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. 2022 Nov 7;14(21):8818–8838. doi: 10.18632/aging.204371

Prognostic and clinicopathological value of m6A regulators in human cancers: a meta-analysis

Zhangci Su 1,2,3,*, Leyao Xu 1,2,3,*, Xinning Dai 1,2,3,*, Mengyao Zhu 1,2,3,4, Xiaodan Chen 1,2,3, Yuanyuan Li 1,2,3, Jie Li 1,2,3, Ruihan Ge 1,2,3, Bin Cheng 1,2,3,✉, Yun Wang 2,3,✉
PMCID: PMC9699754  PMID: 36347025

Abstract

Background: N6-methyladenosine (m6A) is the most abundant epigenetic modification. Although the dysregulation of m6A regulators has been associated with cancer progression in several studies, its relationship with cancer prognosis and clinicopathology is still controversial. Therefore, we evaluated the prognostic and clinicopathological value of m6A regulators in cancers by performing a comprehensive meta-analysis.

Methods: The PubMed, Cochrane Library, Web of Science, and Embase databases were searched up to April 2022. Hazard ratios were used to analyze the association between m6A with prognosis. We also analyze the relationship between m6A and clinicopathology using odds ratios.

Results: METTL3 overexpression predicted poor overall survival and disease-free survival in cancer patients (p < 0.001) such as gastric cancer (p < 0.001), esophageal squamous cell carcinoma (p < 0.001), oral squamous cell carcinoma (p = 0.002) and so on. Additionally, METTL3 overexpression was associated with poor pT stage (p < 0.001), pN stage (p < 0.001), TNM stage (p < 0.001), tumor size >5 cm (p < 0.001) and vascular invasion (p = 0.024). Conversely, METTL14 overexpression was positively associated with better OS (p < 0.001), negatively with poor pT stage (p = 0.001), pM stage (p = 0.002), pN stage (p = 0.011) and TNM stage (p < 0.001). Moreover, KIAA1429 overexpression was associated with poor OS (p = 0.001). YTHDF1 overexpression was also associated with advanced pM stage (p < 0.001) and tumor size >5 cm (p < 0.001). However, ALKBH5 overexpression was negatively associated with vascular invasion (p = 0.032).

Conclusions: High expression of METTL3 predicted poor outcome. In contrast, high expression of METTL14 was associated with better outcome. Thus, we suggest that among all the m6A regulators, METTL3 and METTL14 could be potential prognostic markers in cancers.

Keywords: m6A regulators, cancers, prognosis, clinicopathology, meta-analysis

INTRODUCTION

According to world cancer report 2020, there will be an estimated 60% increase in cancer cases over the next two decades and they will cause about one sixth of deaths worldwide [1]. Although certain progresses have been made in cancer treatment in the past decades, the overall survival of cancer patients is still unsatisfactory. Therefore, biomarkers which can function as prognosticators for the survival time in cancer are necessarily needed. N6-methyladenosine (m6A) modification, an epigenetic modification found in eukaryotes, has been a hot topic in recent years. As the most abundant epigenetic modification in eukaryotes [2], m6A modification is associated with RNA splicing [3–5], maturation [5], stabilization [6] and translation initiation [7]. As a result, m6A modification participates in several biological processes: neural development [8], disease occurrence [9, 10] and tumorigenesis [11–13]. This reversible modification can be added or removed by writers and erasers [14]. Writers are known as m6A methyltransferases, such as METTL3, METTL14, WTAP, KIAA1429 and RBM15/RBM15B. The two major erasers, FTO and ALKBH5, function as m6A demethylases. Furthermore, there are binding proteins called readers [14], represented by YTHDC, IGF2BP and HNRNPC, which recognize specifically modified RNA to exercise different subsequent reactions, including translation and degradation. Recently, emerging studies reported that the above mentioned m6A regulators were of great significance in tumorigenesis [15–17], tumor progression [18, 19] and metastasis [20]. For example, the writer METTL14 could suppress UVB-induced skin tumorigenesis and act as a critical epitranscriptomic mechanism to facilitate global genome repair which is essential for preventing mutagenesis and skin cancer [17]. Moreover, Bo Tang and his colleagues revealed that the eraser ALKBH5 suppressed pancreatic cancer tumorigenesis through promoting transcription of WIF-1 mRNA and inhibiting Wnt signaling pathway in a m6A dependent manner [21]. Additionally, the reader YTHDF1 could promote translation of autophagy-related genes ATG2A and ATG14 by binding to m6A-modified ATG2A and ATG14 mRNA, which facilitated autophagy and autophagy-related human hepatocellular carcinoma progression [15]. YTHDF1 could also enhance ferroptosis by promoting the activation of autophagy and BECN1 mRNA stability in hepatic stellate cells [22]. Overall, there are high-complexity links between m6A and different types of programmed cell death, which are closely related with the initiation, progression and resistance of cancer [23]. Furthermore, there is increasing evidence suggesting that dysregulated expression of m6A regulators exists in major types of cancers and correlates with poor prognosis. However, these survival data were contradictory among different cancer types and regulators, suggesting that a meta-analysis is required to identify prognostic markers. Therefore, in this study, we conducted a systematic review and meta-analysis to assess the prognostic and clinicopathological value of m6A regulators in cancer patients.

