Skip to main content
Download PDF

Prognostic, diagnostic and clinicopathological roles of tsRNAs: a meta-analysis in breast cancer

European Journal of Medical Research volume 29, Article number: 35 (2024) Cite this article

Abstract

Breast cancer (BC) is one of the most common malignancies in women and the leading cause of cancer-related death in women. The newly emerged non-coding RNAs tsRNAs (tRNA-derived small RNAs) play an important role in the occurrence and development of BC. The purpose of this study was to comprehensively evaluate the prognostic, diagnostic and clinicopathological roles of tsRNAs in BC. Through literature screening, a total of 13 BC-related tsRNA studies were included in this meta-analysis, all of which passed quality assessment. Prognostic studies showed upregulated tsRNAs to be associated with poor survival outcomes (HR = 1.64, 95%CI 1.51–1.77) and downregulated tsRNAs to be associated with better outcomes (HR = 0.58, 95%CI 0.50–0.68). Results of diagnostic studies showed a combined sensitivity of 72% (95%CI 68–76%) and combined specificity of 64% (95%CI 61–67%); the AUC was 0.72 (95%CI 0.68–0.75) and the DOR 4.62 (95%CI 3.76–5.68). Finally, correlation analysis of clinicopathological features showed that downregulation of tsRNAs correlated significantly with age, TNM stage and lymphatic metastasis. Sensitivity analysis and publication bias showed no significant difference. In conclusion, BC-associated tsRNAs are closely related to the prognosis and clinicopathological features of patients with this disease and can be used to assist in early diagnosis of BC. Therefore, tsRNAs are potential targets for the diagnosis and treatment of BC.

Introduction

According to the latest cancer statistics data, breast cancer (BC) is the most common malignant tumor in women, accounting for 31% of the total number of female cancers, and the incidence has gradually increased in recent years [1, 2]. With continuous improvement in diagnosis and treatment of BC, early- stage patients have good prognosis, with an overall cure rate of 90% [3]. However, the 5-year survival rate is significantly reduced for patients with advanced stage, poor tissue typing or resistance to combined therapy [4]. Therefore, clarifying the specific molecular mechanism of the occurrence and development of BC to assist in early diagnosis, as well as to find new and more accurate targeted molecules, is crucial for improving the overall survival rate of BC patients.

In recent years, positive roles of a variety of non-coding RNAs (ncRNAs), including micro RNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs) and tRNA-derived small RNAs (tsRNAs), in cancer have received much attention and been widely reported; some have been included in clinical application, fully demonstrating the huge potential of ncRNAs in tumor diagnosis and treatment [5,6,7]. TsRNAs are the products of tRNA or pre-tRNA cleavage during maturation. TsRNAs can be divided according to different fracture sites into tRNA-related fragments (tRFs) and tRNA halves (tiRNAs), which complement each other in terms of formation pathway, cell localization and function [8, 9]. Although tsRNAs were initially thought to be produced by random degradation and have no special function, an increasing number of studies have shown that tsRNAs are not only widely expressed in tumors but also play important roles in the occurrence and development of tumors [10]. In addition, tsRNAs are present in a large number of human body fluids, such as saliva, urine, and semen, providing a new target for early tumor diagnosis and precise treatment [11].

Studies have shown that tsRNAs are expressed specifically in BC and widely exist in the serum of BC patients. TsRNAs differentially expressed in different subtypes of BC, including triple-negative breast cancer, have been mined through RNA sequencing and verified with clinical samples, suggesting the significance of tsRNAs in identifying tumor properties and tumor tissue types [12,13,14]. In terms of mechanism research, tsRNAs are significantly enriched in hormone-dependent BC and are not only directly involved in regulating the occurrence and development of BC but also closely related to drug resistance and tumor recurrence in BC [15,16,17]. Overall, tsRNAs are good detection markers and potential therapeutic targets for BC patients. Therefore, we summarized existing studies on BC and tsRNAs and used meta-analysis to explore the roles and values of tsRNAs in the prognosis, diagnosis and clinicopathological features of BC patients to provide evidence for future applications of tsRNAs.

Methods

Publication search

To collect relevant research literature for meta-analysis, the Boolean logic method was used to search PubMed, Web of Science, Cochrane Library, Embase and other databases as of March 1, 2023. Specific search terms included “breast cancer”, “breast carcinoma”, “tsRNA”, “tRNA-derived small RNA”, “tRF”, “tRNA-derived fragment”, “tiRNA” and “tRNA-derived stress-induced RNA”.

