Sage Journals: Discover world-class research

Abstract

BACKGROUND:

Lung cancer is the leading cause of cancer mortality worldwide. The collection of exhaled breath condensate (EBC) is a non-invasive method that may have enormous potential as a biomarker for the early detection of lung cancer.

OBJECTIVE:

To investigate the proteomic differences of EBC between lung cancer and CT-detected benign nodule patients, and determine whether these proteins could be potential biomarkers.

METHODS:

Proteomic analysis was performed on individual samples from 10 lung cancer patients and 10 CT-detected benign nodule patients using data-independent acquisition (DIA) mass spectrometry.

RESULTS:

A total of 1,254 proteins were identified, and 21 proteins were differentially expressed in the lung adenocarcinoma group compared to the benign nodule group ( $p<$ 0.05). The GO analysis showed that most of these proteins were involved in neutrophil-related biological processes, and the KEGG analysis showed these proteins were mostly annotated to pyruvate and propanoate metabolism. Through protein-protein interactions (PPIs) analysis, ME1 and LDHB contributed most to the interaction-network of these proteins.

CONCLUSION:

Significantly differentially expressed proteins were detected between lung cancer and the CT-detected benign nodule group from EBC samples, and these proteins might serve as potential novel biomarkers of EBC for early lung cancer detection.

Keywords

Exhaled breath condensate proteomics biomarkers benign pulmonary nodule lung cancer

1. Introduction

Lung cancer is a major public health problem, and ranks first among the leading causes of cancer morbidity and mortality worldwide according to the GLOBOCAN 2018 [1]. Over the past several decades, the incidence of lung cancer has been increasing in China due mostly to, tobacco smoking, severe outdoor air pollution and indoor air pollution from cooking fumes [2]. Most lung cancer cases are diagnosed in the late stages (Stage III and IV), which are hard to cure [3]. In contrast, about 80% of low-dose computed tomography (LDCT)-detected lung tumors are found at an early curable stage [4]. Thus, the early detection of lung cancer is essential to reduce mortality from the disease. The most effective method for the early detection of lung cancer in at-risk individuals in China and elsewhere is LDCT. The landmark National Lung Screening Trial (NLST) was carried out to measure whether LDCT could reduce the mortality of lung cancer [5]. The results showed that in asymptomatic long-term smokers, lung cancer mortality could be reduced by 20% through annual LDCT screening. But the NLST also found a high false-positive rate of about 25% [5]. Most of the nodules detected by LDCT are benign, and many patients with positive scans receive unnecessary treatment.

Exhaled breath condensate is a biological fluid that comes from the respiratory tract and consists of water vapor and aerosolized particles [6]. It contains a variety of biological markers, such as proteins, metabolites, and DNA, which have been used to study the causes of pulmonary diseases [7, 8, 9]. The collection of EBC is simple, non-invasive, and cost-efficient, which allows for its potential application in low dose lung cancer screening to help differentiate benign from malignant lesions.

Several studies have considered how various types of biomarkers can be measured in EBC, and have profiled the presence of specific biomarkers in various disease states [7, 10]. Recently, proteomic technology has developed rapidly [11, 12], which has improved the in-depth protein profiling for biomarker identification. Although EBC is considered to be an ideal tool to explore biomarkers for lung cancer, the low protein concentration (approximately 1 $\mu$ g ml ${}^{-1}$ ) in EBC is still a significant limitation in proteomic studies [13]. Thus, improved methods are needed to increase the depth of proteomic coverage.

Data-independent acquisition (DIA) is a new mass spectrometry technology developed in recent years [17, 18]. In contrast with data-dependent acquisition (DDA), DIA uses a different data scanning mode: the entire full scan range of the mass spectrum is divided into several windows, and then all ions in each window are detected and fragmented [19]. Therefore, the information of all ions in the sample can be obtained without omission. Based on these technical advantages, DIA is extremely suitable for traceable analysis of large-scale samples and could complete massive analysis on EBC samples [20, 21, 22, 23].

Thus, we designed a study to explore proteomic differences between lung cancer and CT-detected benign nodule in EBC samples using the DIA method, and conducted bioinformatics to identify potential biomarkers for lung cancer and the underling functions and pathways of differentially expressed proteins. Our goal is to discover new biomarkers might improve the criteria for LDCT and help to reduce the false-positive rate and harms from unnecessary treatment.

Table 1
Baseline characteristics of all the participants

Characteristics	Lung cancer	Benign pulmonary nodule	$p$ -value
No. of subjects	10	10	/
Male, n (%)	7 (70%)	4 (40%)	0.639 ${}^{*}$
Age (mean $\pm$ SD), yr	61.5 $\pm$ 6.5	53.7 $\pm$ 12.6	0.196 ${}^{**}$
BMI (kg/m ${}^{2}$ )	22.3 $\pm$ 1.0	21.8 $\pm$ 1.3	0.417 ${}^{**}$
Disease type	Lung adenocarcinoma	Benign nodule	/
Stage (I/II/III/IV)	4/4/2/0	–	/
Smoking (male/female)	7/0	2/0	0.700 ${}^{*}$

${}^{*}$ Fisher’s exact test; ${}^{**}$ Independent sample $t$ -test.

2. Materials and methods

2.1 Sample collection

A total of 20 subjects were included in this study, including 10 lung cancer patients and 10 benign nodule patients (shown in Table 1). EBC samples were collected from subjects between May 21, 2018 and December, 2018 in Shanghai Chest Hospital (Shanghai, China). R-tubes (Respiratory Research Inc., USA) were used for EBC collection, and nose clips were used to avoid nasal influence according to the guide of the American Thoracic Society and European Respiratory Society (ATS/ERS) [14]. Participants breathed normally through the tube for about 10–12 mins, and the final volume of EBC was 1.5 mL–2 mL. Samples were collected before 10:00 am, and stored at $-$ 80 ${}^{\circ}$ C immediately.

