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Abstract

Background

Death-associated protein kinase (DAPK) has a strong function of tumor suppression involving apoptosis regulation, autophagy, and metastasis inhibition. Hypermethylation of CpG islands in DAPK gene promoter region is one of the important ways to inactivate this tumor suppressor gene, which might promote lung carcinogenesis. However, the clinicopathological significance of the DAPK promoter hypermethylation in lung cancer remains unclear. In this study, we performed a meta-analysis trying to estimate the clinicopathological significance of DAPK promoter hypermethylation in non-small cell lung cancer (NSCLC).

Methods

A detailed literature search for publications relevant to DAPK gene promoter methylation and NSCLC was made in PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, CSTJ, Wanfang databases, and SinoMed (CBM). The random-effects model and fixed-effects model were utilized to pool the relative ratio based on the heterogeneity test in the meta-analysis.

Results

A total of 41 studies with 3348 patients were included. The frequency of DAPK methylation was significantly higher in NSCLC than in non-malignant control (odds ratio (OR) = 6.88, 95% confidence interval (CI): 4.17–11.35, P < 0.00001). The pooled results also showed that DAPK gene promoter hypermethylation was significantly associated with poor prognosis for overall survival in patients with NSCLC (hazard ratio: 1.23, 95% CI:1.01–1.52, P = 0.04). Moreover, DAPK gene promoter hypermethylation was significantly associated with squamous cell carcinoma (OR: 1.25, 95% CI: 1.01–1.54, P = 0.04) and smoking behavior (OR: 1.42, 95% CI: 1.04–1.93, P = 0.03) but not with TNM stage, tumor differentiation, age, or gender.

Conclusion

DAPK promoter hypermethylation might be a candidate diagnostic and prognostic tumor marker for NSCLC.

Keywords

DAPK methylation meta-analysis non-small cell lung cancer

Introduction

Globally, lung cancer is the leading cause of cancer incidence and mortality in all kinds of tumors.¹ It has been estimated that nearly 1.6 million people died of lung cancer in 2012, accounting for approximately 20% of all cancer deaths that year.^1,2 Despite recent improvements in treatment, the prognosis of lung cancer remains poor, with a 5-year overall survival (OS) rate of 19%.³ Of all primary pulmonary malignancies, approximately 85% of cases are non-small cell lung cancer (NSCLC) including adenocarcinoma (ADC), squamous cell carcinoma (SCC), adenosquamous carcinoma, and large cell carcinoma. It is now well established that genetic mutations and epigenetic modifications play an important role in lung cancer development.^4,5 DNA methylation is one of the important molecular mechanisms in epigenetics. In contrast to the feature of genome-wide hypomethylation in malignancies, hypermethylation of CpG islands in tumor suppressor genes (TSGs) promoter regions, leading to inactivation of TSGs, were common in various cancers.^6–8 Death-associated protein kinase (DAPK) belongs to the superfamily of calcium/calmodulin (Ca+2/CaM) regulated serine/threonine kinases (STKs). It is encoded by DAPK gene located on chromosome 9q34.1 and known for its role as a strong tumor suppressor, a regulator of apoptosis and autophagy and an inhibitor of metastasis.^9,10 Several studies indicated that DAPK genes’ promoter methylation occurred frequently in NSCLC, and has a tight relationship with occurrence and development of NSCLC.^11–15 However, the results were varied from different studies, and the clinicopathological significance of DAPK promoter hypermethylation remains unclear. In this study, 41 eligible studies were pooled, and we performed a meta-analysis trying to provide a more accurate estimate of the clinicopathological significance of DAPK promoter hypermethylation in NSCLC, including age, gender, smoking status, histological type, differentiation, tumor node metastasis (TNM) stage, and prognostic outcomes.

Materials and methods

Search strategy

PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, CSTJ, Wanfang databases and SinoMed (CBM) were reviewed by two reviewers comprehensively till April 10, 2021. Studies relevant to DAPK gene promoter methylation and NSCLC were identified using the Medical Subject Headings (MeSH) combined with Free-Word index term of: “Lung Neoplasms”, OR “Pulmonary Neoplasms”, OR “Neoplasms, Lung”, “Lung Neoplasm”, OR “Neoplasm, Lung”, OR “Neoplasms, Pulmonary”, OR “Neoplasm, Pulmonary”, OR “Pulmonary Neoplasm”, OR “Lung Cancer”, OR “Cancer, Lung”, OR “Cancers, Lung”, OR “Lung Cancers”, OR “Pulmonary Cancer”, OR “Cancer, Pulmonary”, OR “Cancers, Pulmonary”, OR “Pulmonary Cancers”, OR “Cancer of the Lung”, OR “Cancer of Lung”, OR “NSCLC”, OR “LC”; AND “death associated protein kinase methylation”, OR “death-associated protein kinase methylation”, OR “DAPK methylation”, OR “death-associated protein kinase”, OR “DAPK”, OR “death-associated protein kinase”, OR “methylation”.

