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Introduction

Colon adenocarcinoma (COAD) is the most common gastrointestinal malignancy in China and the third leading cause of cancer-related death in the US, which affects >1 million individuals annually worldwide (1). It has been reported that COAD develops due to the progressive accumulation of genetic and epigenetic alterations (2). COAD displays an aggressive behavior and patients with COAD have poor survival. According to global cancer statistics, new cases of COAD in 2018 accounted for 6.1% of all newly diagnosed cancer cases and 5.8% of all cancer-associated deaths (3). COAD is frequently diagnosed at an advanced stage, as patients may be asymptomatic at the early stage (3). COAD may be treated with surgical techniques, chemotherapy and radiotherapy (4). Although these treatments have been improved in recent years, the prognosis for patients with COAD with metastasis remains unsatisfactory (4). Therefore, it is necessary to expand the current understanding of the disease and identify and apply disease-specific biomarkers and therapeutic targets for COAD to improve treatment outcomes.

Autophagy is an important and essential cellular mechanism that has a key role in cellular degradation and the recycling process in all eukaryotes. Numerous studies have indicated that autophagy is induced under stress conditions to degrade misfolded or aggregated proteins and clear damaged organelles, leading to cell survival and cellular maintenance (5). Autophagy is also involved in numerous biological functions, including cellular differentiation, development and cell defence. Thus, autophagy is primarily a cytoprotective mechanism; however, excessive self-degradation may be harmful (6). It has been suggested that defects in autophagy regulation are associated with several diseases, including cancer, neurodegeneration and metabolic diseases (7). It has been reported that autophagy may suppress or promote tumor growth depending on the developmental stage and tumor type (8). Autophagy has an important role in tumor suppression at early stages by protecting the cells against inflammation, oxidative stress and DNA damage. However, autophagy may also induce proliferation and metastasis of cancer cells. Increasing evidence indicates that autophagy is increased during chemotherapy, which leads to drug resistance and refractory cancer. Thus, it is important to comprehensively analyze the expression of autophagy-related genes (ARGs) in COAD.

The Cancer Genome Atlas (TCGA) is a web-based database that was developed to discover and explore the major cancer-causing genomic alterations to elucidate the mechanisms of cancer development and progression (9). The Genotype-Tissue Expression (GTEx) database was established to explore the correlation between human genetic variations and tissue-specific gene expression in non-diseased individuals (10). In the present study, differentially expressed ARGs in COAD were identified using the TCGA and GTEx databases. Enrichment analysis and protein-protein interaction analysis of differentially expressed ARGs were performed to improve the understanding of the biological functions of these genes. Furthermore, the association of the expression of ARGs with different clinicopathological features was explored. Clinical and pathological data from our hospital were used for further verification. From the individual prognostic genes identified, a risk signature based on the expression of these genes was developed.

Materials and methods

COAD datasets

Transcriptome data and clinical information were downloaded from TCGA database, including 482 tumor samples and 42 normal samples. The GTEx database (https://www.gtexportal.org/home/index.html) contains transcriptome data of various tissues from postmortem donors. The transcriptome data of 308 normal colon samples were downloaded from the GTEx database in December 2019.

Selection and download of ARGs

The Human Autophagy Database (HADb, http://www.autophagy.lu/) is a publicly available project that provides structural and functional information on autophagy-associated genes. All autophagy-associated genes listed on this website are included in the present study, a list of which (234 ARGs) was obtained from the HADb in December 2019.

Differentially expressed genes (DEGs)

Transcriptome profiles of all COAD datasets were merged and normalized. The R software was employed to search for genes that were differentially expressed between different samples [P<0.05, |log fold change (FC)| >1].

Construction and analysis of the protein-protein interaction network

Protein-protein interactions (PPI) have a key role in the majority of biological functions and processes. The Search Tool for the Retrieval of Interacting Genes/proteins (STRING, https://string-db.org/) is a web-based database that is able to provide and predict the PPI networks. STRING was used to construct the PPI network of the differentially expressed ARGs (minimum required interaction score: Medium confidence=0.400). Cytoscape software (https://cytoscape.org/), a free open-source platform that serves as a tool for biological network analysis and visualization, was used for PPI network analysis.

