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. 2024 Nov 8;23:15330338241285149. doi: 10.1177/15330338241285149

Circular RNA as Diagnostic and Prognostic Biomarkers in Hematological Malignancies:Systematic Review

Liyun Gao 1,2,✉, Junfei Fan 3, Jiayin He 4, Wenyan Fan 1, Xiangxin Che 1, Xin Wang 1, Chunhua Han 5,✉,✉
PMCID: PMC11544746  PMID: 39512224

Abstract

Objectives: While various serum and tissue biomarkers have been explored for tumor diagnosis, the sensitivity and specificity have not yield optimal results. Circular RNAs (circRNAs) are more stable, conserved, and tissue-specific than linear RNA. Recent reports indicate that circRNAs could serve as potential biomarkers in the diagnosis or/and prognosis of tumors. In this study, we systematically examined the relationship between circRNA expression and diagnostic and prognostic outcomes in patients with hematological tumors. Methods: We searched several databases, including Google Scholar, MEDLINE, Scopus, PubMed, Embase, ScienceDirect, Ovid-Medline, Chinese National Knowledge Infrastructure, WanFang and SinoMed, with a cutoff date of June 12, 2024. The study protocol was PROSPERO (CRD42020188627). Result: A total of 73 studies were included in our review, comprising 39 diagnostic studies and 65 prognostic studies. Clinical parameters were assessed based on pooled adds ratios and 95% confidence intervals (CIs). Overall survival (OS) was evaluated using hazard ratios (HRs) and 95% CIs. The pooled area under the curve was 0.86, indicating the potential to identify hematological tumor patients, with sensitivity and specificity of 79% each. The diagnostic score for circRNAs related to hematological malignancies was 2.12. Notably, different hematological malignancies subgroups displayed varying prognoses. Specifically, lymphoid leukemia circRNA showed a negative impacct on prognosis (HR = 1.25, 95% CI: 1.10-1.43, P < 0.001). Conclusion: Our findings provide compelling evidence that circRNA may be serve as a promising alternative for the diagnosis and prognosis of hematological tumors.

Keywords: circular RNA, hematological malignancies, diagnostic accuracy, prognostic effect, hazard ratios

Introduction

Hematological malignancies, which arise from bone marrow cells and the lymphatic system, represent a significant global health issue. According to the Global Cancer Statistics in 2018, there were 1,186,598 new cases and 690,002 deaths reported due to hematologic malignancies, which encompass condition such as leukemia, lymphoma and multiple myeloma. 1 The spectrum of hematological malignancies is varied and includes acute myeloid leukemia (AML), 2 chronic myeloid leukemia (CML), 3 acute lymphocytic leukemia (ALL), 4 chronic lymphocytic leukemia (CLL), 5 diffuse large B cell lymphoma (DLBCL), 6 mantle cell lymphoma (MCL), 7 multiple myeloma (MM), 8 Hodgkin lymphoma (HL), 9 follicular lymphoma (FL), 10 Burkitt lymphoma (BL), 11 among others.

Despite differences in incidence rates among these malignancies, a combination of genetic and environmental factors is recognized as their primary underlying causes. Treatment approaches are typically multifaceted, involving chemotherapy, radiation therapy, and targeted therapies. Hence, the identification of molecular markers that can enhance the diagnosis and treatment of hematologic malignancies is of utmost importance. Circular RNAs (circRNAs), which are a subclass of non-coding RNAs, are characterized by their unique closed circular structure that lacks a 5′ end cap or 3′ end poly-(A) tail. These molecules have garnered attention due to their stability, high conservation across species, and tissue-specific expression, enabling them to resist the degradation processes that ofen affect linear RNAs. Increasing evidence suggests that circRNAs play a significant role in carcinogenesis, emphasizing their importance in pathology of disease.12,13 The aim of this study is to explore the diagnostic and prognostic potential of circRNAs in hematologic malignancies, focusing on their capacity as molecular markers that could improved diagnosis and therapeutic strategies. By exploring the roles of circRNAs in hematologic malignancies, this research aspires to provide valuable insights that could enhance the development of precision medicine in oncology.

Materials and Methods

Protocol

This study was registered in PROSPERO (CRD42020188627) and was conducted according to the preferred reporting items for a systematic review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. 14 Data enrollment and processing adhered to the guideline set forth by the Francis A. Countway Library and the Health Information Research Unit.

Literature Search

We conducted a comprehensive literature search across various databases including Google Scholar, MEDLINE, Scopus, PubMed, Embase, ScienceDirect, Ovid-Medline, Chinese National Knowledge Infrastructure, WanFang and SinoMed. The search was conduct using medical subject headings (MeSH), Entree, and text word from the inception of the databases up to June 12, 2024, without any language limitations. The search terms included “hematologic malignancy”, “hematologic neoplasm”, “leukemia”, “leucocythaemia”, “leucocythemia”, “circular RNAs”, “circRNA”, “circular RNA”, “closed circular RNA”, and “circular intronic RNA”. For non-English publications, we plan to utilize voluntary interpreters to provide PDF full-text access through Cochrane Task Exchange. Additionally, a manual search of relevant reviews, conference abstracts, commentary editorials, and letters was also conducted. Any inconsistencies identified during the review process were referred to a third reviewer for a final determination.