Inclusion and exclusion criteria

Inclusion criteria: (1) the content included the relationship between tsRNAs and BC; (2) all cases passed the gold standard, that is, the histopathological diagnosis was clear; and (3) data related to prognosis, diagnosis or clinicopathological features could be extracted directly or indirectly. Exclusion criteria: (1) the content was not related to tsRNAs or BC; (2) reviews, meta-analyses, letters, case reports, conference abstracts; (3) non-English and nonhuman studies; and (4) failure to report or extract important indicators and data. Two researchers independently read the title and abstract of the retrieved literature for preliminary screening; in cases in which the abstract results were not clear, the full text was read to determine inclusion eligibility. If the screening results were inconsistent, the third researcher decided.

Data extract

The general data extracted from each article included the following: title, first author, tsRNAs, year, country, expression, cut-off, sample size, data source, detected sample, among others. Data related to prognosis included follow-up time, survival outcome (progression-free survival (PFS), recurrence-free survival (RFS), disease-free survival (DFS), overall survival (OS)), survival analysis, hazard ratio (HR), and 95% confidence interval (95%CI), among others. Data related to diagnosis included case, control, area under the receiver operating characteristic curve (AUC), sensitivity (SEN), specificity (SPE), true positive (TP), false positive (FP), true negative (TN), false negative (FN), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and cancer type, among others. Data related to clinicopathologic features included age, TNM stage, and lymphatic metastasis, among others. All records were approved by two independent researchers.

Quality assessment

As the quality of studies may influence the results of meta-analysis, each included study was evaluated using the Newcastle‒Ottawa score (NOS) tables in the Cochrane manual and Quality Assessment for Studies of Diagnostic Accuracy 2 (QUADAS 2). After all evaluations, RevMan 5.3 software was used to output the evaluation results. NOS scores ≥ 7 or QUADAS 2 scores ≥ 4 are generally considered to be high quality [18, 19].

Data synthesis and analysis

Statistical analysis of the data was performed with Stata software (version 15.1). HRs, odds ratios (ORs), and 95%CIs were used to evaluate the effect of tsRNAs on prognosis and clinicopathologic features in BC patients. To evaluate the diagnostic value of tsRNAs, summary receiver operating characteristic (SROC) curves were drawn, and AUCs were calculated, as were Fagan nomograms and scatter plots. For studies in which Kaplan–Meier (KM) and ROC curve data could not be extracted directly, we used Engage Digitizer and GetData Graph Digitizer and calculated HR and the corresponding 95%CI based on the method of Tierney et al. [20,21,22]. The Cochran-Q test and I2 statistics were used to assess heterogeneity. When the heterogeneity was small (P > 0.10 or I2 < 50%), a meta-analysis was conducted using the fixed effects model. Otherwise, random-effects models were employed to combine effect sizes, and further subgroup analysis and meta-regression (MetaDiSc software) were applied to explore possible sources of interstudy heterogeneity. Sensitivity analysis was carried out by excluding the included studies one by one. Finally, Begg’s funnel plot, Deeks’ funnel plot and Egger’s test were used to evaluate publication bias, and P > 0.05 was considered to indicate no publication bias. All statistical tests were bilateral, and P < 0.05 was considered statistically significant.

Results

Literature information and study characteristics

Through systematic analysis using our search terms, a total of 211 studies were retrieved; 101 remained after removing duplicate studies. Then, 45 studies that did not meet the inclusion criteria were excluded, and the full texts of the remaining 56 studies were reviewed in detail. Finally, 13 studies (involving 7 prognosis, 7 diagnosis, and 3 clinicopathological features) were included in the meta-analysis [13, 23,24,25,26,27,28,29,30,31,32,33,34]. The detailed literature selection process is illustrated in Fig. 1.

Fig. 1
figure 1

Workflow of the study and result of document screening

Full size image

NOS and QUADAS 2 scoring methods were used to systematically assess the quality of all included prognostic and diagnostic studies. The results showed that the quality of all the included studies met the standards. Risk of bias graphs and summaries are shown in Additional file 1: Figures S1 and Additional file 2: Figure S2.

Prognostic roles of tsRNAs in BC

There were seven studies, all of which were conducted in China except for one in the USA and published between 2018 and 2022. The sample type used for detection was mainly tissue, with a total of 5257 patients. Clinical outcome indicators included OS, PFS, RFS and DFS. Finally, 10 high expression tsRNAs and 6 low expression tsRNAs related to BC were identified (Table 1).