The lung cancer group (LC) included newly diagnosed patients with histopathologically-confirmed lung adenocarcinoma. Inclusion criteria included FEV1 / FVC greater than 70% (FEV1: forced expiratory volume in one second; FVC: forced vital capacity). The exclusion criteria were having received chemotherapy or undergone lung cancer surgery, or had airway inflammation or other lung infections in the past 3 months, or had other types of cancer. The diagnosis of lung cancer was made according to the IASLC/ATS/ERS classification for lung adenocarcinoma [15] and WHO classification of lung tumors [16].

The benign pulmonary nodule group (PN) included patients who had a positive LDCT scan and were diagnosed with lung nodules. Inclusion criteria included 50–74 years of age, no signs or symptoms of lung cancer, 30-pack years or greater history of tobacco smoking, and being a current smoker or have quit smoking within the last 15 years. The exclusion criteria for this group were having received anti-tumor or anti-inflammatory treatment before the sample collection, or had other lung diseases, or the lesion was larger than 3 cm.

This study was performed according to the Code of Ethics of the World Medical Association and approved by the Ethics Committee of East China University of Science and Technology. An informed consent was signed by each participant.

2.2 Sample preparation

An in-house filter-aided sample preparation (FASP) protocol was used for sample digestion. A 10 KDa filter membrane was used for sample concentration, and mixed with 100 $\mu$ L of UA buffer (8M Urea, 150 mM Tris-HCl, pH 8.0). EBC samples were mixed with 1M dithiothreitol (DTT) and incubated at 37 ${}^{\circ}$ C for 45 min. Next, 100 $\mu$ L of 50 mM IAA (iodoacetamide in UA) was added and samples were centrifuged at 14,000 g for 20 mins at room temperature. We added 40 $\mu$ L NH ${}_{4}$ HCO ${}_{3}$ buffer (0.5 $\mu$ g Lys-C) to the supernatant and vortexed at 600 rpm for 1 min; mixed with 0.5 $\mu$ g Trypsin and digested at 37 ${}^{\circ}$ C overnight. Then 40 $\mu$ L of NH ${}_{4}$ HCO ${}_{3}$ buffer (50 mM) was added to each sample. After lyophilization, 12 $\mu$ L 0.1% formic acid (FA) was added to reconstitute samples, and the peptide concentration was measured at 280 nm.

2.3 LC-MS/MS analysis

The nano-flow HPLC system (Easy nLC-1200) (Thermo Fisher Scientific, CA, USA) was equipped with a C18 column (75 $\mu$ m $\times$ 300 mm, 3 $\mu$ m, Thermo Fisher Scientific, CA, USA) connected to a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific, CA, USA). The LC-MS/MS analysis was conducted by Applied Protein Technology (Shanghai, China).

For DDA analysis, the peptides were separated by a 120 min gradient at the flow rate of 250 nL/min. Mobile phase A consisted of 0.1% FA in water, and mobile phase B consisted of 0.1% FA in acetonitrile. The gradient program was set as follows: 0–97 min, mobile phase B from 8% to 30%; 97–110 min, mobile phase B from 30% to 100%; 110–120 min, held at 100% for mobile phase B.

After separation, full scan was performed on a Q-Exactive HF mass spectrometer between 300–1800 m/z. The detection mode was positive ion, AGC was 3e6, and maximum injection time was 50 ms. followed by 20 ddMS2 scans (MS2 scans). The isolation window was 1.6 Th, the resolution was 30K, AGC target was 3e6, maximum ion injection time was 120 ms, and normalized collision energy was 27.

Figure 1.

Volcano plot of the proteins identified in lung cancer group and benign pulmonary nodule group. Volcano plot showed 21 proteins were differentially expressed in lung cancer group ( $p<$ 0.05), and all of which were upregulated.

2.4 Data-independent acquisition (DIA)

DIA analysis was conducted on the same LC-MS/MS system with DDA. 5 $\mu$ L of peptides (about 0.5 $\mu$ g) was used and mixed with indexed Retention Time (iRT) standard peptides. Peptides were separated using a 120 min linear gradient, and the flow rate was 250 nL/min. The gradient program was carried out as: 10–30% mobile phase B (97 min), 30–100% mobile phase B (13 min), and held for 10 min of mobile phase B.

For DIA analysis, the cycle was carried out as follows: one full MS scan at the resolution of 120K between 350 and 1650 m/z, and AGC target was 3e6, maximum ion injection time was 50 ms; followed by 30 DIA windows acquired at the resolution of 30K. AGC target was 3e6, MS2 activation type was HCD and the energy was 30. The spectra data was recorded as profile.

2.5 Spectral library generation

DDA data were analyzed with MaxQuant (version 1.5.3.17) against the Uniprot human database, and the sequence of iRT was added to the database. Tryptic cleavage was chosen and two missed cleavages were acceptable at most. Fixed modification was Carbamidomethyl, variable modifications were Oxidation(M) and Acetyl(Protein N-term). The results were filtered with an FDR $<$ 1%. Raw data and database search results were imported into Spectronaut (Biognosys, Schlieren, Switzerland) to generate the spectral library. DIA data were analyzed with Spectronaut Pulsar X against the library we built.

Figure 2.

GO classification and enrichment results of differentially expressed proteins. (A) GO classification in biological process, molecular function and cellular component; (B) enriched GO terms in biological process, molecular function and cellular component.