Inclusion and exclusion criteria

The included criteria were as follows: (a) case-control or cohort studies related to DAPK gene promoter methylation and NSCLC; (b) patients in the case group included in each study were confirmed to be NSCLC by cytology or pathology; and (c) research published in English or Chinese. The following exclusion criteria were used: (a) studies published in languages other than Chinese or English; (b) articles with insufficient data; (c) the same population or repeat publication; (d) degree theses, reviews, conference abstracts, editorials, letters, case reports, dissertation and expert opinion; and (e) the studies utilized cell lines or basic research on animal experiments. (For the selection process, see Figure 1) After evaluation, 41 articles fulfilled the entry criteria of this meta-analysis, and the detailed information of these final included articles are listed in Table 1.

Figure 1.

Flow chart of literature search and study selection.

Table 1.

The main characteristics of included studies.

Study	Year	Country	Sample size (M/T)	DAPK methylation rate	Histological type			TNM stage		Differentiation		Age		Smoking status		Gender		Method	NOS score
Study	Year	Country	Sample size (M/T)	DAPK methylation rate	Control	SCC	ADC & other NSCLC	I + II	Ⅲ + Ⅳ	Poor	Well & moderate	≤60	>60	+	−	Male	Female	Method	NOS score
Zhang¹⁶	2011	China	120/200	60.00%	23/200	65/104	54/95	-	-		-	51/97	68/102	92/144	26/54	86/161	15/38	MSP	7
Wu¹⁷	2002	China	11/30	36.70%	0/30	6/17	4/11	3/14	8/16	-	-	6/16	5/14	9/21	2/9	9/25	2/5	MSP	7
Li¹⁸	2020	China	41/60	68.30%	31/65	-	-	-	-	-	-	-	-	-	-	-	-	MSP	8
Chan¹⁹	2002	China	26/75	35.00%	-	11/31	15/44	-	-	-	-	-	-	-	-	20/62	6/13	MSP	6
Zöchbauer-Müller²⁰	2001	UK	20/107	19.00%	6/104	9/43	11/64	17/82	3/25	-	-	-	-	18/98	2/9	19/76	1/31	MSP	6
Jin²¹	2010	China	47/94	50.00%	15/94	-	-	-	-	-	-	-	-	-	-	-	-	MSP	6
Kim²²	2005	Korea	18/61	29.50%	15/61	5/17	13/44	8/39	7/22	-	-	-	-	-	-	-	-	MSP	6
Ansari²³	2017	India	133/160	83.10%	49/60													MSP	6
Tang²⁴	2000	USA	59/135	44.00%	-	16/51	43/84	-	-	-	-	21/53	38/82	-	-	48/100	11/35	MSP	6
Hoffmann¹⁵	2009	Germany	52/76	68.50%	-	-	-	-	-	-	-	-	-	-	-	-	-	qMSP	5
Huang²⁵	2018	China	17/23	73.90%	-	2/3	11/14	14/18	3/5	-	-	9/13	8/10	7/10	10/13	13/17	4/6	Nested MSP	7
Ramirez²⁶	2003	Spain	23/50	46.00%	-	10/25	12/25	14/27	8/23	-	-	-	-	21/49	0/2	21/49	1/2	MSP	6
Safar²⁷	2005	USA	12/32	23.00%	6/32	-	-	16/48	8/57	-	-	-	-	-	-	-	-	MSP	6
Luo²⁸	2018	China	40/79	50.60%	2/40	23/43	17/36	15/27	25/52	-	-	24/41	16/38	25/46	15/33	30/58	10/21	MSP	6
Niklinska²⁹	2009	Poland	24/70	34.30%	-	17/41	5/20	16/55	8/15	-	-	-	-	-	-	-	-	MSP	6
Guo³⁰	2004	USA	5/20	25.00%	0/20	-	-	-	-	-	-	-	-	-	-	-	-	MSP	6
Yanagawa³¹	2003	Japan	21/75	28.00%	10/75	10/29	12/46	16/56	6/19	-	-	-	-	17/55	5/20	16/54	6/21	MSP	7
Kim¹¹	2001	USA	47/185	25.40%	-	44/59	32/124	28/137	18/45	-	-	-	-	-	-	26/101	21/84	MSP	6
Liu¹²	2007	USA	40/122	32.80%	-	-	-	-	-	-	-	-	-	28/81	12/41	-	-	MSP	6
Ramirez³²	2003	Spain	23/51	45.10%	-	12/26	10/25	13/26	9/24	-	-	-	-	-	-	-	-	MSP	6
