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Article

MicroRNA Monitoring in Human Alveolar Macrophages from Patients with Smoking-Related Lung Diseases: A Preliminary Study

by
Davida Mirra
1,†,
Renata Esposito
1,†,
Giuseppe Spaziano
1,*,
Liberata Sportiello
2,3,
Francesca Panico
4,
Antonio Squillante
5,
Maddalena Falciani
6,
Ida Cerqua
7,
Luca Gallelli
4,
Erika Cione
8 and
Bruno D’Agostino
1
1
Department of Environmental Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
2
Campania Regional Centre for Pharmacovigilance and Pharmacoepidemiology, 80138 Naples, Italy
3
Department of Experimental Medicine-Section of Pharmacology “L. Donatelli”, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
4
Department of Health Sciences, University of “Magna Graecia”, 88100 Catanzaro, Italy
5
Department of Medicine, University of Salerno, 84100 Salerno, Italy
6
Pulmonary and Critical Care Medicine, Ospedale Scarlato, 84018 Scafati, Italy
7
Department of Pharmacy, University Federico II of Naples, Via D. Montesano 49, 80131 Naples, Italy
8
Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Rende, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomedicines 2024, 12(5), 1050; https://doi.org/10.3390/biomedicines12051050
Submission received: 8 April 2024 / Revised: 18 April 2024 / Accepted: 1 May 2024 / Published: 9 May 2024
(This article belongs to the Section Gene and Cell Therapy)
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Versions Notes

Abstract

:
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is commonly considered to be a potent driver of non-small cell lung cancer (NSCLC) development and related mortality. A growing body of evidence supports a role of the immune system, mainly played by alveolar macrophages (AMs), in key axes regulating the development of COPD or NSCLC phenotypes in response to harmful agents. MicroRNAs (miRNAs) are small non-coding RNAs that influence most biological processes and interfere with several regulatory pathways. The purpose of this study was to assess miRNA expression patterns in patients with COPD, NSCLC, and ever- or never-smoker controls to explore their involvement in smoking-related diseases. Bronchoalveolar lavage (BAL) specimens were collected from a prospective cohort of 43 sex-matched subjects to determine the expressions of hsa-miR-223-5p, 16-5p, 20a-5p, -17-5p, 34a-5p and 106a-5p by RT-PCR. In addition, a bioinformatic analysis of miRNA target genes linked to cancer was performed. Distinct and common miRNA expression levels were identified in each pathological group, suggesting their possible role as an index of NSCLC or COPD microenvironment. Moreover, we identified miRNA targets linked to carcinogenesis using in silico analysis. In conclusion, this study identified miRNA signatures in AMs, allowing us to understand the molecular mechanisms underlying smoking-related conditions and potentially providing new insights for diagnosis or pharmacological treatment.

1. Introduction

Chronic obstructive pulmonary disease (COPD) is a fatal disease with high mortality worldwide, placing a heavy economic burden on the healthcare systems [1,2]. It is a heterogenous condition characterized by a progressive deterioration of lung function over time and is generally associated with lung inflammation triggered by harmful particles or gases [3,4,5,6,7,8,9]. Additionally, COPD is commonly considered a potent driver of non-small-cell lung cancer (NSCLC) development and related mortality, beyond a common etiology [10,11]. Indeed, lung cancer is up to five times more frequent in smokers with airflow obstruction than in those with normal lung function, and patients with COPD have twice the risk of cancer diagnosis [12]. Inflammation and tumorigenesis are closely linked, and chronic inflammation could lead to an increased rate of cell division and to the chance of mutations and carcinogenesis [13,14,15,16]. Moreover, a growing body of evidence supports the role of the innate and adaptive immune systems, mainly played by alveolar macrophages (AMs), as key axes that regulate the development of COPD or NSCLC phenotypes in response to harmful agents [17,18,19,20,21,22,23]. Despite this increased attention, the mechanistic nature of both diseases remains unclear, making novel biomarkers necessary to improve the prediction of cancer risk in subjects with COPD. Recently, epigenetic changes, including DNA methylation, covalent histone modifications, and microRNA (miRNA) expression, have been reported to play important roles in lung cancer and COPD onset [24,25]. Indeed, miRNAs are small non-coding, single-stranded RNA molecules capable of binding to mRNA sequences, resulting in gene silencing via mRNA translational repression or target degradation. miRNAs expression can influence most biological processes and interfere with several regulatory pathways, playing a crucial role in the development of both innate and adaptive immune systems [26,27,28,29,30]. In addition, several authors have identified serum miRNAs, which could serve as biomarkers to help identify the risk of developing cancer in COPD patients [31,32]. In the present study, we focused on miRNAs that have recently been shown to influence immune system regulation or cancer hallmarks, such as cell proliferation and immunosuppression. Notably, hsa-miR-34a-5p has been reported to be dysregulated in various cancers and considered a tumor suppressor because of its synergistic effect with p53 [33]. Hsa-miR-223-5p was found to be downregulated in the bronchial airway of lung cancer patients and regulated the malignant phenotypes of lung squamous cell carcinoma cells by pairing with OTX1 [34]. Similarly, hsa-miR-16-5p is a tumor inhibitor and a new biomarker for immune-checkpoint-inhibitor-dependent immunotherapy in lung adenocarcinoma by regulating the PD-L1 expression [35]. Furthermore, different authors have shown that hsa-miR-106a-5p is overexpressed in the sera of patients with NSCLC [36] while hsa-miR-20a-5p and hsa-miR-17-5p have been reported as tumor suppressors [37,38,39]. However, miRNAs could be shared in COPD and cancer targeting candidate mRNA families implicated in the pathogenesis of both diseases [40]). Therefore, to obtain miRNA signatures between COPD and NSCLC that could be used to distinguish between smoking-related diseases, we evaluated miRNAs expression in AMs of bronchoalveolar lavage (BAL) from patients with COPD, NSCLC, and ever- or never- smoker controls.