Table 2

Differentially expressed proteins in lung cancer group compared to benign pulmonary nodule group

Uniprot accession	Gene symbol	Protein	Biological process	Fold change	$p$ -value ${}^{*}$
Q14CN4	KRT72	Keratin, type II cytoskeletal 72	GO:0048513 animal organ development GO:0048731 system development GO:0007275: multicellular organism development	17.072	0.026
P07195	LDHB	L-lactate dehydrogenase B chain	GO:0019752 carboxylic acid metabolic process GO:0043436 oxoacid metabolic process GO:0006089 lactate metabolic process GO:0006082 organic acid metabolic process GO:0006090 pyruvate metabolic process GO:0006753 nucleoside phosphate metabolic process GO:0009117 nucleotide metabolic process	10.206	0.044
P63241	EIF5A	Eukaryotic translation initiation factor 5A-1	GO:0006415 translational termination GO:0071427 mRNA-containing ribonucleoprotein complex export from nucleus GO:0006406 mRNA export from nucleus GO:0051028 mRNA transport	9.481	0.049
B7Z4R3	N/A	CCT-beta	GO:0009987 cellular process GO:0006457 protein folding	5.265	0.026
P06748	NPM1	Nucleophosmin	GO:0007098 centrosome cycle GO:0006886 Biological Process GO:0043066 negative regulation of apoptotic process	4.543	0.034
P08603	CFH	Complement factor H	GO:0006957 complement activation, alternative pathway GO:0006956 complement activation	4.015	0.031
O43548	TGM5	Protein-glutamine gamma-glutamyltransferase 5	GO:0070268 cornification GO:0006464 cellular protein modification process	3.776	0.012
P43243	MATR3	Matrin-3	GO:0002218 activation of innate immune response GO:0045087 innate immune response	3.181	0.006
P07741	APRT	Adenine phosphoribosyltransferase	GO:0043312 neutrophil degranulation GO:0002283 neutrophil activation involved in immune response GO:0036230 granulocyte activation GO:0042119 neutrophil activation GO:0002446 neutrophil mediated immunity GO:0043299 leukocyte degranulation	2.426	0.037
Q86YZ3	HRNR	Hornerin	GO:0002443 leukocyte mediated immunity GO:0032940 secretion by cell GO:0048731 system development GO:0001775 cell activation GO:0007275 multicellular organism development	2.360	0.021
Q5T6F0	DCAF12	DDB1- and CUL4-associated factor 12	GO:0070647 protein modification by small protein conjugation or removal GO:0016567 protein ubiquitination GO:0032446 neutrophil mediated immunity GO:0070647 protein modification by small protein conjugation or removal	1.961	0.029
Q96P63	SERPINB12	Serpin B12	GO:0043312 neutrophil degranulation GO:0002283 neutrophil activation involved in immune response GO:0036230 granulocyte activation GO:0042119 neutrophil activation GO:0002446 neutrophil mediated immunity GO:0043299 leukocyte degranulation	1.860	0.0276
Q9UFE4	CCDC39	Coiled-coil domain-containing protein 39	GO:0061512 protein localization to cilium GO:0001578 microtubule bundle formation GO:0036159 inner dynein arm assembly GO:0070286 axonemal dynein complex assembly GO:0061966 establishment of left/right asymmetry GO:0090660 cerebrospinal fluid circulation	1.836	0.0264

Table 2, continued
Uniprot accession	Gene symbol	Protein	Biological process	Fold change	$p$ -value ${}^{*}$
Q9BYJ1	ALOXE3	Hydroperoxide isomerase ALOXE3	GO:0019369 arachidonic acid metabolic process GO:0046513 ceramide biosynthetic process GO:0051122 hepoxilin biosynthetic process	1.770	0.026
Q8NEX9	SDR9C7	Short-chain dehydrogenase/reductase family 9C member 7	–	1.712	0.025
P48163	ME1	NADP-dependent malic enzyme	GO:0009725 response to hormone GO:0009719 response to endogenous stimulus GO:0019752 carboxylic acid metabolic process GO:0043436 oxoacid metabolic process GO:1902031 regulation of NADP metabolic process GO:0006082 organic acid metabolic process	1.655	0.021
B4DJQ8	N/A	Cathepsin C	–	1.618	0.008
Q16610	ECM1	Extracellular matrix protein 1	GO:0001525 angiogenesis- GO:0031214 biomineral tissue development GO:0006954 inflammatory response	1.601	0.003
P05089	ARG1	Arginase-1	GO:0060135 maternal process involved in female pregnancy GO:0043312 neutrophil degranulation GO:0002283 neutrophil activation involved in immune response GO:0036230 granulocyte activation GO:0042119 neutrophil activation GO:0002446 neutrophil mediated immunity	1.567	0.021
Q02413	DSG1	Desmoglein-1	GO:0043312 neutrophil degranulation GO:0002283 neutrophil activation involved in immune response GO:0036230 granulocyte activation GO:0042119 neutrophil activation GO:0002446 neutrophil mediated immunity GO:0009725 response to hormone	1.543	0.020
P28072	PSMB6	Proteasome subunit beta type-6	GO:0005829 cytosol GO:0048513 animal organ development GO:0016567 protein ubiquitination GO:0048731 system development GO:0032446 neutrophil mediated immunity GO:0019752 carboxylic acid metabolic process	1.525	0.0166

${}^{*}$ Student’s $t$ -test.

The DIA data have been deposited to the ProteomeXchange Consortium via the MassIVE partner repository with the dataset identifier PXD020919 and MSV000085953.

2.6 Functional analysis

To ensure the validity and accuracy of subsequent biological information and statistical analysis, according to general principles, proteins with quantitative values in more than 50% of the samples were used for subsequent statistical and bioinformatics analysis. Differentially expressed proteins were identified with a fold change $>$ 0.05 and $p$ -value $<$ 0.05 using Student’s $t$ -test.

