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Abstract

The presence of distant metastasis is associated with an adverse outcome in papillary thyroid cancer. We performed a meta-analysis to investigate the role of molecular markers as predictors for distant metastasis in papillary thyroid cancer. Four electronic databases including PubMed, Web of Science, Scopus, and Virtual Health Library were searched, and odds ratio and its 95% confidence interval concerning the association of BRAF, RAS, and TERT promoter mutations and RET/PTC rearrangements with distant metastasis were calculated using random-effects model. In total, 42 studies with 11,109 papillary thyroid cancers were included for meta-analyses. Overall, the presence of TERT promoter (odds ratio = 5.95; 95% confidence interval = 2.95–11.99), RAS mutations (odds ratio = 2.5; 95% confidence interval = 1.00–6.22), and RET/PTC rearrangements (odds ratio = 1.92; 95% confidence interval = 1.03–3.56) were found to be associated with a significantly increased risk for distant metastasis. BRAF mutations were not associated with an elevated risk for distant metastasis (odds ratio = 0.79; 95% confidence interval = 0.54–1.16). In conclusion, our study demonstrated the promising value of few molecular biomarkers, especially TERT promoter mutations in predicting distant metastasis in papillary thyroid cancers, while BRAF mutations showed no association with distant metastasis. Our study affirms the value of selected mutations for tumor risk stratification and assessment of patients’ prognosis.

Keywords

Papillary thyroid cancer distant metastasis

Introduction

Thyroid cancer is the most common among endocrine malignancies, and its incidence has been increasing rapidly over the years.¹ Papillary thyroid cancer (PTC) is the most common histological type, accounting for about 90% of thyroid cancer.^2,3 Generally, the prognosis of PTC patients is excellent with a survival rate of more than 95% and usually a good response to initial treatment.³ Nonetheless, there is a small group of PTCs that behave aggressively at presentation, develop distant metastasis (DM), and are associated with an increased rate of mortality.⁴ DM is not a frequent event in PTCs, accounting about 2%–5% of cases,^5–8 but the presence of DM has been well known as an adverse prognostic factor in PTCs.^5,9–11 In the latest guidelines of the American Thyroid Association (ATA), differentiated thyroid cancers (DTCs) with DM are classified as ATA high risk.⁴

Several clinicopathological factors such as old age, large size, vascular invasion, and extrathyroidal extension have been demonstrated to be risk factors for DM in DTC.^5,12,13 However, there are ongoing debates in other studies on this topic.^14–17 There has been rapid development in understanding the pathogenesis and genetic profiles of thyroid cancer in recent years. A few genetic events have been shown to be associated with aggressive behaviors and poor outcomes in PTC.^6,18,19 In addition to having worse impact on patients’ survival, the survival rate of thyroid cancer patients with DM has not improved over the last two decades despite the fact that there have been huge improvements in patient management and treatment strategies.²⁰ Therefore, it is really needful to identify reliable molecular markers that are predictive of DM in PTCs.

In this study, we aimed to perform a systematic review and meta-analysis of observational studies to investigate the role of molecular markers in predicting DM in PTCs.

Methods

Literature search

We searched for relevant studies in four electronic databases including PubMed, Scopus, Web of Science, and Virtual Health Library from inception to November 2016. We used the following search terms: “BRAF or TERT or RAS or RET/PTC”; “papillary thyroid”; and “carcinoma or cancer.” Additional studies were found by reviewing the citations within the included publications and other reviews. Our study protocol is primarily based on the recommendation of Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement.²¹

Selection criteria and abstract screening

We imported all search results from four libraries into Endnote (Thomson Reuters) and deleted duplicates. Two reviewers independently screened the titles and abstracts of all included articles using the following inclusion criteria: (1) studies on PTC population and (2) studies in which the authors reported the association of DM with mutational status of any of the following genetic alterations (BRAF, TERT promoter, RAS mutations, and RET/PTC fusions). We excluded studies if they were not focused on (1) PTCs or genetic events described above; (2) reviews; (3) case reports; (4) conference papers, posters, theses, books; or (5) data sets considered as overlapping or duplicated articles. Any discordant results between reviewers were resolved by discussion and consensus.

Full-text screening and data extraction

We downloaded corresponding full-text of all included articles following abstract screening steps, and two reviewers independently screened these full-texts. Data were extracted into a predefined extraction form. Any discrepancies were resolved again by discussion and consensus. The following details were extracted: institution, city, country, publication year, surgical period of included patients, types of genetic events, detection method, gender, age, follow-up duration, number of overall DM, DM at the time of diagnosis, and DM during follow-up. Those studies with a number of cases with DM less than five were further excluded in this step.