Enrichment analysis of differentially expressed ARGs

The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis is able to assign functional meanings to genes and genomes at molecular and higher levels. Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. Metascape (http://metascape.org/gp/index.html) is a web-based database that is able to provide a comprehensive gene list annotation and analysis resources. Gene Set Enrichment Analysis (GSEA; http://www.gsea-msigdb.org/gsea/index.jsp) is a computational method that assesses gene expression data and provides biological pathways. In the present study, enrichment analysis of the differently expressed ARGs was performed using GSEA and Metascape [miminum (Min) overlap=3, P<0.05 and Min enrichment=1.5].

Prognostic value of differentially expressed ARGs

Univariate Cox regression analysis was performed to identify the differentially expressed ARGs that were significantly associated with overall survival (OS). Multivariate Cox regression analysis was used to search for genes that may be used as independent prognostic indicators. Several candidate genes were obtained for prognosis monitoring. The risk score for the signature was calculated using the following formula: Risk score where Coef i is the coefficient and Xi is the relative expression value of each selected z-score-transformed gene expression value, divided into high-risk and low-risk groups according to the median risk score.

Immunohistochemical analysis

All samples were collected from The Third Hospital of Hebei Medical University (Shijiazhuang, China). Clinical samples were collected from December 2018 to September 2019 with written informed consent from the patients. Immunohistochemical analysis was performed using a tissue chip with a diameter of 4.0 mm (Beijing Mairuibo Biotechnology Co., Ltd.). The array was heated in sodium citrate buffer for 10 min in a microwave oven at 95˚C and then sealed in normal goat serum (Beijing Mairuibo Biotechnology Co., Ltd.) at 37˚C for 1 h. The samples were incubated with rabbit anti-human autophagy-related 4B cysteine peptidase (ATG4B; 1:50 dilution; cat. no. A2981), death-associated protein kinase 1 (DAPK1; 1:50 dilution; cat. no. HPA048436) and Serpin family A member 1 (Serpina1; 1:50 dilution; cat. no. SAB2109236; all from Sigma-Aldrich; Merck KGaA) at 37˚C for 1 h. The samples were then incubated with a goat anti-rabbit secondary antibody conjugated with horseradish peroxidase (1:100 dilution; cat. no. F030212; Beijing Biolab Technology Co., Ltd.) at 37˚C for 30 min. A total of three pathologists, blinded to the patients' data, independently analyzed the stained sections under a light microscope. The average number of immune-positive cells in the specimen was determined under a magnification of x400. The staining results were divided into two categories as follows. a) Staining intensity: No staining, 0; buff, 1; dark yellow, 2; tan, 3; b) percentage of stained cells: <1%, 0; 1-25%, 1; 25-50%, 2; 51-80%, 3; >80%, 4. The final scores were added up and based on the staining scores, samples were classified as low (final score 0-3, +), medium (final score 4-7, ++) or high (final score >7, +++). Image-Pro Plus software (version 6; Media Cybernetics, Inc.) was employed for immunohistochemical evaluation in the present study.

Statistical analysis

The χ2 test was used to compare the distribution of clinicopathological parameters between the two risk groups. The Mann-Whitney U test and one-way analysis of variance followed by Bonferroni's post hoc test were used to compare the risk scores of patients with different clinicopathological and molecular pathological characteristics. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of the risk score. To analyze the prediction efficiency, receiver operating characteristic (ROC) curve analysis with the R package ‘survival ROC’ was employed. The OS of the patients was compared using the Kaplan-Meier method with a two-sided log-rank test. R software (version 3.5.3) and SPSS20.0 software (IBM Corp.) were used to perform statistical analysis.

Results

Identification of differentially expressed ARGs

The transcriptional profiles of 482 COAD samples and 350 non-tumor samples were downloaded from the TCGA and GTEx databases and the expression of 234 ARGs was analyzed in these samples (P<0.05, |logFC| >1). A total of 72 differentially expressed ARGs were obtained (Fig. 1), comprising of 32 upregulated and 40 downregulated genes (Fig. 1A and C). The heatmap of differentially expressed ARGs was then drawn with R software (Fig. 1B).