2. Materials and Methods

2.1. Study Population

We performed an observational clinical study on 43 age- and sex-matched subjects, aged 18 years or older, referred to the “Mater Domini” Hospital in Catanzaro, Italy, with a suspected diagnosis of pulmonary neoplasia, undergoing routine bronchoscopy and bronchoalveolar lavage (BAL). This study is a part of the clinical trial recorded at clinicaltrials.gov (NCT04654104) and approved by the local Ethics Committee “Calabria Centro” (n. 361 of 22 October 2020). This study was conducted in compliance with the Institutional Review Board/Human Subjects Research Committee, the Declaration of Helsinki, and the Guidelines for Good Clinical Practice criteria. Before the beginning of the study, all the enrolled patients or their legal guardians signed their informed consent and underwent spirometry according to international guidelines [41]. At enrollment, patients’ demographics and clinical and social history were obtained by physicians: age, gender, smoking history (pack/years), spirometry data, comorbidities, and pharmacological treatments. The exclusion criteria were active pulmonary infections, autoimmune diseases, extrapulmonary neoplasia, or other airflow obstruction, such as asthma or bronchiectasis, or those who did not sign their informed consent. The main clinical and pathological characteristics of cohorts are summarized in Table 1. The enrolled subjects were classified into four groups according to their clinical and pathological (bronchoscopy-guided) diagnoses: (1) healthy never-smokers control (“HNS”; n = 9); (2) healthy ever-smokers control (“HS”; n = 11); (3) smokers with Global Initiative for Obstructive Lung Diseases (GOLD) stage 1–4 (“COPD”; n = 11); and (4) non-small-cell lung cancer (“NSCLC”; n = 12). Within each group, subjects were comparable with respect to COPD severity and/or lung cancer histology. Indeed, only those with NSCLC were enrolled among the subjects presenting cancer. The most frequent comorbidities were hypertension and diabetes mellitus; bronchodilators were the most common medications used.

2.2. Bronchoalveolar Lavage

All subjects underwent bronchoscopy for clinical indications, and BAL was obtained with a bronchoscope according to international guidelines [41] The procedure involved premedication (20 mg codeine per os) and local anesthesia of the larynx and lower airways (0.5% tetracaine in the oropharynx, 8 cc 0.5% tetracaine in lower airways). Transcutaneous oxygen saturation was monitored continuously through an oximeter with a finger probe. BAL was obtained by instilling a total volume of 200 mL of sterile isotonic saline solution (NaCl 0.9%, 37 °C) in the right middle lobe. Specifically, fraction I, returned after instilling 50 mL of saline, and fraction II, returned after instilling 3 × 50 mL of saline, were collected in a siliconized specimen trap, and immediately kept on ice. BAL was filtered through nylon gauze and centrifuged (10 min at 400× g at 4 °C). The cell pellet was washed twice, counted, and resuspended in PBS. Cells were counted in a Bürker chamber and cell yield was determined by the total cell number per fraction/total recovered volume per fraction. Cell viability was determined by Trypan blue and a differential cell count was performed by microscopy on the cytospin slide after staining with QUICK-DIFF KIT (Reastain®, Reagena Toivala, Finland); at least 100 cells were counted. AMs were identified as greater than 95% by morphology.