Lin³³	2006	China	20/65	30.80%	0/35	8/30	12/35	3/15	17/50	-	-	-	-	-	-	-	-	MSP	6
Cao Li³⁴	2016	China	22/63	34.90%	0/25	10/25	12/38	7/31	15/32	14/37	8/26	9/20	13/43	-	-	16/51	6/12	MSP	5
Lin Qing³⁵	2006	China	27/89	30.30%	0/55	10/40	17/49	4/19	23/70	-	-	-	-	-	-	-	-	MSP	5
Lu Degan³⁶	2010	China	23/62	37.10%	0/46	7/20	16/35	3/11	20/51	10/32	13/30	10/23	13/39	-	-	15/40	8/22	MSP	6
Wang³⁷	2016	China	30/50	60.00%	0/50	19/36	5/14	14/21	16/29	15/26	15/24	17/27	13/23	-	-	27/43	3/7	MSP	6
Yan Qi³⁸	2018	China	28/80	35.00%	0/100	-	-	-	-	-	-	-	-	-	-	-	-	MSP	7
Peng³⁹	2010	China	48/82	51.20%	0/25	23/35	20/40	14/42	10/40	20/40	17/42	24/42	25/40	-	-	30/40	25/42	MSP	6
Hua⁴⁰	2007	China	18/50	36.00%	0/10	-	-	-	-	-	-	-	-	-	-	-	-	MSP	5
Toyooka⁴¹	2003	USA	14/38	36.80%	1/15	4/12	8/20	-	-	-	-	-	-	-	-	-	-	COBRA	7
Andrea⁴²	2005	USA	22/49	44.90%	-	-	-	-	-	-	-	-	-	-	-	-	-	MSP	5
Daniel⁴³	2006	Germany	60/91	65.90%	0/91	-	-	-	-	-	-	-	-	-	-	-	-	MethyLight	7
Naoki⁴⁴	2007	Japan	26/101	25.70%	8/101	31/101	23/101	20/75	6/26	-	-	-	-	20/73	6/28	19/72	7/29	MSP	6
Wang⁴⁵	2007	China	14/28	50.00%	2/17	5/7	9/21	-	-	8/13	6/15	6/16	8/12	-	-	10/17	4/11	COBRA	6
Zhang⁴⁶	2010	China	11/78	14.10%	3/78	-	-	-	-	-	-	-	-	-	-	-	-	MSP	8
Milica⁴⁷	2012	Serbia	12/54	22.20%	-	7/29	5/25	10/35	2/18	-	-	7/32	5/22	11/42	1/11	8/39	4/15	MSP	7
Guo⁴⁸	2015	China/USA	35/202	17.30%	0/73	20/91	15/111	16/100	5/27	-	-	11/67	22/122	-	-	24/133	9/56	MSP	7
Belinsky⁴⁹	2007	USA	22/72	30.60%	5/25	-	-	-	-	-	-	-	-	-	-	-	-	MSP	5
Fischer⁵⁰	2007	Germany	24/92	26.10%	-	-	-	-	-	-	-	-	-	-	-	-	-	MSP	6
Fujiwara⁵¹	2005	Japan	10/91	11.00%	5/100	-	-	6/60	4/31	-	-	-	-	-	-	-	-	MSP	6
Kevin⁵²	2005	USA	72/206	35.00%	-	-	72/106	-	-	-	-	29/61	86/176	16/45	45/125	33/110	39/96	MSP	7
Lu⁵³	2004	USA	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	MSP	6

ADC: adenocarcinoma; COBRA: combined bisulfite restriction analysis; DAPK: death-associated protein kinase; M/T: number of NSCLC with methylation/T: total number of the group; MSP: methylation-specific PCR; NOS: Newcastle–Ottawa Scale; NSCLC: non-small-cell lung cancer; PCR: polymerase chain reaction; qMSP: quantitative methylation-specific real-time PCR; SCC: squamous cell cancer; TNM: tumor node metastasis..

Data extraction

The data extraction followed the procedure described by the published literature. Two investigators independently extracted data from eligible studies. Disagreements were resolved by discussion and consensus. Two investigators reviewed all of the articles that met the inclusion and exclusion criteria. The following information was recorded for each study: the first author’s name, country, year of publication, sample source, methylation detection method, number of cases, DAPK methylation status, and clinicopathological parameters including TNM stage, differentiation, age, gender, smoking status, and data of overall survival (OS). The study characteristics and clinicopathological parameters are summarized in Table 1. Heterogeneity of investigation was evaluated to determine whether the data from the various studies could be analyzed in a meta-analysis.