RESULTS

Study characteristics

The literature selection is presented in Figure 1, and the characteristics of eligible studies are shown in Table 1. A total of 3069 relevant studies were retrieved through an initial search. Among them, 915 duplicated records and 1944 unrelated records were excluded based on title or abstract. We subjected 210 studies to full-text screening, of which 159 studies were excluded because they did not meet the inclusion criteria. The remaining 51 articles were further assessed for quality by the Newcastle-Ottawa Scale (NOS) system, and only high-quality studies (NOS ≥ 6) were included in the meta-analysis. Finally, we included 49 cohort studies [6, 15, 24–70] comprising 7006 patients. All studies were published between 2017 and 2022. Forty-eight studies were conducted in Asia and one was conducted in Europe. Sample size ranged from 31 to 603 patients per study. In 49 included studies, 27 studies involved m6A writers, 15 studies referred to erasers and 9 studies were related to readers. The included studies totally reported 20 types of cancers, including digestive system cancer (n = 33), respiratory system cancer (n = 6), urinary system cancer (n = 4), female reproductive system cancer (n = 2) and others (n = 4). With respect to survival data, 48 studies reported overall survival (OS), 9 studies presented disease-free survival (DFS), and 4 studies showed relapse-free survival (RFS).

Figure 1.

Figure 1

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Flow diagram of reviewing and selecting studies.

Table 1. The main characteristics of included studies.

