Serum levels of C-terminal peptides of alpha-1 antitrypsin as potential biomarkers in non-small cell lung cancer

Introduction

Lung cancer is still the leading cause of cancer-related death worldwide. Fewer than 20% of individuals diagnosed with lung cancer survive beyond 5 years. This relates to the fact that by the time clinical symptoms manifest, the disease has often progressed to an advanced stage (1).

The plasma proteome is a valuable resource for cancer research and clinical use, offering insights into disease mechanisms, monitoring, and personalized treatment. Known protein biomarkers include prostate-specific antigen (PSA) for prostate cancer, cancer antigen 125 (CA125) for ovarian cancer, carcinoembryonic antigen (CEA) for colorectal cancer, alpha-fetoprotein (AFP) for liver cancer, and CA19-9 for pancreatic cancer (2). Recent studies have focused on tumor-associated antigens, autoantibodies, and exosome proteins in lung cancer (3). Advances in proteomics, like mass spectrometry, enable high-throughput plasma analysis, aiding the discovery of novel biomarkers, including protein fragments (4). Hypothetically, these peptide fragments, derived from precursor proteins through proteolysis, may serve as dynamic biomarkers for cancer progression, distinguishing healthy and malignant states, assessing treatment efficacy, and/or monitoring disease recurrence.

The acute phase protein and protease inhibitor alpha-1 antitrypsin (AAT), also known as serpin A1, is increased two- to four-fold during inflammation to neutralize proteases (5). High circulating levels of AAT are suggested as a prognostic marker for tumor recurrence and outcomes. We have shown in over 300 patients with non-small cell lung cancer (NSCLC), that higher serum AAT levels predicted poorer outcomes (6). On the other hand, genetic AAT deficiency has been associated with an increased susceptibility lung cancer (7). This link may not only be attributed to low levels of the mutated AAT protein but also to its conformational changes, particularly polymerization. However, the relationship between AAT levels, its molecular forms, and lung cancer progression remains poorly understood.

Short C-terminal peptides of AAT, generated through enzymatic cleavage of AAT by proteases such as trypsin, elastase, proteinase 3, cathepsin G, and matrix metalloproteinases (MMPs), have been identified in various human biological samples. These peptides exhibit biological activities and are potential biomarkers for numerous diseases (8). Altered levels of AAT peptides in plasma or serum seem to reflect proteolytic activity in conditions like sepsis and lung fibrosis (9). Consequently, qualitative and quantitative analyses of these peptides offering insights into disease mechanisms and progression (10).

In this study we analyzed serum levels of nine AAT-derived peptides in 222 patients with NSCLC, examining their prognostic significance and correlations with full-length AAT and clinical parameters. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-178/rc).

Methods

Patient samples

Blood samples were collected at Thoraxklinik Heidelberg between 2006 and 2011 and provided by Lung Biobank Heidelberg, a Member of the BioMaterialBank Heidelberg (BMBH) and the biobank platform of the German Center for Lung Research (DZL). All patients signed an informed consent. The storage and use of biomaterial and data was approved by the local ethics committee of the Medical Faculty Heidelberg (No. S-270/2001) and the retrospective study was approved by the ethics committee of Hannover (No. 9155_BO_K_2020). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. We included 222 patients with NSCLC at pathological stage I–III (see Table 1). Serum samples were collected prior to surgical excision of the lung tumor. Follow-up samples for 32 patients were collected between 80 and 140 days after surgery and at least 4 weeks after the last chemotherapy treatment to avoid any influence by therapy. Serum samples were processed within 1 h after blood draw and stored at −80 ℃ until further use. All histopathological diagnoses were made according to the 2003 and 2009 World Health Organization (WHO) classification for lung cancer by at least two experienced pathologists. Tumor stage was designated according to the 7^th edition of the Union for International Cancer Control tumor-node-metastasis (UICC TNM) classification. Tumor size data were obtained during routine pathological examination and extracted from the clinical tumor registry. Tumor volume was calculated with the following formula: V = 4/3 × Pi × r1 × r2 × r3 (ellipsoid volume).

Measurement of clinical routine parameters and AAT peptides

Neutrophil counts were measured during clinical routine prior to surgical intervention and extracted from the laboratory database.