Quality assessment

We used the Newcastle–Ottawa Scale (NOS) to assess the quality of each study by two authors. The scores included three parts: the selectivity of patients (0–4), comparability of groups (0–2), and assessment of outcome (0–3). Studies with scores > 5 were included in final meta-analysis.

Statistical analysis

Data were analyzed using the RevMan 5.3 software and STATA14.0. When the hazard ratio (HR) values of OS were not directly reported, we obtained additional data from the original authors. When the request was not answered, the HR values were extracted from Kaplan–Meier curves by the Engauge Digitizer 4.1 software. Heterogeneity was calculated by the chi-squared test and I -squared statistics. If I² ≥ 50% or P ≤ 0.01 was established, the results were pooled through a random-effects model; otherwise, a fixed-effects model was selected. In addition, sensitivity analysis was used to minimize the influence of heterogeneity. Publication bias was estimated qualitatively using Begg's and Egger's tests with funnel plots. If Begg's and Egger's results indicated that publication bias exists, the trim and fill method was used to examine the sensitivity of the result. A difference was considered statistically significant if two-sided P < 0.05.

Results

Study characteristics

The initial search information retrieved 1479 articles. According to the selection criteria, 41 studies with 3348 patients were eventually included in the meta-analysis (Figure 1). The basic characteristics of the eligible studies are shown in Table 1.

DAPK methylation and clinicopathological features of NSCLC

The total number of NSCLC from 27 studies is 2160. And 876 of them were with DAPK methylation, the methylation rate was 40.6%. The frequency of DAPK methylation was significantly higher in NSCLC than in non-malignant controls; and the pooled odds ratio (OR) was 6.88 with 95% confidence interval (CI) 4.17–11.35, Z = 7.55, P < 0.00001, I² = 74% (Figure 2(a)). We also observed the similar effect when divided the included populations into two subgroups according to the geographic origin. Figure 2(a) showed that the frequency of DAPK methylation was significantly higher in NSCLC than in non-malignant controls both in European and American countries (OR: 8.71; 95% CI: 1.76–43.09; Z = 2.65, P < 0.001, I² = 78%) and in Asian countries (OR: 6.77; 95% CI: 3.94–11.66; Z = 6.91, P < 0.00001, I² = 74%). When stratified by SCC, ADC, and other NSCLC, the frequency of DAPK hypermethylation was significantly higher in SCC; and the pooled OR was 1.25 with 95% CI: 1.01–1.54, Z = 2.07, P = 0.04, I² = 0% (Figure 2(b)). As shown in Figure 2(c), DAPK hypermethylation was associated with smoking behavior in NSCLC (OR: 1.42; 95% CI: 1.04–1.93; Z = 2.20, P = 0.03, I² = 0%). In addition, the relevant results showed that DAPK hypermethylation was not significantly associated with TNM stage (Stage I + II vs. Stage III + IV, OR: 0.94; 95% CI: 0.72–1.23; Z = 0.43, P = 0.67, I² = 13%), differentiation (poor vs. well and moderate, OR: 1.13; 95% CI: 0.70–1.81; Z = 0.50, P = 0.62, I² = 0%), age (≤60 vs. >60, OR: 0.90; 95% CI: 0.71–1.15; Z = 0.83, P = 0.41, I² = 0%), and gender (male vs. female, OR: 1.18; 95% CI: 0.94–1.49; Z = 1.44, P = 0.15, I² = 0%)(Figure 2(d)–(g)). Analysis of the relationship between DAPK hypermethylation and OS showed that DAPK hypermethylation was significantly associated with poor prognosis in patients with NSCLC (HR: 1.23; 95% CI: 1.01–1.52; Z = 2.01, P = 0.04, I² = 23% (Figure 2(h)).

Figure 2.

Forest plots for the association between DAPK methylation and in cancer patients: (a) NSCLC vs. non-malignant controls; (b) SCC vs. ADC and other NSCLC; (c) smoking vs. non-smoking; (d) TNM stage (Stage I + II vs. Stage III + Iv;); (e) differentiation (poor vs. well and moderate); (f) age (≤60 vs. >60); (g) gender (male vs. female); (h) forest plots for the association between DAPK methylation and OS.

Publication bias

This meta-analysis adopted Begg's test and Egger's test to evaluate publication bias. All the results except DAPK methylation in NSCLC versus non-malignant controls showed no significant publication bias as Begg's test showed pr > |z| = 0.05 or Egger's test showed P > |t| = 0.05 (Figure 3).

Figure 3.