Table 1 Characteristics of all prognostic studies included in the meta-analysis
Full size table

The fixed effect model was used to evaluate the correlation between tsRNAs and prognosis of BC patients according to the level of heterogeneity. According to forest map results, high expression of tsRNAs was associated with poor clinical outcomes (HR = 1.64, 95%CI 1.51–1.77). Conversely, low expression of tsRNAs was associated with better clinical outcomes (HR = 0.58, 95%CI 0.50–0.68). These results indicate that tsRNAs may be important prognostic factors for BC (Fig. 2). In addition, subgroup analysis of differentially expressed tsRNAs was performed, and the results showed that parameters such as country, cut-off, sample size, data source, detected sample, follow-up time, survival outcome, survival analysis, and HR obtained were associated with BC prognosis (Additional file 3: Figure S3 and Additional file 4: Figure S4).

Fig. 2
figure 2

Forest plots of upregulated (A) and downregulated (B) tsRNAs for survival outcome in breast cancer

Full size image

Diagnostic roles of tsRNAs in BC

In Table 2, the SEN, SEP, LPR, NPR and DOR of BC diagnosis are summarized, and the results are shown in the form of a forest map. The combined SEN (Fig. 3A) was 72% (95%CI 68–76%), and the combined SEP (Fig. 3B) was 64% (95%CI 61–67%). The combined PLR (Fig. 3C) was 2.00 (95%CI 1.83–2.20) and the combined NLR (Fig. 3D) 0.43 (95%CI 0.38–0.50). The DOR (Fig. 3E) was 4.62 (95%CI 3.76–5.68). Subsequently, we plotted the SROC curve (Fig. 3G), and the AUC was 0.72 (95%CI 0.68–0.75). These results suggest that tsRNAs have good efficacy in diagnosis of BC and have potential to be used as diagnostic markers. Moreover, bivariable box diagram results (Fig. 4A) showed that most studies were distributed within the 95%CI. To analyze sources of heterogeneity among the studies, we evaluated threshold effects. The results showed that the SROC curve graph (Fig. 3F) did not show a typical "shoulder-arm" distribution, considering that there was no threshold effect. Covariables country, expression, case, and type were used for subgroup analysis (Table 3) and meta-regression analysis (Table 4) to evaluate the nonthreshold effects. The I2 of the DOR for country (China vs. Japan) was 53.5% vs. 0.0%, and the I2 of the DOR for expression (Down vs. Up vs. NA) was 62.9% vs. 40.7% vs. 0.0%, partially reducing heterogeneity. However, in univariate multiple regression analysis, the covariables were all P > 0.05, no significant correlation was found with DOR, and heterogeneity due to experimental design was not considered.

Table 2 Characteristics of all diagnostic studies included in the meta-analysis
Full size table
Fig. 3
figure 3

Forest plots of combined sensitivity (A), specificity (B), PLR (C), NLR (D), DOR (E), ROC plane (F), and SROC curve (G) of tsRNAs for breast cancer

Full size image
Fig. 4
figure 4

Bivariate boxplot (A), Deeks’ funnel plot (B), scatter plot of PLR and NLR (C), and Fagan's nomogram (D) of tsRNAs for breast cancer

Full size image
Table 3 Assessment of diagnostic accuracy and heterogeneity in subgroup analysis
Full size table
Table 4 Results of univariate meta-regression analysis of diagnostic odds ratio
Full size table

To further evaluate the ability of tsRNAs as diagnostic markers in patients with BC, Fagan's nomogram (Fig. 4C) and Scatter plots of PLR and NLR (Fig. 4D) were developed. When the prior probability is 20%, the probability of BC in positive tsRNAs test increases to 33%, and the probability of BC in negative tsRNAs test decreases to 10%. All the above results indicate that tsRNAs have high diagnostic capability of BC and are a good diagnostic test.

Clinicopathological roles of tsRNAs in BC

A total of 3 studies examining 5 types of tsRNAs included clinicopathological data (Table 5) [13, 25, 28]. Considering the heterogeneity among different studies, we only summarized the studies with at least 3 relevant indicators. The results showed that tDR-000620, 5'-tiRNA-Val, tRF-32-Q99P9P9NH57SJ, tRF-17-79MP9PP were all expressed at low levels in BC patients and correlated with patient age (OR = 2.470, 95%CI 1.067–5.718), TNM stage (OR = 3.435, 95%CI 1.468–8.039), lymphatic metastasis (OR = 0.354, 95%CI 0.177–0.709) (Fig. 5). Regarding the correlation between other indicators and tsRNAs, more studies need to be evaluated for confirmation.

