Small Nucleolar RNAs as Diagnostic and Prognostic Biomarkers in Cancer: A Systematic Review and Meta-Analysis

Abstract

Objectives

Small nucleolar RNAs (snoRNAs) form clusters within the genome, representing a mysterious category of small non-coding RNAs. Research has demonstrated that aberrant snoRNAs can contribute to the development of various types of cancers. Recent studies have identified snoRNAs as potentially valuable biomarkers for the diagnosis or/and prognosis of cancers. However, there has been a lack of comprehensive reviews on prognostic and diagnostic snoRNAs across different types of cancers.

Methods

We conducted a systematic search of various databases including Google Scholar, Medline, Cochrane, Scopus, PubMed, Embase, ScienceDirect, Ovid-Medline, Chinese National Knowledge Infrastructure, WanFang, and SinoMed with a time frame reception to December 30, 2022. A total of 49 relevant articles were included in our analysis, consisting of 21 articles focusing on diagnostic aspects and 41 articles focusing on prognostic aspects. Pooled odds ratio, 95% confidence intervals (CIs), and hazard ratio (HR) were utilized to evaluate clinical parameters and overall survival (OS), respectively.

Result

The findings indicated that area under the curve, sensitivity, and specificity were 0.85, 75%, and 80% in cancer, respectively. There was a possibility that snoRNAs had a positive impact on the diagnosis (risk ratio, RR = 2.95, 95% CI: 2.75-3.16, P = 0.000) and OS (HR = 1) in cancer. Additionally, abnormally expressed snoRNAs were associated with a positive impact on OS time for chronic lymphocytic leukemia (HR: 0.88, 95%Cl: 0.69-1.11, P < 0.00001), colon adenocarcinoma (HR: 0.97, 95%Cl: 0.91-1.03, P < 0.0001), and ovarian cancer (HR: 0.98, 95%Cl: 0.98-0.99, P < 0.00001). However, dysregulated snoRNAs of colon cancer and colorectal cancer had a negative impact on OS time (HR = 3.01 and 1.01 respectively, P < 0.0001).

Conclusion

The results strongly suggested that snoRNAs could serve as potential novel indicators for prognosis and diagnosis in cancers. This systematic review followed the guidelines of the Transparent Reporting of Systematic Review and Meta-Analyses (PROSPERO register: CRD42020209096).

Keywords

small nucleolar RNA diagnosis prognosis sensitivity specificity hazard ratio

Introduction

Cancer remains a significant global health concern, with high rates of mortality.^1,2 Despite the current treatment options, such as surgery, radiation therapy, and chemotherapy, their success is limited in advanced cases of cancer. Regrettably, patients with advanced cancer still experience high mortality rates and poor survival outcomes.³ Early detection is crucial in preventing the progression of tumors. However, current biomarkers for diagnosing cancer have limited accuracy. Therefore, investigating the potential of snoRNA biomarkers is necessary. Small nucleolar RNAs (snoRNAs) are non-coding RNAs involved in cellular processes, including cancer cell behavior.⁴ They have been associated with cancer development, diagnosis, and prognosis.^5,6 Sensitivity, specificity, and area under the curve (AUC) are important indicators to evaluate biomarkers effectiveness. Further research is needed to determine the optimal values for different types of cancers. Conducting a comprehensive meta-analysis can provide valuable insight into the potential of snoRNAs as novel cancer biomarkers. This analysis can greatly contribute to early diagnosis and prognosis in cancer research.

Materials and Methods

Literature Search Strategy

We conducted a comprehensive search of the following databases: Chinese National Knowledge Infrastructure, SinoMed, WanFang, PubMed, Google Scholar, Medline, Cochrane, Scopus, Embase, ScienceDirect, and Ovid-Medline databases. The search was conducted from inception to December 30, 2022 and included medical subject headings (MeSH), Entree, text word searches. There were no language limitations. The search terms for cancer were “tumor” or “neoplasm” or “tumors” or “neoplasia” or “neoplasias” or “cancer” or “cancers” or “malignant neoplasm” or “malignancy” or “malignancies” or “malignant neoplasmas” or “neoplasm, malignant” or “neoplasms, malignant.” Additionally, we searched for snoRNAs using the terms: “small nucleolar RNA,” “small nucleolar RNAs,” “snoRNA,” “snoRNAs.” We also manually searched for relevant reviews, conference abstracts, comments, editorials, and letters. Any inconsistencies were reviewed by a third reviewer. This study was registered in PROSPERO (CRD42020209096) and conducted according to the preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies guidelines. Data enrollment and processing followed the guideline of the Francis A. Countway Library and Health Information Research Unit. Non-English publications were translated by voluntary interpreters and posted on Cochrane Task Exchange for full-text analysis.