Funnel plot for publication bias. (a) DAPK methylation in NSCLC and non-malignant controls; (b) SCC vs. ADC and other NSCLC; (c) smoking vs. non-smoking; (d) TNM stage (Stage I + II vs. Stage III + IV); (e) differentiation (poor vs. well & moderate); (f) age (≤60 vs. >60); (g) gender (male vs. female); (h) funnel plots for the association between DAPK methylation and OS.

Discussion

Epigenetic and genetic changes have become established in recent years as being one of the most important molecular mechanisms of human tumors. Plus, DNA methylation constitutes an important molecular mechanism of epigenetics. CpG islands are 200–4000 bp stretches of DNA that have a significantly higher concentration of CpG dinucleotides than the bulk of the genome. Whereas 70–80% of all CpG dinucleotides in the human genome are methylated, CpG islands located in promoter region and exon region large remain unmethylated.^54,55 Hypermethylation of the CpG islands affects certain tumor suppressor genes involved in cell cycle (p16INK4a, p15INK4b, Rb, p14ARF), DNA repair (BRCA1, hMLH1, MGMT), carcinogen-metabolism (GSTP1), cell-adherence (CDH1, CDH13), apoptosis (DAPK, TMS1), etc., which has been proved to be closely related to the occurrence and development of a variety of cancers.⁵⁶ With a strong function of tumor suppression involving apoptosis regulation, autophagy and metastasis inhibition, DAPK has received wide appreciation by researchers. It is established that hypermethylation of CpG islands in the DAPK encoding gene promoter region has a tight relationship with the occurrence and development of NSCLC. The methylation rate of the included studies varied from 11.0% to 83.1%. The present study pooled 27 studies including 2160 NSCLC and 1627 non-malignant controls, and 40.6% (876/2160) were with DAPK gene promoter hypermethylation. Therefore, DAPK promoter hypermethylation indicated high risk of NSCLC compared to non-malignant control (the pooled total OR: 6.88, 95% CI: 4.17–11.35, P < 0.00001). A similar effect was observed in the populations from European, American, and Asian countries (Figure 2(a)). However, due to the publication bias of these 27 included studies, the difference of DAPK promoter hypermethylation between NSCLC and non-malignant controls might be exaggerated to some extent. Based on this evidence, we recommend DAPK hypermethylation to be a candidate tumor marker for lung cancer diagnosis, especially for NSCLC. Moreover, we found that DAPK promoter hypermethylation correlated significantly with SCC (P < 0.05, Figure 2(b)) and smoking behavior in patients with NSCLC (P < 0.05, Figure 2(c)). However, we did not find a significant association of DAPK promoter hypermethylation with differentiation, age, and gender.

Since the first integration of the descriptors of tumor size, nodal status, and metastases by Denoix, the TNM staging system was widely used as an important guideline for treatment and prognostic prediction in various cancers.⁵⁷ With our advanced understanding of molecular pathways on tumor biology, an increasing number of research focused on identifying molecular prognostic factors. A 14-gene expression assay has proven to improve prognostic accuracy beyond the National Comprehensive Cancer Network criteria for stage I high-risk tumors, and differentiated low-risk, intermediate-risk, and high-risk patients within all disease stages.⁵⁸ Subsequently, a novel staging system, the TNMB (with the B denoting biology) system was the develop and validate to significantly improve prediction of OS greater than adoption of the seventh or eighth edition staging systems. This might enable us to better differentiate high risk patients from low-risk patients, which might help to select appropriate early intervention.⁵⁹ Buckingham et al. suggested that hypermethylation of a specific promoter cytosine in the death-associated kinase gene, DAPK was associated with shorter time to recurrence in surgically treated stage I and II NSCLC patients.¹³ Lu et al. showed that DAPK promoter methylation is a significant negative prognostic factor for disease-specific survival in resected stage I NSCLC.⁵³ In this meta-analysis, we confirmed that DAPK gene promoter hypermethylation was significantly associated with poor prognosis for OS in patients with NSCLC (HR: 1.23, 95% CI: 1.01–1.52, P = 0.04), which makes it possible for DAPK gene promoter hypermethylation to be a prognostic predictive factor.

All the included literature used the assay based on a standard sodium bisulfite DNA treatment to assess DAPK gene methylation status, including methylation-specific real-time PCR (MSP),⁶⁰ quantitative (q)MSP, nested MSP, combined bisulfite restriction analysis (COBRA),⁶¹ and MethyLight.⁶² These technologies entail initial modification of DNA by sodium bisulfite, converting all unmethylated, but not methylated, cytosines to uracil, and subsequent amplification with primers specific for methylated versus unmethylated DNA. Thus, primers specificity is the most critical factor to avoid a false positive result and to ensure accuracy. Also, incomplete bisulfite conversion could have a negative impact on the experimental results. Most of the included literature used MSP as the detection assay, and the high enough methylation frequency of CpG sites in CpG islands renders this technique extremely sensitive for such regions. However, MSP theoretically only reflects the average methylation level of the entire regions, for the reason that not all CpG sites in CpG islands were completely methylated. Therefore, this method is mainly used for qualitative or semi-quantitative research.⁶⁰ Although quantitative techniques, such as qMSP, COBRA, and MethyLight, have been used in individual studies, the impact of the above limitations of these sodium bisulfite treatment-based technologies on the pooled results cannot be ignored.