Gene Ontology (GO) analysis was performed on the targeted protein set using Blast2GO (http//www.blast2 go.com/). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was analyzed with KAAS (KEGG Automatic Annotation Server: http://www.genome.jp/kegg/kaas/) software [17]. Fisher’s exact test was used for GO and KEGG enrichment analysis. Protein-protein interactions (PPIs) was performed on differentially expressed proteins with STRING (https://string-db.org/).

2.7 Statistical analysis

The mean $\pm$ standard deviation (SD) was used to present continuous variables that describe subject characteristics including age and body mass index (BMI). Age and BMI were compared between the two groups using independent sample t-test. Due to small sample sizes, categorical data such as gender and smoking were analyzed with Fisher’s exact test. All tests were two-tailed, and a $p$ -value $<$ 0.05 was considered to be significant. SPSS v 23.0 (SPSS, Chicago, IL, USA) was used for statistical analysis.

3. Results

3.1 Identification of differentially expressed proteins (DEPs)

In total, 1,254 proteins were identified in this study, and the full list of identified proteins is shown in Supplementary Table S1. With a cutoff $p$ -value $<$ 0.05, 21 proteins were significantly differentially expressed in the lung cancer group and all of which were upregulated. The information on DEPs is shown in Table 2 and Fig. 1.

3.2 Functional analysis

To further understand the characteristics of these differentially expressed proteins, GO classification and enrichment analysis were conducted (Fig. 2). In molecular function, proteins were mostly enriched on cation and transition metal ion binding. In the biological process, neutrophil degradation, granulocyte activation, neutrophil activation involved in immune response, neutrophil activation, neutrophil mediated immunity, and leukocyte degranulation were the most enriched terms. In cellular components, most proteins were involved in cytosol, secretory granule, and the membrane.

15 pathways were annotated based on the KEGG analysis, and the results suggested that significantly expressed proteins play an essential role in carbohydrate metabolism and amino metabolism. The most enriched pathways were pyruvate metabolism (hsa00620) and propanoate metabolism (has00640), shown in Fig. 3.

Figure 3.

Enriched KEGG pathways in differentially expressed proteins. Enriched KEGG results showed that differentially expressed proteins were mostly involved in propanoate metabolism and pyruvate metabolism.

3.3 Protein-protein interactions (PPIs) analysis

Protein-protein interactions were analyzed on differentially expressed proteins (Fig. 4). We found ME1 and LDHB have the most complicated interactions among these proteins. The PPIs of all the proteins were presented in the Supplementary Fig. S1.

Figure 4.

Protein-protein interactions of the differentially expressed proteins.

4. Discussion

Few studies have conducted proteomic analysis from EBC, and the application of this approach for differentiating lung cancer from benign nodule patients has not yet been explored. Codreanu et al. [18] conducted proteomic profiling on lung cancer and benign lung nodules on biopsied samples, and found that 10 proteins showed nodule-specific abundance. Although the benign group was not ascertained through LDCT, this study provides evidence that proteomic differences between benign nodule and lung adenocarcinoma have the potential to be biomarkers for use in LDCT. In our study, a thorough proteomic study was conducted to explore the EBC proteomic differences in lung cancer and CT-detected benign nodule patients, and the results showed that the differentially expressed proteins held great potential for the early detection of lung cancer.

Other studies have reported that EBC protein profiling has the biomedical potential to discriminate different pulmonary diseases, including asthma, COPD, and lung cancer [19, 20, 21, 22, 23]. Lopez et al. [24] conducted proteomic research on 192 EBC samples from healthy controls, lung cancer and COPD patients, and a total of 348 proteins were identified. In the lung cancer group, the total amount of proteins was significantly higher than in the control group, and the ROC curve suggested the differences had diagnostic value. Like ours, this study also showed the proteomics of EBC could help to develop biomarkers for the early detection of lung cancer. Sun et al. [25] carried out a proteomic analysis using tandem mass tags (TMTs) on COPD patients, and a total of 257 proteins were identified. This current study showed that the proteins in EBC could reflect airway-related disease and might be potential biomarkers. The use of TMTs helped to identify more proteins compared to previous studies, but also had the limitation: only six samples could be analyzed at one time, which may not be suitable for a large sample size study. Although previous studies attempted to investigate the biomedical potentials of EBC using proteomics, the lack of a high-throughput method for protein identification limited the implications from these findings.

Our proteomic analysis was done by a DIA approach, which divided the full scan range of mass spectrum into several windows, and all ions in each window could be detected and fragmented [26]. Different from DIA, DDA is biased to high abundant peptides [27]. One potential application of DIA is its use for the proteomic profile of individual EBC sampling, which is a noninvasive method that could potentially identify biomarkers for the diagnosis of lung cancer. The DIA method we used in this study was previously described [28]. In our previous study, we developed the DIA approach for the proteomics of individual EBC samples and conducted a pilot proteomic study in lung cancer and healthy controls. We had shown that DIA was suitable for EBC proteomics even if only a small amount of EBC sample was used for analysis. The proteins upregulated in lung cancer were related to human disease and mostly involved in the Ras signaling pathway, suggesting the biomedical potential of EBC. Our previous EBC proteomics study showed great potential in studying lung cancer, which provided the basis for further exploration of the proteomic differences in lung cancer and CT-detected benign lung nodules.

In this study, a total of 1,254 proteins were identified, which extremely expanded the EBC proteome compared to previous studies. Through GO and KEGG analysis, we found these DEPs were mostly involved in neutrophil-related activities, and the most enriched pathways were pyruvate metabolism (hsa00620) and propanoate metabolism (has00640), which helped to better understand the potential biological mechanism of lung cancer and benign nodules.

