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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.

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 al¹⁷	2020	China	Circ_0009910	CML	30	35	U	0.660	0.950	0.831	20	2	10	33	1.729
Chen et al¹⁸	2020	China	circ-CDYL	MM	72	13	U	0.817	0.982	0.900	59	0	13	13	NA
Chen et al¹⁹	2021	China	circ-PVT1	AML	68	30	U	0.721	0.900	0.920	49	3	19	27	NA
Gao et al²⁰	2019	China	circ_0007841	MM	86	30	U	0.815	0.829	0.907	70	5	16	25	NA
Gharib et al²¹	2023	Canada	CircKAT6A	CLL	100	39	U	0.667	0.763	0.783	67	9	33	30	0.7262
Gharib et al²¹	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 al²¹	2023	Canada	CircMYH9	CLL	100	39	U	0.587	0.737	0.750	59	10	41	29	0.7262
Gong et al²²	2021	China	circ_0087776	MM	136	74	D	0.728	0.676	0.722	99	24	37	50	NA
Guo et al²³	2020	China	circ_0012152	ALL	43	8	D	0.775	0.886	0.863	33	1	10	7	NA
Guo et al²³	2020	China	circ_0001857	ALL	43	8	U	0.950	0.600	0.735	41	3	2	5	NA
Guo et al²³	2020	China	circ_0012152	AML	49	8	U	0.977	1.000	0.977	47	3	2	5	NA
Guo et al²³	2020	China	circ_0001857	AML	49	8	D	0.875	0.909	0.909	43	1	6	7	NA
Guo et al²⁴	2022	China	circ_0079480	AML	118	118	U	0.822	0.825	0.913	97	10	21	108	NA
Guo et al²³	2020	China	circ_0001857	AML	43	8	U	0.875	0.909	0.909	38	1	5	7	NA
Guo et al²³	2020	China	circ_0012152	AML	43	8	D	0.775	0.886	0.863	37	1	10	7	NA
Han et al²⁵	2020	China	circ0001947	AML	106	15	U	0.933	0.733	0.891	99	4	7	11	NA
Hu et al²⁶	2019	China	circ-APC	LBCL	27	16	D	0.888	0.753	0.888	24	4	3	12	NA
Huang et al²⁷	2022	China	CircNFIX	AML	47	40	U	0.830	0.975	0.927	39	1	8	39	NA
Li et al²⁹	2022	China	circZNF91	CLL	45	20	U	0.690	0.850	0.812	31	3	14	17	NA
Li et al³⁰	2023	China	Circ_0001946	AML	50	50	U	0.680	0.753	0.760	34	12	16	38	NA
Li et al³¹	2024	China	circXPO1	MM	54	17	U	0.842	0.668	0.815	45	6	9	11	NA
Li et al²⁸	2017	China	Circ_0004277	AML	107	8	D	0.950	0.900	0.957	102	1	5	7	2
Lin et al³²	2021	China	circPLXNB2	AML	40	15	U	0.930	0.630	0.853	37	6	3	9	NA
Liu et al³⁵	2022	China	circ_0044907	AML	45	45	U	0.778	0.889	0.945	35	5	10	40	NA
Liu et al³³	2019	China	Circ-SMARCA5	MM	105	36	D	0.933	0.417	0.714	98	21	7	15	2.242
Liu et al³⁴	2021	China	CircRNF220	AML	87	5	U	0.853	0.940	0.926	74	0	13	5	NA
Luo et al³⁶	2020	China	CircMYC	MM	122	54	U	0.808	0.902	0.924	99	5	23	49	1.83
Ma³⁷	2022	China	Circ_0059706	AML	100	33	D	0.913	0.856	0.925	91	5	9	28	0.254
Ma³⁸	2022	China	Circ_0059707	AML	94	23	D	1.000	0.660	0.984	94	8	0	15	0.0084
Mei et al³⁹	2019	China	circ-CLow-expressionYL	MCL	18	17	U	0.667	0.941	0.856	12	1	6	16	NA
Shen et al⁴⁰	2022	China	circ-ANAPC7	AML	18	18	U	0.870	0.850	0.915	15	2	3	16	NA
Su et al⁴¹	2020	China	Circ_0002232	AML	115	48	D	0.813	0.757	0.851	93	12	22	36	0.165