Enrolled Criteria

Eligible studies for the meta-analysis were required to meet the following criteria: The studies must be diagnostic in nature or provide hazard ratios (HR) with 95% confidence intervals (CIs) for overall survival (OS) that include sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Studies assessing the diagnostic and/or prognostic significance of circRNA signatures were included. Data that could be calculated or extracted from the publication, such as information from experimental and control groups, were also eligible. The exclusion criteria included: Studies utilizing non-human models. Studies that did not include a control group. Articles categorized as commentaries, reviews, editorials, or similar types.

Data Extraction

Data from the eligible studies were extracted independently by two authors. The extracted information included: first author, country of publication, year of publication, Number of cases and controls, circRNA detection methods, samples sources(serum or plasma), circRNA name, circRNA expression signatures, SEN and SPE, true positive (TP), false positive (FP), false negative (FN) and true negative (TN) rates, cutoff values and AUC values, follow-up time, HR with 95% CI for OS. The extracted data were obtained and analyzed using RevMan 5.3 (Cochrane, London, UK) or STATA (College Station, USA). The quality of the included studies was assessed using the 14-item Quality Assessment of Diagnostic Accuracy studies (14-item QUADAS) and the Newcastle-Ottawa Scale (NOS).

Heterogeneity Analysis

Heterogeneity among the studies was evaluated using Chi-squared (Chi2) tests and I2 statistics, along with L’Abbe and Galbraith plot analysis. Specifically, the Chi2 tests and the I² statistic were employed to assess the variability between studies. An I² value of less than 50% was interpreted as indicative of no significant heterogeneity among the studies.

Quality and Bias Assessment

The quality and risk of bias assessment were performed independently by two reviewers. For the diagnostic studies, the QUADAS checklist with 14 criteria was employed. 15 Each criterion was rated for risk of bias as “low”, “high” or “unclear”. The scoring for these risk levels was as follows: “low” risk was assigned a score of “1”, “high” risk received “0”, and “unclear” risk was marked as “U”. A cumulative score greater than 8 on the NOS indicated a low risk of bias in the studies evaluated. 16 For bias assessments, the NOS checklist assessed selection, comparability, and outcome, with a cumulative score of 9 applicable to retrospective cohort studies. Studies that achieved an overall evaluation score exceeding 6 were classified as high quality.

Statistical Analysis

Data extraction and effect size calculations were conducted using STATA 16.0 (Stata Corp, TX, USA) and RevMan 5.3 (Nordic Cochrane Center, Copenhagen, Denmark). Endnote 7 (Clarivate Analytics, London, UK) was employed to identify duplicate entries among the enrolled studies. When no significant heterogeneity was detected among studies, a fixed-effect model was chosen for aggregating pooled results. The pooled SEN and SPE, summary receiver operating characteristics (SROC), AUC, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) along with corresponding 95% CIs were calculated. In the prognostic meta-analysis, the hazard ratios (HRs) and their respective 95% CIs were analyzed. To assess potential publication bias across the studies, Deek's funnel plot asymmetry test, visual funnel plot, and the Egger and Begg tests were conducted, with a p-value less than 0.05 being considered statistically significant.

Results

Literature Search

The literature search yielded a total of 2228 publications from various online databases. After removing duplicates, 969 articles remained. An initial screening of titles and abstracts led to the exclusion of 292 articles. From the remaining 385 publication, a full-text eligibility evaluation identified 156 non-randomized controlled trials, 107 animal studies and 49 reviews, all of which were subsequently discarded. Ultimately, 73 studies were included in the final analysis: 40 studies focused on diagnosis,17–56 and 63 studies on prognosis,17–22,24,26,29,31–39,41,43–49,51–53,55–88 as detailed in the PRISMA standards (Figure 1).

Figure 1.

Figure 1.

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Study enrollment procedure in terms of the standards of the PRISMA diagram. PRISMA: preferred reporting items for systematic reviews and meta-analyses.

Study Characteristics and Quality Assessment

The main characteristics of studies included in the diagnostic and prognostic analysis are summarized in Table 1 and Table 2. The publication years for these studies ranged from 2018 to 2024, encompassing a pooled patient population of 11,366 individuals. This included 4314 cases for the diagnostic synthesis and 7052 cases with survival data for the prognostic meta-analysis. Additionally, 1794 and 4289 healthy controls were included in the diagnostic and prognostic synthesis, respectively. The follow-up period varied from 10 to 300 months, providing a robust foundation for our analyses, as shown in supplementary Figure S1 and Tables S1 and S2.

Table 1.

Main Characteristics of Studies for Diagnosis Analysis.

