Identification of differentially expressed genes and biological pathways in para‑carcinoma tissues of HCC with different metastatic potentials
- Authors:
- Published online on: March 29, 2020 https://doi.org/10.3892/ol.2020.11493
- Pages: 3799-3814
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Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Hepatocellular carcinoma (HCC) is the major histological subtype of primary liver cancer accounting for 85% of all liver cancer cases worldwide, and it was reported to be the most common liver malignancy in 2016 (1). HCC is one of the most aggressive types of cancer with a high mortality rate, whereby 326,000 people died of HCC in 2015 (2). There are several definite risk factors for HCC, such as chronic hepatitis B/C virus infection, nonalcoholic fatty liver diseases, aflatoxin consumption and smoking (2). In addition, there have been a number of identified prognostic markers of HCC, such as alpha fetoprotein, vascular endothelial growth factor and transforming growth factor β, in both large-scale clinical trials and research projects. However, the early diagnosis and effective treatment of HCC remains problematic (3).
To date, surgical resection remains the gold standard treatment for HCC; however, postoperative recurrence and metastasis is common (4). In addition, a large number of patients with HCC are diagnosed at the advanced stages, in which the tumor has already metastasized to other organs prior to surgery (4). Therefore, metastasis is a key challenge in the treatment of HCC. Tumor metastasis is a malignant biological process involving multiple factors and complex signaling pathways that depend not only on the genetic changes of malignant tumor cells, but also on the changes in the tumor microenvironment, such as the stroma, blood vessels and infiltrating inflammatory cells (5). Dysfunction of gene expression in the microenvironment surrounding a tumor serves an important role in the metastatic behavior of tumor cells, such as adhesion of tumor cells, degradation of the extracellular matrix, invasion of basal tissues, homing ability to enter specific tissues, movement and migration in the circulatory system and the promotion of the angiogenesis (6). Therefore, the identification of key genes which function in the tumor microenvironment of HCC may be helpful to identify new targeted therapeutic strategies for HCC metastasis.
In the present study, an original dataset (GSE5093) was obtained from the NCBI-Gene Expression Omnibus database (ncbi.nlm.nih.gov/geo/) containing 20 samples of noncancerous tissues surrounding HCC tissues from two distinct groups of patients with HCC, including 9 metastasis-inclined microenvironment (MIM) samples, with detectable metastases and 11 metastasis-averse microenvironment (MAM) samples, without detectable metastases (7). Differentially expressed genes (DEGs) were filtered in using the Morpheus Website with a data processing standard. Gene-Spring software (version 13.1.1; Agilent Technologies Inc.) was employed to screen the DEGs, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG; kegg.jp) pathway enrichment analysis. Furthermore, a protein-protein interaction (PPI) network was established and three significant modules were analyzed. A total of 35 HCC tissue samples were assessed to verify the results of chip analysis. The present study aimed to investigate the genetic molecular mechanisms underlying the metastatic phenotype of HCC and improve the diagnosis and treatment of metastases of primary hepatic carcinoma patients.
Materials and methods
Data collection
The gene expression profile GSE5093 was obtained from Gene Expression Omnibus (GEO) (7). The GSE5093 dataset was based on the GPL1262 dataset [National Cancer Institute (NCI)/Advanced Technology Corner (ATC) Hs-UniGEM2, Advanced Technology Center Microarray Facility (National Institutes of Health (NIH)/NCI/Cancer Research Center (CCR)/ATC)] and contained 20 samples, including 9 MIM samples and 11 MAM samples.
Patients and specimens
The present study was approved by The Institutional Review Board of the Second Affiliated Hospital of Chongqing Medical University (Chongqing, China) and written informed consent was provided by all patients according to The Declaration of Helsinki. A total of 35 HCC specimens (from 21 men and 14 women; age range, 22–73 years; mean age, 53 years) were collected during resection of HCC tumor at The Second Affiliated Hospital of Chongqing Medical University (Chongqing, China) between May 2015 and November 2016. Tissue samples included 16 noncancerous surrounding hepatic tissue samples with MIM and 19 noncancerous surrounding hepatic tissue samples with MAM (<5 cm away from HCC tissue). Specimens were stored in liquid nitrogen for subsequent experimentation. The clinicopathological features of the HCC specimens are listed in Table SI.