Concentrations of C-reactive protein (CRP) and AAT were determined by automated laboratory diagnostics at the Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital using a COBAS^® C502 module (Roche Diagnostics, Mannheim, Germany). The Tina-quant α1-Antitrypsin ver.2 assay (Roche Diagnostics) was used for AAT measurements within a range of 2–60 mg/mL. CRP was determined using the Cardiac C-Reactive Protein (Latex) High Sensitive test (Roche Diagnostics; range, 0.15–20.0 mg/L); if values were above the upper limit of quantification (>20 mg/L), the corresponding sample was again analyzed with the Tina-quant C-Reactive Protein IV assay (Roche Diagnostics; range, 3–350 mg/L).

For peptide quantification by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), 35 µL of thawed serum samples were mixed with 10 µL of internal standard mixture containing isotopic labelled peptides C22^IS, C37^IS and C42^IS (0.8 µL each; sb-PEPTIDE, Saint Egréve, France) and processed as described elsewhere (8). Ten µL of sample were loaded on a C4 UHPLC column (Hypersil GOLD C4 1.9 µM, 2.1×100 mm; Thermo Fisher Scientific, Darmstadt, Germany) and separated with a step gradient as described before (11). In total 9 different peptides (C22, C36, C37, C39, C40, C42, C43, C44 and C45) were simultaneously analyzed. Mass spectrometry data was processed with Analyst Software (version 1.7.1) and peak integration was reviewed individually. The analyte peak areas were normalized to the peak area of their respective internal standard and concentration was calculated from a quadratic fit standard curve with 1/x*x weighting. Data processing was performed using R version 4.3.2 (12) with the imputeLCMD (13) and dbnorm (14) packages.

Total RNA isolation and cDNA synthesis

For RNA isolation from tumors and normal lung tissues of NSCLC patients the RNeasy Mini Kit (Qiagen, Hilden, Germany) was used. The complete procedure is described elsewhere (15).

Quantitative real time polymerase chain reaction (qPCR)

Real-time qPCR was performed in accordance with MIQE-guidelines (16) using a LightCycler^® 480 Real-Time PCR Instrument (Roche, Mannheim, Germany) in a 384-well plate format. Gene-specific primers and probes (Universal ProbeLibrary, Roche) were used in combination with qPCR Probe-MasterMix (SL-9802, Steinbrenner, Wiesenbach, Germany). Five ng single-stranded cDNA (sscDNA) was used for each amplification. The whole procedure is described elsewhere (15). The following forward and reverse primers and probes were used: esterase D (ESD), 5'-TCAGTCTGCTTCAGAACATGG-3' and 5'-CCTTTAATATTGCAGCCACGA-3', UPL50; 40S ribosomal protein S18: 5'-CTTCCACAGGAGGCCTACAC-3' and 5'-CGCAAAATATGCTGGAACTTT-3', UPL46; matrix metalloproteinase-2, 5'-AGAAGGCTGTGTTCTTTGCAG-3' and 5'-AGGCTGGTCAGTGGCTTG-3', UPL1; matrix metalloproteinase-9, 5'-GAACCAATCTCACCGACAGG-3' and 5'-GCCACCCGAGTGTAACCATA-3', UPL6; matrix metalloproteinase-14, 5'-CTGTCAGGAATGAGGATCTGAA-3' and 5'-AGGGGTCACTGGAATGCTC-3', UPL37.

Statistical analysis

Statistical analyses were conducted using GraphPad Prism 9 (GraphPad Software, Boston, MA, USA) and SPSS 29 (IBM, Boeblingen, Germany). Correlations were assessed using Spearman’s rank-order correlation. The Wilcoxon matched-pairs signed-rank test was applied to evaluate differences between groups. Optimal cutoff values for differentiating survival probabilities were determined using the R package Cutoff-Finder (version 2.1.15) (17). The Cox proportional hazards model was performed for univariate and multivariate survival analyses, with survival data visualized through Kaplan-Meier curves. Overall survival was defined as the time from surgical intervention to death or the last recorded follow-up.