Table 5 Meta-analyses of correlation between downregulated tsRNAs and clinicopathological features of breast cancer
Full size table
Fig. 5
figure 5

Forest plots of age (A), TNM stage (B) and lymphatic metastasis (C) of tsRNAs in breast cancer

Full size image

Sensitivity analysis and publication bias

To assess the impact of each study on the results of the meta-analysis, sensitivity analysis was performed on the included literature. The results showed no significant change in the combined total HRs or ORs after the removal of each study, suggesting little difference among studies and relatively stable results (Additional file 4: Figure S4 and Additional file 5: Figure S5).

Begg’s funnel plots and the Egger test were used to assess the existence of publication bias. The funnel plots of prognostic correlation studies were roughly symmetric (Additional file 1: Figure S4). However, one of the lymphatic metastasis indexes in the clinicopathological feature correlation analysis fell outside the funnel plot, suggesting publication bias in the included studies, which may be related to the small amount of data (Additional file 5: Figure S5). Finally, Deeks’ funnel plots were applied to analyze publication bias in diagnostic correlation studies. The results showed a basically symmetrical funnel plot (P = 0.59), suggesting no significant publication bias (Fig. 4B).

Discussion

BC is the malignant tumor with the highest incidence in the female population at present [35, 36]. Its causes are still unclear, and some types lack effective intervention targets, resulting in a high fatality rate. Traditional BC detection methods, such as CEA, CA153, B-ultrasound, and molybdenum targeted therapy, among others, are relatively less traumatic than other methods but have low sensitivity and specificity for early diagnosis, and pathology, the gold standard of diagnosis, is difficult to popularize in the population without disease [37,38,39,40]. Therefore, exploration of more effective markers for early diagnosis and precise treatment is crucial for improving the poor prognosis of BC patients.

As emerging ncRNAs, abnormal expression of tsRNAs has been confirmed in a variety of diseases, including cancers, and plays an important biological role and function [41, 42]. As tRNA fragments, tsRNAs are characterized by low molecular weight and stable expression. In addition, tsRNAs are widely expressed and specific in human tissues and body fluids. These characteristics all render tsRNAs important measures in cancer diagnosis and prognosis prediction [43, 44]. Since Hani et al. discovered that tRFs derived from tRNA-Glu, tRNA-Asp, tRNA-Gly, and tRNA-Tyr competitively bind to the endogenous oncogene YBX1 in BC cells, inhibiting cell growth by interfering with oncogene transcription stability, the role and mechanism of tsRNAs in BC occurrence and development have been extensively explored [15]. Wang et al. conducted tsRNA expression sequencing on plasma samples from 8 BC patients and 4 healthy women, and found that the expression level of tRF-Glu-CTC-003 in BC patients' plasma was lower compared to that in healthy women, and the expression level of tRF-Glu-CTC-003 in TNBC patients' plasma was lower than in other subtypes [31]. These differential results suggest that tsRNA may serve as a potential biomarker for BC. Detecting tsRNA levels in plasma can aid in early BC diagnosis and patient prognosis evaluation. Mechanistically, tsRNAs primarily regulate protein expression at different stages by binding to other molecules, thereby affecting protein biosynthesis through transcription or post-transcriptional processes, and playing a regulatory role in BC. Maurizio et al. found significantly lower expression levels of tRF3E derived from mature tRNA-Glu in BC tissues compared to normal tissues, which could inhibit BC progression by binding to the RNA-binding protein NCL [45]. Zhu et al. identified high expression of tRF-Lys-CTT-010 in TNBC, demonstrating its ability to promote cell proliferation and migration, participate in metabolic pathways, and regulate cell survival and proliferation by manipulating lactic acid production and glycogen consumption [46]. Mo et al. discovered the inhibitory effects of tRF-17-79MP9PP on BC cell invasion and metastasis via the THBS1/TGF-β1/Smad3 axis [30]. These findings collectively indicate that tsRNA plays a crucial role in BC occurrence and development, offering potential avenues for precise BC treatment.