2.3. Clinical Biochemistry Assays and Real Time PCR (RT-PCR)

To extract miRNAs (total RNA) from AMs from BAL, we adopted the best method available: the miRNeasy Mini kit (QIAGEN, Hilden Germany), used according to manufacturer instructions. RNA was eluted at a volume of 15 µL with RNAse-free water. The qubit RNA Integrity and Quality (IQ) assay (catalog number Q33222) was performed to check for RNA degradation, and then microRNAs were quantified with a Qubit™ microRNA assay kit (catalog number Q 32881). For both kits, a Qubit 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) (serial number 2322618032114) was used.
The expression levels of hsa-miR-223-5p, 16-5p, 20a-5p, -17-5p, 34a-5p, and 106a-5p were determined through TaqMan™ Advanced miRNA Assay RT-PCR, following the Thermo Fisher Scientific procedures (Waltham, MA, USA) and selecting U6 snRNA as the housekeeping miRNA (Gallelli et al., 2019). The sequences used were:
  • hsa-miR-223-5p: CGUGUAUUUGACAAGCUGAGUU; (Assay ID 477984_mir);
  • hsa-miR-16-5p: UAGCAGCACGUAAAUAUUGGCG; (Assay ID 477860_mir);
  • hsa-miR-20a-5p: UAAAGUGCUUAUAGUGCAGGUAG; (Assay ID 478317_mir);
  • hsa-miR-17-5p: CAAAGUGCUUACAGUGCAGGUAG; (Assay ID 478447_mir);
  • hsa-miR-34a-5p: UGGCAGUGUCUUAGCUGGUUGU; (Assay ID 478048_mir);
  • hsa-miR-106a-5p: AAAAGUGCUUACAGUGCAGGUAG; (Assay ID 478225_mir);
  • U6 snRNA: GTGCTCGCTTCGGCAGCACATATACTAAAATTGGAACGATACAGAGAAGATTAGCATGGCCCCTGCGCAAGGATGACACGCAAATTCGTGAAGCGTTCCATATTTT; (AssayID: 001973).
Briefly, 5–10 ng of total RNA was subjected to a reverse transcription polymerase chain reaction using the TaqMan MicroRNA Reverse Transcription kit for all the miRNA targets chosen, according to the manufacturer’s instructions. The thermocycling conditions were as follows: 42 °C for 30 min, 85 °C for 5 min and 4 °C for 5 min. RT-PCR was performed using the TaqMan™ Fast Advanced Master Mix (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol and QuantumStudio5TM Real-Time PCR Systems. The thermocycling conditions were as follows: 95 °C for 10 min and 40 cycles of 15 s at 95 °C, followed by 1 min at 60 °C. Nine biological replicates for the HNS group, eleven for HS, eleven for NSCLC, and twelve for COPD were analyzed, and all samples were run in triplicate; after the achievement of the RT-PCR, the cycle threshold (Ct) of the reactions was determined. Gene expression stability was evaluated using RefFinder, a web tool integrating geNorm, Normfinder, BestKeeper, and the comparative ΔΔCt method. Data from all RT–PCR experiments and miRNA expression were analyzed by normalizing to the endogenous miRNA control, applying the comparative 2−ΔΔCt method, where ΔCt = CtmiRNA − Ct housekeeping miRNA, whereas the relative quantification of differences in expression was conducted with ΔΔCt = ΔCtHS/COPD/NSCLC − ΔCtHNS.

2.4. In Silico Analysis

To identify genes as targets of hsa-miR-223-5p, 16-5p, 20a-5p, -17-5p, 34a-5p, and 106a-5p linked to NSCLC and/or COPD, we performed in silico analysis. The in silico identification of the target genes was performed using DIANA Tools (http://diana.imis.athena-innovation.gr/DianaTools/index.php, accessed on 8 January 2024) and miRTargetLink 2.0 (https://ccb-compute.cs.uni-saarland.de/mirtargetlink2 accessed on 10 January 2024) databases. These databases were used to check which miRNA target genes were already validated experimentally. Indeed, all mRNAs’ targets chosen were selected from those validated by techniques such as immunoprecipitation, qPCR, or Western blotting. Possible biochemical pathways were checked using the GeneCard database (https://www.genecards.org/ accessed on18 January 2024), allowing genomic, proteomic, transcriptomic, genetic, and functional information on all known and predicted human genes.

2.5. Statistical Analysis

Unless specified, all data are expressed as mean ± standard deviation (SD). The one-way ANOVA test followed by Tukey’s Multiple Comparison Test was used to evaluate the differences between the groups. We used nominal (sex, comorbidity, and treatment) and categorical variables and the correlation between clinical characteristics was calculated using one-way ANOVA followed by Tukey’s Multiple Comparison Test. GraphPad software (version 9.1.0) was used for statistical analyses (GraphPad Software, San Diego, CA, USA). Differences were considered statistically significant at p < 0.05.