Study m6A regulators Country Ethnicity Cancer types Follow-up (months) Sample size (M/F) TMN stage Cut-off value Outcome HR and 95% CI NOS score Status
Yang 2020 (3) METTL14 China Asian Colorectal cancer NA 37 (27/10) I–IV score > 6 (0–12) RFS Reported 6 Included
Chen 2020 METTL14 China Asian Colorectal cancer NA 112 (74/38) I–IV > median OS Reported 7 Included
Wang 2022 METTL14 China Asian Colorectal cancer 60 72 (44/28) I–IV NA 0S Reported 7 Included
Deng 2019 METTL3 China Asian Colorectal cancer 72–108 181 (97/84) I–IV NA OS Reported 7 Included
Li 2019 (1) METTL3 China Asian Colorectal carcinoma 80 OS:432 (257/175) DFS:389 NA > median OS DFS OS: Reported DFS: Calculated 6 Included
Shengli 2022 METTL3 China Asian Colorectal cancer 60 111 (51/60) I–IV score ≥ 4 (0–12) OS Calculated 7 Included
Ma 2022 KIAA1429 China Asian Colorectal cancer 100 111 (75/36) I–IV NA OS Calculated 7 Included
Yang 2020 (1) ALKBH5 China Asian Colon cancer 80 60 (25/35) I–IV score ≥ 4 (0–12) OS DFS Reported 7 Included
Ruan 2021 FTO China Asian Colorectal cancer 140 369 (209/160) I–III > median OS RFS Reported 6 Included
Nishizawa 2018 YTHDF1 Japan Asian Colorectal cancer NA 63 (41/22) I–IV score = 2+ or 3+ (0–3) OS Reported 7 Included
Yue 2019 METTL3 China Asian Gastric cancer NA 120 (79/41) I–IV NA OS DFS Reported 7 Included
Wang 2020 METTL3 China Asian Gastric cancer 60 83 (61/22) I–IV score > 7 (0-–12) OS Reported 6 Included
Yang 2020 (2) METTL3 China Asian Gastric cancer 21-84 OS:196 (131/65) DFS:156 I–IV score > 145 (0-300) OS DFS Reported 8 Included
Sun 2020 METTL3 China Asian Gastric cancer NA OS:80 RFS:58 (NA) I–IV score = 2+ or 3+ (0–3) OS RFS Reported 7 Included
Wang 2021 (1) METTL16 China Asian Gastric cancer 49.1 231 (155/76) I–IV > median OS Reported 8 Included
Liu 2021 METTL14 China Asian Gastric cancer 100 248 (183/65) I–IV score > 6 (0–12) OS Reported 7 Included
Li 2019 (2) FTO ALKBH5 China Asian Gastric cancer 100 450 (308/142) I–IV score ≥ 6 (0–12) OS Reported 6 Included
Xu 2017 FTO China Asian Gastric cancer 60 128 (68/60) I–IV NA OS Reported 7 Included
Yuan 2022 YTHDC2 China Asian Gastric cancer 80 120 (86/34) I–IV NA OS Reported 6 Included
Xia 2019 METTL3 China Asian Pancreatic cancer 15-26 40 (35/5) I–III > median OS Calculated 6 Included
Guo 2020 ALKBH5 China Asian Pancreatic cancer 60 42 (19/23) I–III median OS Calculated 7 Included
Zeng 2021 FTO China Asian Pancreatic cancer NA 50 (27/23) I–IV > average OS Calculated 8 Included
Tan 2022 FTO China Asian Pancreatic cancer NA 209 (NA) I–IV score > 6 (0–12) OS Reported 8 Included
Li 2021 YTHDF1 China Asian Hepatocellular carcinoma 60 120 (32/88) I–III NA OS DFS Reported 7 Included
Ma 2017 METTL14 China Asian Hepatocellular carcinoma NA 220 (193/27) I–IV > median OS RFS Calculated 3 Not included
Xu 2022 (1) METTL3 China Asian Intrahepatic cholangiocarcinoma NA 96 (53/43) I–IV > median OS DFS OS: Reported DFS: Calculated 6 Included
Ye 2020 FTO China Asian Liver cancer 60 309 (NA) I–III score ≥ 6 (0–12) OS Reported 7 Included
Wang 2021 (2) METTL3 China Asian Oesophageal squamous cell carcinoma NA 81 (64/17) I–IV score > 300 (0–400) OS Calculated 7 Included
Xia 2020 METTL3 China Asian Esophageal squamous cell carcinoma 108 207 (151/56) I–IV score > 8 (0–12) OS DFS Reported 7 Included
Nagaki 2020 FTO ALKBH5 Japan Asian Esophageal squamous cell carcinoma 41.5–60 177 (153/24) NA score = 2+ or 3+ (0–3) OS ALKBH5: Reported FTO: Calculated 6 Included
Liu 2020 METTL3 China Asian Oral squamous cell carcinoma 3–106 101 (68/33) I–IV Youden index OS Reported 7 Included
Xu 2021 METTL3 China Asian Oral squamous cell carcinoma 80 94 (51/43) I–IV score ≥ 4 (0–12) OS Calculated 7 Included
Guo 2022 METTL3 China Asian Head and neck squamous cell carcinoma 80 100 (99/1) I–IV score ≥ 8 (0–12) OS Reported 7 Included
Chen 2021 METTL3 China Asian Gallbladder-cancer NA 120 (57/63) I–IV > median OS DFS Reported 6 Included
Yang 2021 FTO China Asian Lung adenocarcinoma 120 83 (55/28) I–IV score ≥ 6 (0–8) OS Calculated 8 Included
Huang 2018 ALKBH5 China Asian Lung adenocarcinoma 3–125 88 (47/41) I–IV HSCORE > 100% OS Reported 7 Included
Xu 2022 (2) YTHDF2 China Asian Lung squamous cell carcinoma 60 73 (66/7) I–III > median OS Reported 7 Included
Tsuchiya 2021 YTHDF1 and YTHDF2 Japan Asian Non–small-cell lung cancer NA 603 (414/189) I–IV YTHDF1: score > 118(0-300) YTHDF2: score > 118 (0–300) OS RFS Reported 6 Included
Lu 2020 METTL3 China Asian Nasopharyngeal carcinoma 10.33–91.67 55 (30/25) I–IV score > 3 (0–9) OS DFS OS: Reported DFS: Calculated 7 Included
Du 2022 IGF2BP3 China Asian Nasopharyngeal carcinoma 150 70 (56/14) I–IV NA OS Reported 7 Included
Gu 2019 METTL14 China Asian Bladder cancer NA 98 (NA) NA NA OS Calculated 3 Not included
Han 2019 METTL3 China Asian Bladder cancer 60–96 180 (141/39) I–IV score > 3 (0–9) OS Calculated 7 Included
Yu 2021 ALKBH5 China Asian Bladder cancer 60 161 (124/37) I–IV score ≥ 8 (0–12) OS Calculated 7 Included
Li 2017 METTL3 China Asian Renal cell carcinoma 100 145 (89/56) I–IV NA OS Reported 7 Included
Zhang 2020 ALKBH5 China Asian Renal cell carcinoma 100 96 (60/36) I–IV score ≥ 8 (0–12) OS Calculated 7 Included
Niu 2019 FTO China Asian Breast tumor 96 53 (0/53) NA NA OS Calculated 7 Included
Hua 2018 METTL3 China Asian Ovarian carcinoma NA 162 (0/162) I–IV > median OS Reported 8 Included
Lin 2022 METTL3 China Asian Thyroid carcinoma 36 80 (25/55) I–IV > median OS Calculated 6 Included
Orouji E 2020 YTHDF1 Germany European Merkel cell carcinoma NA 31 (NA) NA score > 8 OS Calculated 7 Included
Li 2020 WTAP, KIAA1429, RBM15, RBM15B, METTL3, METTL14, METTL16, HNRNPC, HNRNPA2B1, YTHDF1, YTHDF2, YTHDF3, YTHDC1, FTO, ALKBH5 China Asian Osteosarcoma 60 120 (NA) NA score > 6 OS Reported 6 Included
Wei 2022 YTHDF1 China Asian Osteosarcoma 60 56 (NA) I–IV > median OS Calculated 6 Included
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Abbreviations: CI: confidence interval; HR: hazard ratio; OS: overall survival; DFS: disease-free survival; RFS: relapse-free survival; NA: not available; F: female; M: male.

Expression of m6A regulators and prognosis of cancer patients

Based on the type of m6A writers, we carried out meta-analysis and found that high expression of METTL3 had an unfavorable effect on OS (HR = 1.75; 95% CI: 1.32–2.31, p < 0.001; I2 = 78.1%, p < 0.001; Figure 2, Table 2) and DFS (HR = 2.02; 95% CI: 1.54–2.64, p < 0.001; I2 = 52%, p = 0.052; Figure 3, Table 2) in cancer patients. Similarly, high expression of KIAA1429 was associated with poor OS (HR = 2.35; 95% CI: 1.40–3.93, p = 0.001; I2 = 37.2%, p = 0.207; Figure 2, Table 2). On the contrary, high expression of METTL14 had a favorable effect on OS (HR = 0.55; 95% CI: 0.43–0.69, p < 0.001; I2 = 0.0%, p = 0.392; Figure 2, Table 2). Furthermore, the expression of METTL16 was not significantly associated with OS in cancer patients (Figure 2, Table 2). Similarly, neither erasers nor readers were significantly associated with OS in cancer patients. (Figure 2, Table 2). We did not perform a meta-analysis of m6A regulators and RFS because there were not enough studies.

