autologous stem cell transplantation

NDMM (n = 20) R/RMM (n = 22)

Age

Median—yr 59 60

Range—yr 38–75 44–76

Sex-no. (%)

Male 11 (55.0) 14 (63.6)

Female 9 (45.0) 8 (36.4)

ECOG performance status-no. (%)

0 7 (35.0) 6 (27.3)

1 11 (55.0) 13 (59.1)

2 2 (10.0) 3 (13.6)

R-ISS stage-no. (%)

I 3 (15.0) 3 (13.6)

II 6 (30.0) 7 (31.8)

III 11 (55.0) 12 (54.5)

Cytogenetics-no. (%)

High risk 6 (30.0) 10 (45.5)

Del (17p) 4 (20.0) 7 (31.8)

t (14;20) 2 (10.0) 3 (13.6)

t (14;16) 2 (10.0) 2 (9.1)

Standard risk 12 (60.0) 8 (36.3)

Unknown/missing 2 (10.0) 4 (18.2)

No. of previous regimens-no. (%)

Median – 3

Range – 1–6

Previous therapy-no. (%)

Bortezomib – 18 (81.8)

Lenalidomide – 8 (36.4)

ASCT – 4 (18.2)

Page 6 of 12Xu et al. BMC Bioinformatics (2021) 22:238

tRF‑60:77‑Thr‑TGT‑1 was upregulated and tRF‑1:22‑Lys‑TTT‑1‑M3 was downregulated in R/

RMM and drug‑resistant myeloma cells

qPCR was performed to confirm the expression of three significantly upregulated or downregulated tRFs/tiRNAs in NDMM and R/RMM samples. The expression of all these six tRFs/tiRNAs were consistent with the sequencing data, while the most significantly upregulated and downregulated tRFs/tiRNAs were tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3, respectively (Fig. 3a). For the further validation, we detected expression of tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3 in 22 R/RMM and 20 NDMM patients (Fig. 3b). Their expression was also detected in MM cell lines (U266, RPMI-8226) and drug-resistant cell lines (U266/BTZ, RPMI-8226/BTZ). tRF-60:77-Thr-TGT-1 was upregulated and tRF-1:22-Lys-TTT-1-M3 was downregulated in both R/RMM and drug-resistant mye-loma cells (Fig. 3c, d). There seems to be no significant difference in the expression of tRF-60:77-Thr-TGT-1 or tRF-1:22-Lys-TTT-1-M3 among patients with different cytogenetic

ND4

ND5

ND1

ND3

DT2

R/R

5

R/R

2

R/R

3

R/R

4

R/R

1

0 5 10 15

040

68 43298

R/RMM NDMM

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●●0

5

10

15

0 5 10 15NDMM

R/R

MM

pearson correlation: 0.824●●

●●

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up_regulated tRFs & tiRNAs (238)

not differential expressed (334)

down_regulated tRFs & tiRNAs (265)

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0

1

2

3

−5.0 −2.5 0.0 2.5 5.0log2(Fold Change)

−log

10(p

_val

ue)

●●

●

up_regulated tRFs & tiRNAs (10)

not differential tRFs & tiRNAs (811)

down_regulated tRFs & tiRNAs (16)

a b

dc

Fig. 2 Expression profiles of tRFs/tiRNAs sequencing data in paired NDMM and R/RMM. a Venn diagram based on number of commonly expressed and specifically expressed tRFs/tiRNAs. b The hierarchical clustering heatmap for significantly differentially expressed tRFs/tiRNAs. c The scatter plot between two groups for tRFs/tiRNAs. d The volcano plot of tRFs/tiRNAs

Page 7 of 12Xu et al. BMC Bioinformatics (2021) 22:238

risk in NDMM or R/RMM group. Which may be related to the relatively small sample size. Absolute values of RNA expression levels are shown in Additional file 2: Table 2.

Target gene prediction with bioinformatics tool

TargetScan algorithms were used to explore the putative roles of tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3 in MM. Through this strategy, we predicted 238 conserved targets and 159 conserved targets, respectively. The network dia-grams constructed by Cytoscape show the genes (Fig. 4a, b).