Risk of bias assessment

The methodologic quality of included studies was evaluated using the Newcastle–Ottawa Scale (NOS) for quality of cohort and case-control studies in our meta-analysis.²² Stars were given for each study (maximum nine stars) based on a developed checklist.²² Studies with at least six stars were considered as moderate- to high-quality studies and those with less than six stars were regarded as low-quality studies.

Data analysis

We used Review Manager version 5.3 (Cochrane Collaboration, Oxford, UK) for data analysis. Random-effects model was used to calculate pooled estimates of odds ratio (OR) and 95% confidence interval (CI). We assessed the heterogeneity across the included studies by the I² statistic.²³ An I² statistic <25% indicates low amount of heterogeneity and >50% indicates high amount of heterogeneity.²⁴ We used subgroup analyses and meta-regression to determine whether study-level covariates accounted for the heterogeneity among studies. Meta-regression analysis was calculated by Meta-Essentials: Workbook for meta-analysis.²⁵ Egger’s regression test and funnel plot observation were used to evaluate the presence of publication bias, investigated by using MAVIS version 1.1.2—a R package.²⁶ A p value less than 0.05 was considered to be statistically significant publication bias.

Results

A total of 7505 and 2647 articles were found after searching four electronic databases and deleting duplicates, respectively. We identified 136 potential studies following title and abstract screening. In total, 94 articles were excluded after reading full-text and leaving 42 studies with 11,253 patients for final analyses (Figure 1). Characteristics of all included studies are described in Table 1.

Figure 1.

Study flowchart.

Table 1.

Characteristics of included studies.