First author Publish Year Country CircRNA Cancer Type Case Control Regula-tion SEN SPE AUC TP FP FN TN Cutoff Value
Cao et al 17 2020 China Circ_0009910 CML 30 35 U 0.660 0.950 0.831 20 2 10 33 1.729
Chen et al 18 2020 China circ-CDYL MM 72 13 U 0.817 0.982 0.900 59 0 13 13 NA
Chen et al 19 2021 China circ-PVT1 AML 68 30 U 0.721 0.900 0.920 49 3 19 27 NA
Gao et al 20 2019 China circ_0007841 MM 86 30 U 0.815 0.829 0.907 70 5 16 25 NA
Gharib et al 21 2023 Canada CircKAT6A CLL 100 39 U 0.667 0.763 0.783 67 9 33 30 0.7262
Gharib et al 21 2023 Canada CircLNPEP CLL 100 39 U 0.627 0.711 0.750 63 11 37 28 0.7262
Gharib et al 21 2023 Canada CircMDM2 CLL 100 39 U 0.653 0.790 0.803 65 8 35 31 0.7262
Gharib et al 21 2023 Canada CircMYH9 CLL 100 39 U 0.587 0.737 0.750 59 10 41 29 0.7262
Gong et al 22 2021 China circ_0087776 MM 136 74 D 0.728 0.676 0.722 99 24 37 50 NA
Guo et al 23 2020 China circ_0012152 ALL 43 8 D 0.775 0.886 0.863 33 1 10 7 NA
Guo et al 23 2020 China circ_0001857 ALL 43 8 U 0.950 0.600 0.735 41 3 2 5 NA
Guo et al 23 2020 China circ_0012152 AML 49 8 U 0.977 1.000 0.977 47 3 2 5 NA
Guo et al 23 2020 China circ_0001857 AML 49 8 D 0.875 0.909 0.909 43 1 6 7 NA
Guo et al 24 2022 China circ_0079480 AML 118 118 U 0.822 0.825 0.913 97 10 21 108 NA
Guo et al 23 2020 China circ_0001857 AML 43 8 U 0.875 0.909 0.909 38 1 5 7 NA
Guo et al 23 2020 China circ_0012152 AML 43 8 D 0.775 0.886 0.863 37 1 10 7 NA
Han et al 25 2020 China circ0001947 AML 106 15 U 0.933 0.733 0.891 99 4 7 11 NA
Hu et al 26 2019 China circ-APC LBCL 27 16 D 0.888 0.753 0.888 24 4 3 12 NA
Huang et al 27 2022 China CircNFIX AML 47 40 U 0.830 0.975 0.927 39 1 8 39 NA
Li et al 29 2022 China circZNF91 CLL 45 20 U 0.690 0.850 0.812 31 3 14 17 NA
Li et al 30 2023 China Circ_0001946 AML 50 50 U 0.680 0.753 0.760 34 12 16 38 NA
Li et al 31 2024 China circXPO1 MM 54 17 U 0.842 0.668 0.815 45 6 9 11 NA
Li et al 28 2017 China Circ_0004277 AML 107 8 D 0.950 0.900 0.957 102 1 5 7 2
Lin et al 32 2021 China circPLXNB2 AML 40 15 U 0.930 0.630 0.853 37 6 3 9 NA
Liu et al 35 2022 China circ_0044907 AML 45 45 U 0.778 0.889 0.945 35 5 10 40 NA
Liu et al 33 2019 China Circ-SMARCA5 MM 105 36 D 0.933 0.417 0.714 98 21 7 15 2.242
Liu et al 34 2021 China CircRNF220 AML 87 5 U 0.853 0.940 0.926 74 0 13 5 NA
Luo et al 36 2020 China CircMYC MM 122 54 U 0.808 0.902 0.924 99 5 23 49 1.83
Ma 37 2022 China Circ_0059706 AML 100 33 D 0.913 0.856 0.925 91 5 9 28 0.254
Ma 38 2022 China Circ_0059707 AML 94 23 D 1.000 0.660 0.984 94 8 0 15 0.0084
Mei et al 39 2019 China circ-CLow-expressionYL MCL 18 17 U 0.667 0.941 0.856 12 1 6 16 NA
Shen et al 40 2022 China circ-ANAPC7 AML 18 18 U 0.870 0.850 0.915 15 2 3 16 NA
Su et al 41 2020 China Circ_0002232 AML 115 48 D 0.813 0.757 0.851 93 12 22 36 0.165
Sun et al 42 2021 China circ-G042080 MM 20 5 U 0.908 0.877 0.909 18 1 2 4 2.18
Wu et al 43 2020 China Circ-RPL15 CLL 137 50 U 0.700 0.850 0.840 96 7 41 43 NA
Wu et al 27 2022 China Circ_0009910 AML 37 37 U 0.811 0.865 0.916 30 5 7 32 NA
Wu et al 45 2023 China circTET2 CLL 69 25 U 0.725 0.713 0.760 50 7 19 18 NA
Xia et al 46 2018 China Circ-CBFB CLL 47 21 U 0.750 0.900 0.800 35 2 12 19 NA
Xiang et al 47 2022 China circ_0000190 MM 67 19 D 0.881 0.319 0.655 59 13 8 6 NA
Yan et al 48 2023 China circRNA_100199 AML 114 86 D 0.703 0.847 0.862 80 13 34 73 NA
Yang et al 49 2023 China Circ_0001187 AML 24 24 D 0.798 0.946 0.906 19 1 5 23 NA
Yang et al 50 2023 China circRNA_0010984 AML 13 13 U 0.764 0.853 0.8 10 2 3 11 NA
Ye et al 51 2023 China circ_0032891 AML 40 10 U 0.802 0.654 0.783 32 3 8 7 NA
Ye et al 51 2023 China circ_0076995 AML 40 10 U 0.599 0.873 0.778 24 1 16 9 NA
Ye et al 51 2023 China circ_0014352 AML 40 10 U 0.693 0.652 0.735 28 3 12 7 NA
Ye et al 51 2023 China circ_0047663 AML 40 10 U 0.593 0.774 0.753 24 2 16 8 NA
Ye et al 51 2023 China circ_0025593 AML 40 10 U 0.491 0.924 0.670 20 1 20 9 NA
Ye et al 51 2023 China circ_0007444 AML 40 10 D 0.888 0.548 0.783 36 5 4 5 NA
Ye et al 51 2023 China circ_0001684 AML 40 10 D 0.982 0.511 0.795 39 5 1 5 NA
Ye et al 51 2023 China circ_0006199 AML 40 10 D 0.990 0.297 0.638 40 7 0 3 NA
Ye et al 51 2023 China circ_0000544 AML 40 10 D 0.691 0.664 0.713 28 3 12 7 NA
Ye et al 51 2023 China circ_0005354 AML 40 10 D 0.483 0.846 0.705 19 2 21 8 NA
Yi et al 52 2019 China Circ-vimentin AML 113 42 U 0.600 0.900 0.741 68 4 45 38 2.003
Yu et al 53 2021 China circ-MYBL2 MM 89 23 D 0.992 0.606 0.839 88 9 1 14 NA
Zhong et al 54 2021 China circ_0058493 CML 48 42 U 0.725 0. 620 0.723 36 15 14 25 0.007886
Zhou et al 55 2019 China Circ-Foxo3 AML 116 24 D 0.62 0.75 0.633 72 6 44 18 0.233
Zhou et al 56 2020 China Circ-PTK2 MM 60 30 U 0.667 0.885 0.814 40 3 20 27 NA
Zhou et al 56 2020 China Circ-RNF217 MM 60 30 U 0.303 0.982 0.672 18 1 42 29 NA
Zhou et al 56 2020 China Circ-RERE MM 60 30 U 0.670 0.689 0.725 40 9 20 21 NA
Zhou et al 56 2020 China Circ-NAGPA MM 60 30 U 0.452 0.989 0.804 27 0 33 30 NA
Zhou et al 56 2020 China Circ-KCNQ5 MM 60 30 U 0.521 0.811 0.704 31 6 29 24 NA
Zhou et al 56 2020 China circ-AFF2 MM 60 30 D 0.873 0.625 0.757 52 11 8 19 NA
Zhou et al 56 2020 China circ-WWC3 MM 60 30 D 0.790 0.607 0.773 47 12 13 18 NA
Zhou et al 56 2020 China circ-Low-expressionNAJC5 MM 60 30 D 0.633 0.637 0.672 38 11 22 19 NA
Zhou et al 56 2020 China Circ-KLHL2 MM 60 30 D 0.550 0.752 0.677 33 7 27 23 NA
Zhou et al 56 2020 China circ-IQJAP1 MM 60 30 D 0.784 0.690 0.758 47 9 13 21 NA
Zhou et al 56 2020 China circ-AL137655 MM 60 30 D 0.659 0.672 0.708 40 10 20 20 NA
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CML, chronic myeloid leukemia; CLL, chronic lymphocytic leukemia; AML, acute myelogenous leukemia; DLBCL, diffuse large B cell lymphoma; MCL, mantle cell lymphoma; FL, follicular lymphoma; BL, Burkitt lymphoma; MM, multiple myeloma; U, upregulated expression; D, downregulated expression; SEN, sentivity; SPE, specifity; AUC, area under the curve; TP, true positive; FP, false positive; FN, false negative; TN, true negative.