Identification and analysis of DEGs
MIM and MAM samples were divided into two groups and GEO2R (ncbi.nlm.nih.gov/geo/geo2r/) was used to analyze the DEGs between the two groups. The raw expression data files, which included TXT files (Agilent platform), were used for analysis by processing using the Morpheus Website (software.broadinstitute.org). A unpaired t-test was used to identify the DEGs and |log fold change (FC)|≥1 and P<0.05 were used as the cut-off criteria for statistical significance.
Functional and pathway enrichment of DEGs
After identifying the DEGs, GO enrichment and KEGG pathway analyses were performed for gene annotation and functional enrichment analysis using the online tool Database for Annotation, Visualization and Integrated Discovery (DAVID; david.abcc.ncifcrf.gov/). The resulting GO terms and KEGG pathways with P<0.05 were considered significantly enriched for the obtained DEGs.
Survival analysis of the hub genes in HCC
Kaplan-Meier survival analysis and the log-rank test were employed to determine the association between hub genes and HCC, using the Gene Expression Profiling Interactive Analysis (GEPIA) database (8) and prognostic data from The Cancer Genome Atlas (cancer.gov/tcga) database. The percentage of low and high expression groups was set at 50%.
Constructing the PPI network of DEGs
The Search Tool for the Retrieval of Interacting Genes (STRING) database offers both experimental and predicted interactive information (9). In the present study, the STRING database was used to explore the enrichment analysis results according to the biological process, molecular function and cell components determined for the DEGs. Finally, the interactions were selected, of which combined scores of >0.7 were used to construct the PPI network using the Molecular Complex Detection (MCODE) plugin within Cytoscape software [version 3.7.2; (10,11)].
RNA preparation and reverse transcription-quantitative (RT-q)PCR
Total RNA from tissue samples was extracted using TRIzol® reagent (Takara Bio, Inc.), according to the manufacturer's protocol. RNA was reverse transcribed into cDNA using the PrimeScript RT reagent (Takara Bio, Inc.). The temperature protocol for RT was as follows: 37°C for 5 min, followed by 45°C for 42 min and 75°C for 5 min. qPCR was subsequently performed using SYBR-Green Real-time PCR Master mix (Beijing Transgen Biotech Co., Ltd.). The primer sequences used for qPCR are listed in Table SII. The following thermocycling conditions were used for qPCR: Initial denaturation at 92°C for 15 min; 37 cycles of 95°C for 15 sec, 55°C for 30 sec; and a final extension at 72°C for 30 sec. Relative mRNA levels were measured using the 2−ΔΔCq method (12) and normalized to the internal reference gene GAPDH.
Statistical analysis
All data are presented as the mean ± standard deviation and analyzed using SPSS 20.0 software (IBM Corp.) and GraphPad Prism 7 (GraphPad Software, Inc.). Differences between groups were analyzed using paired Student's t-test and one-way analysis of variance, followed by Newman-Keuls post hoc test. P<0.05 was considered to indicate a statistically significant difference.
Results
Identification of DEGs between MIM and MAM samples
Through the analysis and processing of data in GSE5093, a total of 1,171 DEGs were identified, which included 712 upregulated and 459 downregulated genes. The heat map of DEG expression levels (top 50 upregulated and 50 downregulated genes) is exhibited in (Fig. 1).
GO and pathway enrichment analyses of DEGs
The 1,171 DEGs were selected for functional analysis, performed using the DAVID database. GO analysis of DEGs was carried out from three aspects, which covered molecular function (MF), cellular component (CC) and biological process (BP). BP analysis revealed that the upregulated DEGs were enriched in ‘cell-cell adhesion’, ‘mRNA splicing via spliceosome’ and ‘platelet degranulation’ (Table I), whereas the downregulated DEGs were enriched in ‘cell-cell adhesion’, ‘cell proliferation’ and ‘positive regulation to tyrosine phosphorylation of STAT5 protein’ (Table II). For CC, the upregulated DEGs were enriched in the ‘extracellular exosome and cytosol’, as well as the ‘membrane’ (Table I) and the downregulated DEGs were enriched in ‘receptor complex’, ‘integral component of plasma membrane’ and ‘postsynaptic density’ (Table II). In addition, the MF of upregulated DEGs were enriched in ‘protein binding’, ‘poly(A) RNA binding’ and ‘cadherin binding involved in cell-cell adhesion’ (Table I), whereas the downregulated DEGs were enriched in ‘protein binding’, ‘epidermal growth factor receptor binding’ and ‘amino acid transmembrane transfer activity’ (Table II). These results suggest that the DEGs were associated with the biological processes of cell migration and metastasis.