GO enrichment analysis and KEGG pathway analysis

We performed functional enrichment analysis for target genes of tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3 through DAVID database. GO analysis indicated that 37 GO terms were enriched (P < 0.05) for both target genes of tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3. The most enriched terms of target genes of tRF-60:77-Thr-TGT-1 were ‘plasma membrane’ and ‘membrane’ in cellular component (CC) category, ‘protein binding’ and ‘tran-scription factor activity, sequence-specific DNA binding’ in molecular function (MF) category,

0

2

4

6

8

10

NDMM

R/RMM

rela

tive

expr

essi

on

tRF-60

:77-T

hr-TGT-1

tRF-+1

:T17-P

ro-TGG-3-

2

tRF-57

:75-G

ly-TCC-1-

M3

tRF-1:

22-Ly

s-TTT-1

-M3

tRF-1:

31-Ly

s-CTT-1

-M2

tRF-1:

32-Ly

s-CTT-1

-M2

** **

NDMM

R/RMM

tRF-60

:77-T

hr-TGT-1

tRF-1:

22-Ly

s-TTT-1

-M3

0

2

4

6

8**** ****

U266

U266/BTZ

tRF-60

:77-T

hr-TGT-1

tRF-1:

22-Ly

s-TTT-1

-M3

rela

tive

expr

essi

on

0.5

1.0

1.5

2.0

2.5

* *

tRF-60

:77-T

hr-TGT-1

tRF-1:

22-Ly

s-TTT-1

-M3

rela

tive

expr

essi

on

0.5

0.0

1.0

1.5

2.0

* *

RPMI-8226

RPMI-8226/BTZ

a b

c d

rela

tive

expr

essi

on

0.0

Fig. 3 Relative expression of tRFs/tiRNAs by qPCR. a Relative expression of 3 significantly upregulated or downregulated tRFs/tiRNAs in paired NDMM and R/RMM samples. b Relative expression of tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3 in 22 R/RMM and 20 NDMM patients. c Relative expression of tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3 in U266 and BTZ-resistant U266. d Relative expression of tRF-60:77-Thr-TGT-1 and tRF-1:22-Lys-TTT-1-M3 in RPMI-8266 and BTZ-resistant RPMI-8266