Figure 2.

Figure 2

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Forest plots for the association of m6A writers (A), erasers (B) and readers (C) with OS in cancer patients.

Table 2. Summary of the meta-analysis of m6A regulators and prognosis in cancer patients.

Regulators Outcome Studies HR 95% Cl P-value Heterogeneity Effects model
I2 P-value
METTL3 OS 21 1.75 1.32–2.31 0 78.10% 0 Random
DFS 7 2.02 1.54–2.64 0 52% 0.052 Random
METTL14 OS 4 0.55 0.43–0.69 0 0.00% 0.392 Random
KIAA1429 OS 2 2.35 1.40–3.93 0.001 37.20% 0.207 Random
METTL16 OS 2 1.75 0.84–3.65 0.137 82.20% 0.018 Random
ALKBH5 OS 8 1.09 0.75–1.57 0.657 62.50% 0.009 Random
FTO OS 10 1.01 0.72–1.41 0.966 74.70% 0 Random
YTHDF1 OS 6 1.21 0.67–2.18 0.532 72.10% 0.003 Random
YTHDF2 OS 3 0.9 0.52–1.57 0.715 80.10% 0.007 Random
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Abbreviations: CI: confidence interval; HR: hazard ratio; OS: overall survival; DFS: disease-free survival.

Figure 3.

Figure 3

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Forest plots for the association of m6A regulators with DFS in cancer patients.

Subgroup analysis for different m6A regulators and cancer types

For further exploration, subgroup analyses were conducted according to cancer types. As shown in Table 3, high expression of METTL3 was correlated with poor OS (HR = 2.72; 95% CI: 1.81–4.07, p < 0.001; I2 = 64.2%, p = 0.039) and DFS (HR = 2.58; 95% CI: 1.92–3.47, p < 0.001; I2 = 37.9%, p = 0.205) in gastric cancer. Moreover, high expression of METTL3 was significantly associated with poor OS in esophageal squamous cell carcinoma (HR = 2.20; 95% CI: 1.59–3.05, p < 0.001; I2 = 0.0%, p = 0.436) and oral squamous cell carcinoma (HR = 2.16; 95% CI: 1.33–3.49, p = 0.002; I2 = 0.0%, p = 0.602). However, the expression of METTL3 or METTL14 was not significantly associated with OS in colorectal cancer. The expression of FTO was also not significantly associated with OS in gastric cancer and pancreatic cancer. Furthermore, we did not find a significant association between YTHDF1 and OS in osteosarcoma.

Table 3. Subgroup analysis of the correlation between m6A regulators and cancer prognosis based on cancer types.

Regulators Cancer types Outcome Studies HR 95% Cl P-value Heterogeneity Effects model
I2 P-value
METTL3 oral squamous cell carcinoma OS 2 2.16 1.33–3.49 0.002 0.00% 0.602 Fix
esophageal squamous cell carcinoma OS 2 2.2 1.59–3.05 0 0.00% 0.436 Fix
gastric cancer OS 4 2.72 1.81–4.07 0 64.20% 0.039 Random
gastric cancer DFS 2 2.58 1.92–3.47 0 37.90% 0.205 Fix
colorectal cancer OS 3 1.59 0.48–5.26 0.448 92.9%, 0 Random
METTL14 colorectal cancer OS 2 0.51 0.26–1.00 0.051 53.50% 0.142 Random
FTO pancreatic cancer OS 2 1.32 0.48–3.60 0.586 65.90% 0.087 Random
gastric cancer OS 2 1.15 0.47–2.81 0.756 92.40% 0 Random
YTHDF1 osteosarcoma OS 2 0.95 0.58–1.54 0.833 0.00% 0.337 Fix
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Abbreviations: CI: confidence interval; HR: hazard ratio; OS: overall survival; DFS: disease-free survival.

Expression of m6A regulators and the clinicopathological parameters

As shown in Figure 4 and Table 4, high expression of METTL3 was associated with advanced pT stage (OR = 1.85; 95% CI: 1.40–2.45, p < 0.001; I2 = 47.4%, p = 0.055), pN stage (OR = 2.37; 95% CI: 1.58–3.56, p < 0.001; I2 = 63.7%, p = 0.001), TNM stage (OR = 2.61; 95% CI: 2.03–3.36, p < 0.001; I2 = 12.7%, p = 0.323), tumor size >5 cm (OR = 2.33; 95% CI: 1.51–3.61, p < 0.001; I2 = 0.0%, p = 0.886) and vascular invasion (OR = 1.47; 95% CI: 1.05–2.05, p = 0.024; I2 = 0.0%, p = 0.508). Conversely, high expression of METTL14 correlated negatively with pT stage (OR = 0.27; 95% CI: 0.13–0.58, p = 0.001; I2 = 0.0%, p = 0.739), pM stage (OR = 0.12; 95% CI: 0.03–0.46, p = 0.002; I2 = 0.0%, p = 0.497), pN stage (OR = 0.26; 95% CI: 0.09–0.73, p = 0.011; I2 = 60.6%, p = 0.079) and TNM stage (OR = 0.21; 95% CI: 0.13–0.34, p < 0.001; I2 = 0.0%, p = 0.575). Meanwhile, there was a statistical association between overexpression of ALKBH5 and negative vascular invasion (OR=0.39; 95%CI: 0.17-0.92, p = 0.032; I2 = 6.3%, p = 0.301, Figure 5). Furthermore, overexpression of YTHDF1 was associated with advanced pM stage (OR = 8.59; 95% CI: 2.58–28.60, p < 0.001; I2 = 0.0%, p = 0.863, Figure 5) and tumor size >5 cm (OR = 4.75; 95% CI: 2.47–9.14, p < 0.001; I2 = 0.0%, p = 1.000, Figure 5).