Table I.Gene ontology analysis of upregulated differentially expressed genes associated with metastasis-inclined microenvironment and metastasis-averse microenvironment. |
Table II.Gene ontology analysis of downregulated differentially expressed genes associated with metastasis-inclined microenvironment and metastasis-averse microenvironment. |
KEGG pathway analysis of MIM and MAM DEGs
To further analysis the functions of DEGs, KEGG pathway analysis was conducted to determine the most significantly enriched pathways of the upregulated DEGs and downregulated DEGs. The upregulated DEGs were enriched in ‘protein processing in the endoplasmic reticulum’, ‘antigen processing and presentation’ and ‘phagosome’ pathways (Table III). The downregulated DEGs were enriched in the ‘arrhythmogenic right ventricular cardiomyopathy’, ‘phosphatidylinositol signaling system’ and ‘inositol phosphate metabolism’ pathways (Table III).
Table III.Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs associated with metastasis-inclined microenvironment and metastasis-averse microenvironment. |
Analysis of the PPI network with DEGs in MIM and MAM
According to the information provided by the STRING, the interaction of the DEGs and acquired hub genes of potential diagnosis and treatment associated DEGs were analyzed. As depicted in Figs. 2A and S1, there were 955 nodes and 7,589 edges, of which 638 nodes represented upregulated DEGs and nodes represented downregulated DEGs (Fig. 2B). The top 10 hub genes with the highest degrees included: Heat shock protein family A member 8 (HSPA8); PH domain and leucine rich repeat protein phosphatase 1 (PHLPP1); phosphoribosylglycinamide formyltransferase (GART); carbamoyl-phosphate synthetase 2 (CAD); actin beta (ACTB); cadherin 1 (CDH1); phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG); nuclear factor κB subunit 1 (NFκB1); signal transducer and activator of transcription 3 (STAT3); and heat shock protein family A (Hsp70) member 5 (HSPA5). Then, the expression of the top 10 hub genes were verified using RT-qPCR. There were 4 hub upregulated genes and 6 downregulated genes in MAM, compared with MIM samples (Fig. 3). Moreover, the top 3 significant modules were selected from the DEG PPI network using MCODE (Fig. 4A-C). Module 1 included 31 nodes and 458 edges, module 2 included 37 nodes and 267 edges and module 3 included 45 nodes. Furthermore, GO and KEGG pathway analysis results were used to analyze the functional and signal pathway enrichment of the three modules. The results showed that module 1 was primarily associated with ‘RNA splicing’, ‘catalytic step 2 spliceosome’, ‘nucleotide binding’ and ‘spliceosome’ pathways (Table IV). Module 2 was primarily enriched in ‘protein modification by small protein conjugation’, ‘catalytic complex’, ‘ubiquitin-protein transferase activity’ and ‘chemokine signaling’ pathways (Table V). Module 3 was most enriched in ‘protein folding’, ‘extracellular exosomes’, ‘protein disulfide isomerase activity’ and ‘chemokine signaling’ pathways (Table VI). These results indicated that the 10 hub genes may function in the biological behavior processes of cell migration and metastasis.
The Kaplan-Meier plot of hub genes in MIM and MAM
Through GEPIA prediction of the association between the 10 hub genes and HCC patient prognosis, it was observed that expression of CAD [Hazard Ratio (HR), 1.9; P<0.001], GART (HR, 1.8; P=0.018), HSPA5 (HR, 1.5; P=0.016), ACTB (HR, 1.6; P=0.0074), CDH1 (HR, 0.66; P=0.018) and HSPA8 (HR, 1.6; P=0.011) were associated with poor overall survival in patients with HCC (Fig. 5).