Inclusion and Exclusion Criteria

Eligible studies for meta-analysis met the following criteria: ① The studies provided diagnostic data such as sensitivity (SEN), specificity (SPE) and the AUC, or the hazard ratio (HR) with 95% confidence interval (CI) for overall survival (OS); ② The studies evaluated diagnostic and/or prognostic significance of snoRNA signature; ③ Data including the experimental and control groups were calculated or extracted from the articles. The exclusion criteria were ① Studies using non-human models; ② Studies without a control group; ③ Article types such as commentaries, reviews, editorials, comments, etc.

Data Extraction and Quality Assessment

Eligible studies were independently extracted by more than two authors. The extracted data included the first author, country, year of publication, number of cases and controls, snoRNA detection methods, sample sources (solid, serum and plasma), snoRNA name, snoRNA expression signatures, SEN, SPE, true positive (TP), false positive (FP), false negative (FN) and true negative (TN), cutoff value and AUC values, follow-up time, and HR with 95% CI for OS. The extracted data were calculated using RevMan 5.3 (Cochrane, London, UK) or Stata16.0 (College Station, TX, USA). The quality assessment of the included studies utilized the 14-item quality assessment for diagnostic accuracy studies tool (14-item QUADAS) and Newcastle-Ottawa Scale (NOS) checklist.^7,8 For studies representing data as plots or graphs, we retrieved data using GetData Graph Digitizer, v2.26 (http://getdata-graph-digitizer.com/), Engauge Digitizer 4.1 (http://digitizer.sourceforge.net/).

Quality and Bias Assessment

Heterogeneity was assessed using Chi² and I² tests as well as the L’Abbe and Galbraith plot analysis. Pooled effect sizes of heterogeneous were considered significant with P < 0.01 for Chi² test or I²> 50% for I² test. Quality and bias assessment were conducted by more two reviewers independently. The quality assessment diagnostic studies were conducted using the 14-item QUADAS checklist. The risk of bias for each criterion was classified as “low,” “high,” or “unclear” and scored as “1,” “0,” and “U,” respectively. A cumulative score higher than 8 on the NOS checklist indicated a low risk of bias in the retrospective cohort studies. Studies with an overall evaluation score greater than 6 were considered to be of high quality.

Statistical Analysis

Stata 16.0 and RevMan 5.3 were used for data analysis. Duplicates among the enrolled eligible studies were deleted using Endnote 7 (Clarivate Analytics, London, UK). A fixed-effect model was used for aggregation of pooled results in the absence of heterogeneity among the studies. Pooled SEN and SPE, summary receiver operating characteristics (SROC), AUC, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with corresponding 95% CIs were obtained. The prognostic meta-analysis involved the analysis of HRs with their corresponding 95% CIs using multivariate analytic methods. Bias assessments in the publications were detected using Deek's funnel plot asymmetry test, visual funnel plot, Egger, and Begg test. All P-values were less than 0.05 and indicated statistically significance.

Results

Study Selection

We conducted a study to analyze the expression of snoRNAs and evaluated their clinical value of cancer. We initially retrieved 473 records from online databases, and after removing duplicates, we excluded 2702 studies that were unrelated to diagnostic or/and prognostic cancer. We also excluded 79 basic studies and 26 reviews from the remaining 105 eligible studies. Finally, we included a total of 49 studies in our qualitative synthesis, with 21 studies focused on diagnosis,^9–29 and 41 studies focused on prognosis.^{11,13–15,17,18,20,22,24,25,27,28,30–56} Figure 1 provided detailing information about the selection process, which based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards.

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 characteristics of studies are shown in Tables 1, 2 and Supplementary Table1 (Table S1). The years of publication were from 2010 to 2022. There were 3155 cases and 3118 healthy controls in diagnostic synthesis, and there were 12 415 cases and 7413 healthy controls in prognostic synthesis, respectively. The following-up period varied from 1 to 60 months. All studies indicated the relatively reliable foundation of our analysis in Tables S2 and S3.

Table 1.