Results

Serum levels of AAT peptides and their correlations with full-length AAT protein and other clinical parameters

Our previous study showed an association between AAT levels and the prognosis of patients with NSCLC (6). Since the investigated peptides are fragments of AAT (8), we sought to characterize their serum levels, profiles and correlations with AAT protein. Of all peptides, C22 and C39 were non-detectable in all individual samples and only C36, C37 and C42 showed concentrations above the lower limit of quantification (LLOQ; 0.025 µM for C36 and C42, 0.01 µM for C37). All other peptides had concentration below the LLOQ (0.01 µM) and were excluded from further analysis. In a cohort of 222 patients with early-stage NSCLC (stages I–III), we observed significant differences in the levels of the three AAT-derived peptides-C36, C37, and C42 (Figure 1A). Among them, C42 displayed the highest concentration, with a median level of 0.084 µM.

Figure 1 Analysis of AAT-derived peptides and laboratory parameters in NSCLC. (A-E) Distribution of investigated parameters in the patient cohort. Please note that not all parameters were available for each patient. (F) Correlation plot of AAT, its peptides and clinical parameters at the baseline, i.e., serum samples collected prior to surgical excision of the lung tumor. A correlation r>0.5 was considered reliable (red: 0.75≤r<1, yellow: 0.5≤r<0.75, green: 0.25≤r<0.5 and blue: r<0.25). (G) Correlation plot of MMP2, MMP9 and MMP14 mRNA expression in tumor tissue in a subset of patients (n=141 adenocarcinoma of the lung) and serum concentrations of AAT and its peptides. A correlation r>0.5 was considered reliable. (H) Comparison of peptide levels in relation to the smoking status of the patients. A P value <0.05 was considered significant; *, P<0.05; **, P<0.01; ***, P<0.005; ****, P<0.001. Ø, median; AAT, alpha 1-antitrypsin; CRP, C-reactive protein; Ex, person who stopped smoking; MMP2, matrix metalloproteinase-2; MMP9, matrix metalloproteinase-9; MMP14, matrix metalloproteinase-14; NS, person who never smoke; NSCLC, non-small cell lung cancer; S, person who smokes.

Furthermore, we measured baseline serum levels of full-length AAT (Figure 1B). AAT levels were within the normal range, ruling out the presence of severe AAT deficiency genotypes in our cohort. However, it cannot be excluded that in some patients AAT level might be influenced by an increased baseline inflammation.

We next examined the correlations between the concentration of each peptide and full-length AAT, tumor volume (Figure 1C), CRP levels (Figure 1D), and neutrophil counts (Figure 1E). While the correlations among the three peptides were very strong (r=0.79–0.93; Figure 1F), the correlations between full-length AAT and individual peptide levels were moderate (r=0.42–0.63). Tumor volume and neutrophil counts showed no significant association with peptide abundance, although a slight trend toward correlation with CRP levels was observed. Notably, we found no correlation between MMP2, MMP9, and MMP14 expression in tumor (Figure 1G) and normal tissue (data not shown) and serum levels of AAT or its peptides C36, C37 and C42 while the metalloproteases were correlated to each other (Figure 1G).

Smoking status, however, significantly influenced peptide levels (Figure 1H). Patients who were current smokers or had quit within 6 months prior to surgery displayed significantly higher serum peptide concentrations compared to those who had quit smoking longer ago or had never smoked. However, a Spearman ranked correlation analysis showed no clear correlation between the individual peptide levels and the smoking status (r=−0.27 for C36, r=−0.20 for C37 and r=−0.23 for C42, data not shown). No differences in peptide levels were identified based on histology, age, pathological cancer stage, lymph node status, sex, or chronic obstructive pulmonary disease (COPD) status (Figure S1A-S1F).

Serum levels of AAT peptides as prognostic markers for overall survival in NSCLC

We asked whether AAT peptides could serve as prognostic markers in NSCLC patients. We confirmed our previous findings that higher serum levels of full-length AAT are associated with poor prognosis (Figure 2A). Moreover, we found that higher serum concentrations of AAT-derived peptides are also significantly correlate with lower survival probabilities (Figure 2B-2D).