Discussion
HCC is the most common malignant tumor with high morbidity and mortality, and was reported to be one of the major causes of cancer-associated mortalities worldwide, in 2002 (13). Patients with HCC are often diagnosed with extrahepatic metastasis, which poses a great challenge to the diagnosis and treatment of HCC (14). Currently, high-throughput gene chip technology can be used to explore the occurrence and development of diseases from the whole genome or transcriptome level, which has been widely used in gene expression analysis and biomarker discovery for a variety of illnesses (14). In order to further understand the molecular mechanisms underlying the recurrence and metastasis of HCC, the GSE5093 Biochip dataset on the metastatic microenvironment of HCC was obtained from the GEO database. This was used to perform a systematic and bioinformatics analysis, including enrichment analysis of differences in gene expression and protein-protein interaction.
In the present study, a total of 1,171 DEGs were identified, of which 712 were upregulated and 459 were downregulated. GO term functional analysis demonstrated that the upregulated genes were primarily involved in ‘cell-cell adhesion’, ‘extracellular exosome’ and ‘cadherin binding’, while downregulated DEGs were involved in ‘cell-cell adhesion’, ‘integral component of plasma membrane’ and ‘epidermal growth factor receptor binding’. The tumor cells leave the primary site, invade the extracellular matrix, adhere to macromolecular protein components in the basement membrane, degrade the basement membrane and the extracellular matrix, and move through the extracellular matrix to invade the surrounding tissues (15). Once in the circulation system, tumor cells evade the surveillance of the immune system. When passing through the vessel wall to the secondary site, tumor cells adhere to the secondary site, proliferate and eventually metastasize (16,17). Cell adhesion, receptor complex, integral component of plasma membrane, postsynaptic density and epidermal growth factor receptor binding are key auxiliary components (18–20). Cell adhesion molecules suppress the adhesive function of tumor cells and glycoproteins located on the cell surface (21). Moreover, KEGG pathway analysis indicated that the upregulated DEGs influenced ‘protein processing in endoplasmic reticulum’, ‘antigen processing and presentation’ and ‘phagosome’; the downregulated DEGs were associated with ‘arrhythmogenic right ventricular cardiomyopathy’, ‘phosphatidylinositol signaling system’ and ‘inositol phosphate metabolism’. Tumor metastasis involves detachment from the primary site of the tumor, entrance into the surrounding stroma and the circulation or lymphatic system, adhesion to endothelial cell walls, extravasation and invasion through vascular proliferation for the formation a novel metastatic lesion (22,23). GO and KEGG analyses suggested that the aforementioned DEGs may influence signaling pathways associated with tumor cell metastasis. The identified DEGs may provide novel research directions and targets for the treatment of HCC metastasis.
Based on the established PPI network with DEGs, the following top 10 hub genes were identified: HSPA8, PHLPP1, GART, CAD, ACTB, CDH1, PIK3CG, NFκB1, STAT3 and HSPA5. HSPA8 is a member of the heat shock proteins family, which influences molecular signal transduction, apoptosis and protein regulation (24). HSPA8 is located in the cytoplasm and lysosomes and is involved in mediating cell autophagy by binding to the substrate protein and transporting it into the lysosomal cavity (25,26). Novel research has shown that the Hsp70 protein may inhibit apoptosis by regulating the caspase-dependent pathway (27). Moreover, tumor vaccines against HSP70S have been successfully completed in animal models and clinical trials are underway (27).
PHLPP1 is an important regulator of Akt serine-threonine kinases and conventional/novel protein kinase C isoforms (28). PHLPP1 can inhibit growth factor-induced signal transduction pathways in cancer cells, so it may be used as a growth inhibitor in several types of cancer (28). In the human breast cancer cell line 21T, AK294 phosphorylation was inhibited by LY294002, and the expression of PHLPP1 in two metastatic cell lines (MT1 and MT2) was much lower compared with early breast cancer cells (29). PHLPP1 can negatively regulate the activity of AKT and its downstream kinase by phosphorylating the hydrophobic group of AKT at the Serine473 site. This resulted in inhibition of the PI3K/AKT signaling pathway, and the activation of various cancer promoting signaling pathways, including amplification or gain-of-function mutations in upstream receptor protein tyrosine kinases (RPTKs), activating mutations in PI3K and Akt and loss-of-function mutations in the regulatory phosphatase PTEN, which affect differentiation and proliferation of cancer cells and apoptosis in breast cancer cells (29). Therefore, it is hypothesized that PHLPP1 is associated with tumor metastasis.