Author	Institution	Mean age	Number of cases		Genetic events	Detection method	NOS quality assessment
Author	Institution	Mean age	With DM	Without DM	Genetic events	Detection method	Selection	Comparability	Exposure
Abubaker et al.²⁷	King Faisal Specialist Hospital	ND	30	257	BRAF	DS	3	0	2
Alzahrani et al.⁶²	King Faisal Specialist Hospital	15.3	8	46	BRAF	DS	3	1	3
Bae et al.¹²	Seoul St. Mary’s Hospital	46.1	27	172	BRAF, TERT	DS	3	0	2
Bastos et al.²⁸	University of São Paolo	ND	5	113	BRAF, RAS	DS	3	0	3
					RET/PTC	RT-PCR	3	0	3
Daliri et al.²⁹	Ghaem Hospital	40.4	12	57	BRAF	PCR-RFLP	3	0	3
Elisei et al.³⁰	University of Pisa	42.8	6	94	BRAF	PCR-SSCP/DS	3	1	3
Fernandez et al.³¹	University of Bologna	50.1	5	292	BRAF	DS	3	0	3
Fraser et al.⁸	University of Sydney	47.5	9	487	BRAF	IHC	3	0	2
Fugazzola et al.⁷	University of Pisa, University of Perugia, and University of Milan	40.0	10	250	BRAF	PCR-SSCP/DS	3	1	3
Galuppini et al.³²	University of Padua	49.0	5	179	BRAF	DS/MASA-PCR	4	1	2
Gandolfi et al.³³	Arcispedale S. Maria Nuova	48.1	43	78	BRAF, TERT	DS	3	0	3
George et al.³⁴	MD Anderson Cancer Center	45.3	62	194	BRAF, TERT	NGS	3	0	3
Givens et al.³⁵	University of Utah	13.6^a	5	14	BRAF	Pyroseq	3	1	2
Gouveia et al.³⁶	University of California at San Francisco	46.8	11	418	BRAF	FMCA	3	0	2
Hara et al.³⁷	University of Chicago Medical Center	ND	29	62	RAS	OH	3	0	3
Henke et al.³⁸	Washington University	45.5	15	493	BRAF	PCR-RFLP	3	0	3
Ito et al.³⁹	Kuma Hospital	50.8	37	729	BRAF	DS	3	1	3
Jing et al.⁴⁰	Peking Union Medical College Hospital	37.5	42	64	BRAF	qPCR	3	0	2
Joung et al.⁴¹	Samsung Medical Center	32.0	5	32	BRAF, RET	qPCR	3	0	2
Kim et al.⁴²	Samsung Medical Center	44.0^a	25	384	TERT	DS	3	0	3
McKelvie et al.⁴³	St. Vincent’s Hospital	46.0^a	7	70	BRAF	IHC	3	0	3
Melo et al.⁴⁴	Multicenter	44.8	36	193	TERT	DS	3	0	3
Ming et al.⁴⁵	Union Hospital	ND	7	57	BRAF	FMCA	3	1	2
					RAS	LH-MSA
					RET/PTC	Nested PCR
Mond et al.⁴⁶	Victorian Hospital	ND	6	84	BRAF	DS	3	0	2
Musholt et al.⁴⁷	Hanover Medical School	48.4	35	245	BRAF	DS	3	0	3
					RET/PTC	RT-PCR	3	0	3
Namba et al.⁴⁸	Nagasaki University	52.0	12	114	BRAF	DS	3	0	3
Nasirden et al.⁴⁹	Juntendo University of Medicine	56.4	21	123	BRAF, TERT	DS	3	0	3
O’Neill et al.⁵⁰	The University of Sydney	45.8	5	96	BRAF	PCR-RFLP	3	0	3
Onder et al.⁵¹	Istanbul University	14.7	5	45	BRAF	DS	3	1	3
Qasem et al.⁵²	King Faisal Specialist Hospital	31.3	18	133	TERT	DS	3	0	2
Romitti et al.⁵³	Hospital de Clinicas de Porto Alegre	37.0	6	19	BRAF	DS	3	0	2
					RET/PTC	Nested PCR	3	0	2
Sancisi et al.⁵⁴	Arcispedale S. Maria Nuova	48.6	47	75	BRAF	DS	3	0	3
Sassolas et al.⁵⁵	Laennec School of Medicine	25.6	7	83	BRAF	DS	3	0	2
					RAS	FMCA
					RET/PTC	RT-PCR
Song et al.⁶	Seoul National University Hospital	45.5	7	411	BRAF, TERT	DS	3	0	2
Stanojevic et al.⁵⁶	Institute of Oncology and Radiology of Serbia	48.1	9	257	BRAF, RAS	DS	3	1	3
					RET/PTC	RT-PCR	3	1	3
Sykorova et al.⁵⁷	Multicenter (Czech)	47.0^a	12	179	BRAF	DS	3	1	2
Tallini et al.⁵⁸	Yale University	ND	7	194	RET/PTC	RT-PCR	3	0	2
Xing et al.¹⁹	Multicenter (seven countries)	46.0^a	110	1716	BRAF	Various methods	3	0	3
Xing et al.⁵⁹	The John Hopkins Hospital	45.6	22	485	BRAF, TERT	DS	3	1	2
Zheng et al.⁶⁰	Tianjin Medical University Cancer Institute	43.5	30	482	BRAF	DS	3	0	3
Zheng et al.⁶¹	Tianjin Medical University Cancer Institute	45.9	15	962	BRAF	DS	3	0	3

NOS: Newcastle–Ottawa Scale; DM: distant metastasis; ND: no description; DS: direct sequencing; RT-PCR: reverse transcription polymerase chain reaction; PCR-RFLP: PCR–restriction fragment length polymorphism; PCR-SSCP: PCR–single-strand conformation polymorphism; IHC: immunohistochemistry; MASA-PCR: mutant allele–specific amplification–PCR; NGS: next generation sequencing; Pyroseq: pyrosequencing; FMCA: fluorescent melting curve analysis; OH: oligonucleotide hybridization; qPCR: quantitative real-time PCR; LH-MSA: loop-hybrid mobility shift assay.

Median value of age.

TERT promoter mutations

Overall, nine studies with 2434 PTC patients had sufficient data to be included for meta-analysis.^{6,12,33,34,42,44,49,52,59} Our analysis showed that 38.2% and 8.8% of PTCs with and without DM, respectively, had TERT promoter mutations. The presence of these mutations was associated with a significantly increased risk for DM in PTCs (OR = 5.95; 95% CI = 2.95–11.99; Figure 2(a)). A high amount of heterogeneity was found among studies (I² = 69%). Between-study inconsistency remained significant following the leave-one-out method but the significant effects were unaffected.

Figure 2.

Forest plots concerning the association of distant metastasis with (a) TERT promoter, (b) BRAF, (c) RAS mutations, and (d) RET/PTC fusions.