Table 2.

Clinical Characteristics of Articles Enrolled in the Prognosis Analysis

Author Year Country CircRNA Cancer Regulation Cutoff value Follow-up (m) HR HR UL
Type Case Control
Cao et al 17 2020 China Circ_0009910 CML 30 35 U 1.729 60 2. 2.00 0.940 5.170
Chen et al 19 2021 China circ-PVT1 AML 60 30 U NA NA 1.688 1.057 2.696
Chen et al 58 2024 China Circ_0012152 AML 247 40 U 8.63 2.651 1.374 5.114
Chen1 et al 57 2020 China Circ_0069767 MM 66 21 U NA 60 1.000 0.950 1.050
Chen2 et al 18 2020 China circ-CDYL MM 72 13 U NA 60 1.000 0.960 1.040
Dahl et al 59 2022 Denmark circSCORE MCL 163 156 U? NA NA 3.600 1.600 8.300
Du et al 60 2023 China circRNA-001264 AML 50 50 U NA NA 1.000 0.964 1.039
Fang et al 61 2021 China CircRERE MM 125 30 U NA 60 1.240 0.660 2.330
Feng et al 62 2019 China circ_0000190 MM 47 10 D NA 40 1.000 0.960 1.040
Feng et al 63 2020 China CircHIPK3 CML 100 100 U NA NA 4.630 1.460 14.590
Gao et al 20 2019 China circ_0007841 MM 86 30 U NA 60 3.090 1.190 8.020
Gharib1 et al 21 2023 Canada CircKAT6A CLL 100 39 U 0.7262 58 3.156 2.225 4.306
Gharib2 et al 21 2023 Canada CircLNPEP CLL 100 39 U 0.7262 58 4.113 2.346 5.288
Gharib3 et al 21 2023 Canada CircMDM2 CLL 100 39 U 0.7262 58 2.309 1.884 4.093
Gharib4 et al 21 2023 Canada CircMYH9 CLL 100 39 U 0.7262 58 3.259 1.847 4.303
Gong et al 22 2021 China circ_0087776 MM 136 74 D NA NA 4.191 2.371 7.406
Guo et al 24 2022 China circ_0079480 AML 118 118 U NA NA 1.000 0.980 1.030
Hu et al 26 2019 China circ-APC DLBCL 80 80 D NA 120 0.511 0.374 0.837
Lei et al 64 2019 China Circ_0009910 AML 70 70 U NA 80 2.200 0.940 5.170
Lei et al 65 2021 China Circ_100053 CML 150 100 U NA 30 3.210 1.322 7.797
Li et al 66 2021 China circ_POLA2 AML 50 50 U NA 100 0.660 0.390 1.090
Li et al 29 2022 China circZNF91 CLL 45 20 U NA 83 1.010 0.940 1.080
Li et al 67 2023 China CircZBTB46 AML 116 124 U 36 0.780 0.430 1.420
Li et al 31 2024 China circXPO1 MM 27 27 U NA NA 1.000 0.960 1.040
Lin1 et al 32 2021 China circPLXNB2 AML 40 15 U NA 50 0.590 0.120 2.920
Lin2 et al 68 2021 China circTASP1 AML 30 30 U NA NA 20.778 5.268 81.953
Liu et al 33 2019 China Circ-SMARCA5 MM 105 36 D 2.242 5.0–36.0 0.345 0.127 0.941
Liu et al 69 2020 China circITCH MM 56 56 D NA 60 1.000 0.960 1.040
Liu et al 35 2022 China circ_0044907 AML 45 45 U NA NA 0.999 0.962 1.038
Liu1 et al 70 2021 China circ_0001821 MM 115 115 U NA 60 1.802 1.0190 2.940
Liu2 et al 34 2021 China CircRNF220 AML 87 5 U NA NA 5.866 1.230 27.969
Luo et al 36 2020 China CircMYC MM 122 54 U 1.83 60 3.640 2.240 5.380
Ma1 et al 37 2022 China Circ_0059706 AML 100 33 D 0.254 NA 0.340 0.166 0.699
Ma2 etal 38 2022 China Circ_0059707 AML 94 23 D 0.0084 NA 0.441 0.210 0.929
Mei et al 39 2019 China CircCDYL MCL 18 17 U NA 25 1.000 0.940 1.060
Papaioannou1 et al 71 2020 USA circKLHL8 AML 365 365 U NA NA 0.530 0.320 0.880
Papaioannou2 et al 71 2020 USA circFCHO2 AML 365 365 U NA NA 0.450 0.270 0.750
Shang et al 72 2021 China circ_0012152 AML 60 60 U NA 60 0.540 0.190 1.520
Shen et al 40 2022 China circ-ANAPC7 AML 18 18 U NA NA 1.000 0.960 1.040
Sheng et al 74 2023 China circPVT1 AML 23 20 U NA 50 1.000 0.970 1.030
Su et al 41 2020 China Circ_0002232 AML 115 48 D NA 100 1.817 1.024 3.223
Sun1 et al 75 2024 China circNBAS AML 6 6 U NA NA 1.000 0.960 1.040
Sun2 et al 75 2024 China circKAT6A AML 98 98 U NA NA 1.000 0.960 1.040
Sun3 et al 75 2024 China circRABEP1 AML 98 98 U NA NA 0.9999 0.963 1.039
Tan et al 76 2021 China F-circBA1 CML 10 10 U NA 3.5 1.050 0.1600 6.760
Wang et al 77 2020 China circ_0007841 MM 52 25 U NA 60 1.000 0.950 1.050