Figure 2 Kaplan-Meier survival analysis of AAT and its peptides in NSCLC. (A) Prognostic impact of AAT serum concentration at the time of surgical intervention on overall survival in NSCLC patients. (B-D) Prognostic impact of the AAT-derived peptides C36, C37 and C42 on overall survival. Cut-off values distinguishing high and low survival probability were determined using the R package cutoff-finder (version 2.1.15) (17). The following cut-offs were used: AAT: 1.315 mg/mL, C36: 0.027 µM, C37: 0.013 µM, C42: 0.0858 µM. A P value <0.05 was considered statistically significant. AAT, alpha-1 antitrypsin; NSCLC, non-small cell lung cancer.

In a multivariate Cox proportion hazard model adjusted for clinical parameters such as age, sex, Eastern Cooperative Oncology Group (ECOG) status, pathological stage and histology, serum concentration of all peptides C37 and C42 turned out to be significant prognostic factors [P=0.03 (C37) and P=0.02 (C42), see Table 2]. Age and pathological stage were identified as the major clinical factors influencing overall survival in our cohort.

Serum concentrations of AAT and its peptides increase after tumor excision

To further evaluate the impact of NSCLC on AAT levels and the formation of C-terminal peptide of AAT, we analyzed follow-up serum samples from 32 patients, collected 80–140 days after surgical tumor resection, and compared these to baseline measurements (Figure 3). We found significantly higher post-surgery concentrations of peptides C37 (1.7-fold, P<0.001) and C42 (1.57-fold, P<0.001). Additionally, C36 levels showed a tendency to increase (mean fold change of 1.12), though this was not statistically significant (Figure 3A and Figure S2). Similarly, full-length AAT concentrations increased significantly after surgery (1.18-fold, P=0.02, Figure 3B and Figure S2), while median CRP levels remained unchanged (Figure 3C and Figure S2). Since the analyzed peptides are derived from AAT, we also calculated the AAT-to-peptide ratios. As expected, the ratios increased post-tumor excision for all peptides, with significant differences observed for C37 and C42 (Figure 3D).

Figure 3 Changes in serum levels of AAT peptide and CRP following surgical tumor resection. (A) Serum concentrations of AAT-derived peptides (C36, C37, and C42) before (filled circles) and after (open circles) surgical lung tumor resection. Serum concentration of full-length AAT (B) or CRP (C) before and after surgery. (D) Ratio of each peptide to full-length AAT before and after surgery. A P value <0.05 was considered significant; ns, P>0.05; *, P<0.05; ****, P<0.001. Ø, median; AAT, alpha-1 antitrypsin; CRP, C-reactive protein.

Discussion

Elevated serum AAT levels were found to be associated with poorer survival in patients with lung cancer (6). AAT is traditionally known for its anti-protease and anti-inflammatory and immunomodulatory functions; therefore, hypothetically, its higher levels could be a consequence rather than a cause of poor prognosis. Other factors, such as tissue damage or metabolic changes in advanced cancer, might indirectly increase AAT levels and/or induce post-translational modifications. It is known that serine protease-mediated cleavage of AAT produces a 36-amino-acid C-terminal peptide (this sequence corresponds to residues 369–404 of full-length AAT, C36), whereas specific MMPs cleave AAT as a substrate and generate longer peptides of AAT, like C37 and C42. These AAT-derived C-terminal peptides may possess distinct biological activities and potential diagnostic significance (18,19). However, their diagnostic or prognostic relevance in lung cancer has remained unexplored until now.

In this study, serum samples retrospectively collected from 222 patients with NSCLC revealed varying levels of AAT-derived peptides C36, C37 and C42, with C42 being the most abundant. Unlike acute inflammatory conditions like sepsis and severe COVID-19, patients with NSCLC showed lower overall peptide levels. Notably, C36 and C42 levels were comparable to those seen in chronic inflammatory conditions, such as COPD (20). CRP levels were only mildly elevated in most cancer patients, suggesting that acute inflammation or infection is unlikely to be a major factor in peptide generation. Notably, serum levels of C36, C37, and C42 peptides were strongly correlated in patients with NSCLC. However, their correlation with AAT, particularly for C36, was weaker than that observed in patients with stable COPD (20). This difference may reflect the involvement of distinct proteases in each disease. In stable COPD, neutrophil serine proteases likely play a dominant role in AAT cleavage and peptide generation whereas other proteases may be more active in NSCLC (21,22). These disease-specific peptide profiles warranting further investigation.

