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: [email protected]; [email protected] 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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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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