Figure 1 DEGs between tumor and normal groups in colon cancer datasets. (A) Volcano plots of DEGs in colon cancer. Red color represents upregulated, whereas green color represents downregulated genes. (B) Heat maps of ARGs. (C) Boxplot of ARGs. (D) Protein-protein interaction network of the differentially expressed ARGs. The size of the nodes is relative to the number of connections, indicating that the more the number of connections is, the bigger is the size of the nodes. DEG, differentially expressed gene; ARG, autophagy-related gene; FC, fold change; N, normal sample; T, tumor sample.

PPI network

The PPI network was built on the basis of all DEGs using the STRING online database and drawn with the software Cytoscape (Fig. 1D). A total of 74 nodes and 509 edges were identified from PPI networks. The top 5 hub genes were CASP3, TP53, PIK3C3, GAPDH and BCL2L1.

Enrichment analysis of differentially expressed ARGs

To understand the biological roles of the 72 differentially expressed ARGs, GO and KEGG analyses were performed using Metascape and GSEA.

The GO terms significantly enriched in GSEA were autophagosome, autophagosome membrane and cell body (Fig. 2A). KEGG analysis using GSEA revealed that the significantly enriched pathways included pathways in cancer, regulation of autophagy and apoptosis (Fig. 2B).

Figure 2 Functional analysis of ARGs. (A) GO analysis of ARGs by GSEA. (B) KEGG analysis of ARGs by GSEA. (C) GO analysis of ARGs by Metascape. (D) KEGG analysis of ARGs by Metascape. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes. In the right panels, the darker colour indicates a smaller P-value. ARG, autophagy-related gene; GSEA, Gene Set Enrichment Analysis.

GO analysis using Metascape revealed that the differentially expressed ARGs were mainly enriched in autophagy, regulation of autophagy, organelle disassembly, ubiquitin-like protein ligase binding, response to starvation, positive regulation of programmed cell death, response to topologically incorrect protein, cytokine-mediated signaling pathway, phagophore assembly site, protein kinase binding, inclusion body, regulation of reactive oxygen species metabolic process, response to interferon-gamma, regulation of protein kinase activity, lysosome, peptidyl-serine phosphorylation, protein kinase activity, protein folding, and protein localization to the membrane and perinuclear region of the cytoplasm (Fig. 2C). KEGG analysis using Metascape revealed that the DEGs were mainly enriched in autophagy-animal, mitophagy-animal, protein processing in endoplasmic reticulum, toxoplasmosis, pathways in cancer, NOD-like receptor signaling pathway, measles, ErbB signaling pathway, Chagas disease (American trypanosomiasis), mTOR signaling pathway, estrogen signaling pathway, Parkinson's disease, endocytosis, Jak/STAT signaling pathway, Alzheimer's disease, HIF-1 signaling pathway, phagosome, Hippo signaling pathway and regulation of actin cytoskeleton (Fig. 2D).

Identification of prognostic genes

The differently expressed ARGs were analyzed by to identify the prognostic genes (Figs. 3 and 4). The analysis revealed five prognostic genes (Fig. 3A). In order to identify the genes with an independent prognostic capability, multivariate Cox regression analysis was performed with SPSS software (Fig. 3A). The ARGs with a significant independent prognostic value were SERPINA1, DAPK1 and ATG4B (Table I). DAPK1 and ATG4B were closely associated with low OS of patients with COAD. Furthermore, downregulation of SERPINA1 was associated with low OS in patients with COAD (Fig. 3C). Based on these genes, a risk score was calculated. According to the median risk score, the COAD patients were divided into a high-risk group and low-risk group. Kaplan-Meier plots were employed to determine the performance of the risk score in predicting the clinical outcome of patients with COAD. The results suggested that the survival rate of the high-risk group was significantly lower than that of the low-risk group (Fig. 3B). Furthermore, after adjusting for clinicopathological features (age, gender, TNM, T, N and M stage) by univariate and multivariate analyses, the risk score remained a useful independent prognostic indicator (Fig. 4C and D). The risk score distribution of COAD patients, the number of patients in different risk groups, a thermogram of three prognostic genes and risk score distributions in patients are provided in Fig. 3D. The prognostic value of the risk score in patients with COAD was analyzed by ROC curve analysis, revealing an area under the ROC curve of 0.646, with a cut-off value of 1.565 (Fig. 4B).