Cigarette smoking, a known risk factor for lung cancer, significantly affects peptide levels (23). Patients with active tobacco use or a previous smoking history had significantly higher peptide levels compared to patients who never smoked. This supports the idea that elevated peptide levels are linked to a protease-antiprotease imbalance, as smoking increases AAT oxidation and susceptibility to proteolytic cleavage (24,25). In laboratory models, cigarette smoke exposure has been shown to increase MMP expression, particularly MMP-12 and MMP-9, in the lungs (26). These MMPs degrade extracellular matrix components and cleave AAT, producing biologically active peptides, like C42 and C37, which may contribute to lung tissue remodeling, inflammation, and lung disease development.

Given the value of serum AAT levels in NSCLC prognosis (6), we now assessed the prognostic value of AAT-derived peptides. Higher concentrations of C36, C37 and C42 peptides were linked to significantly lower survival probabilities. In a multivariate regression model, adjusted for relevant clinical factors, all three peptides emerged as prognostic indicators. This suggests that serum levels of AAT and its peptides may serve as valuable indicators of patient outcomes. Both markers are expected to be reduced in individuals with AAT deficiency, a condition caused by the inherited mutations in SERPINA1 gene. Notably, a recent study has demonstrated that AAT deficiency is associated with an increased risk of mortality following a lung cancer diagnosis (27). We assume that none of our patients carry a deficiency genotype of AAT; however, sequencing of the SERPINA1 gene would be required to confirm this hypothesis. While this analysis is beyond the scope of our study, it could provide valuable insights in future research.

Previous studies have shown that overexpression of MMPs and transmembrane serine proteases in NSCLC tumors correlates with poorer chemotherapy efficacy and prognosis (28,29). It is important to note that in our patient cohort, we found no relationship between serum peptide levels and gene expression of previously analyzed MMPs, including MMP2, MMP9, and MMP14, either in tumor or in normal lung tissue. This finding supports the idea that the activity of MMPs, rather than their expression levels, is linked to the generation of AAT-derived peptides. However, due to the sample limitation we were not able to assess the activity of serum MMPs.

We also analysed serum AAT and peptide levels in follow-up samples from 32 patients who had undergone tumor excision. We hypothesized that tumor removal would lead to a reduction in serum AAT and its peptide levels compared to pre-surgical levels. However, despite unchanged CRP levels, the mean serum AAT increased by 1.18-fold, and C42 and C37 levels rose by 1.57- and 1.7-fold, respectively. While the C36/AAT ratio remained unchanged, the C42/AAT and C37/AAT ratios significantly increased after surgery. The elevated C42 levels, linked to worse prognosis in our NSCLC cohort, suggest increased MMP activity post-surgery, which may reflect tissue remodelling, and wound healing during recovery (30,31). Conversely, if MMP levels remain elevated beyond the expected healing period, it could negatively impact patient outcomes. A previous study found that high MMP-2 expression in non-tumorous lung tissue after tumor resection is linked to recurrence and poor survival (32). Unfortunately, as mentioned earlier, we did not have enough sample to measure serum MMP levels and activity in the pre- and post-surgical samples. On the other hand, measuring serum MMP activity presents several challenges that can affect the reliability of results. For example, MMPs are sensitive to changes in temperature and storage conditions (33). Many assays fail to differentiate between the active and inactive forms of MMPs, which can complicate the interpretation of results, particularly when studying disease-related activity (34). Moreover, serum MMP levels do not always reflect the actual proteolytic activity (35).

Most experimental models and clinical data suggest that development and progression of cancer is implicated to differential MMP activity (36). Controversially, however, there are situations where expression of MMPs appear to be associated with a favorable prognosis. For example, in colon cancer, the expression of MMP-12 by tumor cells and MMP-9 expression by infiltrating macrophages have been reported to correlate with reduced metastatic potential (37). Angiostatin, a cleavage product of plasminogen generated by MMP-2, -3, -9, and -12, and endostatin, a cleavage product of basement membrane collagen generated by active MMP-3, -9, -12, and -13, have been suggested to inhibit endothelial cell proliferation and block MMP-14 activity (38-40). This is further supported by the fact that several synthetic MMP inhibitors have failed to meet expectations, as in some cases, patients with cancer experienced poorer survival outcomes due to MMP inhibitor therapy (41-43).