Tumor microenvironment is vital for tumorigenesis, and chronic inflammatory response is one of its main characteristics [29]. Neutrophils are an important part of tumor microenvironment and play a critical role in local inflammatory response [30]. Tumor-associated neutrophils (TANs) are the neutrophils infiltrated in the tumor, which originate from blood and enter the tumor tissue after attracted by the chemokines [31]. The function of TANs in tumor microenvironment is complicated, which is reported to be playing a dual role in tumor progression [32]. In the early stage of lung cancer, TANs could stimulate T cell responses and inhibit the development of cancer [33]. Houghton et al. [34] reported that neutrophil elastase could promote the proliferation of tumor cells in lung adenocarcinoma by degrading insulin receptor substrate-1. Wcuek and Malanchi [35] reported that neutrophils are the driver of lung colonization of metastasis-initiating breast cancer cells, suggesting neutrophils could help tumors spread. The functional analysis of our study showed that 5 DEPs were annotated to neutrophil-related activities: APRT, SERPINB12, HRNR, ARG1, DSG1. Majer et al. [36] observed the elevation of APRT in cell bronchogenic carcinoma. ARG1 was reported to inhibit the CD8+ response to promote tumor development [37], and Colegio et al. [38] reported the expression of ARG1 was significantly related to tumor growth. Saaber et al. [39] found the downregulated expression of DSG1 in lung cancer cell lines and suggesting its role as potential diagnostic markers. Most of these proteins (except HRNR) have not been reported in previous EBC studies, and might be novel biomarkers of lung cancer.

Pyruvate has been reported to be a critical molecule related to the human metabolism activities [40]. It is the end-product of glycolysis, plays an essential role in mitochondrial ATP generation and central carbon metabolism [41]. Some studies have reported that altered pyruvate metabolism may lead to cancer [40] including lung cancer [42]. Warburg effect is a metabolic switch that is used to define cancer, which indicates glycolytic carbon flux is always upregulated in cancer cells [43, 44, 45]. In our study, 7 proteins were annotated to pyruvate metabolism pathway: MDH2, LDHA, LDHB, PKM, MDH1, ME1, GLO1. LDHB and ME1 were also in our list of significantly expressed proteins, so these two proteins might play an essential role in lung cancer.

LDHB is one of the isomers of lactate dehydrogenase (LDH), which is an important metabolic enzyme in tumor microenvironment [46]. It plays a vital role in the conversion of pyruvate to lactate and has been proven to be an essential player in cancer metabolism [47]. Some studies have reported the dysregulation of LDHB in several tumor cells, such as breast cancer [48, 49], pancreatic cancer [50, 51, 52], and lung cancer [53, 54], suggesting that LDHB might have a role in tumorigenesis, progression, and tumor cell survival [52]. McClelland et al. have reported that LDHB can regulate cell proliferation in lung adenocarcinoma, and its upregulated expression may be related to poor clinical outcome, suggesting that LDHB could be a novel therapeutic target [54]. But for now, none of the previous studies have shown LDHB in EBC, especially in lung cancer and CT-detected benign nodule. Our study might be the first study that reported significant differential expression of LDHB in EBC samples.

ME1 is a NADP ${}^{+}$ - dependent malic enzyme and is a catalyst in the transformation of malate to pyruvate [55]. Several studies have reported the elevation of ME1 in different human cancers, including gastric cancer [56], breast cancer [55, 57], and lung cancer [58]. ME1 is well known to be regulated by tumor suppressors such as K-ras or p53 [59]. Several published studies have reported the function of ME1 in tumor progression, which is essential for regulating cellular metabolism and redox balance [60]. Recently, Chakrabarti has conducted a multi-database translational study and found K-ras mutations regulate the expression of glutamine metabolism genes, ME1 and GOT, which might be novel biomarkers for lung cancer [58]. In brief, elevated expression of ME1 links cancer progression and is negatively correlated with treatment outcome in lung cancer [55]. Our study has reported the same result with regard to significant differential expression of ME1 in EBC between lung cancer and benign lung nodules.

Although the NLST project shows that the use of LDCT could reduce the mortality of lung cancer by 20%, it also has a high false-positive rate (23.3%) [5]. Considering the radiation risk and the cost of LDCT, alternative or complimentary method for early detection are needed [61]. EBC sampling might be an ideal method because it measures physiologic changes in the airway and alveolar spaces, and is a non-invasive method that causes no harm to patients [62]. Our study provided evidence for discriminating lung cancer and benign nodule patients through the EBC protein profile.

The limitations of this study are the lack of an independent validation set, and that biomarkers used in conjunction with LDCT screening need to consider the variability in lung cancer diagnosis. For example, our group of subjects included only lung adenocarcinomas without COPD.

In summary, this study compared the proteomic differences between CT-detected benign pulmonary nodule and lung cancer, and the use of the DIA approach helped to extend our knowledge of differentially expressed proteins in EBC. Our protein data could provide evidence for the diagnostic potential of EBC in lung cancer and benign pulmonary nodules.

5. Conclusion

This was the first study comparing the proteomic differences between CT-detected benign pulmonary nodule and lung cancer patients using DIA approach. 21 proteins were differentially expressed, and all of them were upregulated in the lung adenocarcinoma group. GO, and KEGG analysis suggested these proteins were annotated to neutrophil-related biological processes and mostly involved in pyruvate metabolism. Our study showed that EBC has the potential for early detection of lung cancer, and might help to reduce the false-positive rate in low dose CT screening.

Author contributions

Conception: Guangli Xiu, Guanghong Xiu.

Interpretation and analysis of data: Lin Ma, Raghu Sinha.

Preparation of the manuscript: Lin Ma.

Revision for important intellectual content: Joshua Muscat, Dongxiao Sun.

Supervision: Guangli Xiu.

Supplementary data

The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-203269.

Footnotes

Acknowledgments

This research was supported by the Key R & D Plan Project of Yantai, Shandong Province, China, grant number 2017YT06000331, and National Natural Science Foundation of China, grant number 21707035).