BRAF mutations

Relevant data were found in 36 studies,^{6–8,12,19,27–36,38–41,43,45–51,53–57,59–62} PTC population from the following studies^{7,8,30–32,50,57,59} could overlap with PTCs from the multicenter study by Xing et al.¹⁸ In addition, patients from the studies by Gandolfi et al.,³³ Zheng et al.,⁶¹ and Alzahrani et al.⁶² possibly overlapped with patients from the studies by Sancisi et al.,⁵⁴ Zheng et al.,⁶⁰ and Abubaker et al.,²⁷ respectively. We selected studies with higher number of cases for meta-analysis.

Finally, 25 studies comprising 6884 PTC patients were included for this meta-analysis. BRAF mutations were detected in 51.3% and 51.6% of PTCs with DM and without DM, respectively. The pooled result indicated that the presence of BRAF mutations was associated with a decreased risk of DM but the difference was not statistically significant (OR = 0.79; 95% CI = 0.54–1.16; Figure 2(b)). A high amount of heterogeneity among included studies was found (I² = 64%). The overall results were robust following the sensitivity analysis but the heterogeneity across studies still existed.

RAS mutations

Overall, five studies including 629 patients were included for analysis^{28,37,45,55,56} RAS mutations were identified in 15.8% of DM-PTCs and 6.5% of non-DM-PTCs. RAS mutations were associated with a significantly higher risk for DM (OR = 2.5; 95% CI = 1.00–6.22; Figure 2(c)). The significant result was dominated by a single study.³⁷ No among-study heterogeneity was found (I² = 0%).

RET/PTC rearrangements

There were nine studies comprising 1200 PTCs included for meta-analysis.^{28,41,45,47,53,55,56,58,63} The population from the two studies^47,63 could duplicate with each other, and the study with higher number of cases was selected. The prevalence of RET/PTC in groups of PTC with and without DM were 24.7% and 21.9%, respectively. There was a significant association of RET/PTC with an increased risk for DM (OR = 1.92; 95% CI = 1.03–3.56; Figure 2(d)). There was no heterogeneity among the included studies (I² = 0%).

Coexisting mutations

We performed an additional meta-analysis to examine the impact of PTCs with coexisting mutations on DM as compared with PTCs with each mutation alone. There were very limited data on this topic, and we could only find data regarding the association of coexisting mutations with DM in only four studies which provided data on interaction between BRAF and TERT mutations.^6,33,44,59 Our pooled analysis demonstrated that PTCs with concomitant BRAF and TERT mutations were associated with a significantly increased risk for DM as compared with PTCs harboring BRAF mutation alone (OR = 17.49; 95% CI = 5.05–60.54; I² = 44%). Meanwhile, PTCs with dual mutations showed no increased risk for DM as compared with PTCs with TERT mutation only (OR = 0.92; 95% CI = 0.20–4.18; I² = 54%).

Quality of studies

The NOS tool was used to assess the quality of included studies. The number of stars awarded to each study ranged from five to seven stars. Details of given stars within each domain of NOS are described in Table 1.

Subgroup analyses and meta-regression

We performed subgroup analyses according to the region of origin (Caucasian and Asian), time of DM (at presentation and during follow-up), and mutation detection methods. TERT promoter mutation showed its significant risk for DM in all subgroups, except subgroup of DM at the time of diagnosis. Details of subgroup analyses are described in Table 2.

Table 2.

Subgroup analyses.

Subgroup	Number of studies	Number of patients	OR	95% CI	I² (%)
Study origin
TERT promoter mutations
Caucasian	4	1099	5.13	2.93–8.96	38
Asian	4	1191	10.67	1.19–95.83	86
BRAF mutations
Caucasian	19	3870	0.69	0.49–0.97	13
Asian	10	2584	0.88	0.44–1.74	76
RAS mutations
Caucasian	4	565	2.62	1.01–6.80	0
Asian	1	64	1.48	0.06–33.88	NA
RET/PTC rearrangements
Caucasian	6	980	1.73	0.79–3.79	19
Asian	2	101	2.81	0.67–11.72	0
Time of DM
TERT promoter mutations
At presentation	4	1222	4.14	0.79–21.65	82
During follow-up	2	628	7.49	3.45–16.28	26
BRAF mutations
At presentation	18	4694	0.75	0.49–1.13	29
During follow-up	6	2067	1.26	0.80–1.98	0
RAS mutations
At presentation	2	330	1.28	0.15–10.76	0
During follow-up	2	208	1.23	0.21–7.33	0
RET/PTC rearrangements
At presentation	4	784	1.24	0.55–2.80	0
During follow-up	2	208	3.78	1.10–13.03	5
Detection methods
BRAF mutations
Direct sequencing	17	4337	0.88	0.56–1.37	58
Other methods	11	2321	0.57	0.33–0.99	36
TERT promoter mutations
Direct sequencing	7	2048	6.81	2.53–18.34	77
Other methods	1	242	4.9	2.62–9.13	NA
RAS mutations
Direct sequencing	2	384	0.96	0.12–7.62	0
Other methods	3	245	3.14	1.14–8.68	0
RET/PTC rearrangements
Reverse transcription PCR	5	955	1.62	0.67–3.92	33
Other methods	3	126	2.95	0.82–10.65	0