Page 8 of 12Xu et al. BMC Bioinformatics (2021) 22:238

MAPKAPK3

RCC2

LTBP2

SCN3B

MEF2D

MINK1 SENP5

LRP4

RAPGEF5

MAPK8IP2

SDCBP

SERBP1

SFRS1

MRPL17SLC4A8

SPATA2SNX1

SDF2

NLGN2

UBTF

NR2C2

SEC22C

OLFML2A

WEE1

OTUB2

ALDH1A3

SLC35C1 SKAP2

TPD52L1

FAM20BNFIB

EPB41L1NMT2

SP6

VGLL3

SIX3

EPS15NSL1ZBTB3

ABL2

MGAT3

MED29UBL3

TUB

TRAPPC3

DIO2

DNAJA2

NFAT5

NHSUTP15

ACSL5

STK4 SYNGR1VAMP1

UNC5D tRF-60:77-Thr-TGT-1

AICDA

TSPAN5LZTR1

TANC2MRVI1

SMARCC2 LL22NC03-75B3.6LPL

NIPA2

MRFAP1L1DCBLD2

CUGBP2TTC39A

MED1CUL3

CRHR1TNPO1

UBE2E2TOMM20

SELK

PRIC285

PDZD4

ANKRD12

ABI2

FAM53CCRY2ARFIP2AK2 BTN3A2

C3orf70CMTM6 FBXL18

RAB18

DALRD3

DPY19L1

RAB8B

FAM131BESRRADGKI DHDDS

RGS5

PLXND1

PLAG1B3GALTL

POU3F1

BRD4

C2orf7 RIMS3

PLCXD3PLXNA4

SHOX2 CACNA2D2

RAB30CUL5

PTPN1

CDH2FOXJ2

FOXO4

RGS6

GABRA1PIGK FRMD5 GATA4BSDC1

S1PR1 KCNK5C3orf64 HSPC105 JUB

SEPHS2 MAML3

SEC13

KIAA0247

KIAA0232 KDRIL1F5

ISG20L2RWDD2B

ZNF703TOMM40L

CALM1

IGF2BP1

KCTD12

CADM1

COPS2

ITIH5

LIFR

TIMP2

TMEM194A

YIPF4TGIF2

ZBTB41

ZNF398

LHX6

RAG1RAP1A

RAP2B

SLC26A1SIGLEC1

LIMD1LMNB2

SCN2B

N-PAC KIAA0430

NUFIP2 KIAA1045

POU3F4

SESN2

KIAA1467

SFXN5

KIAA1549

SCAMP5

HTR2CC6orf168 KIAA0182SAMD12 KCMF1

RTF1 HUNK

GPR68

C2orf65 INSR

ROCK2HNRNPUC12orf54

HSF2

IL10

RUNDC3B

FBRSAMBRA1

VASN

WSB2

WSCD1HIP1RBTG2

RNF111WNT3 HEXA

ATP1B4GPR173

GORASP2

BACE1

FOXJ3ELK1

RAVER2

FLJ25404

FZD5

ARSB

PHF6

ZNF707FAM5B

ZNF81

ATP9A

B3GALT5

PEX5SH3TC2

ZNF24

ZNF512

OGFOD1

PANK3 FAM155BARHGEF12

F3APOL6

PSD3 RNF215HLA-AC12orf4XYLT1VAMP3

SYNPO2LAKNA FBXW11SYTL4

IDH3A

RHEB FBN1HDGF

BPTF

BBS5ATP1A2

HEG1

RNF180

PLA2G3PRCPPSD3

PKP2

ACVR2B

ADAMTS4ADAM19

AHCTF1

MFN1

FAM160B1MREG

WWOX

PDCD10

SCN2B

ZMYND11

COL4A6

CC2D1B

CALCR

CASR METT11D1ESRRGSEC14L5 WDR33

SORCS1

RNF26

CDK6

NR1H2GNG2

RCL1ATP1B4

RBM3GALNT17

DLX6

BAIAP2

RANBP6

BICD2

PYGO2

PVRL1

CALM2

AKAP5

GABRB2SHROOM3

NPTXRKIAA0152

NMNAT2

NOTCH2NL

SMARCC2 FGA

SNX13

CTDSP2 C1orf88SYT9 NFYA SCARB2 MEIS2

RAF1ZEB2

CSRP2BP

XYLT1 THNSL1

LMOD1

TM9SF4

PAPPA

HDDC3HS3ST4GLIS2

FYCO1

GABBR2CLDN12

GSR IQGAP1

TRIM62SKAP2

COL11A1PRKCH

TRIM8

PLD2H6PD

PPP2R1B CREB3L2SLC7A11

IMPACTWDR81HUS1B

KSR2

SOBPSTEAP4

SMG1WFDC6

STK4STK16

TACC1

MDGA2

EFNB3

RNF165

MED1

USP15

VSIG4

API5PRKCA

FOXP4WDR40B

MICALCL MECP2

DRAM LOC388503

RGS6 ZIC3

TXLNB

LIN28XRRA1

ALCAM

AMTLOC285636DIP2BAAK1 TMEM2

RAB22AUNC119B BDH2LRFN2

FOXA3METTL9 MEF2D

tRF-1:22-Lys-TTT-1-M3GLCCI1 FAM131BJHDM1D

NRIP1GPC2

THRB

SH3BGRL2

FIGNL2

TFAP2ESCG3

TGFBR1 INSRJARID1A

SP8SMARCC1

BET1L

FAM100A

EDARADD

RORC

C1orf176

MLL2

RUNX1T1

C12orf4

RC3H1

FNDC5 SH3PXD2B

SH3PXD2A

ORC6L

TMEM26

GPR77

CCDC147SIRPA TMEM87A

CALB1

GABPA

a

b

Fig. 4 Target gene prediction of tRFs/tiRNAs. a Target genes of tRF-60:77-Thr-TGT-1. b Target genes of tRF-1:22-Lys-TTT-1-M3