References

Bray

Ferlay

Soerjomataram

Siegel

R.L.

Torre

L.A.

and Jemal

, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin 68 (2018), 394–424.

Fan

Sun

Ren

Han

and Zhao

, Texture recognition of pulmonary nodules based on volume local direction ternary pattern, Bioengineered 11 (2020), 904–920.

Morgensztern

S.H.

Gao

and Govindan

, Trends in stage distribution for patients with non-small cell lung cancer: A National Cancer Database survey, J Thorac Oncol 5 (2010), 29–33.

Zheng

and Chen

, Lung cancer screening in China: early-stage lung cancer and minimally invasive surgery 3.0, J Thorac Dis 10 (2018), S1677–s1679.

T. National Lung Screening Trial Research Aberle

D.R.

Adams

A.M.

Berg

C.D.

Black

W.C.

Clapp

J.D.

Fagerstrom

R.M.

Gareen

I.F.

Gatsonis

Marcus

P.M.

and Sicks

J.D.

, Reduced lung-cancer mortality with low-dose computed tomographic screening, N Engl J Med 365 (2011), 395–409.

Wallace

M.A.G.

and Pleil

J.D.

, Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols, Anal Chim Acta 1024 (2018), 18–38.

Campanella

De Summa

and Tommasi

, Exhaled breath condensate biomarkers for lung cancer, J Breath Res 13 (2019), 044002.

Zou

Zhang

Chen

Ying

and Wang

, Optimization of volatile markers of lung cancer to exclude interferences of non-malignant disease, Cancer Biomarkers 14 (2014), 371–379.

Wang

Zou

Zhao

Zhang

Wang

and Ying

, The analysis of volatile organic compounds biomarkers for lung cancer in exhaled breath, tissues and cell lines, Cancer Biomarkers 11 (2012), 129–137.

10.

Tufman

and Huber

R.M.

, Biological markers in lung cancer: A clinician’s perspective, Cancer Biomarkers 6 (2010), 123–135.

11.

Aebersold

and Mann

, Mass spectrometry-based proteomics, Nature 422 (2003), 198–207.

12.

Mallick

and Kuster

, Proteomics: a pragmatic perspective, Nat Biotechnol 28 (2010), 695–709.

13.

Muccilli

Saletti

Cunsolo

Gili

Conte

Sichili

Vancheri

and Foti

, Protein profile of exhaled breath condensate determined by high resolution mass spectrometry, J Pharm Biomed Anal 105 (2015), 134–49.

14.

Horvath

Hunt

Barnes

P.J.

Alving

Antczak

Baraldi

Becher

van Beurden

W.J.

Corradi

Dekhuijzen

Dweik

R.A.

Dwyer

Effros

Erzurum

Gaston

Gessner

Greening

L.P.

Hohlfeld

Jobsis

Laskowski

Loukides

Marlin

Montuschi

Olin

A.C.

Redington

A.E.

Reinhold

van Rensen

E.L.

Rubinstein

Silkoff

Toren

Vass

Vogelberg

Wirtz

and Condensate

A.E.T.F.o.E.B.

, Exhaled breath condensate: methodological recommendations and unresolved questions, Eur Respir J 26 (2005), 523–48.

15.

Travis

W.D.

Brambilla

Noguchi

Nicholson

A.G.

Geisinger

K.R.

Yatabe

Beer

D.G.

Powell

C.A.

Riely

G.J.

Van Schil

P.E.

Garg

Austin

J.H.

Asamura

Rusch

V.W.

Hirsch

F.R.

Scagliotti

Mitsudomi

Huber

R.M.

Ishikawa

Jett

Sanchez-Cespedes

Sculier

J.P.

Takahashi

Tsuboi

Vansteenkiste

Wistuba

Yang

P.C.

Aberle

Brambilla

Flieder

Franklin

Gazdar

Gould

Hasleton

Henderson

Johnson

Kerr

Kuriyama

Lee

J.S.

Miller

V.A.

Petersen

Roggli

Rosell

Saijo

Thunnissen

Tsao

and Yankelewitz

, International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma, J Thorac Oncol 6 (2011), 244–85.

16.

Travis

W.D.

Brambilla

Nicholson

A.G.

Yatabe

Austin

J.H.M.

Beasley

M.B.

Chirieac

L.R.

Dacic

Duhig

Flieder

D.B.

Geisinger

Hirsch

F.R.

Ishikawa

Kerr

K.M.

Noguchi

Pelosi

Powell

C.A.

Tsao

M.S.

and Wistuba

, The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification, J Thorac Oncol 10 (2015), 1243–1260.

17.

Moriya

Itoh

Okuda

Yoshizawa

A.C.

and Kanehisa

, KAAS: an automatic genome annotation and pathway reconstruction server, Nucleic Acids Res 35 (2007), W182–5.

18.

Codreanu

S.G.

Hoeksema

M.D.

Slebos

R.J.C.

Zimmerman

L.J.

Rahman

S.M.J.

Chen

S.C.

Chen

Eisenberg

Liebler

D.C.

and Massion

P.P.

, Identification of Proteomic Features To Distinguish Benign Pulmonary Nodules from Lung Adenocarcinoma, J Proteome Res 16 (2017), 3266–3276.

19.

Antus

Barta

Csiszer

and Kelemen

, Exhaled breath condensate pH in patients with cystic fibrosis, Inflammation Research 61 (2012), 1141–1147.

20.

Bloemen

Hooyberghs

Desager

Witters

and Schoeters

, Non-invasive biomarker sampling and analysis of the exhaled breath proteome, Proteomics Clin Appl 3 (2009), 498–504.

21.

Borrill

Z.L.

Roy

and Singh

, Exhaled breath condensate biomarkers in COPD, European Respiratory Journal 32 (2008), 472–486.