Figure 4.

Figure 4

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Forest plots for the association of METTL3 (A) and METTL14 (B) with clinicopathological parameters in cancer patients.

Table 4. The correlations between m6A regulators with clinicopathological characteristics in cancer patients.

m6A regulator Clinicopathological feature Studies (n) Patients (n) References OR (95% CI) P value Heterogeneity Effects model
I² (%) P value
METTL3 Depth of invasion (T3–T4 vs. T1–T2) 9 1057 Hua 2018; Lu 2020; Wang 2020; Xia 2020; Xu 2021; Yang 2020 (2); Xia 2019; Chen 2021; Guo 2022 1.85 (1.40–2.45) 0.000 47.4 0.055 Fix
Lymph Node Metastasis 13 1421 Liu 2020; Chen 2021; Guo 2022; Sun 2020; Lin 2022; Xia 2020; Xu 2022 (1); Xia 2019; Hua 2018; Lu 2020; Wang 2020; Yang 2020 (2); Xu 2021 2.37 (1.58–3.56) 0.000 63.7 0.001 Random
TNM Stage (T3–T4 vs. T1–T2) 11 1303 Xia 2019; Sun 2020; Xia 2020; Chen 2021; Lin 2022; Yang 2020 (2); Xu 2022 (1); Shengli 2022; Wang 2020; Deng 2019; Liu 2020 2.61 (2.03–3.36) 0.000 12.7 0.323 Fix
Tumor size (>5 cm vs ≤ 5 cm) 3 375 Wang 2020; Xu 2022 (1); Yang 2020 (2) 2.33 (1.51–3.61) 0.000 0.0 0.886 Fix
Vascular invasion 4 781 Sun 2020; Li 2019 (1); Xu 2022 (1); Yue 2019 1.47 (1.05–2.05) 0.024 0.0 0.508 Fix
Distant metastasis 9 1091 Chen 2021; Shengli 2022; Xu 2022 (1); Deng 2019; Sun 2020; Hua 2018; Lu 2020; Wang 2020; Yang 2020 (2) 1.93 (0.99–3.78) 0.054 67.5 0.002 Random
Clinical stage III–IV vs. II–II 4 688 Guo 2022; Li 2019 (1); Liu 2020; Lu 2020 1.05 (0.28–3.91) 0.936 89.7 0.000 Random
Differentiation (Poor vs. Moderate/Well) 4 997 Li 2019 (1); Xia 2020; Yang 2020 (2); Hua 2018 1.22 (0.65–2.30) 0.529 73.3 0.011 Random
Nerve invasion 3 724 Xu 2022 (1); Yue 2019; Li 2019 (1) 1.26 (0.92–1.74) 0.150 0.0 0.666 Fix
METTL14 Depth of invasion (T3–T4 vs. T1–T2) 2 285 Liu 2021; Yang 2020 (3) 0.27 (0.13–0.58) 0.001 0.0 0.739 Fix
Lymph Node Metastasis 3 357 Wang 2022; Yang 2020 (3); Liu 2021 0.26 (0.09–0.73) 0.011 60.6 0.079 Random
Distant metastasis 2 285 Liu 2021; Yang 2020 (3) 0.12 (0.03–0.46) 0.002 0.0 0.497 Fix
TNM Stage (T3–T4 vs. T1–T2) 4 466 Chen 2020; Liu 2021; Wang 2022; Yang 2020 (3) 0.21 (0.13–0.34) 0.000 0.0 0.575 Fix
Tumor size (>5 cm vs. ≤5 cm) 2 285 Liu 2021; Yang 2020 (3) 0.32 (0.05–2.14) 0.241 79.6 0.027 Random
ALKBH5 Vascular invasion 2 102 Guo 2020; Yang 2020 (1) 0.39 (0.17–0.92) 0.032 6.3 0.301 Fix
Clinical stage (III–IV vs. I– II) 2 148 Yang 2020 (1); Huang 2018 0.98 (0.07–13.96) 0.988 91.9 0.000 Random
Depth of invasion (T3–T4 vs. T1–T2) 4 775 Nagaki 2020; Huang 2018; Li 2019 (2); Yang 2020 (1) 0.84 (0.45–1.54) 0.564 56.7 0.074 Random
Differentiation (Poor vs. Moderate/Well) 4 729 Guo 2020; Li 2019 (2); Yang 2020 (1); Nagaki 2020 0.81 (0.41–1.59) 0.532 54.8 0.085 Random
Distant metastasis 2 510 Li 2019 (2); Yang 2020 (1) 0.37 (0.02–5.60) 0.475 71.7 0.060 Random
Lymph Node Metastasis 5 936 Li 2019 (2); Yu 2021; Nagaki 2020; Huang 2018; Yang 2020 (1) 0.94 (0.51–1.75) 0.851 65.4 0.021 Random
TNM Stage (T3–T4 vs. T1–T2) 3 715 Huang 2018; Nagaki 2020; Li 2019 (2) 1.03 (0.52–2.06) 0.925 69.3 0.039 Random
FTO Depth of invasion (T3-T4 vs. T1–T2) 2 578 Xu 2017; Li 2019 (2) 0.89 (0.62–1.28) 0.533 0 0.623 Fix
Differentiation (Poor vs. Moderate/Well) 4 997 Ruan 2021; Xu 2017; Li 2019 (2); Zeng 2021 0.77 (0.34–1.77) 0.537 78.3 0.003 Random
Distant metastasis 3 902 Xu 2017; Li 2019 (2); Ye 2020 1.19 (0.72–1.95) 0.502 0 0.515 Fix
Lymph Node Metastasis 3 628 Xu 2017; Li 2019 (2); Zeng 2021 0.76 (0.22–2.67) 0.671 83.5 0.002 Random
Nerve invasion 2 419 Ruan 2021; Zeng 2021 0.71 (0.42–1.22) 0.218 0 0.687 Fix
TNM Stage (T3–T4 vs. T1–T2) 4 997 Ruan 2021; Zeng 2021; Xu 2017; Li 2019 (2) 0.98 (0.42–2.29) 0.969 82.8 0.001 Random
YTHDF1 Distant metastasis 2 113 Nishizawa 2018; Wei 2022 8.59 (2.58–28.60) 0.000 0.0 0.863 Fix
Tumor size (>5 cm vs ≤5 cm) 2 170 Li 2021; Wei 2022 4.75 (2.47–9.14) 0.000 0.0 1.000 Fix
Lymph Node Metastasis 3 716 Wei 2022; Nishizawa 2018; Tsuchiya 2021 1.73 (0.38–7.80) 0.476 84.1 0.002 Random
TNM Stage (T3–T4 vs. T1–T2) 3 716 Wei 2022; Nishizawa 2018; Tsuchiya 2021 1.83 (0.42–7.94) 0.418 88.4 0.000 Random
Vascular invasion 2 183 Li 2021; Nishizawa 2018 1.55 (0.21–11.37) 0.665 85.7 0.008 Random
YTHDF2 Lymph Node Metastasis 2 676 Tsuchiya 2021; Xu 2022 (2) 1.59 (0.20–12.53) 0.660 92.9 0.000 Random
TNM Stage (T3–T4 vs. T1–T2) 2 676 Tsuchiya 2021; Xu 2022 (2) 1.85 (0.30–11.54) 0.512 90.0 0.002 Random
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Abbreviations: CI: confidence interval; OR: odds ratio.