Page 9 of 12Xu et al. BMC Bioinformatics (2021) 22:238

‘positive regulation of transcription from RNA polymerase II promoter’ and ‘transcription from RNA polymerase II promoter’ in biological process (BP) category (Fig. 5a). Whereas, the most enriched terms of target genes of tRF-1:22-Lys-TTT-1-M3 were ‘nucleoplasm’ and ‘cell junction’ in CC category, ‘transcription factor activity, sequence-specific DNA binding’ and ‘RNA polymerase II core promoter proximal region sequence-specific DNA binding’ in MF category, ‘positive regulation of transcription, DNA-templated’ and ‘positive regulation of transcription from RNA polymerase II promoter’ in BP category (Fig. 5b).

KEGG pathway analysis showed that targets of tRF-60:77-Thr-TGT-1 might partici-pate in Ras signaling pathway, cGMP-PKG signaling pathway, thyroid hormone signaling pathway and FoxO signaling pathway (P < 0.05) (Fig.  5c). For targets of tRF-1:22-Lys-TTT-1-M3, the most enriched pathways were Ras signaling pathway, PI3K-Akt signaling pathway and pathways in cancer (Fig. 5d).

DiscussionMM is an incurable hematological malignancy. Even patients who are initially sensitive to treatment will eventually develop relapsed and refractory disease. So far, many genes or ncR-NAs related to relapse and drug resistance of MM have been identified. For example, J Xia demonstrated that NEK2 induces autophagy-mediated bortezomib resistance by stabilizing Beclin-1 in multiple myeloma [15]. MicroRNA-497 was found to inhibit myeloma growth and increase susceptibility to bortezomib by targeting Bcl-2 [16]. B-Z Zhu reported that LncRNA HOTAIR activated the expression of NF-κB and promoted the proliferation of myeloma cells [17]. However, it is still unclear whether tRFs/tiRNAs are involved in the relapse and drug resistance of MM. Therefore, for the first time, we conducted a preliminary evaluation of the role of tRFs/tiRNAs in R/RMM

GO:00

60828~reg

ulat ion o

f canonical W

nt...

GO:00

51966~regulation

ofs ynaptictransmission

...

GO:00

50715~positive

regulation

ofcytok

ine...

GO:00

30100~regulation

ofendocytosi s

GO:00

06887~exocytosis

GO:00

16477~cell m

igration

GO:00

98609~cell-cella dhesio

n

GO:00

45893~posi tive

regulation

oftranscription

...

GO:00

06366~transcription

f romRN

Apolym

erase...

GO:00

45944~posi tive

regulation

oftranscription

...

GO:00

31901~early

endosome m

embrane

GO:00

30027~lam

ellipo

dium

GO:00

45202~synapse

GO:00

05768~endosome

GO:00

05913~cell-cella dherensjun

c tion

GO:00

30054~cell junc tion

GO:00

00139~Golgimembr ane

GO:00

09986~cell surface

GO:00

16020~me

mbrane

GO:00

05886~pla

sma m

embrane

GO:00

42043~neurexinfam

i lyprote

inbin

ding

GO:00

19838~growthfacto

r bind

ing

GO:00

61630~ubiqu

it inprotein

l igase activity

GO:00

19904~prote

indomai nspecif ic

b inding

GO:00

01077~transcription

alactivato

r activity,RN

A...

GO:00

00978~RN

Apolym

erase II core p

romo

ter...

GO:00

43565~sequence-spec ificDN

Abind ing

GO:00

03700~transcription

f acto

r activity,sequence...

GO:00

05515~prote

inbin

ding

0

50

100

150

coun

t

BPCCMF

GO:00

18105~pept idyl-serine

phosphorylat ion

GO:00

06367~transcription

initiat ion fromRN

A...

GO:00

46777~protein

autophosphorylat ion

GO:00

07507~heart

develop

men t

GO:00

07399~nervous syst emdevelop

ment

GO:00

30154~cell dif ferent iat ion

GO:00

06366~transcription

f romRN

Apolym

erase...