Main Characteristics of Studies for Diagnosis Analysis

First author	Year	Country	SnoRNA	Cancer type	Case	Control	Regulation	SEN	SPE	AUC	TP	FP	FN	TN	Sample type
Baraniskin et al.⁹	2013	Germany	RNU2-1f	PDAC	191	129	U	0.98	0.91	0.97	187	12	4	117	Tissue
Dong et al.¹⁰	2021	China	SNORD55	LUAD	204	189	U	0.78	0.68	0.79	159	60	45	129	Tissue
Dong et al.¹⁰	2021	China	SNORD55	LUAD	68	189	U	0.90	0.50	0.76	61	95	7	94	Tissue
Dong et al.¹⁰	2021	China	SNORD55	LUSC	76	189	U	0.72	0.78	0.83	55	42	21	147	Tissue
Dong et al.¹⁰	2021	China	SNORD55	LUSC	19	189	U	0.68	0.93	0.85	13	13	6	176	Tissue
Duan et al.¹¹	2020	China	SNORA71B	BRCA	103	103	U	0.64	0.59	0.63	66	42	37	61	Tissue
Faucher-Giguère et al.¹²	2022	Canada	SNORA6	OC	18	19	U	0.92	0.50	0.75	9	2	9	17	Tissue
Faucher-Giguère et al.¹²	2022	Canada	SNORA19	OC	18	19	U	0.92	0.44	0.37	8	2	10	17	Tissue
Faucher-Giguère et al.¹²	2022	Canada	SNORA56	OC	18	19	U	0.78	0.71	0.82	14	6	4	13	Tissue
Faucher-Giguère et al.¹²	2022	Canada	SNORA72	OC	18	19	U	0.70	0.77	0.76	13	4	5	15	Tissue
Faucher-Giguère et al¹²	2022	Canada	SNORA81	OC	18	19	U	0.63	0.94	0.83	11	1	7	18	Tissue
He et al.¹³	2020	China	SNORD16	CC	20	20	U	0.70	0.69	0.70	13	8	7	12	Tissue
Hu et al.¹⁴	2022	China	SNORA71A	BRCA	39	39	U	0.769	0.615	0.72	30	15	9	24	Tissue
Liao et al.¹⁶	2010	China	SNORD33	NSCLC	37	22	U	0.73	0.86	0.82	27	9	10	13	Tissue
Liao et al.¹⁶	2010	China	SNORD66	NSCLC	37	22	U	0.76	0.77	0.81	28	5	9	17	Tissue
Liao et al.¹⁶	2010	China	SNORD76	NSCLC	37	22	U	0.70	0.91	0.81	26	2	11	20	Tissue
Liu et al.¹⁷	2021	China	SNORD1C	CRC	122	50	U	0.80	0.57	0.75	97	21	25	29	Serum
Okugawa et al.¹⁵	2017	USA	SNORA42	CRC	250	24	U	0.57	0.96	0.75	21	1	16	21	Tissue
Qin et al.¹⁸	2017	China	SNORA74B	GBC	72	72	U	0.91	0.72	0.87	41	3	31	69	Tissue
Shang et al.¹⁹	2021	China	SNORD63	ccRCC	55	40	D	0.95	0.27	0.52	52	29	3	11	Plasma
Shang et al.¹⁹	2021	China	SNORD63	ccRCC	75	96	D	0.48	0.87	0.71	36	13	39	83	Urine
Shang et al.¹⁹	2021	China	SNORD63	ccRCC	23	40	D	0.95	0.39	0.61	22	24	1	16	Plasma