Sun et al⁴²	2021	China	circ-G042080	MM	20	5	U	0.908	0.877	0.909	18	1	2	4	2.18
Wu et al⁴³	2020	China	Circ-RPL15	CLL	137	50	U	0.700	0.850	0.840	96	7	41	43	NA
Wu et al²⁷	2022	China	Circ_0009910	AML	37	37	U	0.811	0.865	0.916	30	5	7	32	NA
Wu et al⁴⁵	2023	China	circTET2	CLL	69	25	U	0.725	0.713	0.760	50	7	19	18	NA
Xia et al⁴⁶	2018	China	Circ-CBFB	CLL	47	21	U	0.750	0.900	0.800	35	2	12	19	NA
Xiang et al⁴⁷	2022	China	circ_0000190	MM	67	19	D	0.881	0.319	0.655	59	13	8	6	NA
Yan et al⁴⁸	2023	China	circRNA_100199	AML	114	86	D	0.703	0.847	0.862	80	13	34	73	NA
Yang et al⁴⁹	2023	China	Circ_0001187	AML	24	24	D	0.798	0.946	0.906	19	1	5	23	NA
Yang et al⁵⁰	2023	China	circRNA_0010984	AML	13	13	U	0.764	0.853	0.8	10	2	3	11	NA
Ye et al⁵¹	2023	China	circ_0032891	AML	40	10	U	0.802	0.654	0.783	32	3	8	7	NA
Ye et al⁵¹	2023	China	circ_0076995	AML	40	10	U	0.599	0.873	0.778	24	1	16	9	NA
Ye et al⁵¹	2023	China	circ_0014352	AML	40	10	U	0.693	0.652	0.735	28	3	12	7	NA
Ye et al⁵¹	2023	China	circ_0047663	AML	40	10	U	0.593	0.774	0.753	24	2	16	8	NA
Ye et al⁵¹	2023	China	circ_0025593	AML	40	10	U	0.491	0.924	0.670	20	1	20	9	NA
Ye et al⁵¹	2023	China	circ_0007444	AML	40	10	D	0.888	0.548	0.783	36	5	4	5	NA
Ye et al⁵¹	2023	China	circ_0001684	AML	40	10	D	0.982	0.511	0.795	39	5	1	5	NA
Ye et al⁵¹	2023	China	circ_0006199	AML	40	10	D	0.990	0.297	0.638	40	7	0	3	NA
Ye et al⁵¹	2023	China	circ_0000544	AML	40	10	D	0.691	0.664	0.713	28	3	12	7	NA
Ye et al⁵¹	2023	China	circ_0005354	AML	40	10	D	0.483	0.846	0.705	19	2	21	8	NA
Yi et al⁵²	2019	China	Circ-vimentin	AML	113	42	U	0.600	0.900	0.741	68	4	45	38	2.003
Yu et al⁵³	2021	China	circ-MYBL2	MM	89	23	D	0.992	0.606	0.839	88	9	1	14	NA
Zhong et al⁵⁴	2021	China	circ_0058493	CML	48	42	U	0.725	0. 620	0.723	36	15	14	25	0.007886
Zhou et al⁵⁵	2019	China	Circ-Foxo3	AML	116	24	D	0.62	0.75	0.633	72	6	44	18	0.233
Zhou et al⁵⁶	2020	China	Circ-PTK2	MM	60	30	U	0.667	0.885	0.814	40	3	20	27	NA
Zhou et al⁵⁶	2020	China	Circ-RNF217	MM	60	30	U	0.303	0.982	0.672	18	1	42	29	NA
Zhou et al⁵⁶	2020	China	Circ-RERE	MM	60	30	U	0.670	0.689	0.725	40	9	20	21	NA
Zhou et al⁵⁶	2020	China	Circ-NAGPA	MM	60	30	U	0.452	0.989	0.804	27	0	33	30	NA
Zhou et al⁵⁶	2020	China	Circ-KCNQ5	MM	60	30	U	0.521	0.811	0.704	31	6	29	24	NA
Zhou et al⁵⁶	2020	China	circ-AFF2	MM	60	30	D	0.873	0.625	0.757	52	11	8	19	NA
Zhou et al⁵⁶	2020	China	circ-WWC3	MM	60	30	D	0.790	0.607	0.773	47	12	13	18	NA
Zhou et al⁵⁶	2020	China	circ-Low-expressionNAJC5	MM	60	30	D	0.633	0.637	0.672	38	11	22	19	NA
Zhou et al⁵⁶	2020	China	Circ-KLHL2	MM	60	30	D	0.550	0.752	0.677	33	7	27	23	NA
Zhou et al⁵⁶	2020	China	circ-IQJAP1	MM	60	30	D	0.784	0.690	0.758	47	9	13	21	NA
Zhou et al⁵⁶	2020	China	circ-AL137655	MM	60	30	D	0.659	0.672	0.708	40	10	20	20	NA