3. Results

3.1. miRNA Expression Levels

3.1.1. hsa-miR-223-5p

We identified common and discriminating miRNA signatures for each pathological entity (NSCLC and COPD) or smoking habit (HS) compared to the control group (HNS). RT-PCR analysis indicated that HS, COPD, and NSCLC shared a significant negative modulation of hsa-miR-223-5p expression levels compared to HNS (p < 0.0001, respectively) (Figure 1). Specifically, among the three groups, NSCLC showed the lowest values (p < 0.0001, respectively) (Figure 1).

3.1.2. hsa-miR-16-5p and 20a-5p

hsa-miR-16-5p and 20a-5p levels were significantly reduced in HS and NSCLC patients with respect to HNS (p < 0.0001). Moreover, the expression levels of these miRNAs in COPD patients showed no significant differences from healthy subjects (Figure 2 and Figure 3).

3.1.3. hsa-miR-17-5p

HS and COPD shared a significantly positive modulation of hsa-miR-17-5p expression levels compared to HNS (p < 0.05 and p < 0.0001, respectively) and particularly to NSCLC (p < 0.0001, respectively), in which a significant downregulation was observed with respect to healthy subjects (p < 0.05) (Figure 4).

3.1.4. hsa-miR-34a-5p

Interestingly, RT-PCR results showed that hsa-miR-34a-5p was exclusively upregulated in patients with COPD (p < 0.0001), while HS and NSCLC shared a significant negative modulation of this miRNA compared to HNS (p < 0.01, respectively) (Figure 5).

3.1.5. hsa-miR-106a-5p

The three pathological groups shared upregulated hsa-miR-106a-5p expression levels as compared with the control group (p < 0.0001 for HS and COPD group, p < 0.01 for NSCLC group), with the highest levels observed in HS subjects versus COPD and NSCLC groups (p < 0.0001, respectively) (Figure 6).
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Figure 1. Analysis of hsa-miR-223-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. **** p < 0.0001.
Figure 1. Analysis of hsa-miR-223-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. **** p < 0.0001.
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Figure 2. Analysis of hsa-miR-16-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. **** p < 0.0001.
Figure 2. Analysis of hsa-miR-16-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. **** p < 0.0001.
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Figure 3. Analysis of hsa-miR-20a-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. **** p < 0.0001.
Figure 3. Analysis of hsa-miR-20a-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. **** p < 0.0001.
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Figure 4. Analysis of hsa-miR-17-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. * p < 0.05; **** p < 0.0001.
Figure 4. Analysis of hsa-miR-17-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. * p < 0.05; **** p < 0.0001.
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Figure 5. Analysis of hsa-miR-34a-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. ** p < 0.01; **** p < 0.0001.
Figure 5. Analysis of hsa-miR-34a-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. ** p < 0.01; **** p < 0.0001.
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Figure 6. Analysis of hsa-miR-106a-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. ** p < 0.01; **** p < 0.0001.
Figure 6. Analysis of hsa-miR-106a-5p AMs expression levels in HNS (green column: biological replicates n = 9), HS (blue column: biological replicates n = 11), COPD (purple column: biological replicates n = 11) and NSCLC (red column: biological replicates n = 12). All samples were run in triplicate, and results are shown as means ± SD. The statistical tests used in these analyses were one-way analysis of variance followed by Tukey’s Multiple Comparison Test. ** p < 0.01; **** p < 0.0001.
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3.2. In Silico Identification of Target mRNAs

To assess the potential interconnection of miRNAs discriminating each pathological entity studied, we performed in silico prediction analysis based on sequence similarity between miRNAs and mRNAs. Two different databases were used for the analysis and data were compared with respect to the number of target genes experimentally validated. The results are reported in Table 2. The numbers of validated target genes were higher in miR target Link 2.0; therefore, it was used for bioinformatics analysis. We analyzed all selected miRNA targets, focusing on those that are common to several miRNAs and implicated in the cellular pathways responsible for carcinogenesis. The results showed that miRNA may be involved in the regulation of several lung cancer driver genes, such as BCL2, CYCS, E2F2, MCL1, or MYC, among others. However, other target genes linked to tumor invasion and metastasis were found, as shown in Table 3. Given that several authors have experimentally confirmed all genes, miRNAs might modulate these targets in a coordinated or individual manner, affecting several hallmarks of lung carcinogenesis. Abbreviations, names of the selected genes, methods, tissues of validation, and references are reported in Table 4.