Shang et al.¹⁹	2021	China	SNORD63	ccRCC	39	96	D	0.48	0.82	0.69	19	17	20	79	Urine
Shang et al.¹⁹	2021	China	SNORD96A	ccRCC	55	40	D	0.90	0.80	0.89	50	8	5	32	Plasma
Shang et al.¹⁹	2021	China	SNORD96A	ccRCC	75	96	D	0.53	0.77	0.68	40	22	35	74	Urine
Shang et al.¹⁹	2021	China	SNORD96A	ccRCC	23	40	D	0.95	0.87	0.94	22	5	1	35	Plasma
Shang et al.¹⁹	2021	China	SNORD96A	ccRCC	39	96	D	0.53	0.80	0.67	21	20	18	76	Urine
Shen et al.²⁰	2022	China	SNORD15B	CRC	41	41	U	0.99	0.86	0.83	40	6	1	35	Tissue
Shen et al.²⁰	2022	China	SNORA5C	CRC	41	41	U	0.97	0.81	0.70	40	8	1	33	Tissue
Shen et al.²⁰	2022	China	SNORD48	CRC	41	41	U	0.97	0.80	0.86	40	8	1	33	Tissue
Su et al.²¹	2015	China	snoRD66	NSCLC	59	61	D	0.76	0.74	0.81	34	2	25	59	Tissue
Su et al.²¹	2015	China	snoRD78	NSCLC	59	61	D	0.66	0.87	0.81	39	8	20	53	Tissue
Su et al.²¹	2015	China	snoRD42	NSCLC	59	61	D	0.76	0.70	0.74	45	18	14	43	Tissue
Su et al.²¹	2015	China	snoRD33	NSCLC	59	61	D	0.58	0.82	0.72	34	11	25	50	Tissue
Wan et al.²²	2021	China	TIISR	LUAD	240	239	D	0.83	0.62	0.83	199	91	41	148	Tissue
Wang et al.²³	2022	China	snoRD83A	NSCLC	150	150	D	0.76	0.60	0.74	114	60	36	90	Plasma
Wu et al.²⁴	2017	China	ACA11	HCC	92	92	U	0.87	0.72	0.81	54	17	38	75	Tissue
Wu et al.²⁵	2018	China	SNORD76	HCC	66	66	U	0.86	0.50	0.73	38	12	28	54	Tissue
Yang et al.²⁶	2017	China	snoRA16	CRC	44	44	U	0.59	0.93	0.81	26	3	18	41	Tissue
Yang et al.²⁶	2017	China	snoRA41	CRC	44	44	D	0.84	0.64	0.88	37	16	7	28	Tissue
Yang et al.²⁶	2017	China	SnoRA33	CRC	44	44	U	0.86	0.94	0.94	38	3	6	41	Tissue
Yang et al.²⁶	2017	China	snoRA16	CRC	28	28	U	0.54	0.89	0.76	15	3	13	25	Tissue
Yang et al.²⁶	2017	China	snoRA41	CRC	28	28	U	0.64	0.96	0.76	18	1	10	27	Tissue
Yang et al.²⁶	2017	China	SnoRA33	CRC	28	28	U	0.39	0.96	0.66	11	1	17	27	Tissue
Yoshida et al.²⁷	2017	USA	SNORA21	CRC	215	22	U	0.93	0.76	0.93	201	5	14	17	Tissue
Zheng et al.²⁸	2015	China	SNORD78	NSCLC	56	56	U	0.91	0.22	0.55	51	5	44	12	Tissue
Zhou et al.²⁹	2022	China	SNORD60	LUAD	12	12	U	0.74	0.75	0.828	9	3	3	3	Tissue