Figure 5.

Figure 5

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Forest plots for the association of ALKBH5 (A), FTO (B), YTHDF1 (C) and YTHDF2 (D) with clinicopathological parameters in cancer patients.

Sensitivity analysis

We omitted individual studies successively to estimate the impact of each study in our meta-analysis. No individual study modified the pooled HR of included studies reporting OS or DFS significantly, which proved that the results were stable (Figure 6).

Figure 6.

Figure 6

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Sensitivity analysis of METTL3 (A), METTL14 (B), ALKBH5 (C), FTO (D), and YTHDF1 (E) for OS. Sensitivity analysis of METTL3 (F) for DFS.

Publication bias

Funnel plots were generated to detect publication bias (Figure 7). The studies were distributed uniformly around the axis, indicating no obvious publication bias. Meanwhile, no obvious publication bias was found according to Begg’s test and Egger’s test (Table 5).

Figure 7.

Figure 7

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Funnel plot of METTL3 (A), METTL14 (B), ALKBH5 (C), FTO (D) and YTHDF1 (E) for OS. Funnel plot of METTL3 (F) for DFS.

Table 5. Publication bias test of included studies in our meta-analysis.

Regulators Outcome Begg’s test (P value) Egger’s test (P value)
METTL3 OS 0.415 0.319
METTL4 OS 0.308 0.229
ALKBH5 OS 0.174 0.290
FTO OS 0.592 0.571
YTHDF1 OS 0.260 0.117
METTL3 DFS 0.230 0.083
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Abbreviations: OS: overall survival; DFS: disease-free survival.