4. Discussion

In this study, we assessed the role of miRNAs in two smoking-related diseases, COPD and NSCLC, by discriminating between common and selectively dysregulated miRNAs associated with each condition or smoking habit. NSCLC and COPD are lung diseases that share etiological and pathogenic factors [10,11,12]. Indeed, inflammation and tumorigenesis are closely linked, and chronic inflammation in COPD can lead to an increased rate of cell division and the chance of mutations and carcinogenesis [13,14,15,16]. Given the high prevalence of lung cancer in COPD patients, the search for new biomarkers has prompted in-depth epidemiological studies [42,43]. Here, we analyzed a miRNA panel identifying miRNAs that may be potentially useful as biomarkers or for therapeutic purposes. This analysis was carried out in AMs recovered from BAL, a precious biological sample that is highly representative of the pulmonary microenvironment [44]. First, we identified two similarly dysregulated miRNAs in HS, COPD patients, and NSCLC patients, suggesting common pathological mechanisms underlying these conditions. Specifically, hsa-miR-223-5p levels were decreased and hsa-miR-106a-5p levels were increased in these groups compared with those in healthy never-smokers. hsa-miR-223-5p was often reported to be downregulated in NSCLC tissue and cells, leading in turn to the upregulation of many target genes that contribute to tumor proliferation, migration, and invasion [45,46]. In addition, our findings in the ever-smokers and COPD group suggested that hsa-miR-223-5p expression could indicate a common pathway between COPD and NSCLC, probably sustained by cigarette smoke exposure. Several authors have described the role of hsa-miR-223-5p in macrophage differentiation, neutrophil recruitment, and pro-inflammatory responses, which are the key features of lung inflammation and remodeling [47]. For instance, Schembri et al. reported lower hsa-miR-223-5p expression levels in bronchial epithelial cells from current smokers than in those from never-smokers [48]. BTG3, a member of the B-cell translocation gene/transducer of the ErbB2 antiproliferative protein family, which regulates cell cycle progression and differentiation in various cell types, has been reported to be downregulated in NSCLC specimens and cell lines, with the upregulation of hsa-miR-106a-5p [49]. Furthermore, Ye et al. showed that hsa-miR-106a-5p is overexpressed in the serum of patients with NSCLC compared to healthy subjects, thus playing a pivotal role in the onset and development of the disease [36]. Consistent with these data, we observed a significant positive modulation of hsa-miR-106a-5p in the NSCLC, COPD, and HS groups compared to healthy individuals, thus providing evidence of cancer-like dysregulation in COPD and smoking habits. Although a few studies have investigated hsa-miR-106a-5p expression patterns associated with cigarette smoke and chronic lung diseases, Sharma et al. reported that it negatively regulates IL-10 expression in an in vitro and in vivo model of airway inflammation, which is in accordance with our data [50]. Interestingly, COPD exhibited different trends in hsa-miR-16-5p, 20a-5p, and 34a-5p expression compared to HS and NSCLC. This evidence suggests a crucial role for cigarette smoking in the mechanisms responsible for the development of a cancer-like milieu, and that the dysregulation of these miRNAs in COPD subjects towards the levels found in NSCLC could be indicative of an overlap between the two phenotypes. Programmed cell death ligand-1 (PDL1) is a well-known immune checkpoint that is upregulated in patients with lung cancer, allowing tumors to escape immune attack, cell growth, and metastasis [51]. Chen et al. reported that the overexpression of PD-L1 accelerated tumor growth and decreased exosomal hsa-miR-16-5p content in cell culture media, while exosomal miR-16-5p overexpression in cell culture media inhibited tumor development by decreasing PD-L1 expression [35]. Wei et al. reported a 5.5-fold suppressed expression of hsa-miR-16-5p in lung cancer tissues in comparison to the normal adjacent tissues [52]. Consistent with these data, we observed a significant decrease in hsa-miR-16-5p in both HS and NSCLC patients with respect to subjects with COPD. Similarly, hsa-miR-20a-5p showed a different trend between ever-smokers and NSCLC patients compared with COPD subjects. Angiogenesis is an essential feature of carcinogenesis, involving molecules in the lung microenvironment that recruit endothelial cells to promote the formation of intra-tumoral capillaries and primary tumor growth, proliferation, and metastasis [53]. Among these molecules, RRM2 plays a crucial regulatory role in controlling intratumoral