There are still many limitations in our study: First, Begg's test and Egger's test showed that the comparison of DAPK methylation in NSCLC versus non-malignant controls existed significant publication bias, which might lead to exaggerated interpretation of the frequency of DAPK hypermethylation in NSCLC. In the future, we need to adopt the counterpart publications indicated in the published bias graph adjusted by the trim and fill method. Second, the majority of samples included in the analysis were serum and lung tissue. One of the original studies used pleural fluid samples to detect DAPK gene promoter methylation, while two of the original studies used sputum samples, which may have increased heterogeneity and had a subsequent influence on the pooled results. Third, although studies included in this meta-analysis were all ≥ 5 stars, and 35 out of 41 publications were of high quality (NOS ≥ 6 stars), most of included studies were retrospective, which means selection bias is inevitable. Therefore, further prospective research is warranted to investigate the clinical significance of DAPK promoter hypermethylation.

Conclusion

DAPK gene promoter methylation occurred frequently in NSCLC, and has a tight relationship with occurrence and development of it. DAPK promoter hypermethylation might be a candidate diagnostic and prognostic tumor marker for NSCLC.

Footnotes

Acknowledgments

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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.

Author contributions

Zhimao Chen and Yu Fan contributed equally to this work.

ORCID iDs

Zhimao Chen

Xiangzheng Liu

Shijie Zhang

References

Torre

Bray

Siegel

, et al. Global cancer statistics, 2012. CA Cancer J Clin 2015; 65: 87–108.

Ferlay

Soerjomataram

Dikshit

, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136: E359–E386.

Siegel

Miller

Jemal

. Cancer statistics, 2019. CA Cancer J Clin 2019; 69: 7–34.

Jones

Baylin

. The fundamental role of epigenetic events in cancer. Nat Rev Genet 2002; 3: 415–428.

Sato

Shames

Gazdar

, et al. A translational view of the molecular pathogenesis of lung cancer. J Thorac Oncol 2007; 2: 327–343.

Herman

Baylin

. Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med 2003; 349: 2042–2054.

Das

Singal

. DNA methylation and cancer. J Clin Oncol 2004; 22: 4632–4642.

Merlo

Herman

Mao

, et al. 5′CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers. Nat Med 1995; 1: 686–692.

Farag

Roh

. Death-associated protein kinase (DAPK) family modulators: current and future therapeutic outcomes. Med Res Rev 2019; 39: 349–385.

10.

Shiloh

Bialik

Kimchi

. The DAPK family: a structure-function analysis. Apoptosis 2014; 19: 286–297.

11.

Kim

Nelson

Wiencke

, et al. Promoter methylation of DAP-kinase: association with advanced stage in non-small cell lung cancer. Oncogene 2001; 20: 1765–1770.

12.

Liu

Gao

Siegfried

, et al. Promoter methylation of RASSF1A and DAPK and mutations of K-ras, p53, and EGFR in lung tumors from smokers and never-smokers. BMC Cancer 2007; 7: 74.

13.

Buckingham

Penfield Faber

Kim

, et al. PTEN, RASSF1 and DAPK site-specific hypermethylation and outcome in surgically treated stage I and II non-small cell lung cancer patients. Int J Cancer 2010; 126: 1630–1639.

14.

Lin

Geng

, et al. RASSF1A, APC, ESR1, ABCB1 and HOXC9, but not p16INK4A, DAPK1, PTEN and MT1G genes were frequently methylated in the stage I non-small cell lung cancer in China. J Cancer Res Clin Oncol 2009; 135: 1675–1684.

15.

Hoffmann

Kaifi

Vallböhmer

, et al. Lack of prognostic significance of serum DNA methylation of DAPK, MGMT, and GSTPI in patients with non-small cell lung cancer. J Surg Oncol 2009; 100: 414–417.

16.

Zhang

Jin

, et al. Relationship between promoter methylation of p16, DAPK and RARB genes and the clinical data of non-small cell lung cancer. Chin J Med Genet 2011; 28: 23–28.

17.

Jun

Liang

Jianyou

, et al. Study on detection of aberrant promoter hypermethylation of p16 and DAP kinase in serum DNA from patients with non-small cell lung cancer. Chin J Lung Cancer 2002; 5: 188–190.