To our knowledge, this is the first meta-analysis exploring the value of tsRNAs in BC, with a view to providing evidence-based medical evidence for future clinical applications of tsRNAs. According to our inclusion and exclusion criteria, 13 studies on the correlation between tsRNAs and BC were identified as eligible for meta-analysis, and no studies that did not meet the criteria were found after quality assessment. In the study on the correlation between tsRNAs and BC patient prognosis, we included 7 studies involving a total of 5257 patients. The results showed that tsRNA expression correlated positively with prognostic indicators (PFS, RFS, DFS, OS), which was consistent with the study on the mechanism of differentially expressed tsRNAs playing a role in promoting or suppressing BC. It should be noted that the cut-off method for classifying tsRNA expression was not unified among the included studies, and there is still a lack of quantitative analysis standards for tsRNAs, posing challenges for practical clinical application of tsRNAs in the future. In addition, the vast majority of studies focused on Asia, and more solid research is needed to confirm whether our results are biased by race and/or region. Wang et al. found that the AUC of the combination of six tsRNAs in the plasma of early-stage BC patients was 0.844, superior to any single identified tsRNA [32]. In addition, Zhang et al. reported that when three tsRNAs were combined with the traditional tumor markers CEA, CA125 and CA153, the AUC increased to 0.801 [33]. These results suggest that tsRNAs have reliable potential for BC diagnosis. Therefore, we systematically reviewed studies on the diagnostic value of tsRNAs in BC, aiming to identify biomarkers that can be used for diagnosis. Eventually, 7 studies involving 21 different tsRNAs were included in our analysis. After meta-analysis of all tsRNAs, the combined SEN was 72% (95%CI 68–76%), the combined SEP was 64% (95%CI 61–67%), and the AUC of the SROC curve was 0.72 (95%CI 0.68–0.75), indicating that tsRNAs have good diagnostic efficacy. As an independent prognostic indicator, the DOR value can indicate the degree of association between diagnosis results and diseases, whereby a higher value indicates a more reliable diagnosis. In diagnostic assessment of tsRNAs, the combined DOR reached 4.62 (95 CI 3.76–5.68), further indicating reliable accuracy of tsRNA diagnosis in BC. Finally, Fagan's nomogram was used to analyze the clinical value of tsRNAs. The results showed that when the pretest probability was set to 20%, the probability of BC in positive tsRNA test results increased to 30% and that in negative tsRNA test results decreased to 10%. Regarding clinicopathological studies, 3 studies were included in total. The results showed that low expression of tsRNAs correlated significantly with age, TNM stage and lymphatic metastasis, suggesting a role in inhibiting BC progression. In addition, we found a certain publication bias in the included studies, which affected the accuracy of the results obtained; the reason may be that there were few studies and that some of them had small sample sizes. For example, in the study of Wang et al. ts-32-Q99P9P9NH57SJ and ts-17-79MP9PP showed low expression in BC, though the trend in lymphatic metastasis was the opposite, which may also be related to the small number of samples (n = 16) [28]. In conclusion, we believe that tsRNAs may become a new target in BC treatment and provide new ideas for BC treatment. However, more literature needs to be examined to obtain reliable data support.

At present, research on tsRNAs is still in the initial stage, and the specific molecular mechanism of the role of a large number of differentially expressed tsRNAs in tumors, especially BC, has not been clarified. Nevertheless, we hope that our meta-analysis will be helpful for future clinical applications of tsRNAs. Of course, there are several limitations of this study that should not be ignored. First, as the patients included were mostly from China, the conclusion may not be applicable to different regions or populations. Second, some data and their 95%CIs were obtained indirectly through software, which may lead to deviation from the real data, resulting in certain publication bias. In addition, there are few related studies on different subtypes, especially triple-negative breast cancer; thus, the positive role of tsRNAs in the early diagnosis and prognosis of these patients should be explored. Finally, comprehensive treatment tolerance is one of the main factors affecting the survival of BC patients at present, but there are few studies focusing on such factors. Future studies with more and larger samples should be designed and performed to guide individualized clinical therapy (Additional file 6: Figure S6).

Conclusion

In summary, our study found that tsRNAs have important value in diagnosis and prognosis assessment of BC and correlate significantly with some clinicopathological features, suggesting that tsRNAs can be used as an effective diagnostic and treatment marker for BC patients. However, due to the limited sample size, the results may be unstable. Therefore, more high-quality research data are needed to support or update our conclusions in the future.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

BC:

Breast cancer

ncRNA:

Non-coding RNA

miRNA:

Micro RNA

lncRNA:

Long non-coding RNA

circRNA:

Circular RNA

tsRNA:

TRNA-derived small RNA

tRF:

TRNA-related fragment

tiRNA:

TRNA halve

PFS:

Progression-free survival

RFS:

Recurrence-free survival

DFS:

Disease-free survival

OS:

Overall survival

HR:

Hazard ratio

95%CI:

95% Confidence interval

AUC:

Area under the curve

SEN:

Sensitivity

SPE:

Specificity

TP:

True positive

FP:

False positive

TN:

True negative

FN:

False negative

PLR:

Positive likelihood ratio

NLR:

Negative likelihood ratio

DOR:

Diagnostic odds ratio

NOS:

Newcastle–ottawa score

QUADAS 2:

Quality assessment for studies of diagnostic accuracy 2

OR:

Odds ratio

SROC:

Summary receiver operator characteristic

References

  1. Kashyap D, Pal D, Sharma R, Garg VK, Goel N, Koundal D, et al. Global increase in breast cancer incidence: risk factors and preventive measures. Biomed Res Int. 2022;2022:9605439.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48.