GO:00

45893~posi tive

regulation

oftranscription

...

GO:00

00122~negati ve regula

tion o

f transcription

…

GO:00

45944~positive

regulation

oft ranscripti on…

GO:00

01726~ruf fle

GO:00

30054~cell junc tion

GO:00

05654~nucleo plas

m

GO:00

03712~transcription

cofac

toract ivity

GO:00

35091~phosphatidyli nosit ol bind

ing

GO:00

00981~RN

Apolym

erase II transcripti on...

GO:00

03714~transcription

corepressor activity

GO:00

01077~transcription

alact ivator a

ct ivi ty,RN

A…

GO:00

04672~protein

ki nase act ivity

GO:00

04674~protein

ser in

e/threon in

e kina

seact ivity

GO:00

43565~sequence-spec ificDN

Abind ing

GO:00

00978~RN

A polymer aseII core…

GO:00

0370

0~tra

nscr ip

tion f a

ctor ac

ti vit y

, sequ

ence

…

0

10

20

30

40

coun

t

BPCCMF

Hsa04068:FoxO signaling pathway

Hsa04919:Thyroid hormone signaling pathway

Hsa04022:cGMP−PKG signaling pathway

Hsa04014:Ras signaling pathway

0 1 2 3 4 5Gene Ratio

1.8

2.0

2.2

2.4

2.6lo g 10(P Value)

Gene counts

6

7

8

9

Hsa05161:Hepatitis B

Hsa04261:Adrenergic signaling in cardiomyocytes

Hsa04728:Dopaminergic synapse

Hsa04270:Vascular smooth muscle contraction

Hsa04919:Thyroid hormone signaling pathway

Hsa04024:cAMP signaling pathway

Hsa04022:cGMP−PKG signaling pathway

Hsa05200:Pathways in cancer

Hsa04151:PI3K−Akt signaling pathway

Hsa04014:Ras signaling pathway

0 2 4 6 8Gene Ratio

2.0

2.5

3.0

3.5log10(P Value)

Gene counts

5

6

7

8

9

a

b

c

d

Fig. 5 Functional analysis of tRFs/tiRNAs. a GO enrichment analysis of tRF-60:77-Thr-TGT-1. b GO enrichment analysis of tRF-1:22-Lys-TTT-1-M3. c KEGG pathway analysis of tRF-60:77-Thr-TGT-1. d KEGG pathway analysis of tRF-1:22-Lys-TTT-1-M3

Page 10 of 12Xu et al. BMC Bioinformatics (2021) 22:238

We used the standardized tDR naming system to name the tRFs/tiRNAs involved in this study [18]. The 4 parts in the name represent prefix, position, source tRNA and matching tRNA transcripts in turn. Two potentially significant tRFs were finally screened out by RNA sequencing and qPCR validation. To explore the pathological processes that they may be involved in, we conducted functional analysis of the predicted target genes. For tRF-60:77-Thr-TGT-1, its predicted target genes were mainly located on membrane and implicated in transcription initiation, cell adhesion and migration. Whereas, target genes of tRF-1:22-Lys-TTT-1-M3 were primarily localized in nucleoplasm. Similarly, they principally regulated transcription initiation by exerting transcription factor activity. This is consistent with the three major functional categories of tRFs/tiRNAs summarized in the previous section. The mechanism of tRFs/tiRNAs-mediated translation regulation is complicated. For example, it was reported that 5’-tiRNA-associated translational silencer Y-Box-Binding Protein 1(YB-1) could contribute to stress-induced translational repression [19]. A universal conserved ‘GG’ di-nucleotide in 5’-tRFs was also involved in the process of protein translation [20]. Moreover, tRFs/tiRNAs could regulate translation by interacting with ribosomes [21, 22]. The transcrip-tional regulation mechanism of tRFs/tiRNAs in R/RMM needs further investigation.