22.

Fedorchenko

K.U.

Ryabokon

A.M.

Kononikhin

A.S.

Mitrofanov

S.I.

Barmin

V.V.

Pikin

O.V.

Anaev

E.H.

Gachok

I.V.

Popov

I.A.

Nikolaev

E.N.

Chuchalin

A.G.

and Varfolomeev

S.D.

, Early diagnosis of lung cancer based on proteome analysis of exhaled breath condensate, Moscow University Chemistry Bulletin 71 (2016), 134–139.

23.

Harshman

Grigsby

and Ott

, Exhaled Breath Condensate for Proteomic Biomarker Discovery, Chromatography 1 (2014), 108–119.

24.

López-Sánchez

L.M.

Jurado-Gámez

Feu-Collado

Valverde

Cañas

Fernández-Rueda

J.L.

Aranda

and Rodríguez-Ariza

, Exhaled breath condensate biomarkers for the early diagnosis of lung cancer using proteomics, American Journal of Physiology-Lung Cellular and Molecular Physiology 313 (2017), L664–L676.

25.

Sun

Zhou

Xie

Gao

Liao

and Wang

, Proteomics of exhaled breath condensate in stable COPD and non-COPD controls using tandem mass tags (TMTs) quantitative mass spectrometry: A pilot study, J Proteomics 206 (2019), 103392.

26.

Reubsaet

Sweredoski

M.J.

and Moradian

, Data-Independent Acquisition for the Orbitrap Q Exactive HF: A Tutorial, J Proteome Res 18 (2019), 803–813.

27.

Doerr

, DIA mass spectrometry, Nature Methods 12 (2015), 35–35.

28.

Muscat

Sinha

Sun

and Xiu

, Proteomics of exhaled breath condensate in lung cancer and controls using data-independent acquisition (DIA): a pilot study, J Breath Res (2020).

29.

Grecian

Whyte

M.K.B.

and Walmsley

S.R.

, The role of neutrophils in cancer, Br Med Bull 128 (2018), 5–14.

30.

Coffelt

S.B.

Wellenstein

M.D.

and de Visser

K.E.

, Neutrophils in cancer: neutral no more, Nat Rev Cancer 16 (2016), 431–46.

31.

Kim

and Bae

J.S.

, Tumor-Associated Macrophages and Neutrophils in Tumor Microenvironment, Mediators Inflamm 2016 (2016), 6058147.

32.

Shaul

M.E.

Levy

Sun

Mishalian

Singhal

Kapoor

Horng

Fridlender

Albelda

S.M.

and Fridlender

Z.G.

, Tumor-associated neutrophils display a distinct N1 profile following TGFβ modulation: A transcriptomics analysis of pro- vs. antitumor TANs, Oncoimmunology 5 (2016), e1232221.

33.

Eruslanov

E.B.

Bhojnagarwala

P.S.

Quatromoni

J.G.

Stephen

T.L.

Ranganathan

Deshpande

Akimova

Vachani

Litzky

Hancock

W.W.

Conejo-Garcia

J.R.

Feldman

Albelda

S.M.

and Singhal

, Tumor-associated neutrophils stimulate T cell responses in early-stage human lung cancer, J Clin Invest 124 (2014), 5466–80.

34.

Houghton

A.M.

Rzymkiewicz

D.M.

Gregory

A.D.

Egea

E.E.

Metz

H.E.

Stolz

D.B.

Land

S.R.

Marconcini

L.A.

Kliment

C.R.

Jenkins

K.M.

Beaulieu

K.A.

Mouded

Frank

S.J.

Wong

K.K.

and Shapiro

S.D.

, Neutrophil elastase-mediated degradation of IRS-1 accelerates lung tumor growth, Nat Med 16 (2010), 219–23.

35.

Wculek

S.K.

and Malanchi

, Neutrophils support lung colonization of metastasis-initiating breast cancer cells, Nature 528 (2015), 413–7.

36.

Mejer

Hørbov

and Nygaard

, Purine metabolizing enzymes in lymphocytes from patients with solid tumors, Acta Med Scand 215 (1984), 5–11.

37.

Hurt

Schulick

Edil

El Kasmi

K.C.

and Barnett

, Jr., Cancer-promoting mechanisms of tumor-associated neutrophils, Am J Surg 214 (2017), 938–944.

38.

Colegio

O.R.

Chu

N.Q.

Szabo

A.L.

Chu

Rhebergen

A.M.

Jairam

Cyrus

Brokowski

C.E.

Eisenbarth

S.C.

Phillips

G.M.

Cline

G.W.

Phillips

A.J.

and Medzhitov

, Functional polarization of tumour-associated macrophages by tumour-derived lactic acid, Nature 513 (2014), 559–63.

39.

Saaber

Chen

Cui

Yang

Mireskandari

and Petersen

, Expression of desmogleins 1–3 and their clinical impacts on human lung cancer, Pathol Res Pract 211 (2015), 208–13.

40.

Gray

L.R.

Tompkins

S.C.

and Taylor

E.B.

, Regulation of pyruvate metabolism and human disease, Cell Mol Life Sci 71 (2014), 2577–604.

41.

Israelsen

W.J.

and Vander Heiden

M.G.

, Pyruvate kinase: Function, regulation and role in cancer, Semin Cell Dev Biol 43 (2015), 43–51.

42.

Sellers

Fox

M.P.

Bousamra

, 2nd Slone

S.P.

Higashi

R.M.

Miller

D.M.

Wang

Yan

Yuneva

M.O.

Deshpande

Lane

A.N.

and Fan

T.W.

, Pyruvate carboxylase is critical for non-small-cell lung cancer proliferation, J Clin Invest 125 (2015), 687–98.

43.