OR: odds ratio; CI: confidence interval; NA: not available; DM: distant metastasis; PCR: polymerase chain reaction.

In the meta-regression of the 24 studies on the association between BRAF and DM, heterogeneity across studies was not explained by year of publication (β = −0.04; p = 0.39), geographic origin (β = −0.10; p = 0.71), mutation detection method (β = −0.62; p = 0.13), or quality of studies (β = 0.43; p = 0.306). We did not perform meta-regression analysis on the association between TERT promoter mutations and DM because of small amount of included studies.

Publication bias

Funnel plot observation and Egger’s regression test did not show strong evidence of publication bias among the set of studies except for the meta-analysis of RAS mutations (p = 0.009; see Supplementary Figures 1–4).

Discussion

DM is a well-known prognostic factor in thyroid cancer, and its presence is associated with an increased risk of mortality.⁴ Several studies have investigated the clinicopathological risk factors for DM in DTCs but there are inconsistencies among various studies.^14,15,20 Recently, in three decades, novel genetic alterations in thyroid cancer have been discovered in thyroid cancer with the development of translational medicine. It is very essential to investigate the usefulness of these molecular markers to discriminate aggressive tumors from those with an indolent course to further tailor the initial therapeutic decisions and the long-term surveillance as well as to avoid overtreatment. There have been a number of studies reporting the association of molecular biomarkers and DM with PTCs but there are still conflicting results in the literature. As a result, there is a definite need to perform a meta-analysis to clarify the results.

RAS mutations have long been identified in thyroid cancer.⁶⁴ RAS-positive follicular thyroid adenoma is currently hypothesized to be the precursor lesion for RAS-mediated development of follicular thyroid cancer (FTC) and follicular variant PTC.⁶⁵ In FTC, RAS mutations were reported to associate with DM.^46,66,67 Our pooled results indicate that RAS mutations are also associated with an increased risk for DM in PTCs. This statistically significant result is largely driven by a single study³⁷ and the number of included studies is quite small. In addition, the result for RAS mutations is in any case only just statistically significant, with 1.00 as one of the CI endpoints. This result, therefore, should be interpreted with caution. Additional studies with larger numbers of PTC cases with DM are required to clarify this risk. The presence of RAS mutations in PTCs has been known for a long time, yet so few studies with data on association with DM have been found in our study. One possible reason is that RAS mutations are not common in conventional PTCs and only prevalent in follicular variant PTCs which behave an indolent course.⁶⁸ RAS-positive tumors only behave aggressively when coexisting with other genetic alterations, especially TERT promoter mutations.^6,69

The RET proto-oncogene is located on chromosome 10q11.2 and encodes for a transmembrane tyrosine-kinase receptor which regulates the cell growth and differentiation. It is well established that RET/PTC rearrangements are highly prevalent in radiation-induced PTCs.^58,70 A correlation between advanced tumor stage and RET/PTC rearrangements, especially RET/PTC3, has been reported previously.^71–73 However, there is ongoing debate and no consensus has been reached so far concerning the association of RET/PTC with clinical course.⁷⁴ Our meta-analysis demonstrates a significant association of RET/PTC with an increased risk for DM.

Over the last two decades, the association of BRAF mutations, especially BRAF V600E, with clinicopathological characteristics in PTCs has been extensively studied. This mutation has been reported to be associated with aggressive features and poor outcomes^18,75 but there are remarkable inconsistencies among the literature.^38,39 The correlation of BRAF mutations with clinicopathological features such as nodal metastasis or extrathyroidal extension has been reported in a number of studies but information regarding its relationship with spread to distant organs is very poor.⁷⁶ BRAF V600E was found to be more prevalent in PTCs with DM in a multicenter study¹⁸ but controversial results were found in other studies.^12,33,54 Therefore, a more powerful statistical method, meta-analysis, is required to clarify this result. Our pooled analysis shows that BRAF mutations were not significantly associated with an increased risk for DM. Our results further suggest that BRAF mutations mostly correlate with locoregional invasion (extrathyroidal extension and cervical nodal metastasis) which has been clearly demonstrated in previous meta-analyses^76,77 but do not associate with potential spread to distant sites. The role of BRAF mutations as a negative prognostic factor, therefore, should be carefully considered.