BRCA, breast cancer; CC, colon cancer; ccRCC, clear cell renal cell carcinoma; CRC, colorectal cancer; ESCC, oesophageal squamous carcinoma cells; HCC, hepatocellular carcinoma; GBC, gallbladder cancer; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; NSCLC, non-small cell lung cancer; OC, ovarian carcinoma; PDAC, pancreatic ductal adenocarcinoma; USA, United States of America; snoRNA, small nucleolar RNA; U, up-regulation expression; D, down-regulation expression; SEN, sensitivity; SEP, sepectivity; AUC, area under the curve; TN, True negative; FP, False positive; FN, False negative; TP, True positive.

Table 2.

Clinical Characteristics of Articles Enrolled in the Prognosis Analysis

Author	Publish year	Country	SnoRNA	Type	Case	Control	Method	Regulation	AUC	Follow-up (months)	(HR)	(LL)	(UL)
Cao et al.³⁰	2018	China	SNORA18L5	HCC	1583	1540	qRT-PCR	U	NC	60	2.38	1.18	4.80
Crea et al.³¹	2016	Canada	SNORA55	PCa	55	55	qRT-PCR	U	NC	Median 54 ± 7.6	1.00	0.96	1.04
Cui et al.³²	2017	Japan	SNORA23	PDAC	150	11	qRT-PCR	U	NC	Median 19.1-26.3	0.99	0.95	1.04
Ding et al.⁴⁹	2017	China	SNORDll3-1	HCC	112	112	qRT-PCR	D	NA	Median 99.8	0.76	0.29	2.00
Ding et al.³³	2021	China	SNORA52	HCC	145	145	qRT-PCR	D	NA	60	0.36	0.16	0.80
Duan et al.¹¹	2020	China	SNORA71B	BRCA	62	173	qRT-PCR	U	0.634	NA	1.84	1.25	2.71
Fang et al.³⁴	2017	China	SNORD126	HCC	30	28	qRT-PCR	U	NA	NA	1.00	0.95	1.05
Fang et al.³⁴	2017	China	SNORD126	CRC	28	22	qRT-PCR	U	NA	NA	1.00	0.95	1.06
Gee et al.³⁵	2011	UK	RNU44	BRCA	219	219	qRT-PCR	D	NA	NA	0.57	0.28	1.15
Gee et al.³⁵	2011	UK	RNU44	HNSCC	46	46	qRT-PCR	D	NA	NA	1.00	0.96	1.04
Gong et al.³⁶	2017	USA	SNORD46	ACC	75	75	qRT-PCR	U	NA	60	1.00	0.97	1.03
He et al.¹³	2020	China	SNORD16	CC	20	20	qRT-PCR	U	0.70	NA	3.01	1.59	5.71
Hu et al.¹⁴	2022	China	SNORA71A	BRCA	538	538	qRT-PCR	U	NC	NA	0.78	0.66	0.93
Huang et al.³⁷	2020	China	SNORD14E	COAD	473	41	qRT-PCR	U	NA	NA	1.27	1.10	1.47
Huang et al.³⁷	2020	China	SNORD67	COAD	473	41	qRT-PCR	U	NA	NA	2.52	1.21	5.28
Huang et al.³⁷	2020	China	SNORD12C	COAD	473	41	qRT-PCR	U	NA	NA	0.92	0.85	1.00
Huang et al.³⁷	2020	China	SNORD17	COAD	473	41	qRT-PCR	U	NA	NA	0.86	0.75	0.99
Li et al.³⁸	2017	China	SNORA47	HCC	60	60	qRT-PCR	U	NC	48	1.00	0.97	1.03
Li et al.³⁹	2021	China	U50A	BRCA	97	17	qRT-PCR	D	NC	120	0.519	0.239	1.128
Liu et al.²	2017	China	SNORA21	GC	79	79	qRT-PCR	U	NC	NA	2.59	1.10	4.90
Liu et al.¹⁷	2022	China	SNORD1C	CRC	108	108	qRT-PCR	U	NC	NA	1.00	0.97	1.03
Ma et al.⁴⁰	2016	China	SNORD78	HCC	47	47	qRT-PCR	U	0.726	5 −36	1.57	1.12	2.19
Mannoor et al.⁴¹	2014	USA	snoRA3	NSCLC	82	82	qRT-PCR	U	NA	NA	1.00	0.99	1.01
Mannoor et al.⁴¹	2014	USA	snoRA42	NSCLC	82	82	qRT-PCR	U	NA	NA	1.00	0.99	0.99
McMahon et al.⁴²	2019	Canada	SNORA24	HCC	268	243	qRT-PCR	D	NA	NA	1.00	0.98	1.01
Mei et al.⁴³	2016	USA	SNORA42	NSCLC	64	64	qRT-PCR	U	NA	NA	1.00	0.97	1.03
Okugawa1et al.¹⁵	2017	USA	SNORA42	CRC	250	24	qRT-PCR	U	0.75	NA	2.11	1.12	3.98
Qin et al.¹⁸	2017	China	SNORA74B	GBC	72	72	qRT-PCR	U	0.871	NA	3.31	1.26	8.71
Ronchetti et al.⁴⁴	2013	Italy	SNORD1A	CLL	211	18	qRT-PCR	U	NA	Median 30	0.47	0.27	0.82