Wang et al 78 2021 China circGMDS AML 218 218 U NA NA 1.592 1.067 2.377
Wang et al 79 2022 China circ_0040823 AML 68 30 D NA NA 2.198 1.2228 3.9527
Wei et al 80 2023 China circSLC25A13 AML 120 65 U NA NA 1.504 1.023 2.213
Wu et al 83 2022 China CircRIC8B CLL 32 31 U 0.3 NA 0.239 0.131 0.438
Wu et al 45 2023 China circTET2 CLL 69 25 U NA NA 3.666 3.5346 3.803
Wu1 et al 43 2020 China Circ-RPL15 CLL 137 50 U NA 300 1.869 1.105 3.161
Wu2 et al 81 2020 China circRNAmc-COX2 CLL 54 40 U NA 10 1.570 0.500 4.940
Wu3 et al 82 2021 China circ-KEL AML 116 40 U NA 80 1.820 1.090 3.010
Wu4 et al 44 2021 China Circ_0009910 AML 37 37 U NA NA 0.654 0.238 1.797
Xia et al 46 2018 China Circ-CBFB CLL 47 21 U NA 114 2.062 0.808 5.263
Xiang et al 47 2022 China circ_0000190 MM 86 30 D NA 48 0.351 0.128 0.961
Xiong et al 84 2023 China circ-SPI1 AML 80 20 U NA 60 3.554 1.286 9.822
Yan et al 48 2023 China circRNA_100199 AML 114 86 D NA NA 0.999 0.962 1.038
Yang et al 49 2023 China Circ_0001187 AML 57 57 D NA NA 1.001 0.968 1.035
Yang et al 85 2024 China circFAM193B AML 40 40 U NA NA 2.435 1.571 3.775
Ye et al 86 2022 China circ_0003602 AML 50 50 U NA 60 1.000 0.96 1.04
Ye1 et al 51 2023 China circ_0032891 AML 40 10 U NA NA 0.999 0.962 1.039
Ye10 et al 51 2023 China circ_0076995 AML 40 10 U NA NA 0.999 0.962 1.038
Ye2 et al 51 2023 China circ_0014352 AML 40 10 U NA NA 0.999 0.962 1.037
Ye3 et al 51 2023 China circ_0047663 AML 40 10 U NA NA 1.000 0.963 1.039
Ye4 et al 51 2023 China circ_0025593 AML 40 10 U NA NA 1.000 0.963 1.039
Ye5 et al 51 2023 China circ_0007444 AML 40 10 D NA NA 1.000 0.964 1.037
Ye6 et al 51 2023 China circ_0001684 AML 40 10 D NA NA 1.000 0.963 1.039
Ye7 et al 51 2023 China circ_0006199 AML 40 10 D NA NA 0.999 0.962 1.038
Ye8 et al 51 2023 China circ_0000544 AML 40 10 D NA NA 1.000 0.963 1.039
Ye9 et al 51 2023 China circ_0005354 AML 40 10 D NA NA 0.999588958 0.962271907 1.038353169
Yi et al 52 2019 China Circ-vimentin AML 113 42 U 2.003 10 2.034 1.016 4.069
Yi et al 87 2021 China Circ-PTK2 AML 40 40 U NA NA 3.210 0.440 23.110
Yu et al 53 2021 China circ-MYBL2 MM 89 23 D NA 50 1.000 0.960 1.040
Zhang et al 88 2022 China circ_0002078 CLL 49 14 U NA 18 0.515 0.177 1.495
Zhou 1et al 56 2020 China Circ-RERE MM 60 30 U NA 40 1.000 0.900 1.100
Zhou 2et al 56 2020 China Circ-NAGPA MM 60 30 U NA 40 1.000 0.900 1.110
Zhou 3et al 56 2020 China Circ-KCNQ5 MM 60 30 U NA 40 1.000 0.900 1.110
Zhou 4et al 56 2020 China circ-AFF2 MM 60 30 D NA 40 1.000 0.860 1.160
Zhou 5et al 56 2020 China circ-WWC3 MM 60 30 D NA 40 1.000 0.900 1.110
Zhou 6et al 56 2020 China circ-DNAJC5 MM 60 30 D NA 40 1.000 0.900 1.110
Zhou 7et al 56 2020 China Circ-KLHL2 MM 60 30 D NA 40 1.000 0.900 1.110
Zhou 8et al 56 2020 China circ-IQJAP1 MM 60 30 D NA 40 1.000 0.900 1.110
Zhou 9et al 56 2020 China circ-AL137655 MM 60 30 D NA 40 1.000 0.900 1.110
Zhou et al 55 2019 China Circ-Foxo3 AML 116 24 D 0.233 8.5 1.208 0.664 2.200
Zhou10 et al 56 2020 China Circ-PTK2 MM 60 30 U NA 40 1.000 0.880 1.130
Zhou11 et al 56 2020 China Circ-RNF217 MM 60 30 U NA 40 1.000 0.900 1.120
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CML, chronic myeloid leukemia; CLL, chronic lymphocytic leukemia; AML, acute myelogenous leukemia; DLBCL, diffuse large B cell lymphoma; MCL, mantle cell lymphoma; FL, follicular lymphoma; BL, burkitt lymphoma; MM, multiple myeloma; MCL, Mantle cell lymphoma; U, upregulated expression; D, downregulated expression; qRT-PCR, real-time polymerase chain reaction; OS, overall survival; HR, hazard ratio; LL, lower limit; UL, upper limit; m, months.