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
Author	Year	Country	CircRNA	Type	Case	Control	Regulation	Cutoff value	Follow-up (m)	HR	HR	UL
Cao et al¹⁷	2020	China	Circ_0009910	CML	30	35	U	1.729	60	2. 2.00	0.940	5.170
Chen et al¹⁹	2021	China	circ-PVT1	AML	60	30	U	NA	NA	1.688	1.057	2.696
Chen et al⁵⁸	2024	China	Circ_0012152	AML	247	40	U		8.63	2.651	1.374	5.114
Chen1 et al⁵⁷	2020	China	Circ_0069767	MM	66	21	U	NA	60	1.000	0.950	1.050
Chen2 et al¹⁸	2020	China	circ-CDYL	MM	72	13	U	NA	60	1.000	0.960	1.040
Dahl et al⁵⁹	2022	Denmark	circSCORE	MCL	163	156	U?	NA	NA	3.600	1.600	8.300
Du et al⁶⁰	2023	China	circRNA-001264	AML	50	50	U	NA	NA	1.000	0.964	1.039
Fang et al⁶¹	2021	China	CircRERE	MM	125	30	U	NA	60	1.240	0.660	2.330
Feng et al⁶²	2019	China	circ_0000190	MM	47	10	D	NA	40	1.000	0.960	1.040
Feng et al⁶³	2020	China	CircHIPK3	CML	100	100	U	NA	NA	4.630	1.460	14.590
Gao et al²⁰	2019	China	circ_0007841	MM	86	30	U	NA	60	3.090	1.190	8.020
Gharib1 et al²¹	2023	Canada	CircKAT6A	CLL	100	39	U	0.7262	58	3.156	2.225	4.306
Gharib2 et al²¹	2023	Canada	CircLNPEP	CLL	100	39	U	0.7262	58	4.113	2.346	5.288
Gharib3 et al²¹	2023	Canada	CircMDM2	CLL	100	39	U	0.7262	58	2.309	1.884	4.093
Gharib4 et al²¹	2023	Canada	CircMYH9	CLL	100	39	U	0.7262	58	3.259	1.847	4.303
Gong et al²²	2021	China	circ_0087776	MM	136	74	D	NA	NA	4.191	2.371	7.406
Guo et al²⁴	2022	China	circ_0079480	AML	118	118	U	NA	NA	1.000	0.980	1.030
Hu et al²⁶	2019	China	circ-APC	DLBCL	80	80	D	NA	120	0.511	0.374	0.837
Lei et al⁶⁴	2019	China	Circ_0009910	AML	70	70	U	NA	80	2.200	0.940	5.170
Lei et al⁶⁵	2021	China	Circ_100053	CML	150	100	U	NA	30	3.210	1.322	7.797
Li et al⁶⁶	2021	China	circ_POLA2	AML	50	50	U	NA	100	0.660	0.390	1.090
Li et al²⁹	2022	China	circZNF91	CLL	45	20	U	NA	83	1.010	0.940	1.080
Li et al⁶⁷	2023	China	CircZBTB46	AML	116	124	U		36	0.780	0.430	1.420
Li et al³¹	2024	China	circXPO1	MM	27	27	U	NA	NA	1.000	0.960	1.040
Lin1 et al³²	2021	China	circPLXNB2	AML	40	15	U	NA	50	0.590	0.120	2.920
Lin2 et al⁶⁸	2021	China	circTASP1	AML	30	30	U	NA	NA	20.778	5.268	81.953
Liu et al³³	2019	China	Circ-SMARCA5	MM	105	36	D	2.242	5.0–36.0	0.345	0.127	0.941
Liu et al⁶⁹	2020	China	circITCH	MM	56	56	D	NA	60	1.000	0.960	1.040
Liu et al³⁵	2022	China	circ_0044907	AML	45	45	U	NA	NA	0.999	0.962	1.038
Liu1 et al⁷⁰	2021	China	circ_0001821	MM	115	115	U	NA	60	1.802	1.0190	2.940
Liu2 et al³⁴	2021	China	CircRNF220	AML	87	5	U	NA	NA	5.866	1.230	27.969