Trifunctional purine biosynthetic protein adenosine-3 is an enzyme that is encoded by the GART gene (30). GART participates in several aspects of energy metabolism, including as a ATP binding source, metal ion binding source and phosphoribosylamine-glycine ligase activity source (30,31). CAD is a fusion gene that encodes three enzymes involved in pyrimidine biosynthesis; carbamoyl-phosphate synthetase 2 and aspartate transcarbamylase fused with dihydroorotase (31). CAD is a key enzyme the regulates multiple biological processes in the first three steps of pyrimidine biosynthesis (32). These three genes serve a role in the synthesis and metabolism of nucleic acids and proteins in cells, and their synthesized products provide essential substances and energy for cell proliferation (32).
Actin is a major constituent of the muscular contractile apparatus and ACTB is one of six different actin isoforms that have been identified in humans (33). ACTB binds RNA-binding protein Sam68 and participates in the regulation of the synaptic formation of dendritic spines (34). ACTB supports the muscular cytoskeleton during the formation of novel tumor cells following tumor cell proliferation and metastasis. Thus, ACTB is considered to be associated with cell proliferation and metastasis (34).
CDH1, also known as epithelial cadherin (E-cadherin), is a protein encoded by the CDH1 gene (35). Mutations in CDH1 are closely associated with liver, colorectal and gastric cancer. Moreover, a functional deficit of CDH1 promotes the proliferation and invasiveness of tumor cells, and promotes the malignant development of tumors (35). E-cadherin is a member of the cadherin family and downregulation of E-cadherin reduces cell-cell adhesion, which results in cells that are more susceptible to migration (36). This change can easily result in cancer cells passing through the cell basement membrane and invading surrounding tissues (36). A previous study demonstrated that E-cadherin is an important protein that regulates cell-cell adhesion. On the cell membrane, E-cadherin binds to β-catenin via its cytoplasmic tail, allowing epithelial cells to bind tightly together. Knockdown of E-cadherin expression results in β-catenin release into the cytoplasm and translocation into the nucleus, and this process can result in the expression of epithelial mesenchymal transition (EMT)-inducible transcription factors. Therefore, E-cadherin is an important gene that regulates EMT (37).
The PIK3CG subunit λ isoform is an enzyme encoded by the PIK3CG gene (38). Several studies have demonstrated that the function of PIK3CG is to regulate the transmission of extracellular signals, including E-cadherin-mediated cell-cell adhesion, which serves an important role in maintaining the structural and functional integrity of the epithelium (39,40).
The NFκB1 gene is a cellular transcription factor and encodes the NF-κB p105 subunit protein. The expression of NF-κB is activated via various stimuli, such as cytokines, oxidative free radicals and bacterial or viral products (41). Activated NF-κB is transported to the nucleus to participate in the transcription and synthesis of various proteins and signaling molecules, regulating various cell behaviors (41). It has been revealed that osteopontin can regulate the expression of MTI-MMP via the NF-κB signaling pathway and promote the migration of cancer cells and invasion into the extracellular matrix (42). A previous study confirmed that several adhesion molecules, such as ICAM-1, VCAM-1 and ELAM-1, are regulated by NF-κB, which serves an important role in tumor metastasis (43).
STAT3, which contains SH2 and SH3 domains, binds to specific phosphotyrosine-containing peptides. When STAT3 is activated by phosphorylation, it serves as a transcriptional activator, in either its homodimeric or heterodimeric form (44). Phosphorylated STAT3 binds a specific site in the target gene promoter sequence and promotes transcription of the gene (44). Dysregulation of this pathway in tumors leads to increased angiogenesis surrounding the tumor, promotes blood supply to the tumor, increases tumor survival and promotes malignant development. It has been demonstrated that tissue-specific inactivation of STAT3 in the lungs of mice is associated with the occurrence and progression of lung adenocarcinoma, which reduced the survival rate. Notably, decreased expression of STAT3 promotes the expression of NF-κB in the cytoplasm, thus regulating NF-κB-induced IL-8 expression (45). This process promotes IL-8-mediated tumor invasion, angiogenesis and progression (45).