The dynamics of MMP profiles, their activity levels, and their cleavage products like peptides of AAT generated in physiological and/or tumor conditions are not fully understood. Therefore, future prospective studies are needed to gain deeper insights into protease-generated peptides, such as those derived from AAT cleavage, before and after tumor resection or treatment in patients with NSCLC, and to explore their potential as prognostic biomarkers.

Conclusions

Previously, we demonstrated the prognostic value of serum AAT levels in NSCLC. In this study, we explored the significance of peptides generated by the proteolytic cleavage of full-length AAT. Our findings show that higher serum concentrations of the peptides C36, C37, and C42 are associated with reduced survival probabilities in NSCLC patients. Furthermore, in a multivariate regression model adjusted for relevant clinical factors, all three peptides remained reliable prognostic indicators, emphasizing their potential as independent biomarkers. These results highlight the importance of peptide-level analysis in cancer prognosis. Advanced mass spectrometry techniques for protein fragment analysis may offer new opportunities for biomarker discovery, improved patient monitoring, and the identification of potential therapeutic targets in NSCLC and other cancers.

Acknowledgments

The authors thank Cora Richert (Institute of Clinical Chemistry and Laboratory Diagnostics, Jena University Hospital, Jena, Germany) for excellence experimental support and processing of samples for automated laboratory diagnostics.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-178/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-178/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-178/prf

Funding: This work was partly supported by the German Center for Lung Research (DZL, grant numbers 82DZL00402, 82DZL002C1 to S.J.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-178/coif). M.A.S., T.M. and S.J. received funding from the German Center for Lung Research. M.K. is the principal inventor of a patent application covering the utilized LC-MS/MS method as a tool for characterizing systemic inflammation (applicant: Jena University Hospital; inventor: M.K.; published as EP4224163A1). The Jena University Hospital is owner of a patent related to methods determining the origin of an infection (EP3239712: granted; inventor: M.K.) and a patent covering the initial identification of C42 (CN104204808B, JP6308946B2, US10712350B2, EP2592421B1, EP2780719B1; inventor: M.K.). The other author has no conflicts of interest to declare.