Diagnostic Accuracy Analysis

A total of 39 publications were identified that examined the clinical value of circRNAs in diagnosis. These studies included 4314 cases of leukemia and 1794 control cases. The forest plots illustrating pooled SEN, SPE, SROC curve and Fagan's plot are displayed in Figure 2. Specifically, the pooled SEN and SPE were each found to be 0.79 (95% CI, 0.75–0.83). The SROC curve illustrated an AUC was 0.86 (95% CI, 0.83–0.89) (Figure 2C). Using Fagan's nomogram, a pre-test probability of 20% was determined (Figure 2D). In supplementary Figure S1, the forest plots of pooled PLR, NLR, diagnostic score and DOR were presented. The PLR and NLR were calculated to be 3.82 (95% CI, 3.23–4.52) and 0.26 (95% CI, 0.22–0.32), respectively, (Figure S1A). The summary assessment found that the circRNA diagnostic score leukemia was 2.67 (95% CI, 2.42–2.92)with a DOR of 14.43 (95% CI, 11.21 −18.59) (Figure S1B). A likelihood ratio scattergram was employed to evaluate the clinical utility of various diagnostic tests, which were categorized into four quadrants (Figure S1C). Most studies fell into the lower right quadrants, indicating a consistent trend of circRNAs being useful for diagnosing hematologic malignancies, although one study showed an outlier. These results strongly suggest that circRNAs are promising diagnostic tools for detecting of hematologic malignancies, enhancing diagnostic accuracy and overall clinical management.

Figure 2.

Figure 2.

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Diagnostic accuracy analysis. Forest plots of pooled sensitivity (A), specificity (B), SROC curve (C) and Fagan's plot (D) for the overall combined diagnostic effect size. SROC: summary receiver operating characteristic.