Ronchetti et al.⁴⁴	2013	Italy	SNORD56	CLL	211	18	qRT-PCR	U	NA	Median 30	0.5	0.3	0.83
Ronchetti et al.⁴⁴	2013	Italy	SNORA70F	CLL	211	18	qRT-PCR	U	NA	Median 30	0.4	0.24	0.66
Ronchetti et al.⁴⁴	2013	Italy	SNORD116-18	CLL	211	18	qRT-PCR	U	NA	Median 30	2.49	1.46	4.27
Ronchetti et al.⁴⁴	2013	Italy	SNORA74A	CLL	211	18	qRT-PCR	U	NA	Median 30	2.37	1.4	4
Shan et al.⁴⁵	2021	China	SNORA42	ESCC	185	10	qRT-PCR	U	NA	NA	1.00	0.94	1.06
Shen et al.²⁰	2022	China	SNORD15B	CRC	41	41	qRT-PCR	U	NA	NA	2.198	1.33	3.62
Shen et al.²⁰	2022	China	SNORA5C	CRC	41	41	qRT-PCR	U	NA	NA	2.128	1.28	3.55
Su et al.⁴⁶	2021	China	SNORD50A/B	BRCA	309	188	qRT-PCR	D	NA	NA	1.00	0.98	1.02
Su et al.⁴⁶	2021	China	SNORD50A/B	HCC	106	28	qRT-PCR	D	NA	NA	1.00	0.96	1.03
Su et al.⁴⁶	2021	China	SNORD50A/B	LUAD	91	21	qRT-PCR	D	NA	NA	1.00	0.96	1.05
Wan et al.²²	2021	China	TIISR	LUAD	54	54	qRT-PCR	D	0.83	NA	2.04	1.13	3.68
Wang et al.⁴⁷	2018	China	snoU2_19	HCC	80	80	qRT-PCR	U	NA	NA	2.37	1.24	4.54
Wang et al.⁴⁸	2021	China	SNORA42	HCC	30	30	qRT-PCR	U	NA	NA	2.467	1.094	5.56
Wu et al.²⁴	2017	China	ACA11	HCC	92	92	qRT-PCR	U	0.81	NA	1.00	0.97	1.03
Wu et al.²⁵	2018	China	SNORD76	HCC	66	66	qRT-PCR	U	0.73	NA	1.00	0.97	1.03
Xu et al.⁴⁹	2014	China	SNORD113-1	HCC	112	112	qRT-PCR	D	NA	NA	1.00	0.96	1.04
Xu et al.⁵⁰	2017	China	SNORD47	Gliomas	124	124	qRT-PCR	D	NA	NA	1.00	0.97	1.03
Xu et al.⁵¹	2021	China	snoRD126	HCC	68	68	qRT-PCR	U	NA	NA	1.89	1.05	3.40
Yi et al.⁵²	2018	China	SNORA42	PCa	218	149	qRT-PCR	U	NA	NA	1.00	0.94	1.06
Yoshida et al.²⁷	2017	USA	SNORA21	CRC	215	22	qRT-PCR	U	0.93	NA	2.37	1.24	4.54
Zheng et al.²⁸	2015	China	SNORD78	NSCLC	56	56	qRT-PCR	U	0.551	NA	5.50	1.68	17.86
Zhou et al.²⁹	2022	China	SNORD60	LUAD	12	12	qRT-PCR	U	NA	NA	1.14	0.80	1.62
Zhu et al.⁵³	2019	China	SNORD89	OC	379	379	qRT-PCR	U	NA	NA	1.294	0.48	3.52
Zhu et al.⁵³	2019	China	SNORD19	OC	379	379	qRT-PCR	D	NA	NA	0.9996	0.98	1.01
Zhu et al.⁵³	2019	China	SNORA2B	OC	379	379	qRT-PCR	D	NA	NA	1.00	0.99	1.01
Zhu et al.⁵³	2019	China	SNORD116-4	OC	379	379	qRT-PCR	U	NA	NA	1.00	0.99	1.01
Zhu et al.⁵⁴	2022	China	SNORD126	OC	127	36	qRT-PCR	D	NA	NA	3.04	0.85	5.64
Zhu1 et al.⁵⁴	2022	China	SNORA70J	OC	127	36	qRT-PCR	D	NA	NA	9.49	1.33	34.09
Zhu2 et al.⁵⁴	2022	China	SNORD3C	OC	127	36	qRT-PCR	D	NA	NA	24.00	1.58	170.8
Zhu3 et al.⁵⁴	2022	China	SNORA75B	OC	127	36	qRT-PCR	D	NA	NA	65.39	94.71	994.06
Zhu4 et al.⁵⁴	2022	China	SNORA58	OC	127	36	qRT-PCR	D	NA	NA	0.63	1.15	0.43
Zhu5 et al.⁵⁴	2022	China	SNORA11B	OC	127	36	qRT-PCR	D	NA	NA	0.50	0.26	0.28
Zhu6 et al.⁵⁴	2022	China	SNORA36C	OC	127	36	qRT-PCR	D	NA	NA	2.03	3.17	3.88
Zhu7 et al.⁵⁴	2022	China	SNORD105B	OC	127	36	qRT-PCR	D	NA	NA	0.63	0.50	0.40
Zhu8 et al.⁵⁴	2022	China	SNORD89	OC	127	36	qRT-PCR	D	NA	NA	1.27	0.48	1.61
Zhu9 et al.⁵⁴	2022	China	SNORD11625	OC	127	36	qRT-PCR	U	NA	NA	2.67	0.25	7.01
Zhu10 et al.⁵⁴	2022	China	SNORA30B	OC	127	36	qRT-PCR	U	NA	NA	10.92	7.80	115.19
Zhu11 et al.⁵⁴	2022	China	SNORD1162	OC	127	36	qRT-PCR	U	NA	NA	1.41	2.28	1.97
Zhu12 et al.⁵⁴	2022	China	SNORD105	OC	127	36	qRT-PCR	U	NA	NA	0.62	0.62	0.38