DISCUSSION

m6A modification, a reversible epigenetic modification regulated by three types of proteins (writers, erasers and readers), plays a complicated role in cancer initiation and development [14, 71, 72]. Recent studies have explored how m6A regulators influenced the prognosis of cancer patients. However, results were frequently inconsistent among different cancer types. Therefore, a comprehensive study to summarize the results from current publications is necessary. To report prognostic value of m6A regulators in cancer patients, we analyzed the survival time and clinicopathological features of 7006 patients from 49 studies who expressed different levels of m6A regulators. Results showed that expression level of m6A writers was related to cancer prognosis. In addition, different m6A writers had opposite associations with the prognosis and clinicopathological features in cancer patients. According to the results, there was a possible trend for poor OS and DFS in patients with the high expression of METTL3. Similarly, previous bioinformatic analysis from databases like TCGA, GEO and HPA, supported that high expression of METTL3 was correlated with unfavorable prognosis in various cancers, including gastric cancer [73], colorectal cancer [74], liver cancer [75], prostate cancer [76] and glioma [77]. In most of these databases, RNA-seq was used to detect the level of METTL3. Moreover, a previous meta-analysis including 9 studies showed that high METTL3 expression was associated with poor prognosis in cancer patients, and the expression of METTL3 in included 9 studies were all detected by qRT-PCR. While in the studies included in our analysis, METTL3 was detected only by IHC staining. Combining our studies with the results from databases, we can conclude that METTL3 is related to cancer prognosis at protein level, which strongly suggests that it could be a prognostic predictor. Additionally, this tendency was more prominent in gastric cancer. Previous studies indicated that in human gastric cancer cells, high expression of METTL3 stimulates the expression of GLUT4 and ENO2 via the METTL3/HDGF axis, thereby promoting tumor angiogenesis and glycolysis [6]. Moreover, Ben Yue et al. unveiled that METTL3 stabilized ZMYM1 mRNA in gastric cancer cells, which facilitated EMT and metastasis by repressing E-cadherin promoter [26]. These might account, at least to some extent, for the poor survival of patients with gastric cancer. Furthermore, aberrant expression of METTL3 was involved in the dysfunction of cellular signaling pathways, such as MAPK [74], JAK/STAT [78], PI3K/AKT [79, 80] and Wnt/β-catenin [81] cascades, which are involved in tumor progression, metastasis, migration and stemness. We also found that high expression of METTL3 was associated with advanced TNM stage and pT stage, pN stage, tumor size >5 cm and vascular invasion respectively. Therefore, based on these current results, we believe that METTL3 plays an important role in multiple stages of cancer progression and ultimately affects prognosis. Interestingly, in contrast to METTL3, METTL14, another m6A methylation writer, might be a positive prognosticator. Previous studies have shown that METTL14 might have various functions that have not been fully identified yet, thus its role in cancer remained controversial [82]. In this study, our result confirmed that high level of METTL14 was associated with better OS. Different studies have reported that METTL14 suppressed progression and metastasis in several cancers, such as colorectal cancer [83] and hepatocellular carcinoma [84]. Besides, Panneerdoss et al. found that in METTL14-silenced breast cancer cells, RhoA and PI3K-AKT signaling pathways were highly enriched, which are well-known to be mediators of cancer progression and angiogenesis [85]. Moreover, our study showed that high expression of METTL14 was inversely associated with poor TNM stage, pT stage, pN stage and pM stage. Combining the results of other studies and ours, we inferred that METTL14 plays a role in cancer suppression and could be a favorable index of cancer progression and prognosis. Moreover, METTL3 and METTL14 show completely contrary effects on cancer progression, indicating that METTL3 and METTL14 may have some biological functions that are independent of m6A modification, which deserves further study.

Besides, from the analysis results of clinicopathological features, high expression of YTHDF1 was associated with advanced pM stage and tumor size >5 cm, while high expression of ALKBH5 was negatively associated with vascular invasion. Consistently, a recent study reported that YTHDF1 regulates CRC tumorigenesis and metastasis by promoting ARHGEF2 translation and RhoA signaling in colorectal cancer [20]. High YTHDF1 level is significantly associated with metastatic gene signature in colorectal cancer, while YTHDF1-knockout mice inhibited tumor growth in vivo [20]. Therefore, targeting the YTHDF1-m 6 A-ARHGEF2 axis may be a promising therapeutic strategy to inhibit tumor growth, invasion, and metastasis. In addition, ALKBH5, as the second m6A demethylated enzyme discovered after FTO, was reported to promote tumor stem formation in gliomas and promote tumor progression in breast cancer, colon cancer and hepatocellular carcinoma [85, 86]. Conversely, ALKBH5 could inhibit tumor growth in bladder cancer and pancreatic cancer. These findings suggest the complexity of the action of ALKBH5 in cancers. However, no significant relationship was found between high expressions of m6A erasers or readers and poor prognosis. Limitation of sample size and a certain degree of heterogeneity may partly account for this. Additionally, the mechanisms of m6A modification and cancers are complicated [87]. Therefore, more studies are needed to provide further mechanistic insights.

To the best of our knowledge, this is the first study to conduct a meta-analysis of the association between m6A regulators and the prognosis and clinicopathology in cancer patients systematically. Nonetheless, there are still several limitations in our meta-analysis. First, several original data were not available, therefore we had to extract data from the Kaplan-Meier survival curves and this might increase the inaccuracy in our study. Secondly, the ethnicity of included patients was mostly Asian, which may increase the population selection bias. Thirdly, IHC was adopted to detect the expression of m6A regulators in all studies, but the IHC protocols, antibodies and cut-off values were not consistent across the included studies, which may have led to significant heterogeneity between included studies. Therefore, future research should standardize the cut-off values for the expression of m6A regulators, detection antibodies used and IHC staining protocols to better compare the results of different studies. In summary, our meta-analysis provides evidence that the expression level of m6A writers is related to cancer progression and prognosis. Different m6A writer proteins play different roles in patients’ outcome: high expression level of METTL3 is significantly associated with poor prognosis, while high expression of METTL14 leads to better survival rate. Both m6A regulators possess a great potential to become practicable prognosticators in various cancers. Meanwhile, future studies with more complete and representative datasets are required for further exploration.