Figure 3 Establishment of a prognostic index model for ARGs. (A) Univariate Cox regression and multivariate Cox regression analyses for ARGs. (B) Kaplan-Meier curves for risk score. (C) Kaplan-Meier curves of ARGs that underwent univariate Cox regression and multivariate Cox regression analysis. The shadow indicates the 95% CI, and the + symbols indicate censored data-points. (D) Risk plot of the ARGs. (E) Boxplots of risk scores value from clinical trials. ARG, autophagy-related gene; M, metastasis stage; N, nodal stage; SERPINA1, Serpin family A member 1; DAPK1, death-associated protein kinase 1; ATG4B, autophagy-related 4B cysteine peptidase.

Figure 4 Verification of the accuracy of the prognostic index model for ARGs. (A) Heatmap of colon cancer datasets. (B) Multi-ROC curve from clinical trials. The cut-off value for the age was 68 years. For stage, the difference between stage I-Ⅱ and stage III-IV was compared. For the risk score, the cut-off value was 1.565. (C) Univariate and (D) multivariate analysis of the single parameters. Hazard ratios are provided with 95% CI. (E) Clinical relevance analysis of ATG4B and SERPINA1, the expression value of genes was shown in boxplots. AUC, area under the ROC curve; ROC, receiver operating characteristic; ARG, autophagy-related gene; M, metastasis stage; N, nodal stage; T, tumor stage; SERPINA1, Serpin family A member 1; ATG4B, autophagy-related 4B cysteine peptidase.

Table I GO summary for prognostic genes.

Table I

GO summary for prognostic genes.

Gene symbol	GO summary
SERPINA1	GO:0048208 COPII vesicle coating; GO:0048207 vesicle targeting, rough ER to cis-Golgi; GO:0048199 vesicle targeting, to, from or within Golgi
DAPK1	GO:0071447 cellular response to hydroperoxide; GO:2000310 regulation of NMDA receptor activity; GO:0043280 positive regulation of cysteine-type endopeptidase activity involved in apoptotic process
ATG4B	GO:0051697 protein delipidation; GO:0000045 autophagosome assembly; GO:1905037 autophagosome organization

[i] SERPINA1, Serpin family A member 1; DAPK1, death-associated protein kinase 1; ATG4B, autophagy-related 4B cysteine peptidase; ER, endoplasmic reticulum; GO, gene ontology; NMDA, N-methyl-D-aspartic acid; COPII, coat protein complex II.

The Mann-Whitney U test was performed to explore the association between the risk score and clinicopathological parameters. Regarding the metastasis (M) stage, the results suggested that the risk score of the M1 group was higher than that of the M0 group (P=0.009). Furthermore, the risk score in the TNM stage III/IV group was higher than that in the stage I/II group (P=0.001). Regarding the nodal (N) stage, the risk score in the N1/N2 group was higher than that in the N0 group (P=0.001; Fig. 3E).

A heatmap depicting the expression of the three ARGs in high-risk and low-risk patients in the TCGA dataset is presented in Fig. 4A. The Mann-Whitney U test indicated that high expression of ATG4B was significantly associated with N (P=0.002) and advanced stages (P=0.002; Fig. 4E). Furthermore, a significant association between low expression of SERPINA1 and advanced M stage (P=0.001), advanced pathological stage (P=0.001), advanced pathological T stage (P=0.026) and advanced N stage (P=0.009) was determined (Fig. 4E).

Immunohistochemical analysis

Representative images of ATG4B, DAPK1 and SERPINA1 staining of COAD tissues are presented in Fig. 5. The clinicopathological data of the patients are presented in Table SI and the association of the expression levels with the patients' clinicopathological characteristics are presented in Table II. A total of 75.6% (34/45) patients were >60 years old, while 24.4% (11/45) were ≤60 years old (age range, 34-88 years; median, 69 years). A total of 62.2% (28/45) were males, while 37.8% (17/45) were females. The results suggested that in COAD tissues, ATG4B expression was low in 20.0% (9/45), moderate in 51.1% (23/45) and high in 28.9% (13/45) of cases and DAPK1 expression was low in 33.3% (15/45), moderate in 64.4% (29/45) and high in 2.2% (1/45) of cases, whereas SERPINA1 expression was low in 2.2% (1/45), moderate in 84.4% (38/45) and high in 13.3% (6/45) of cases. ATG4B expression was significantly associated with age (P=0.0043) and SERPINA1 expression was significantly associated with tumor size (P=0.0034; Table II).