The recently discovered genetic event in thyroid cancer, TERT promoter mutations, has also been reported to be in association with aggressive phenotypes and poor outcomes.^78–80 TERT promoter mutations were found to be an independent risk factor for DM in DTCs.^12,33,44 Following primary and subgroup analyses, we demonstrate a strong association between TERT promoter mutations and DM. Concomitant mutations in thyroid cancer have been proposed to enhance tumor aggressiveness and worsen patients’ survival as well, especially coexisting TERT promoter and BRAF mutations in PTCs.^81–83 Our results show that PTCs with coexisting BRAF and TERT mutations showed a significantly increased risk for DM when compared with PTCs harboring BRAF only (OR = 17.49; 95% CI = 5.05–60.54) but did not differ in risk for DM when compared with PTCs harboring TERT alone (OR = 0.92; 95% CI = 0.20–4.18). These findings indicate that the risk of patient is not increased by the interaction between BRAF and TERT but mainly linked to TERT status alone, thus supporting the independent prognostic value of TERT, irrespective of BRAF status. Our results strongly affirm the usefulness of TERT promoter mutation as an independent and reliable molecular marker to predict DM in PTCs and thus can be considered highly useful for risk stratification and to guide therapeutic approach. The findings from this study are strongly consistent with the results from a recent study in which DM tumors show an enrichment of TERT and NRAS mutations and a decrease in BRAF mutations.⁸⁴

This study is the first meta-analysis to investigate the use of various genetic markers to predict the most unfavorable prognostic factor in PTCs, DM. Our study can help clinicians and thyroid oncologists to choose reliable molecular markers to assess patient prognosis and select appropriate management. However, there are a few limitations in our study that need to be addressed. The first limitation is that the majority of included studies are retrospective studies so selection bias is inevitable. Another concern is that considerable heterogeneity exists among the studies assessing the association of BRAF mutations with DM, and we could not determine the sources of heterogeneity following subgroup analyses and meta-regression. Those heterogeneities might stem from differences in the diagnostic criteria of DM applied, which are not clearly described in many studies.

In conclusion, TERT promoter mutations and RET/PTC fusions, especially TERT promoter mutations, are reliable and useful genetic markers to predict DM in PTCs. In contrast, BRAF mutations, which have been believed for many years to be the molecular marker of choice to help clinicians predict tumor aggressiveness and prognosis, shows no role in predicting DM. Understanding the associations between these genetic alterations with distant spread probability can add more value in selecting suitable treatments and more accurately predicting patients’ prognosis.

Supplemental Material

Supplemtary_Figures – Supplemental material for Role of molecular markers to predict distant metastasis in papillary thyroid carcinoma: Promising value of TERT promoter mutations and insignificant role of BRAF mutations—a meta-analysis

Supplemental material, Supplemtary_Figures for Role of molecular markers to predict distant metastasis in papillary thyroid carcinoma: Promising value of TERT promoter mutations and insignificant role of BRAF mutations—a meta-analysis by Huy Gia Vuong, Ahmed MA Altibi, Uyen NP Duong, Hanh TT Ngo, Thong Quang Pham, Hung Minh Tran, Naoki Oishi, Kunio Mochizuki, Tadao Nakazawa, Lewis Hassell, Ryohei Katoh and Tetsuo Kondo in Tumor Biology

Footnotes

Acknowledgements

H.G.V., A.M.A.A., and L.H. were responsible for conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; original draft preparation, reviewing, and editing; visualization, supervision, and project administration. U.N.P.D. was responsible for methodology, validation, formal analysis, investigation, resources, data curation, and original draft preparation, reviewing, and editing. H.T.T.N., T.Q.P, H.M.T., N.O., K.M., and T.N. were responsible for methodology, resources, data curation, and original draft preparation, reviewing, and editing. R.K. and T.K. were responsible for conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; original draft preparation, reviewing, and editing; visualization; supervision; project administration; and funding support.

Declaration of conflicting interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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