Luo et al³⁶	2020	China	CircMYC	MM	122	54	U	1.83	60	3.640	2.240	5.380
Ma1 et al³⁷	2022	China	Circ_0059706	AML	100	33	D	0.254	NA	0.340	0.166	0.699
Ma2 etal³⁸	2022	China	Circ_0059707	AML	94	23	D	0.0084	NA	0.441	0.210	0.929
Mei et al³⁹	2019	China	CircCDYL	MCL	18	17	U	NA	25	1.000	0.940	1.060
Papaioannou1 et al⁷¹	2020	USA	circKLHL8	AML	365	365	U	NA	NA	0.530	0.320	0.880
Papaioannou2 et al⁷¹	2020	USA	circFCHO2	AML	365	365	U	NA	NA	0.450	0.270	0.750
Shang et al⁷²	2021	China	circ_0012152	AML	60	60	U	NA	60	0.540	0.190	1.520
Shen et al⁴⁰	2022	China	circ-ANAPC7	AML	18	18	U	NA	NA	1.000	0.960	1.040
Sheng et al⁷⁴	2023	China	circPVT1	AML	23	20	U	NA	50	1.000	0.970	1.030
Su et al⁴¹	2020	China	Circ_0002232	AML	115	48	D	NA	100	1.817	1.024	3.223
Sun1 et al⁷⁵	2024	China	circNBAS	AML	6	6	U	NA	NA	1.000	0.960	1.040
Sun2 et al⁷⁵	2024	China	circKAT6A	AML	98	98	U	NA	NA	1.000	0.960	1.040
Sun3 et al⁷⁵	2024	China	circRABEP1	AML	98	98	U	NA	NA	0.9999	0.963	1.039
Tan et al⁷⁶	2021	China	F-circBA1	CML	10	10	U	NA	3.5	1.050	0.1600	6.760
Wang et al⁷⁷	2020	China	circ_0007841	MM	52	25	U	NA	60	1.000	0.950	1.050
Wang et al⁷⁸	2021	China	circGMDS	AML	218	218	U	NA	NA	1.592	1.067	2.377
Wang et al⁷⁹	2022	China	circ_0040823	AML	68	30	D	NA	NA	2.198	1.2228	3.9527
Wei et al⁸⁰	2023	China	circSLC25A13	AML	120	65	U	NA	NA	1.504	1.023	2.213
Wu et al⁸³	2022	China	CircRIC8B	CLL	32	31	U	0.3	NA	0.239	0.131	0.438
Wu et al⁴⁵	2023	China	circTET2	CLL	69	25	U	NA	NA	3.666	3.5346	3.803
Wu1 et al⁴³	2020	China	Circ-RPL15	CLL	137	50	U	NA	300	1.869	1.105	3.161
Wu2 et al⁸¹	2020	China	circRNAmc-COX2	CLL	54	40	U	NA	10	1.570	0.500	4.940
Wu3 et al⁸²	2021	China	circ-KEL	AML	116	40	U	NA	80	1.820	1.090	3.010
Wu4 et al⁴⁴	2021	China	Circ_0009910	AML	37	37	U	NA	NA	0.654	0.238	1.797
Xia et al⁴⁶	2018	China	Circ-CBFB	CLL	47	21	U	NA	114	2.062	0.808	5.263
Xiang et al⁴⁷	2022	China	circ_0000190	MM	86	30	D	NA	48	0.351	0.128	0.961
Xiong et al⁸⁴	2023	China	circ-SPI1	AML	80	20	U	NA	60	3.554	1.286	9.822
Yan et al⁴⁸	2023	China	circRNA_100199	AML	114	86	D	NA	NA	0.999	0.962	1.038
Yang et al⁴⁹	2023	China	Circ_0001187	AML	57	57	D	NA	NA	1.001	0.968	1.035
Yang et al⁸⁵	2024	China	circFAM193B	AML	40	40	U	NA	NA	2.435	1.571	3.775
Ye et al⁸⁶	2022	China	circ_0003602	AML	50	50	U	NA	60	1.000	0.96	1.04
Ye1 et al⁵¹	2023	China	circ_0032891	AML	40	10	U	NA	NA	0.999	0.962	1.039
Ye10 et al⁵¹	2023	China	circ_0076995	AML	40	10	U	NA	NA	0.999	0.962	1.038
Ye2 et al⁵¹	2023	China	circ_0014352	AML	40	10	U	NA	NA	0.999	0.962	1.037