Figure 5 Detection of significant prognostic autophagy-related genes in the colon adenocarcinoma tissues by immunohistochemical staining and H&E staining (magnification, x100 or 400; scale bars, 100 µm). Pink colour indicates positive staining, and all proteins were localized in the cytoplasm. SERPINA1, Serpin family A member 1; DAPK1, death-associated protein kinase 1; ATG4B, autophagy-related 4B cysteine peptidase.

Table II Clinicopathological variables and the expression of ATG4B, DAPK1 and SERPINA1.

Table II

Clinicopathological variables and the expression of ATG4B, DAPK1 and SERPINA1.

		ATG4B (%)				DAPK1 (%)				SERPINA1 (%)
Parameters	n	-/+	++	+++	P-value	-/+	++	+++	P-value	-/+	++	+++	P-value
Age (years)					0.0043				0.7265				0.0779
>60	34	3	20	11		12	21	1		0	28	6
≤60	11	6	3	2		3	8	0		1	10	0
Sex					0.1244				0.6398				0.6057
Female	17	1	9	7		5	12	0		0	14	3
Male	28	8	14	6		10	17	1		1	24	3
Tumor size (cm)					0.7515				0.0825				0.0034
>5	37	8	18	11		12	25	0		1	34	2
≤5	8	1	5	2		3	4	1		0	4	4
Lymphatic metastasis					0.1014				0.5365				0.2428
Yes	17	2	7	8		7	10	0		1	15	1
No	28	7	16	5		8	19	1		0	23	5
Recurrence and metastasis					0.1217				0.8593				0.4660
Yes	7	0	6	1		2	5	0		0	7	0
No	38	9	17	12		13	24	1		1	31	6

[i] Pearson's χ² test was used for statistical comparisons. SERPINA1, Serpin family A member 1; DAPK1, death-associated protein kinase 1; ATG4B, autophagy-related 4B cysteine peptidase.

Discussion

COAD is the most common subtype of colon cancer, with high mortality and a small number of systemic treatment options. Despite commendable advances in the treatment modalities for COAD, it remains one of the most common causes of cancer-associated death worldwide. Thus, it is essential to develop novel and non-invasive diagnostic and prognostic biomarkers for COAD at a molecular level (11-13). Autophagy is a highly conserved pathway that has a key role in cellular self-digestion to provide energy and metabolic precursors under conditions of starvation and to maintain cellular homeostasis (14). It has been reported that autophagy is increased during tumorigenesis and progression of various cancer types, including non-small cell lung cancer (15), liver cancer (16) and breast cancer (17). Chen et al (18) indicated that autophagy has an important role in the innate immune response and regulation of autophagy in tumor-associated macrophages may limit cancer growth and progression. A growing body of evidence indicates that autophagy has a key role in multidrug resistance after long-term chemotherapy, which may result in refractory cancer and tumor recurrence (19-21). In addition, autophagy promotes tumorigenesis and the development of cancer cells through various mechanisms (22). Thus, modification of differentially expressed ARGs may improve the responsiveness of cancer cells to treatments and provide novel targeted therapy options for COAD. In the present study, key prognostic ARGs in patients with COAD were identified, which may be utilized for the treatment of COAD.

In recent years, with the continuous improvements in next-generation sequencing technology and cost reduction, bioinformatics analysis has been widely used for studying clinical markers and identifying potential targets of diseases, including diabetes and tumors (23). Furthermore, the number of DNA and RNA sequences submitted to public databases, including TCGA and gene expression omnibus (GEO), has markedly increased in recent years. In the present study, 72 differentially expressed ARGs between COAD samples and normal samples were identified. GO and KEGG analysis were performed using Metascape and GSEA was applied to identify connections between genes and the potential molecular mechanisms of COAD. Of note, it was observed that the common term enriched in the KEGG analysis using Metascape and GSEA was ‘Pathways in cancer’. The results suggested that certain ARGs were closely associated with tumorigenesis. It has been reported that autophagy is able to inhibit cancer initiation at early stages, which may be due to autophagy limiting oncogenic signaling and preventing the toxic accumulation of organelles and damaged proteins (19,24). However, Guo and White (25) indicated that autophagy is induced in certain types of cancer and cancer cells rely on autophagy to survive. Cancer cells may promote autophagy-mediated recycling to maintain mitochondrial function and energy homeostasis, which has a key role in regulating tumor growth and proliferation (25). Therefore, autophagy has complex and variable effects on tumor cells and has different roles in different tumor types.