18.

Fan

. The role of lung cancer related gene methylation in the early diagnosis of lung cancer. Chin J Oncol 2020; 42: 644–647.

19.

Chan

Lam

Tsang

, et al. Aberrant promoter methylation in Chinese patients with non-small cell lung cancer: patterns in primary tumors and potential diagnostic application in bronchoalevolar lavage. Clin Cancer Res 2002; 8: 3741–3746.

20.

Zöchbauer-Müller

Fong

Virmani

, et al. Aberrant promoter methylation of multiple genes in non-small cell lung cancers. Cancer Res 2001; 61: 249–255.

21.

Jin

Zhang

, et al. Association of abnormal methylation of CpG islands in promoter domains of multiple tumor. Suppressor genes with non-small cell lung cancer. Chin J Clin Oncol 2010; 37: 1109–1114.

22.

Kim

Lee

Sung

, et al.

Can aberrant promoter hypermethylation of CpG islands predict the clinical outcome of non-small cell lung cancer after curative resection?

Ann Thorac Surg 2005; 79: 1180–1188. Discussion 1180–1188.

23.

Ali

Kumar

Kakaria

, et al. Detection of promoter DNA methylation of APC, DAPK, and GSTP1 genes in tissue biopsy and matched serum of advanced-stage lung cancer patients. Cancer Invest 2017; 35: 423–430.

24.

Tang

Khuri

Lee

, et al. Hypermethylation of the death-associated protein (DAP) kinase promoter and aggressiveness in stage I non-small-cell lung cancer. J Natl Cancer Inst 2000; 92: 1511–1516.

25.

Huang

, et al. Methylation analysis for multiple gene promoters in non-small cell lung cancers in high indoor air pollution region in China. Bull Cancer 2018; 105: 746–754.

26.

Ramirez

Sarries

de Castro

, et al. Methylation patterns and K-ras mutations in tumor and paired serum of resected non-small-cell lung cancer patients. Cancer Lett 2003; 193: 207–216.

27.

Safar

Spencer

3rd , Su

, et al. Methylation profiling of archived non-small cell lung cancer: a promising prognostic system. Clin Cancer Res 2005; 11: 4400–4405.

28.

Luo Jianglong

Feng

. Value of p16, DAPK and APC gene methylation in early diagnosis of lung cancer. JMinim Invasive Med2018; 13: 12–16.

29.

Niklinska

Naumnik

Sulewska

, et al. Prognostic significance of DAPK and RASSF1A promoter hypermethylation in non-small cell lung cancer (NSCLC). Folia Histochem Cytobiol 2009; 47: 275–280.

30.

Guo

House

Hooker

, et al.

Promoter hypermethylation of resected bronchial margins: a field defect of changes?

Clin Cancer Res 2004; 10: 5131–5136.

31.

Yanagawa

Tamura

Oizumi

, et al. Promoter hypermethylation of tumor suppressor and tumor-related genes in non-small cell lung cancers. Cancer Sci 2003; 94: 589–592.

32.

Ramirez

Taron

Balaña

, et al. Serum DNA as a tool for cancer patient management. Rocz Akad Med Bialymst (1995) 2003; 48: 34–41.

33.

Lin

Chen

Tang

, et al. The promoter hypermethylation of DAPK gene and p16 gene in sera from Chinese non-small cell lung cancer patients. Chin Ger J Clin Oncol 2006; 5: 184–188.

34.

Cao Li

SYCR

Zhang

Wang

, et al. Observation on the serum methylation status of RUNX3, RASSF1A, 3-OST-2, DAPK and PTPRO genes in patients with non-small cell lung cancer. Shandong Med J 2016; 56: 84–87.

35.

Lin Qing

CLTY

Wang

. Combined detection of serum DAPK gene and p16 gene methylation in patients with non-small cell lung cancer. Chin J Gerontol 2006; 26: 1304–1306.

36.

Lu Degan

. Detection and significance of serum abnormal methylation of death associated protein kinase gene in patients with non-small cell lung cancer. Shandong Med J 2010; 50: 69–70.

37.

Cuiying

WPZ

. Methylation of RASSF1A, p16, DAPK and RUNX3 gene and its role in inner Mongolia non-small-cell lung cancer. J Inner Mongolia Med Univ 2016; 38: 89–102.

38.

Yan Qi

. Combined detection of CpG island methylation of 8 tumor suppressor gene promoters in serum [in Chinese]. Chin J Cancer Prev Treat 2018; 25: 8–10.

39.

Peng

Shan

Wang

. Value of promoter methylation of RASSF 1A, p16, and DAPK genes in induced sputum in diagnosing lung cancers. J Cent South Univ (Med Sci) 2010; 35: 247–253.