Diagnostic and Prognostic Value of circRNA Expression Inhematologic Malignancy

The relative ratio (RR) and corresponding 95%CI from 39 diagnostic studies yielded a pooled RR of 2.12 (95% CI: 2.01–2.22, P < 0.001) (Figure 3A). For overall survival analysis, data from 65 studies were used to calculate HRs based on Kaplan-Meier curves. The analysis of oncogenic and tumor-suppressor circRNAs provided a pooled HR of 1.02 (95% CI: 1.00–1.05, P < 0.00001) (Figure 3B).

Figure 3.

Figure 3.

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Diagnostic and prognostic value of circRNA expression in cancer. Diagnostic value (RR) of circRNA expression in different cancer (A). Forest plots for HR according to circRNA expression in cancer(B). circRNA: circular RNAs; RR: risk ratio; HR: Hazard ratio.OS: overall survival.

Influence Analysis and Subgroup Study

Influence analysis was conducted to identify outlier values among combined effect sizes. The fixed effect estimates indicated no outlier in either in diagnostic and prognostic studies (Figure 4A and 4B), reinforcing the reliability of our results. A stratified analysis based on clinicopathologic features revealed relationships between circRNA expression and clinical characteristics in diagnosing and prognosing patients with leukemia (Figure 5). In addition, further evaluation of the reasons for influence analysis in diagnosis and prognosis revealed that there were diagnostic abilities in different subgroups (the RR value of myeloid leukemia: 2.37, 95% CI:2.19–2.57, P < 0.001; circRNA of lymphoid leukemia: 1.86, 95% CI: 1.70–2.03, P = 0.026; circRNA of multiple myeloma: 2.01, 95% CI:1.84–2.19) (Figure 5A). Additionally, varying prognoses in different subgroups were observed: myeloid leukemia: HR value of 1.01 (95% CI: 0.99–1.03, P < 0.001); lymphoid leukemia: HR value of 1.25 (95% CI: 1.10–1.43, P < 0.001); multiple myeloma: HR value of 1.01 (95% CI:0.98–1.05) (Figure 5B). These findings demonstrate significant correlations between clinicopathological factors and circRNA expression levels in patients with leukemia.

Figure 4.

Figure 4.

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Sensitivity analysis. The sensitivity analysis of the overall combined diagnostic meta-analysis (A) and prognostic meta-analysis (B).

Figure 5.

Figure 5.

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Subgroup analysis of diagnostic and prognostic meta-analysis in different types of hematological malignancies. Subgroup study for RR value and OS according to different types of hematological malignancies in diagnostic meta-analysis (A) and prognostic meta-analysis (B). RR: relative risk; OS: overall survival.

Publication Bias and Sensitivity Analyses

The Deeks’ test revealed no evidence of publication bias in the diagnostic studies (P = 0.29, Figure 6A). Egger's tests of diagnostic and prognostic articles indicated no publication biases, as depicted in Figures 6B and 6C. Quality evaluations of diagnostic and prognostic accuracy for the studies included in the analysis are shown in figure S2. L’Abbe and Galbraith plots asymmetry tests further confirmed the absence of publication bias in diagnostic studies (Figures S2A and S2B), while Galbraith plot asymmetry for prognostic studies is displayed in Figure S2C.

Figure 6.

Figure 6.

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Funnel plots of publication bias. Deeks’ funnel plot asymmetry test and Egger’ s funnel plot for diagnostic studies (A and B); Egger’ s funnel plot for prognostic tests (C).

Discussion

The diagnosis of malignant leukemia continues to rely on biomarker, yet the SEN and SPE of these markers often fall short of optimal standards. As a result, many patients are diagnosed only at advanced stages of the disease, when leukemia is associated with higher mortality rates and significant financial burdens. Thus, the development of highly sensitive biomarkers is critical for the early detection of hematologic malignancies. In recent years, circular RNAs (circRNAs) have emerged as promising biomarkers in various types of malignant tumors, including liver and bladder cancers. The unique closed-loop structure of circRNAs contributes to their stability compared to linear RNA, making them attractive candidates for diagnostic and prognostic applications.89,90 While there was a traditional review of circRNAs in hematological malignancies in 2019, it did not adhere to the relevant Equator network guidelines and lacked comprehensive visual representation. 91 Previous meta-analyses have focused on specific conditions like multiple myeloma, 92 but our study represents the first comprehensive meta-analysis examining circRNAs across all hematological malignancies. Our findings reveal the potential of circRNAs as effective diagnostic and prognostic biomarkers for hematologic malignancies in clinical settings. If circRNAs can be validated as reliable markers for early diagnosis, both patients and clinicians stand to benefit significantly from their implementation. In summary, the integration of circRNAs into diagnostic and prognostic frameworks could enhance early detection strategies for malignant leukemia, ultimately improving patient outcomes and reducing the financial impact of late-stage disease diagnosis. Further research is essential to fully understand the clinical applications of circRNAs in hematologic malignancies.