Upadhyay

Samal

Kandpal

Singh

O.V.

and Vivekanandan

, The Warburg effect: Insights from the past decade, Pharmacology & Therapeutics 137 (2013), 318–330.

44.

House

S.W.

Warburg

Burk

and Schade

A.L.

, On Respiratory Impairment in Cancer Cells, Science 124 (1956), 267–272.

45.

Warburg

, On the Origin of Cancer Cells, Science 123 (1956), 309–314.

46.

Valvona

C.J.

Fillmore

H.L.

Nunn

P.B.

and Pilkington

G.J.

, The Regulation and Function of Lactate Dehydrogenase A: Therapeutic Potential in Brain Tumor, Brain pathology (Zurich, Switzerland) 26 (2016), 3–17.

47.

Pereira-Nunes

Afonso

Granja

and Baltazar

, Lactate and Lactate Transporters as Key Players in the Maintenance of the Warburg Effect, Adv Exp Med Biol 1219 (2020), 51–74.

48.

Dennison

J.B.

Molina

J.R.

Mitra

Gonzalez-Angulo

A.M.

Balko

J.M.

Kuba

M.G.

Sanders

M.E.

Pinto

J.A.

Gomez

H.L.

Arteaga

C.L.

Brown

R.E.

and Mills

G.B.

, Lactate dehydrogenase B: a metabolic marker of response to neoadjuvant chemotherapy in breast cancer, Clin Cancer Res 19 (2013), 3703–13.

49.

Hussien

and Brooks

G.A.

, Mitochondrial and plasma membrane lactate transporter and lactate dehydrogenase isoform expression in breast cancer cell lines, Physiol Genomics 43 (2011), 255–64.

50.

Cui

Quan

Jiang

Jiao

Jin

Wang

and Wang

, Suppressed expression of LDHB promotes pancreatic cancer progression via inducing glycolytic phenotype, Medical oncology (Northwood, London, England) 32 (2015), 143–143.

51.

Feng

Xiong

Cao

Yang

Zheng

Qiu

You

Zheng

Zhang

and Zhao

, LAT2 regulates glutamine-dependent mTOR activation to promote glycolysis and chemoresistance in pancreatic cancer, Journal of experimental & clinical cancer research: CR 37 (2018), 274–274.

52.

Luo

Yang

Liu

Yang

Zou

and Yuan

, LDHB and FABP4 are Associated With Progression and Poor Prognosis of Pancreatic Ductal Adenocarcinomas, Appl Immunohistochem Mol Morphol 25 (2017), 351–357.

53.

Chen

Zhang

Liu

Huang

Yang

and Xiao

, Elevation of serum l-lactate dehydrogenase B correlated with the clinical stage of lung cancer, Lung cancer (Amsterdam, Netherlands) 54 (2006), 95–102.

54.

McCleland

M.L.

Adler

A.S.

Deming

Cosino

Lee

Blackwood

E.M.

Solon

Tao

Shames

Jackson

Forrest

W.F.

and Firestein

, Lactate dehydrogenase B is required for the growth of KRAS-dependent lung adenocarcinomas, Clinical cancer research: an official journal of the American Association for Cancer Research 19 (2013), 773–784.

55.

Liao

Ren

Liu

Chen

Cao

and Dong

, ME1 promotes basal-like breast cancer progression and associates with poor prognosis, Sci Rep 8 (2018), 16743.

56.

Y.X.

H.Q.

Liu

Z.X.

Chen

D.L.

Wang

Zhao

Q.N.

Zeng

Z.L.

Qiu

H.B.

P.S.

Wang

Z.Q.

Zhang

D.S.

Wang

and Xu

R.H.

, ME1 Regulates NADPH Homeostasis to Promote Gastric Cancer Growth and Metastasis, Cancer Res 78 (2018), 1972–1985.

57.

Avagliano

Ruocco

M.R.

Aliotta

Belviso

Accurso

Masone

Montagnani

and Arcucci

, Mitochondrial Flexibility of Breast Cancers: A Growth Advantage and a Therapeutic Opportunity, Cells 8 (2019), 401.

58.

Chakrabarti

, Mutant KRAS associated malic enzyme 1 expression is a predictive marker for radiation therapy response in non-small cell lung cancer, Radiat Oncol 10 (2015), 145.

59.

Jiang

Mancuso

Wellen

K.E.

and Yang

, Reciprocal regulation of p53 and malic enzymes modulates metabolism and senescence, Nature 493 (2013), 689–93.

60.

Zheng

F.J.

H.B.

M.S.

Lian

Y.F.

Qian

C.N.

and Zeng

Y.X.

, Repressing malic enzyme 1 redirects glucose metabolism, unbalances the redox state, and attenuates migratory and invasive abilities in nasopharyngeal carcinoma cell lines, Chin J Cancer 31 (2012), 519–31.

61.

Hasan

Kumar

and Kavuru

M.S.

, Lung cancer screening beyond low-dose computed tomography: the role of novel biomarkers, Lung 192 (2014), 639–48.

62.

Hayes

S.A.

Haefliger

Harris

Pavlakis

Clarke

S.J.

Molloy

M.P.

and Howell

V.M.

, Exhaled breath condensate for lung cancer protein analysis: a review of methods and biomarkers, J Breath Res 10 (2016), 034001.

Comparative proteomic analysis of exhaled breath condensate between lung adenocarcinoma and CT-detected benign pulmonary nodule patients

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

Table 1 Baseline characteristics of all the participants

2.1 Sample collection

2.2 Sample preparation

2.3 LC-MS/MS analysis

2.5 Spectral library generation

2.7 Statistical analysis

3. Results

3.1 Identification of differentially expressed proteins (DEPs)

3.2 Functional analysis

5. Conclusion

Author contributions

Supplementary data

Footnotes

Acknowledgments

References

Table 1
Baseline characteristics of all the participants