Ethical Statement: 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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The storage and use of biomaterial and data was approved by the local ethics committee of the Medical Faculty Heidelberg (No. S-270/2001) and the retrospective study was approved by the ethics committee of Hannover (No. 9155_BO_K_2020). All patients signed an informed consent before collection of biospecimens.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Araghi M, Mannani R, Heidarnejad Maleki A, et al. Recent advances in non-small cell lung cancer targeted therapy; an update review. Cancer Cell Int 2023;23:162. [Crossref] [PubMed]
2. Huang H, Yang Y, Zhu Y, et al. Blood protein biomarkers in lung cancer. Cancer Lett 2022;551:215886. [Crossref] [PubMed]
3. Casagrande GMS, Silva MO, Reis RM, et al. Liquid Biopsy for Lung Cancer: Up-to-Date and Perspectives for Screening Programs. Int J Mol Sci 2023;24:2505. [Crossref] [PubMed]
4. Petersen EV, Chudakova DA, Skorova EY, et al. The Extracellular Matrix-Derived Biomarkers for Diagnosis, Prognosis, and Personalized Therapy of Malignant Tumors. Front Oncol 2020;10:575569. [Crossref] [PubMed]
5. Hubbard RC, Brantly ML, Sellers SE, et al. Anti-neutrophil-elastase defenses of the lower respiratory tract in alpha 1-antitrypsin deficiency directly augmented with an aerosol of alpha 1-antitrypsin. Ann Intern Med 1989;111:206-12. [Crossref] [PubMed]
6. Ercetin E, Richtmann S, Delgado BM, et al. Clinical Significance of SERPINA1 Gene and Its Encoded Alpha1-antitrypsin Protein in NSCLC. Cancers (Basel) 2019;11:1306. [Crossref] [PubMed]
7. Janciauskiene S, Wrenger S, Günzel S, et al. Potential Roles of Acute Phase Proteins in Cancer: Why Do Cancer Cells Produce or Take Up Exogenous Acute Phase Protein Alpha1-Antitrypsin? Front Oncol 2021;11:622076. [Crossref] [PubMed]
8. Bigalke A, Sponholz C, Schnabel C, et al. Multiplex quantification of C-terminal alpha-1-antitrypsin peptides provides a novel approach for characterizing systemic inflammation. Sci Rep 2022;12:3844. [Crossref] [PubMed]
9. Carleo A, Landi C, Prasse A, et al. Proteomic characterization of idiopathic pulmonary fibrosis patients: stable versus acute exacerbation. Monaldi Arch Chest Dis 2020; [Crossref] [PubMed]
10. Noël A, Gutiérrez-Fernández A, Sounni NE, et al. New and paradoxical roles of matrix metalloproteinases in the tumor microenvironment. Front Pharmacol 2012;3:140. [Crossref] [PubMed]
11. Scherr F, Darisipudi MN, Börner FR, et al. Alpha-1-antitrypsin as novel substrate for S. aureus' Spl proteases - implications for virulence. Front Immunol 2024;15:1481181. [Crossref] [PubMed]
12. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.
13. Lazar C. imputeLCMD: A collection of methods for left-censored missing data imputation. R package version 2.0. 2015. Available online: http://CRANR-projectorg/package=imputeLCMD
14. Bararpour N, Gilardi F, Carmeli C, et al. DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies. Sci Rep 2021;11:5657. [Crossref] [PubMed]
15. Schneider MA, Granzow M, Warth A, et al. Glycodelin: A New Biomarker with Immunomodulatory Functions in Non-Small Cell Lung Cancer. Clin Cancer Res 2015;21:3529-40. [Crossref] [PubMed]
16. Bustin SA, Benes V, Garson JA, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 2009;55:611-22. [Crossref] [PubMed]
17. Budczies J, Klauschen F, Sinn BV, et al. Cutoff Finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization. PLoS One 2012;7:e51862. [Crossref] [PubMed]
18. Desrochers PE, Mookhtiar K, Van Wart HE, et al. Proteolytic inactivation of alpha 1-proteinase inhibitor and alpha 1-antichymotrypsin by oxidatively activated human neutrophil metalloproteinases. J Biol Chem 1992;267:5005-12. [Crossref] [PubMed]
19. Gronski TJ Jr, Martin RL, Kobayashi DK, et al. Hydrolysis of a broad spectrum of extracellular matrix proteins by human macrophage elastase. J Biol Chem 1997;272:12189-94. [Crossref] [PubMed]
20. Börner FR, Lechowicz U, Wrenger S, et al. Plasma levels of α1-antitrypsin-derived C-terminal peptides in PiMM and PiZZ COPD patients. ERJ Open Res 2023; [Crossref] [PubMed]
21. Janciauskiene S, Wrenger S, Immenschuh S, et al. The Multifaceted Effects of Alpha1-Antitrypsin on Neutrophil Functions. Front Pharmacol 2018;9:341. [Crossref] [PubMed]