40.

Hua

Jin

Ruan

, et al. Expression and aberrant methylation of DAPK gene of lung cancer tissues in Yunnan tin miners. Chin Occup Med 2007; 34: 87–90.

41.

Toyooka

Miyajima

, et al. Epigenetic down-regulation of death-associated protein kinase in lung cancers. Clin Cancer Res 2003; 9: 3034–3041.

42.

Russo

Thiagalingam

Pan

, et al. Differential DNA hypermethylation of critical genes mediates the stage-specific tobacco smoke-induced neoplastic progression of lung cancer. Clin Cancer Res 2005; 11: 2466–2470.

43.

Vallböhmer

Brabender

Yang

, et al. DNA Methyltransferases messenger RNA expression and aberrant methylation of CpG islands in non-small-cell lung cancer: association and prognostic value. Clin Lung Cancer 2006; 8: 39–44.

44.

Yanagawa

Tamura

Oizumi

, et al. Promoter hypermethylation of RASSF1A and RUNX3 genes as an independent prognostic prediction marker in surgically resected non-small cell lung cancers. Lung Cancer 2007; 58: 131–138.

45.

Wang

Zhang

Zheng

, et al. Multiple gene methylation of non-small cell lung cancers evaluated with 3-dimensional microarray. Cancer 2008; 112: 1325–1336.

46.

Zhang

Wang

Song

, et al. Methylation of multiple genes as a candidate biomarker in non-small cell lung cancer. Cancer Lett 2011; 303: 21–28.

47.

Kontic

Stojsic

Jovanovic

, et al. Aberrant promoter methylation of CDH13 and MGMT genes is associated with clinicopathologic characteristics of primary non-small-cell lung carcinoma. Clin Lung Cancer 2012; 13: 297–303.

48.

Guo

Alumkal

Drachova

, et al. CHFR Methylation strongly correlates with methylation of DNA damage repair and apoptotic pathway genes in non-small cell lung cancer. Discov Med 2015; 19: 151–158.

49.

Belinsky

Grimes

Casas

, et al. Predicting gene promoter methylation in non-small-cell lung cancer by evaluating sputum and serum. Br J Cancer 2007; 96: 1278–1283.

50.

Fischer

Ohnmacht

Rieger

, et al. Prognostic significance of RASSF1A promoter methylation on survival of non-small cell lung cancer patients treated with gemcitabine. Lung Cancer 2007; 56: 115–123.

51.

Fujiwara

Fujimoto

Tabata

, et al. Identification of epigenetic aberrant promoter methylation in serum DNA is useful for early detection of lung cancer. Clin Cancer Res 2005; 11: 1219–1225.

52.

Divine

Pulling

Marron-Terada

, et al. Multiplicity of abnormal promoter methylation in lung adenocarcinomas from smokers and never smokers. Int J Cancer 2005; 114: 400–405.

53.

Soria

Tang

, et al. Prognostic factors in resected stage I non-small-cell lung cancer: a multivariate analysis of six molecular markers. J Clin Oncol 2004; 22: 4575–4583.

54.

Cross

Bird

. CpG islands and genes. Curr Opin Genet Dev 1995; 5: 309–314.

55.

Ohlsson

Kanduri

. New twists on the epigenetics of CpG islands. Genome Res 2002; 12: 525–526.

56.

Esteller

. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene 2002; 21: 5427–5440.

57.

Burke

. Outcome prediction and the future of the TNM staging system. J Natl Cancer Inst 2004; 96: 1408–1409.

58.

Kratz

Van Den Eeden

, et al. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet (London, England) 2012; 379: 823–832.

59.

Kratz

Haro

Cook

, et al. Incorporation of a molecular prognostic classifier improves conventional non-small cell lung cancer staging. J Thorac Oncol 2019; 14: 1223–1232.

60.

Herman

Graff

Myöhänen

, et al. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci USA 1996; 93: 9821–9826.

61.

Xiong

Laird

. COBRA: a sensitive and quantitative DNA methylation assay. Nucleic Acids Res 1997; 25: 2532–2534.

62.

Eads

Danenberg

Kawakami

, et al. Methylight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 2000; 28: E32.

Clinicopathological significance of DAPK gene promoter hypermethylation in non-small cell lung cancer: A meta-analysis

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction

Materials and methods

Search strategy

Inclusion and exclusion criteria

Data extraction

Quality assessment

Statistical analysis

Results

Study characteristics

DAPK methylation and clinicopathological features of NSCLC

Publication bias

Discussion

Conclusion

Footnotes

Acknowledgments

Declaration of conflicting interests

Funding

Author contributions

ORCID iDs

References