This meta-analysis was constructed using 73 qualifying studies retrieved from various databases.To our best knowledge, this represents the inaugural meta-analytic examination of hematologic malignancies. Our synthesized meta-analytic metrics for circRNAs—SEN, SPE, and DOR were 79.00%, 79.00%, and 14.43, respectively. SEN and SPE are pivotal indicators of diagnostic accuracy, while the DOR encapsulates the combined impact of both. The DOR value of 14.43 for circRNAs indicates a highly significant diagnostic utility for hematologic malignancies patients. The PLR and NLR were 3.82 and 0.26, respectively. The PLR implies that hematologic malignancies patients are 14.43 times more likely to test positive for circRNAs than healthy individuals, while the NLR suggests a 26.00% chance of a false-negative outcome from circRNA signature detection. The AUC or the circRNA's combined receiver operating characteristic (ROC) curve in diagnosing hematologic malignancies was 0.86. The AUC of SROC is a measure of the overall diagnostic test performance, with values ranging from 0.75 to 0.92 considered acceptable and indicative of excellent performance. The AUC indicates that circRNAs may be reliable for diagnosing hematologic malignancy diagnostic. But publication bias was detected across the studies included in this meta-analysis, thus subgroup was analyzed in diagnostic and prognostic articles. Myeloid, lymphoid leukemia, and multiple myelom circRNA showed that they were different. Specially, lymphoid leukemia had a negative impacct on prognosis (HR = 1.25, 95% CI: 1.10–1.43, P < 0.001). While Subgroup analysis based on circRNA expression levels were conducted for prognostic studies, revealing that down-expression an overexpression of circRNAs was associated with no different prognosis (Figure S3). The impact of circRNAs on prognosis varied by geographic origin, with foreign studies a negative effect (HR = 1.88, 95% CI: 0.99–3.57, P < 0.001; Figure S4). Additionally, circRNAs of different leukemia were significantly different in different years. The circRNA is bad favorable prognosis in 2021 and 2022. (HR: 1.61 and 0.98, 95% CI: 1.16–2.24 and 0.92–1.04, P < 0.05, respectively; Figure S5). Thus, circRNAs may emerge as potential markers for both diagnosis and prognosis in hematological malignancies. This study, however, has several limitations. Firstly, the data mainly originated from China, with limited representation from other regions. Secondly, the study is at the manuscript level rather than the patient level, precluding access to detailed raw data from the authors. Thirdly, demographic factors such as gender, age, and ethnicity may influence the interpretation of the meta-analysis results. Lastly, the studies on circRNAs as diagnostic and prognostic biomarkers in leukemia are relatively recent, and the number of studies could affect the meta-analysis outcomes.

The current meta-analysis indicated that circRNAs have the potential to serve as valuable diagnostic and prognostic markers in hematologic malignancies. Additionally, the molecular mechanism of circRNAs in leukemia necessitates further investigation. Thus, future large-scale prospective studies are essential to definitively establish reliable diagnostic and prognostic biomarkers for hematologic malignancy. Consequently, it is hypothesized that circRNAs could be utilized by clinician for precise diagnostics and prognostics in the future.

Supplemental Material

sj-doc-1-tct-10.1177_15330338241285149 - Supplemental material for Circular RNA as Diagnostic and Prognostic Biomarkers in Hematological Malignancies:Systematic Review
sj-doc-1-tct-10.1177_15330338241285149.doc (9.7MB, doc)

Supplemental material, sj-doc-1-tct-10.1177_15330338241285149 for Circular RNA as Diagnostic and Prognostic Biomarkers in Hematological Malignancies:Systematic Review by Liyun Gao, Junfei Fan, Jiayin He, Wenyan Fan, Xiangxin Che, Xin Wang and Chunhua Han in Technology in Cancer Research & Treatment

Acknowledgements

Not applicable.

Footnotes

Availability of Data and Materials: Data and materials will be provided to those who are interested in this meta-analysis by the correspondence.

Authors’ Contributions: Liyun Gao contributed to the study conception. Materials were prepared by Junfei Fan. Data collection and analysis were performed by Jiayin He. The first draft of the manuscript was written by Xiangxin Che. Wenyan Fan and Xin Wang modified the manuscript. Chunhua Han designed the study. All authors read and approved the final manuscript.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the grants of the National Natural Science Foundation of China (81502843, 81360447), Jiangxi Natural Science Foundation Project (20202BAB206067, 20232BAB206141), Jiujiang base and talent plan-high level scientific and technological innovation talent project (S2020QNZZ011), Future project of Jiujiang administration of traditional Chinese medicine (2021b697), Special Research Project of Jiangxi Cognitive Science and Interdisciplinary Research Center (RZYB202206), Science and Technology Plan Project of Jiangxi Provincial Health Commission (SKJP220225877, 202311158). Innovation and Entrepreneurship Training Program for College Students (202311843003), Department of Science and Technology of Hubei Province with the Project (2022BCE045), Talent Introduction Projects of Hubei Polytechnic University (22xjz16R).

Ethics Approval and Consent to Participate: Our study was approved by The Mercy Health Research Ethics Committee (approval no. XYZ123).

Patient Consent for Publication: All patients provided written informed consent prior to enrollment in the study.

Supplemental Material: Supplemental material for this article is available online.

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Supplementary Materials

sj-doc-1-tct-10.1177_15330338241285149 - Supplemental material for Circular RNA as Diagnostic and Prognostic Biomarkers in Hematological Malignancies:Systematic Review
sj-doc-1-tct-10.1177_15330338241285149.doc (9.7MB, doc)

Supplemental material, sj-doc-1-tct-10.1177_15330338241285149 for Circular RNA as Diagnostic and Prognostic Biomarkers in Hematological Malignancies:Systematic Review by Liyun Gao, Junfei Fan, Jiayin He, Wenyan Fan, Xiangxin Che, Xin Wang and Chunhua Han in Technology in Cancer Research & Treatment

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