    Article  PubMed  Google Scholar 

  3. EBCTCG. Aromatase inhibitors versus tamoxifen in premenopausal women with oestrogen receptor-positive early-stage breast cancer treated with ovarian suppression: a patient-level meta-analysis of 7030 women from four randomised trials. Lancet Oncol. 2022;23(3):382–92.

    Article  Google Scholar 

  4. Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. 2022;95(1130):20211033.

    Article  PubMed  Google Scholar 

  5. Goodall G, Wickramasinghe V. RNA in cancer. Nat Rev Cancer. 2021;21(1):22–36.

    Article  PubMed  CAS  Google Scholar 

  6. Winkle M, El-Daly S, Fabbri M, Calin G. Noncoding RNA therapeutics—challenges and potential solutions. Nat Rev Drug Discovery. 2021;20(8):629–51.

    Article  PubMed  CAS  Google Scholar 

  7. Chen Q, Li J, Shen P, Yuan H, Yin J, Ge W, et al. Biological functions, mechanisms, and clinical significance of circular RNA in pancreatic cancer: a promising rising star. Cell Biosci. 2022;12(1):97.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Magee R, Rigoutsos I. On the expanding roles of tRNA fragments in modulating cell behavior. Nucleic Acids Res. 2020;48(17):9433–48.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Li J, Zhu L, Cheng J, Peng Y. Transfer RNA-derived small RNA: a rising star in oncology. Semin Cancer Biol. 2021;75:29–37.

    Article  PubMed  CAS  Google Scholar 

  10. Lee A, Aifantis I, Thandapani P. Emerging roles for tRNAs in hematopoiesis and hematological malignancies. Trends Immunol. 2022;43(6):466–77.

    Article  PubMed  CAS  Google Scholar 

  11. Yang M, Mo Y, Ren D, Liu S, Zeng Z, Xiong W. Transfer RNA-derived small RNAs in tumor microenvironment. Mol Cancer. 2023;22(1):32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Telonis A, Loher P, Honda S, Jing Y, Palazzo J, Kirino Y, et al. Dissecting tRNA-derived fragment complexities using personalized transcriptomes reveals novel fragment classes and unexpected dependencies. Oncotarget. 2015;6(28):24797–822.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Feng W, Li Y, Chu J, Li J, Zhang Y, Ding X, et al. Identification of tRNA-derived small noncoding RNAs as potential biomarkers for prediction of recurrence in triple-negative breast cancer. Cancer Med. 2018;7(10):5130–44.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Telonis AG, Rigoutsos I. Race disparities in the contribution of miRNA isoforms and tRNA-derived fragments to triple-negative breast cancer. Cancer Res. 2018;78(5):1140–54.

    Article  PubMed  CAS  Google Scholar 

  15. Goodarzi H, Liu X, Nguyen HC, Zhang S, Fish L, Tavazoie SF. Endogenous tRNA-derived fragments suppress breast cancer progression via YBX1 displacement. Cell. 2015;161(4):790–802.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Cui Y, Huang Y, Wu X, Zheng M, Xia Y, Fu Z, et al. Hypoxia-induced tRNA-derived fragments, novel regulatory factor for doxorubicin resistance in triple-negative breast cancer. J Cell Physiol. 2019;234(6):8740–51.

    Article  PubMed  CAS  Google Scholar 

  17. Honda S, Loher P, Shigematsu M, Palazzo JP, Suzuki R, Imoto I, et al. Sex hormone-dependent tRNA halves enhance cell proliferation in breast and prostate cancers. Proc Natl Acad Sci USA. 2015;112(29):E3816–25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36.

    Article  PubMed  Google Scholar 

  19. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.

    Article  PubMed  Google Scholar 

  20. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wang R, Yao Q, Chen W, Gao F, Li P, Wu J, et al. Stem cell therapy for Crohn’s disease: systematic review and meta-analysis of preclinical and clinical studies. Stem Cell Res Ther. 2021;12(1):463.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Zhou Z, Shi B, Xu Y, Zhang J, Liu X, Zhou X, et al. Neural stem/progenitor cell therapy for Alzheimer disease in preclinical rodent models: a systematic review and meta-analysis. Stem Cell Res Ther. 2023;14(1):3.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Huang Y, Ge H, Zheng M, Cui Y, Fu Z, Wu X, et al. Serum tRNA-derived fragments (tRFs) as potential candidates for diagnosis of nontriple negative breast cancer. J Cell Physiol. 2020;235(3):2809–24.