capillary formation through the PI3K/Akt axis [54]. Han et al. found that the expression of RRM2 was upregulated, whereas the expression of hsa-miR-20a-5p was downregulated in cancer tissues compared to adjacent tissues in NSCLC patients, identifying hsa-miR-20a-5p as a potential tumor suppressor [37]. Accordingly, our findings showed a negative modulation of hsa-miR-20a-5p not only in the NSCLC group but also in ever-smokers, suggesting the ability of cigarette smoke to modulate its expression in a cancer-like manner. hsa-miR-34a-5p has been extensively evaluated in NSCLC and is commonly considered a tumor suppressor because of its modulation of cellular proliferation, apoptosis, and immune evasion [33]. In a previous study, we reported that NSCLC and COPD patients differed in hsa-miR-34a-5p lung tissue levels, showing an increase and decrease, respectively, compared to healthy controls [55]. In line with these observations, AMs recovered from the BAL of lung cancer and COPD subjects showed an opposite trend in hsa-miR-34a-5p levels. Moreover, there were no statistical differences between ever-smokers and patients with lung cancer, suggesting a similar hsa-miR-34a-5p modulation. Cigarette smoking directly damages bronchial epithelial cells, the first barrier for the respiratory tract, and leads to the infiltration of immune cells into the lung tissue, including macrophages and neutrophils, that sustain inflammation [56]. SIRPα, a member of the immunoglobulin superfamily, modulates many aspects of the inflammatory response, including activation, chemotaxis, and phagocytosis [57]. In macrophages, the upregulation of hsa-miR-17-5p by lipopolysaccharides (LPS) serves as the mechanism underlying LPS-induced SIRPα reduction and macrophage activation [58]. This was consistent with our finding of higher hsa-miR-17-5p levels in both COPD patients and ever-smokers, supporting the importance of smoking in the mechanisms underlying COPD. Intriguingly, NSCLC showed the lowest levels of hsa-miR-17-5p, in accordance with several studies that reported it as a tumor suppressor [38,39] However, hsa-miR-17-5p’s regulatory effects on tumor progression are different and cannot be generalized, making its role controversial [59]. It is important to point out that although all subjects with COPD were current smokers, our data revealed that hsa-miR-16-5p, 20-5p, and 34a-p expression levels differed from those in ever-smokers. Generally, the intensity of the reaction against immunogenic antigens produced in response to cigarette smoke varies across a wide range of disease manifestations, such as COPD or lung cancer. Moreover, recent studies have highlighted the crucial role of immune responses in the regulation of the development of COPD or NSCLC phenotypes in response to smoke, assuming that COPD may be associated with a shift from an innate response to an adaptive immune response with hallmarks typical of autoimmune processes [60,61]. In this context, the dysregulation of these specific miRNAs could reflect phenotype switching or the onset of different lung manifestations, underlining the prominent but not exclusive role of cigarette smoking. Indeed, miRNA expression profiles could be confounded by other environmental conditions, which could further modulate the correlation between the expression of miRNAs and their target mRNAs in COPD [62]. Finally, to assess the potential interconnection of miRNAs discriminating each pathological entity, we performed in silico prediction of hsa-miR target genes. Bioinformatic prediction results revealed that the miRNAs analyzed may potentially be involved in the regulation of several lung cancer driver genes, such as BCL2, CYCS, E2F3, MCL1, MFN2, MYC, RNF115, SENP1, SLC1A5, SLC6A4, SLC7A11, and TMOD3, which are involved in cancer key regulatory pathways [63,64,65,66,67,68,69,70,71,72,73,74,75]. Since several authors have experimentally confirmed all miRNA-regulated genes, miRNAs may modulate these targets in combination or individually, affecting different hallmarks of lung carcinogenesis. For instance, Lu et al. assessed the regulation of hsa-miR-17-5p on MFN2 expression using a luciferase reporter assay system. The authors reported that hsa-miR-17-5p regulates proliferation and apoptosis in human pulmonary artery smooth muscle cells (hPASMCs) treated with hypoxia through MFN2 modulation and binding to its 3′-UTR [76]. Christoffersen et al. showed that the 3′-UTR sequence of MYC revealed the presence of a perfectly complementary 6-nucleotide seed match to hsa-miR-34a-5p. Western blotting revealed that the overexpression of hsa-miR-34a-5 p resulted in a marked downregulation of endogenous MYC protein, emphasizing its significance as a tumor suppressor [77].