Zhuo et al.²⁹	2022	China	SNORA38B	NSCLC	20	10	qRT-PCR	U	NA	NA	1.00	0.95	1.05
Zou et al.⁵⁶	2015	USA	SNORD35B	HNSCC	109	109	qRT-PCR	D	NA	NA	2.93	1.56	5.52

HCC, hepatocellular carcinoma; PCa, Prostate cancer; BRCA, breast cancer; CRC, colorectal cancer; HNSCC, head and neck squamous cell carcinoma; ACC, adrenocortical carcinoma; CC, colon cancer; COAD, colon adenocarcinoma; GC, gastric cancer; CLL, chronic lymphocytic leukemia; ESCC, Oesophageal squamous carcinoma cells; GBC, gallbladder cancer; LUAD, lung adenocarcinoma; NSCLC, non-small cell lung cancer; OC, ovarian carcinoma; PDAC, pancreatic ductal adenocarcinoma; USA, United States of America; snoRNA, small nucleolar RNA; U, up-regulation expression; D, down-regulation expression; qRT-PCR, real-time polymerase chain reaction; OS, overall survival; AUC, area under the curve; HR, hazard ratio; LL, lower limit of confidence interval; UL, upper limit of confidence interval.

Diagnostic Accuracy Analysis

We identified 21 publications investigating the clinical value of snoRNAs in diagnosis. Figure 2 shows the forest plots of sensitivity, specificity, SROC curve and Fagan's plot. The sensitivity and specificity of snoRNAs were 0.75 (95% CI, 0.69-0.80; P = 0.00) and 0.80 (95% CI, 0.75-0.84; P = 0.00), respectively. The SROC curve was generated and the AUC was 0.85 (95% CI, 0.81-0.88) in Figure 2c. The Fagan's nomogram indicated a pre-test probability of 20% in Figure 2d. The forest plots of pooled PLR, NLR, diagnostic score and DOR are presented in Figure S1. The PLR was 3.80 (95% CI, 3.06-4.73; P = 0.00), and the NLR was 0.31 (95% CI, 0.25-0.38; P = 0.00) in Figure S1A. The summary evaluation of snoRNAs in the diagnosis of cancer indicated that a diagnostic score was 2.51 (95% CI,2.17-2.84; P = 0.00), and DOR was 12.26 (95% CI, 8.74-17.19; P = 0.00) in Figure S1B. An LR scattergram was utilized to assess the clinical value of different diagnostic tests with the majority of eligible studies falling into the right lower quadrants, except for two studies in Figure S1C. These findings suggested that snoRNA may serve as valuable diagnostic tool for cancer detection.

Figure 2.

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

Diagnostic and Prognostic Value of snoRNA Expression in Cancer

The pooled risk ratio (RR) from 21 diagnostic studies for snoRNAs expression in cancer was 2.95, 95% CI: 2.75-3.16 (P < 0.001) (Figure 3a). The HR from 41 prognostic studies was 1.00(Figure 3b). These findings suggested that snoRNA may serve as a diagnostic or prognostic biomarker in cancer.

Figure 3.

Diagnostic and prognostic value of snoRNA expression in cancer. Diagnostic value of snoRNA expression in different types of cancers (a). Forest plots for OS according to snoRNA expression in cancer (b). OS: overall survival; snoRNA: small nucleolar RNA.

Inﬂuence Analysis and Subgroup Study

The influence analysis was conducted for both diagnostic and prognostic studies, as shown in Figure 4a and b, respectively. The analysis indicated that the results of the study were relatively reliable. The heterogeneity was observed in the meta-analysis of prognostic role for snoRNAs in cancers (Figure 3b), and further subgroup analysis is needed basing on potential factors such as study country, sample type, and HR analytic method. The reasons for the influence analysis in the prognosis of different cancers were investigated, and the results are presented in Figure 5 (chronic lymphocytic leukemia, HR: 0.88, 95%Cl: 0.69-1.11, P < 0.00001; colon adenocarcinoma, HR: 0.97, 95%Cl: 0.91-1.03, P < 0.0001; ovarian cancer, HR: 0.98, 95%Cl: 0.98-0.99, P < 0.00001). However, the dysregulated snoRNAs in colon cancer and colorectal cancer had a negative impact on OS time (HR = 3.01 and 1.01, 95%Cl: 1.59-5.70 and 0.98-1.03, respectively, P < 0.0001). This indicated that snoRNA may also serve as diagnostic and/or prognostic biomarkers in cancer.

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 cancers. Subgroup study for RR value and OS according to different types of cancers in diagnostic meta-analysis (a) and prognostic meta-analysis (b). RR: relative risk; OS: overall survival.

Publication Bias and Sensitivity Analyses

The Deeks’ test for publication bias of diagnostic studies is displayed in Figure 6a (P = 0.53). The Egger's test for diagnostic studies is shown in Figure 6b and for prognostic studies in Figure 6c. The quality evaluation of diagnostic and prognostic accuracy is shown in Figure S2, with Figure S2A and S2B representing L’Abbe and Galbraith plots asymmetry tests for diagnostic studies, and Figure S2C representing Galbraith plots asymmetry tests for prognostic studies.

Figure 6.

Funnel plots of publication bias. Deeks’ funnel plot asymmetry test and Egger’ s funnel plot for diagnostic studies (a, b); Egger’ s funnel plot for prognostic tests (c).