    Article  PubMed  CAS  Google Scholar 

  24. Koi Y, Tsutani Y, Nishiyama Y, Ueda D, Ibuki Y, Sasada S, et al. Predicting the presence of breast cancer using circulating small RNAs, including those in the extracellular vesicles. Cancer Sci. 2020;111(6):2104–15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Mo D, Jiang P, Yang Y, Mao X, Tan X, Tang X, et al. A tRNA fragment, 5’-tiRNA(Val), suppresses the Wnt/β-catenin signaling pathway by targeting FZD3 in breast cancer. Cancer Lett. 2019;457:60–73.

    Article  PubMed  CAS  Google Scholar 

  26. Shan N, Li N, Dai Q, Hou L, Yan X, Amei A, et al. Interplay of tRNA-derived fragments and T cell activation in breast cancer patient survival. Cancers. 2020;12(8):2230.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Sun C, Yang F, Zhang Y, Chu J, Wang J, Wang Y, et al. tRNA-derived fragments as novel predictive biomarkers for trastuzumab-resistant breast cancer. Cell Physiol Biochem. 2018;49(2):419–31.

    Article  PubMed  CAS  Google Scholar 

  28. Wang X, Yang Y, Tan X, Mao X, Wei D, Yao Y, et al. Identification of tRNA-derived fragments expression profile in breast cancer tissues. Curr Genomics. 2019;20(3):199–213.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Liu X, Mei W, Padmanaban V, Alwaseem H, Molina H, Passarelli MC, et al. A pro-metastatic tRNA fragment drives Nucleolin oligomerization and stabilization of its bound metabolic mRNAs. Mol Cell. 2022;82(14):2604-17.e8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Mo D, He F, Zheng J, Chen H, Tang L, Yan F. tRNA-derived fragment tRF-17-79MP9PP attenuates cell invasion and migration via THBS1/TGF-β1/Smad3 axis in breast cancer. Front Oncol. 2021;11: 656078.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Wang J, Ma G, Ge H, Han X, Mao X, Wang X, et al. Circulating tRNA-derived small RNAs (tsRNAs) signature for the diagnosis and prognosis of breast cancer. NPJ Breast Cancer. 2021;7(1):4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Wang J, Ma G, Li M, Han X, Xu J, Liang M, et al. Plasma tRNA fragments derived from 5′ ends as novel diagnostic biomarkers for early-stage breast cancer. Mol Ther Nucleic Acids. 2020;21:954–64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Zhang Y, Bi Z, Dong X, Yu M, Wang K, Song X, et al. tRNA-derived fragments: tRF-Gly-CCC-046, tRF-Tyr-GTA-010 and tRF-Pro-TGG-001 as novel diagnostic biomarkers for breast cancer. Thoracic cancer. 2021;12(17):2314–23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Zhang Z, Liu Z, Zhao W, Zhao X, Tao Y. tRF-19-W4PU732S promotes breast cancer cell malignant activity by targeting inhibition of RPL27A (ribosomal protein-L27A). Bioengineered. 2022;13(2):2087–98.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Davis Lynn B, Chernyavskiy P, Gierach G, Rosenberg P. Decreasing incidence of estrogen receptor-negative breast cancer in the United States: trends by race and region. J Natl Cancer Inst. 2022;114(2):263–70.

    Article  PubMed  Google Scholar 

  36. Qin B, Kim K, Goldman N, Rundle A, Chanumolu D, Zeinomar N, et al. Multilevel factors for adiposity change in a population-based prospective study of black breast cancer survivors. J Clin Oncol. 2022;40(20):2213–23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Lee J, Park S, Park J, Cho J, Kim S, Park B. Elevated levels of preoperative CA 15–3 and CEA serum levels have independently poor prognostic significance in breast cancer. Ann Oncol. 2013;24(5):1225–31.

    Article  PubMed  CAS  Google Scholar 

  38. Mazza S, Drago A, Tanzi G, Soro S, Verga M, Elvo B, et al. Long-term major papilla metastasis from lobular breast cancer mimicking ampullary neoplasm: an endoscopic ultrasound diagnosis. Am J Gastroenterol. 2023;118(5):772–3. https://doi.org/10.14309/ajg.0000000000002231.

    Article  PubMed  CAS  Google Scholar 

  39. Michiels S, Pugliano L, Marguet S, Grun D, Barinoff J, Cameron D, et al. Progression-free survival as surrogate end point for overall survival in clinical trials of HER2-targeted agents in HER2-positive metastatic breast cancer. Ann Oncol. 2016;27(6):1029–34.