5. Conclusions

Our study identified distinct miRNAs in AMs obtained from BAL samples that could discriminate between smoking-related diseases like NSCLC and COPD. One potential limitation was the small sample size used for miRNA analysis, which did not allow our findings to be applied to a broader population or for a subanalysis to be performed according to COPD severity. Moreover, it might be interesting in the future to analyze the direct effect of smoke in the correlation between miRNAs and their targets linked to carcinogenesis, as well as the interplay between AMs and structural alveolar cells. However, considering the nature of the lung samples used, our results, even if preliminary, could be of clinical relevance and lead to future studies involving larger populations. Our data on common and specific deregulated miRNAs in NSCLC and COPD could allow us to understand the underlying regulatory mechanisms involved in the pathogenesis of these smoking-related conditions and potentially provide new tools for diagnosis or pharmacological treatment.

Author Contributions

Conceptualization, D.M., G.S., E.C. and B.D.; Data curation, G.S., I.C. and E.C.; Formal analysis, D.M., R.E., F.P. and M.F.; Funding acquisition, L.S.; Investigation, D.M., R.E., F.P., A.S. and I.C.; Methodology, D.M.; Project administration, G.S. and B.D.; Resources, L.G.; Supervision, L.G. and E.C.; Validation, G.S., L.S. and L.G.; Visualization, R.E.; Writing—original draft, D.M.; Writing—review and editing, D.M., G.S., E.C. and B.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Institutional Review Board Statement: This study was part of a clinical trial recorded at clini-caltrials.gov (NCT04654104) and was approved by the local ethics committee “Calabria Centro” (n. 361 del 22 Ottobre 2020). This work was conducted in compliance with the Institutional Review Board/Human Subjects Research Committee requirements, the Declaration of Helsinki, and the Guidelines for Good Clinical Practice criteria.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