22. Dinusha NU, Sebastian OW, Hongwang W, et al. Early detection of non-small cell lung cancer in liquid biopsies by ultrasensitive protease activity analysis. Journal of Cancer Metastasis and Treatment 2020;6:25.
23. Pfeifer GP, Denissenko MF, Olivier M, et al. Tobacco smoke carcinogens, DNA damage and p53 mutations in smoking-associated cancers. Oncogene 2002;21:7435-51. [Crossref] [PubMed]
24. Hubbard RC, Ogushi F, Fells GA, et al. Oxidants spontaneously released by alveolar macrophages of cigarette smokers can inactivate the active site of alpha 1-antitrypsin, rendering it ineffective as an inhibitor of neutrophil elastase. J Clin Invest 1987;80:1289-95. [Crossref] [PubMed]
25. Alam S, Li Z, Janciauskiene S, et al. Oxidation of Z α1-antitrypsin by cigarette smoke induces polymerization: a novel mechanism of early-onset emphysema. Am J Respir Cell Mol Biol 2011;45:261-9. [Crossref] [PubMed]
26. Vlahos R, Bozinovski S, Jones JE, et al. Differential protease, innate immunity, and NF-kappaB induction profiles during lung inflammation induced by subchronic cigarette smoke exposure in mice. Am J Physiol Lung Cell Mol Physiol 2006;290:L931-45. [Crossref] [PubMed]
27. Hernández-Pérez JM, Ramos-Izquierdo C, Figueira-Gonçalves JM, et al. The impact of alpha-1 antitrypsin deficiency alleles on lung cancer survival. Transl Cancer Res 2024;13:1125-30. [Crossref] [PubMed]
28. Li S, Chen Z, Zhang W, et al. Elevated expression of the membrane-anchored serine protease TMPRSS11E in NSCLC progression. Carcinogenesis 2022;43:1092-102. [Crossref] [PubMed]
29. Kim S. TMPRSS4, a type II transmembrane serine protease, as a potential therapeutic target in cancer. Exp Mol Med 2023;55:716-24. [Crossref] [PubMed]
30. Iizasa T, Fujisawa T, Suzuki M, et al. Elevated levels of circulating plasma matrix metalloproteinase 9 in non-small cell lung cancer patients. Clin Cancer Res 1999;5:149-53. [PubMed]
31. Gillard JA, Reed MW, Buttle D, et al. Matrix metalloproteinase activity and immunohistochemical profile of matrix metalloproteinase-2 and -9 and tissue inhibitor of metalloproteinase-1 during human dermal wound healing. Wound Repair Regen 2004;12:295-304. [Crossref] [PubMed]
32. Kim SH, Choi HY, Lee J, et al. Elevated activities of MMP-2 in the non-tumorous lung tissues of curatively resected stage I NSCLC patients are associated with tumor recurrence and a poor survival. J Surg Oncol 2007;95:337-46. [Crossref] [PubMed]
33. Fasciglione GF, Marini S, D'Alessio S, et al. pH- and temperature-dependence of functional modulation in metalloproteinases. A comparison between neutrophil collagenase and gelatinases A and B. Biophys J 2000;79:2138-49. [Crossref] [PubMed]
34. Krizkova S, Zitka O, Masarik M, et al. Assays for determination of matrix metalloproteinases and their activity. Trends in Analytical Chemistry 2011;30:1819-32. [Crossref]
35. de Almeida LGN, Thode H, Eslambolchi Y, et al. Matrix Metalloproteinases: From Molecular Mechanisms to Physiology, Pathophysiology, and Pharmacology. Pharmacol Rev 2022;74:712-68. [Crossref] [PubMed]
36. Stamenkovic I. Extracellular matrix remodelling: the role of matrix metalloproteinases. J Pathol 2003;200:448-64. [Crossref] [PubMed]
37. Pezeshkian Z, Nobili S, Peyravian N, et al. Insights into the Role of Matrix Metalloproteinases in Precancerous Conditions and in Colorectal Cancer. Cancers (Basel) 2021;13:6226. [Crossref] [PubMed]
38. Cornelius LA, Nehring LC, Harding E, et al. Matrix metalloproteinases generate angiostatin: effects on neovascularization. J Immunol 1998;161:6845-52. [Crossref] [PubMed]
39. Ferreras M, Felbor U, Lenhard T, et al. Generation and degradation of human endostatin proteins by various proteinases. FEBS Lett 2000;486:247-51. [Crossref] [PubMed]
40. Kim YM, Jang JW, Lee OH, et al. Endostatin inhibits endothelial and tumor cellular invasion by blocking the activation and catalytic activity of matrix metalloproteinase. Cancer Res 2000;60:5410-3. [PubMed]
41. Krüger A, Kates RE, Edwards DR. Avoiding spam in the proteolytic internet: future strategies for anti-metastatic MMP inhibition. Biochim Biophys Acta 2010;1803:95-102. [Crossref] [PubMed]
42. Coussens LM, Fingleton B, Matrisian LM. Matrix metalloproteinase inhibitors and cancer: trials and tribulations. Science 2002;295:2387-92. [Crossref] [PubMed]
43. Raeeszadeh-Sarmazdeh M, Do LD, Hritz BG. Metalloproteinases and Their Inhibitors: Potential for the Development of New Therapeutics. Cells 2020;9:1313. [Crossref] [PubMed]