We noticed that both tRFs were involved in the Ras signaling pathway in KEGG pathway analysis. The Ras pathway has been found to regulate many fundamental biological pro-cesses, like cell proliferation, differentiation, survival, and apoptosis. Dysregulation of this pathway generate the emergence, development, and progression of multiple cancers [23–25]. In R/RMM, Ras mutations may increase from 23–54 to 45–81% compared with NDMM [26, 27]. The Ras/ Raf/MEK/Erk pathway is associated with drug resistance and a more aggres-sive phenotype in MM [28]. In addition, these differentially expressed tRFs/tiRNAs may also participate in a variety of cancer-related signaling pathways such as FoxO signaling pathway, PI3K-Akt-mTOR signaling pathway. FOXO3a transcription factor can be negatively regu-lated by mTOR complex 2(mTORC2) and then induces a pro-survival response, which sug-gests potential new mechanism of inhibition of mTORC2 in MM [29]. Sorafenib, an orally compound that acts predominantly by inhibition of Raf kinase and VEGF receptor 2 can inhibit the proliferation of MM cells. It has also been tested in combination with rapamycin to inhibit mTOR, and this shows synergistic effects on MM cells in vitro [30]. In short, these provide reference for the design of more precision and individualized clinical trials. To date, both up-regulated or down-regulated expression tRFs/tiRNAs has been reported to play a pro-tumor role in tumor tissues [10, 31]. Therefore, additional experiments need to be con-ducted to validate these hypotheses.

There remain several limitations in this study. For example, tRFs/tiRNAs can also perform their biological functions through regulating miRNA activity [32]. Regrettably, current bio-informatics methods are not able to predict the target miRNAs of tRFs/tiRNAs. Therefore, we failed to get the ceRNA network. We plan to explore how tRFs/tiRNAs regulate miRNA in MM in future molecular biology experiments. Besides, according to serum M protein, myeloma can be categorized into eight subclasses (type IgG, type IgA, type IgD, type IgE, type IgM, light chain type, non-secreted type and polyclonal type). Different types of MM may have different phenotypes. In this study, we used two cell lines (IgE or IgG secreting) for in vitro verification. Although this is the practice in most myeloma-related studies, but addi-tional more cell lines would be beneficial to confirm the data.

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In summary, we evaluated the potential function of tRFs/tiRNAs in R/RMM and the results provide the basis for preclinical research. Studies on tRFs/tiRNAs are still in their infancy and deep insights into their roles in carcinogenesis and progression remain lacking. tRFs/tiRNAs have the potential as clinical biomarkers for drug resist-ance and prognosis of MM and may be used as treatment targets in the future.

Supplementary InformationThe online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12859- 021- 04167-8.

Additional file 1. RNA quality Quantification and Quality Assurance.

Additional file 2. Absolute values of RNA expression (×10−3).

AcknowledgementsNot applicable.

Authors’ contributionsCX and YF designed and performed the study. CX wrote the manuscript. FZ collected samples. TL and JL performed the analytic calculations and statistical analysis. All authors read and approved the final manuscript.

FundingThis study was supported by National Natural Science Foundation of China (Grant Nos. 82002452 and 81670203).

Availability of data and materialsAll data generated or analyzed during this study are included in this published article. Sequencing data has been uploaded to GEO database (available at https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE17 1003). The accession number is GSE171003. We are currently studying the mechanism of these tRFs/tiRNAs. In order to ensure the novelty of key molecules, we have not provide the sequencing data publicly for the time being.

Declarations

Ethics approval and consent to participateThe studies involving human participants were reviewed and approved by ethics committee of the third Xiangya hos-pital. The approval ID was 2016121. All protocols are carried out in accordance with relevant guidelines and regulations. The patients/participants provided their written informed consent to participate in this study.

Consent for publicationNot applicable.

Competing interestsThe authors declare that they have no competing interests.

Author details1 Department of Hematology, The Third Xiangya Hospital of Central South University, Changsha 410000, China. 2 Depart-ment of Blood Transfusion, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518000, China. 3 Depart-ment of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha 410000, China.

Received: 2 January 2021 Accepted: 4 May 2021

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