Binding immunoglobulin protein, also known as HSPA5, is a protein that is encoded by the HSPA5 gene (46). A large number of studies have revealed the high expression of HSPA5 in all malignant tumors (47,48). Therefore, inhibiting HSPA5 gene expression may be used as an adjuvant therapy for cancer. In addition, reducing the large amount of HSPA5 produced during the stress response can increase the apoptosis of tumor cells and inhibit tumor growth (49).
In addition, tumor metastasis is a complex multi-step process, whereby tumor cells not only interact with each other and host cells, but also with extracellular matrix components (50). Certain characteristics, such as adhesion, metastasis, migration, skeleton assembly, signal transduction and proliferation are associated with tumor metastasis (50,51). Migration is considered a key factor in the process of tumor cell metastasis (51). Previous studies have demonstrated that RNA splicing, protein modification by small protein conjugation, catalytic complex, ubiquitin-protein transferase activity, chemokine signaling pathways, protein folding, extracellular exosomes, protein disulfide isomerase activity and chemokine signaling pathways are also involved in the process of tumor cell migration and metastasis, such as the expansion of the cell front, the formation of new adhesion sites and the release of the original adhesion sites in the tail of the cell (52–56).
A study reported that miR-26b-5p exerts metastatic properties and maintains epithelial cell adhesion molecule + cancer stem cells via HSPA8 in HCC (57). Moreover, a study investigating the role of PHLPP1 in HCC reported that miR-190 influences EMT by regulating the expression of PHLPP1, thus affecting the malignant biological behavior of HCC cells (58). Isobaric tags for relative and absolute quantification analysis of clinical samples of HCC indicated that ACTB serves an important role in the initiation stage of HCC (59). Long non-coding RNA ATRERNA1 promotes the metastasis and invasiveness of HCC cells by recruiting EHMT2 and/or ehmt2/snai1 complexes to inhibit CDH1 (60). Loss of NFκB1 promotes the occurrence of age-related chronic liver disease (CLD) which is characterized by steatosis, neutrophil proliferation, fibrosis, telomere damage of hepatocytes and HCC (61). The results of the present study demonstrated that STAT3 is one of the hub genes. Phosphorylated RPN2 activates the signal transducer and activator of STAT3 and is also responsible for the RPN2-stimulated elevated expression of MMP-9 and for invading HCC cells (62).
In the current study, biological function and signal pathway enrichment analysis revealed that the genes in module 1, 2 and 3 were primarily enriched in ‘RNA splicing’, ‘G-protein coupled receptor signaling pathway’ and ‘protein ubiquitination’. Studies have demonstrated that these signaling pathways are required for cell survival and metabolism and serve an important role in tumor recurrence and metastasis (63–65). Therefore, it is hypothesized that these molecular pathways may provide novel insight and potential targets for the diagnosis and treatment of HCC.
In conclusion, 1,171 DEGs were identified (including 712 upregulated and 459 downregulated genes, 1,033 nodes, 7,589 edges and 10 hub genes) via gene profile dataset and integrated bioinformatics analysis in noncancerous surrounding hepatic tissues. The 10 hub genes were significantly enriched in several signaling pathways which serve an important role in tumor metastasis. According to the present research and analysis, CAD, GART, HSPA5, ACTB, CDH1 and HSPA8 may have potential as targets for the diagnosis and treatment of metastatic HCC. The present study may provide a scientific basis for the investigation of these genes in HCC in the future.
Supplementary Material
Supporting Data
Acknowledgements
Not applicable.
Funding
The present study was supported by The Foundation of Scientific Research of Sichuan Medical Association (grant. no. S16007), The Sichuan Health Planning Commission Key Project Foundation (grant. no. 17ZD008), The Sichuan Science and Technology Project (grant. no. 2018JY0276) and The Chengdu Science and Technology Bureau Technology Innovation Project (grant. no. 2018-YF05-01228-SN).
Availability of data and materials
The datasets generated and/or analyzed during the present study are available in the [NCBI] repository, [https://www.ncbi.nlm.nih.gov/geo].
Authors' contributions
YL and HW performed the experiments, analyzed the data and drafted the manuscript. YW, HW, MD and CL conceptualized the study design and critically revised the manuscript. YL, MD, CL and HW wrote the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The present study was approved by The Institutional Review Board of the Second Affiliated Hospital of Chongqing Medical University (Chongqing, China) and written informed consent was provided by all patients according to the Declaration of Helsinki.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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