METHODS

Literature search

Relevant articles published up to April 2022 were obtained from PubMed, Embase, Web of Science and the Cochrane library. There were no restrictions on language or date of publication. “N (6)-methyladenosine” and “cancers” were the two main key words we used. The comprehensive search strategy for each database is provided in Supplementary Table 1. All references were managed using EndNote X9. Three reviewers independently analyzed search results. Any disagreements between reviewers were resolved by discussion.

Inclusion and exclusion criteria

The process of selecting eligible studies was conducted by three reviewers independently. Articles were included when they met the following inclusion criteria: (1) the text evaluated the relation between m6A regulators expression and cancer prognosis; (2) HR and 95% CI were reported or could be calculated from the text; (3) original research; (4) the expression of m6A regulators in tissues was detected by immunohistochemistry; (5) patients were confirmed cancers definitively. The exclusion criteria were: (1) reviews, letters, meeting abstracts; (2) nonhuman studies; (3) sample cases were from databases; (4) duplicate data; (5) studies did not provide necessary and complete data.

Data extraction and quality assessment

The following information were extracted from eligible studies independently by three researchers: author, published year, country, m6A regulators, cancer types, cancer stage, sample size, gender, cut-off value of m6A regulators and survival data including OS, DFS and RFS. The HR with its 95% CI were extracted from the text directly or calculated from Kaplan-Meier survival curve using Engauge Digitizer. The quality of the included studies was evaluated using the Newcastle Ottawa Scale (NOS) criteria. NOS scores range from 0 to 9. It would be considered as high-quality study if score was more than 5; otherwise, it would be considered as low-quality study. Only studies with NOS ≥ 6 were finally selected for inclusion in meta-analysis. Disagreements were resolved by discussion.

Statistical analysis

The pooled HR and 95% CI were used to evaluate the relation between m6A regulators and cancer prognosis (OS, DFS and RFS). The pooled odds ratio (OR) and 95%CI were used to evaluate the relationship between m6A regulators and clinicopathological parameters. HRs or ORs >1 represented a poor prognosis in cancer. Heterogeneity among the studies was evaluated by Coltrane’s Q statistic and the I2 statistic. If a p < 0.1 or I2 > 50%, we applied a random-effect model. Otherwise, a fixed-effect model was applied. Subgroup analysis was conducted according to cancer types. In the sensitivity analysis, we omitted individual studies successively to estimate the impact of each study in our meta-analysis. Begg’s test and Egger’s test were used to evaluate publication bias. A two-tailed p value < 0.05 was considered statistically significant in all statistical tests. All data analyses were performed using StataSE15.1 (Stata Corporation, College Station, TX, USA).

Supplementary Materials

Supplementary Table 1

ACKNOWLEDGMENTS

We would like to thank all researchers for their contributions.

Abbreviations

METTL3

Methyltransferase Like 3

METTL14

Methyltransferase Like 14

METTL16

Methyltransferase Like 16

RBM15

RNA-binding protein 15

RBM15B

Putative RNA-binding protein 15B

HNRNPC

Heterogeneous nuclear ribonucleoproteins

HNRNPA2B1

Heterogeneous nuclear ribonucleoproteins A2/B1

YTHDF1

YTH domain-containing family protein 1

YTHDF2

YTH domain-containing family protein 2

YTHDF3

YTH domain-containing family protein 3

YTHDC1

YTH domain-containing protein 1

FTO

Alpha-ketoglutarate-dependent dioxygenase FTO

ALKBH5

RNA demethylase ALKBH5

OS

overall survival

DFS

disease-free survival

RFS

recurrence-free survival

HR

hazard ratio

OR

odds ratio

M/F

male/female

NA

not available

cut-off value

the value that can be diagnosed as positive/high expression of a m6A regulator

IHC

immunohistochemistry

IF

immunofluorescence

qRT-PCR

quantitative reverse transcription polymerase chain reaction

P

prospective

CI

confidence interval

AUTHOR CONTRIBUTIONS: Zhangci Su, Leyao Xu, Xinning Dai and Yun Wang conceived and designed the study. Zhangci Su, Leyao Xu and Xinning Dai analyzed the data, prepared the figures and tables, and wrote the paper. Mengyao Zhu validated the data. Xiaodan Chen, Yuanyuan Li, Jie Li and Ruihan Ge contributed analysis tools and materials. Yun Wang and Bin Cheng reviewed drafts of the paper and participated in its coordination. All authors read and approved the final manuscript.

CONFLICTS OF INTEREST: The authors declare no conflicts of interest related to this study.

FUNDING: This work was supported by Guangzhou Science and Technology Project (Grant Number: 201804010040); Guangdong Basic and Applied Basic Research Foundation (Grant Number: 2019A1515011203); Sun Yat-sen University Young Teacher Cultivation Project (Grant Number: 18ykpy29); Science and Technology Planning Project of Guangzhou, China (Grant Number: 201704020063).

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Supplementary Table 1
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