    Article  PubMed  CAS  Google Scholar 

  40. Prat A, Brasó-Maristany F, Martínez-Sáez O, Sanfeliu E, Xia Y, Bellet M, et al. Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer. Nat Commun. 2023;14(1):1157.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Yu M, Lu B, Zhang J, Ding J, Liu P, Lu Y. tRNA-derived RNA fragments in cancer: current status and future perspectives. J Hematol Oncol. 2020;13(1):121.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zhu L, Ge J, Li T, Shen Y, Guo J. tRNA-derived fragments and tRNA halves: the new players in cancers. Cancer Lett. 2019;452:31–7.

    Article  PubMed  CAS  Google Scholar 

  43. Zeng T, Hua Y, Sun C, Zhang Y, Yang F, Yang M, et al. Relationship between tRNA-derived fragments and human cancers. Int J Cancer. 2020;147(11):3007–18.

    Article  PubMed  CAS  Google Scholar 

  44. Zhang Y, Qian H, He J, Gao W. Mechanisms of tRNA-derived fragments and tRNA halves in cancer treatment resistance. Biomarker research. 2020;8:52.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Falconi M, Giangrossi M, Zabaleta ME, Wang J, Gambini V, Tilio M, et al. A novel 3′-tRNA(Glu)-derived fragment acts as a tumor suppressor in breast cancer by targeting nucleolin. Faseb j. 2019;33(12):13228–40.

    Article  PubMed  CAS  Google Scholar 

  46. Zhu P, Lu J, Zhi X, Zhou Y, Wang X, Wang C, et al. tRNA-derived fragment tRFLys-CTT-010 promotes triple-negative breast cancer progression by regulating glucose metabolism via G6PC. Carcinogenesis. 2021;42(9):1196–207.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the researchers of the included studies and the participants for their contributions.

Funding

This study was supported by the Suzhou Science and Education Youth Project (KJXW2022071), Suzhou (Taicang) Science and Technology Development Plan (SKYD2022059), and Taicang Basic Research Program Projects (TC2021JCYL05).

Author information

Authors and Affiliations

  1. Taicang Hospital of Traditional Chinese Medicine, Suzhou, China

    Lu-Jue Gao, Ying-Yi Wei, Hua-Wei Meng, Jing Gu, Hao Zhang & Li-Juan Dai

  2. Jiangyin Traditional Chinese Medicine Hospital, Jiangyin, China

    Si-Xun Zhu

  3. Nanjing University of Chinese Medicine, Nanjing, China

    Lu-Jue Gao, Si-Xun Zhu, Ying-Yi Wei, Hua-Wei Meng, Jing Gu, Hao Zhang & Li-Juan Dai

Authors
  1. Lu-Jue Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Si-Xun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying-Yi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua-Wei Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Li-Juan Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LG designed and fully participated in this study. SZ and YW were responsible for collecting the literature related to tsRNAs and breast cancer, HM and GJ were responsible for summarizing and analyzing the results related to prognosis, diagnosis and clinicopathological indicators. HZ and LD were responsible for the final review of the text. The authors thank XL for his help. All the authors agreed to publish the final version.

Corresponding authors

Correspondence to Hao Zhang or Li-Juan Dai.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1 Figure S1.

NOS risk of bias assessment.

Additional file 2 Figure S2.

QUADAS 2 risk of bias assessment.

Additional file 3 Figure S3.

Subgroup analyses for upregulated tsRNAs in breast cancer, including country (A), cut-off (B), sample size (C), data source (D), detected sample (E), follow-up time (F), survival outcome (G), survival analysis (H) and HR obtained (I)

Additional file 4 Figure S4.

Subgroup analyses for downregulated tsRNAs in breast cancer, including cut-off (A), sample size (B), data source (C), detected sample (D), follow-up time (E), survival outcome (F), survival analysis (G) and HR obtained (H).

Additional file 5 Figure S5.

Sensitivity analysis (A and D), publication bias judged by Begg’s (B and E) and Egger’s (C and F) funnel plots of tsRNAs for survival outcome of breast cancer.

Additional file 6 Figure S6.

Sensitivity analysis (A, D and G), publication bias judged by Begg’s (B, E and H) and Egger’s (C, F and I) funnel plots of tsRNAs for age, TNM stage and lymphatic metastasis of breast cancer.

Rights and permissions

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 material. 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://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, LJ., Zhu, SX., Wei, YY. et al. Prognostic, diagnostic and clinicopathological roles of tsRNAs: a meta-analysis in breast cancer. Eur J Med Res 29, 35 (2024). https://doi.org/10.1186/s40001-023-01617-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40001-023-01617-2

Keywords

European Journal of Medical Research

ISSN: 2047-783X

Contact us