In the present study, three key prognostic ARGs (SERPINA1, DAPK1 and ATG4B) were identified by univariate and multivariate survival analyses. SERPINA1 encodes a serine protease inhibitor whose targets include elastase, plasmin, thrombin, trypsin, chymotrypsin and plasminogen activator. Specific mutations in the SERPINA1 gene may lead to alpha-1-antitrypsin deficiency. The Z variant of alpha-1-antitrypsin cannot be polymerized in the endoplasmic reticulum of hepatocytes and cannot be secreted, which may lead to hepatocellular carcinoma (26,27). Furthermore, Griffith et al (28) indicated that SERPINA1 may be a potential biomarker with sufficient sensitivity and specificity for the diagnosis of thyroid tumors. However, the functional mechanism of SERPINA1 in COAD is not clear. DAPK1 encodes a structurally unique 160-kDa calmodulin-dependent serine-threonine kinase that carries eight ankyrin repeats and two putative P-loop consensus sites. DAPK1 is a positive regulator of gamma-interferon-induced programmed cell death. Previous studies have indicated that DAPK1 is a candidate tumor suppressor and DAPK1methylation is a potential biomarker for the early diagnosis of gastrointestinal cancer (29). Furthermore, Singh et al (30) reported that DAPK1 mediates a wide range of cellular processes such as apoptosis and autophagy. However, the roles of DAPK1 in COAD have remained largely elusive. ATG4B, a member of the autophagin protein family, has a key role in cell homeostasis and cellular remodeling during differentiation. Numerous studies have reported that targeting ATG4B may suppress tumor growth by activating the AMP-activated protein kinase energy-sensing pathway (31). Fu et al (32) indicated that ATG4B is an independent positive regulator of tumor proliferation. These studies suggested that ATG4B is a potential target for COAD.

With the development of large-scale public databases, the identification of prognostic factors in cancer patients based on expression spectrum analysis has been proposed. For instance, Li et al (33) identified 20 genes related to malignant tumors, such as triple-negative breast cancer, COAD, ovarian cancer and glioblastoma multiforme from the GEO database. Wan et al (34) comprehensively analyzed 311 CRC samples from TCGA and GEO databases. However, these studies are not combined with the corresponding clinical information and the molecular markers obtained are of low prognostic value. In the present study, transcriptome information was combined with corresponding clinical information to obtain molecular markers with prognostic value.

In conclusion, three key prognostic ARGs (SERPINA1, DAPK1 and ATG4B) were identified by re-analyzing public datasets. These genes may be potential biomarkers for COAD. In addition, a novel risk score model was constructed based on the expression levels and HR value of these genes, which may predict the survival rate of patients with COAD.

Supplementary Material

Clinicopathological data of the patients.

Acknowledgements

Not applicable.

Funding

Funding: No funding was received.

Availability of data and materials

RNA-seq data were downloaded for the TCGA COAD cohort and the mRNA splicing pattern data were obtained with the SpliceSeq tool from TCGA. The raw data are available from TCGA (https://tcga-data.nci.nih.gov/tcga/) and GTEx (https://www.gtexportal.org/home/index.html). In addition, the data of the present cohort are available from the corresponding author on reasonable request.

Authors' contributions

Conception and design: XZ, RX and JM. Administrative support: WF. Collection and collation of data: JX. Data analysis and interpretation: XZ, RX, JM, WF, YL and JX. Manuscript writing: JM. Confirmation of the authenticity of the raw data: JM and XZ. All authors read and approved the final version of the manuscript. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

This study complied with the Declaration of Helsinki and was approved by the Ethics Committees of The Third Hospital of Hebei Medical University (Shijiazhuang, China). Written informed consent was obtained from all patients.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References