Ye3 et al⁵¹	2023	China	circ_0047663	AML	40	10	U	NA	NA	1.000	0.963	1.039
Ye4 et al⁵¹	2023	China	circ_0025593	AML	40	10	U	NA	NA	1.000	0.963	1.039
Ye5 et al⁵¹	2023	China	circ_0007444	AML	40	10	D	NA	NA	1.000	0.964	1.037
Ye6 et al⁵¹	2023	China	circ_0001684	AML	40	10	D	NA	NA	1.000	0.963	1.039
Ye7 et al⁵¹	2023	China	circ_0006199	AML	40	10	D	NA	NA	0.999	0.962	1.038
Ye8 et al⁵¹	2023	China	circ_0000544	AML	40	10	D	NA	NA	1.000	0.963	1.039
Ye9 et al⁵¹	2023	China	circ_0005354	AML	40	10	D	NA	NA	0.999588958	0.962271907	1.038353169
Yi et al⁵²	2019	China	Circ-vimentin	AML	113	42	U	2.003	10	2.034	1.016	4.069
Yi et al⁸⁷	2021	China	Circ-PTK2	AML	40	40	U	NA	NA	3.210	0.440	23.110
Yu et al⁵³	2021	China	circ-MYBL2	MM	89	23	D	NA	50	1.000	0.960	1.040
Zhang et al⁸⁸	2022	China	circ_0002078	CLL	49	14	U	NA	18	0.515	0.177	1.495
Zhou 1et al⁵⁶	2020	China	Circ-RERE	MM	60	30	U	NA	40	1.000	0.900	1.100
Zhou 2et al⁵⁶	2020	China	Circ-NAGPA	MM	60	30	U	NA	40	1.000	0.900	1.110
Zhou 3et al⁵⁶	2020	China	Circ-KCNQ5	MM	60	30	U	NA	40	1.000	0.900	1.110
Zhou 4et al⁵⁶	2020	China	circ-AFF2	MM	60	30	D	NA	40	1.000	0.860	1.160
Zhou 5et al⁵⁶	2020	China	circ-WWC3	MM	60	30	D	NA	40	1.000	0.900	1.110
Zhou 6et al⁵⁶	2020	China	circ-DNAJC5	MM	60	30	D	NA	40	1.000	0.900	1.110
Zhou 7et al⁵⁶	2020	China	Circ-KLHL2	MM	60	30	D	NA	40	1.000	0.900	1.110
Zhou 8et al⁵⁶	2020	China	circ-IQJAP1	MM	60	30	D	NA	40	1.000	0.900	1.110
Zhou 9et al⁵⁶	2020	China	circ-AL137655	MM	60	30	D	NA	40	1.000	0.900	1.110
Zhou et al⁵⁵	2019	China	Circ-Foxo3	AML	116	24	D	0.233	8.5	1.208	0.664	2.200
Zhou10 et al⁵⁶	2020	China	Circ-PTK2	MM	60	30	U	NA	40	1.000	0.880	1.130
Zhou11 et al⁵⁶	2020	China	Circ-RNF217	MM	60	30	U	NA	40	1.000	0.900	1.120

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.

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.

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.

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

Figure 5.

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.

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

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

Footnotes

Acknowledgements

Not applicable.

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.

Declaration of Conflicting Interests

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).

ORCID iD

Liyun Gao

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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