Discussion

SnoRNAs, a homogeneous group of non-coding RNAs that guide modifications of ribosomal RNA, have emerged as important players contributors in cancer.^57,58 Unlike microRNAs and lncRNAs, they possess distinct characteristics. Numerous studies have demonstrated that snoRNAs can be utilized as diagnostic and prognostic biomarkers in cancer.^14,59 In the early stages of cancer, patients often exhibit few or no symptoms. However, as the disease progresses, patients experience worse clinical outcomes and are frequently diagnosed at advanced stages. Treating patients with advanced cancer poses significant challenges for doctors. Consequently, the development of multiple biomarkers is necessary to confirm the presence of a tumor. However, there was only one systematic review about snoRNAs in colorectal cancer.⁶⁰ Therefore, we conducted a comprehensive analysis of 49 studies to evaluate the expression profiles of snoRNAs as novel biomarkers for the diagnosis and prognosis of cancer. This research had potentially contributed to the development of new diagnostic tools and treatment strategies.

This is the first comprehensive report to systematically reviewing snoRNAs in various types of cancer. Our analysis revealed that snoRNAs exhibited a sensitivity of 0.75, specificity of 0.80, and DOR of 12.26. The DOR combines the strengths of sensitivity and specificity, indicating the significant diagnostic value in cancer. The PLR was 3.80, suggesting that cancer patients are 12.26 times more likely to exhibit abnormal snoRNA expression compared to healthy individuals. The NLR indicated a 31.00% probability of obtaining a false negative result. The AUC of SROC for snoRNAs in the diagnosis of cancer was 0.85, which is considered acceptable. These findings demonstrated that snoRNAs had the potential to serve as reliable diagnostic markers in cancer.

We further assessed whether snoRNAs could serve as biomarkers for the diagnosis and prognosis of cancer. The result revealed that abnormally expressed snoRNAs were a significantly associated with RR (RR = 2.95, 95% CI: 2.75-3.16, P = 0.000) and OS time for cancer (HR = 1) in this systematic analysis. Correspondingly, we observed considerable heterogeneity among the prognostic studies. To explain this heterogeneity, we conducted subgroup analysis basing on different cancer types. We found that dysregulated snoRNAs had varying impacts on OS time depending on the types of cancer. For instance, dysregulated snoRNAs were associated with a positive impact on OS time in chronic lymphocytic leukemia, colon adenocarcinoma, and ovarian cancer (HR = 0.88, 0.97 and 0.98, respectively, P < 0.00001). Conversely, dysregulated snoRNAs in colon cancer and colorectal cancer had negative impacts on OS time (HR = 3.01 and 1.01, respectively, P < 0.0001). Furthermore, we observed heterogeneity among the studies based on different countries, years, and snoRNA expression levels (Figures S3-S5). Dysregulated snoRNAs were associated with positive impacts on OS time in China and Italy (HR = 0.99 and 0.88, respectively, P < 0.00001, Figure S3). Additionally, dysregulated snoRNAs were associated with positive impacts on OS time in 2013 and 2022 (HR = 0.88 and 0.86, respectively, P < 0.00001, Figure S4). Down-expressed snoRNAs were also associated with positive impacts on OS time (HR = 0.97, P < 0.00001, Figure S5). Moreover, the lack of a standardized reference gene among studies may contribute to heterogeneity. However, we employed standardized scientific methods to identify and exclude outlier studies, enhancing the reliability of the results. Nonetheless, our study had some limitations. The small sample sizes and lack of comprehensive data in some studies limited the applicability of the findings. Additionally, the number of available studies on snoRNAs as biomarkers for cancer was still relatively limited, which may affect the overall conclusions. Despite these limitations, the meta-analysis provided valuable insights into the diagnostic and prognostic potential of abnormal snoRNA expression profiling in cancer.

In Conclusion

Our analysis suggested that dysregulated snoRNA expression profiling had the potential to serve as diagnostic and prognostic biomarkers for various types of cancer.

Supplemental Material

sj-doc-1-tct-10.1177_15330338241245939 - Supplemental material for Small Nucleolar RNAs as Diagnostic and Prognostic Biomarkers in Cancer: A Systematic Review and Meta-Analysis

Supplemental material, sj-doc-1-tct-10.1177_15330338241245939 for Small Nucleolar RNAs as Diagnostic and Prognostic Biomarkers in Cancer: A Systematic Review and Meta-Analysis by Liyun Gao, Junfei Fan, Jiayin He, Xiangxin Che, Xin Wang and Chunhua Han in Technology in Cancer Research & Treatment

Footnotes

Abbreviations

Acknowledgements

We thank for Freescience Information Technology Co., LTD (Ningbo, China) for English language editing.

Availability of Data and Materials

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

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) declared the following potential conflicts of interest with respect to 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).

ORCID iD

Liyun Gao

Supplemental Material

Supplemental material for this article is available online.

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