References

  1. López-Campos, J.L.; Tan, W.; Soriano, J.B. Global burden of COPD. Respirology 2016, 21, 14–23. [Google Scholar] [CrossRef]
  2. Safiri, S.; Carson-Chahhoud, K.; Noori, M.; Nejadghaderi, S.A.; Sullman, M.J.M.; Ahmadian Heris, J.; Ansarin, K.; Mansournia, M.A.; Collins, G.S.; Kolahi, A.A.; et al. Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990–2019: Results from the Global Burden of Disease Study 2019. BMJ 2022, 378, e069679. [Google Scholar] [CrossRef] [PubMed]
  3. Pauwels, R.A.; Rabe, K.F. Burden and Clinical Features of Chronic Obstructive Pulmonary Disease (COPD). Lancet 2004, 364, 613–620. [Google Scholar] [CrossRef] [PubMed]
  4. Kotlyarov, S. The Role of Smoking in the Mechanisms of Development of Chronic Obstructive Pulmonary Disease and Atherosclerosis. Int. J. Mol. Sci. 2023, 24, 8725. [Google Scholar] [CrossRef] [PubMed]
  5. Urbanek, K.; De Angelis, A.; Spaziano, G.; Piegari, E.; Matteis, M.; Cappetta, D.; Esposito, G.; Russo, R.; Tartaglione, G.; De Palma, R.; et al. Intratracheal Administration of Mesenchymal Stem Cells Modulates Tachykinin System, Suppresses Airway Remodeling and Reduces Airway Hyperresponsiveness in an Animal Model. PLoS ONE 2016, 11, e0158746. [Google Scholar] [CrossRef] [PubMed]
  6. D’Agostino, B.; Advenier, C.; De Palma, R.; Gallelli, L.; Marrocco, G.; Abbate, G.F.; Rossi, F. The Involvement of Sensory Neuropeptides in Airway Hyper-Responsiveness in Rabbits Sensitized and Challenged to Parietaria judaica: Sensory Neuropeptides in Airway Hyper-Responsiveness. Clin. Exp. Allergy 2002, 32, 472–479. [Google Scholar] [CrossRef] [PubMed]
  7. D’Agostino, B.; Marrocco, G.; De Nardo, M.; Calò, G.; Guerrini, R.; Gallelli, L.; Advenier, C.; Rossi, F. Activation of the Nociceptin/Orphanin FQ Receptor Reduces Bronchoconstriction and Microvascular Leakage in a Rabbit Model of Gastroesophageal Reflux: N/OFQ Effects in the Airways in a GER Animal Model. Br. J. Pharmacol. 2005, 144, 813–820. [Google Scholar] [CrossRef] [PubMed]
  8. Rouget, C.; Cui, Y.Y.; D’Agostino, B.; Faisy, C.; Naline, E.; Bardou, M.; Advenier, C. Nociceptin Inhibits Airway Microvascular Leakage Induced by HCl Intra-Oesophageal Instillation: Nociceptin and Gastro-Oesophageal Reflux. Br. J. Pharmacol. 2004, 141, 1077–1083. [Google Scholar] [CrossRef] [PubMed]
  9. Gallelli, L.; D’Agostino, B.; Marrocco, G.; De Rosa, G.; Filippelli, W.; Rossi, F.; Advenier, C. Role of Tachykinins in the Bronchoconstriction Induced by HCl Intraesophageal Instillation in the Rabbit. Life Sci. 2003, 72, 1135–1142. [Google Scholar] [CrossRef] [PubMed]
  10. Durham, A.L.; Adcock, I.M. The Relationship between COPD and Lung Cancer. Lung Cancer 2015, 90, 121–127. [Google Scholar] [CrossRef]
  11. Papi, A. COPD Increases the Risk of Squamous Histological Subtype in Smokers Who Develop Non-Small Cell Lung Carcinoma. Thorax 2004, 59, 679–681. [Google Scholar] [CrossRef] [PubMed]
  12. Young, R.P.; Hopkins, R.J. Link between COPD and Lung Cancer. Respir. Med. 2010, 104, 758–759. [Google Scholar] [CrossRef] [PubMed]
  13. Schetter, A.J.; Heegaard, N.H.H.; Harris, C.C. Inflammation and Cancer: Interweaving MicroRNA, Free Radical, Cytokine and P53 Pathways. Carcinogenesis 2010, 31, 37–49. [Google Scholar] [CrossRef] [PubMed]
  14. Caramori, G.; Adcock, I.M.; Casolari, P.; Ito, K.; Jazrawi, E.; Tsaprouni, L.; Villetti, G.; Civelli, M.; Carnini, C.; Chung, K.F.; et al. Unbalanced Oxidant-Induced DNA Damage and Repair in COPD: A Link towards Lung Cancer. Thorax 2011, 66, 521–527. [Google Scholar] [CrossRef] [PubMed]
  15. Schaible, A.M.; Filosa, R.; Temml, V.; Krauth, V.; Matteis, M.; Peduto, A.; Bruno, F.; Luderer, S.; Roviezzo, F.; Di Mola, A.; et al. Elucidation of the Molecular Mechanism and the Efficacy in Vivo of a Novel 1,4-Benzoquinone That Inhibits 5-Lipoxygenase. Br. J. Pharmacol. 2014, 171, 2399–2412. [Google Scholar] [CrossRef] [PubMed]
  16. Schaible, A.M.; Filosa, R.; Krauth, V.; Temml, V.; Pace, S.; Garscha, U.; Liening, S.; Weinigel, C.; Rummler, S.; Schieferdecker, S.; et al. The 5-Lipoxygenase Inhibitor RF-22c Potently Suppresses Leukotriene Biosynthesis in Cellulo and Blocks Bronchoconstriction and Inflammation In Vivo. Biochem. Pharmacol. 2016, 112, 60–71. [Google Scholar] [CrossRef] [PubMed]
  17. Mark, N.M.; Kargl, J.; Busch, S.E.; Yang, G.H.Y.; Metz, H.E.; Zhang, H.; Hubbard, J.J.; Pipavath, S.N.J.; Madtes, D.K.; Houghton, A.M. Chronic Obstructive Pulmonary Disease Alters Immune Cell Composition and Immune Checkpoint Inhibitor Efficacy in Non–Small Cell Lung Cancer. Am. J. Respir. Crit. Care Med. 2018, 197, 325–336. [Google Scholar] [CrossRef] [PubMed]
  18. Punturieri, A.; Szabo, E.; Croxton, T.L.; Shapiro, S.D.; Dubinett, S.M. Lung Cancer and Chronic Obstructive Pulmonary Disease: Needs and Opportunities for Integrated Research. JNCI J. Natl. Cancer Inst. 2009, 101, 554–559. [Google Scholar] [CrossRef] [PubMed]
  19. Hodge, S.; Hodge, G.; Ahern, J.; Jersmann, H.; Holmes, M.; Reynolds, P.N. Smoking Alters Alveolar Macrophage Recognition and Phagocytic Ability: Implications in Chronic Obstructive Pulmonary Disease. Am. J. Respir. Cell Mol. Biol. 2007, 37, 748–755. [Google Scholar] [CrossRef] [PubMed]
  20. de Groot, L.E.S.; van der Veen, T.A.; Martinez, F.O.; Hamann, J.; Lutter, R.; Melgert, B.N. Oxidative Stress and Macrophages: Driving Forces behind Exacerbations of Asthma and Chronic Obstructive Pulmonary Disease? Am. J. Physiol. Lung Cell. Mol. Physiol. 2019, 316, L369–L384. [Google Scholar] [CrossRef] [PubMed]
  21. Vlahos, R. Role of Alveolar Macrophages in Chronic Obstructive Pulmonary Disease. Front. Immunol. 2014, 5, 110822. [Google Scholar] [CrossRef] [PubMed]
  22. Finicelli, M.; Digilio, F.A.; Galderisi, U.; Peluso, G. The Emerging Role of Macrophages in Chronic Obstructive Pulmonary Disease: The Potential Impact of Oxidative Stress and Extracellular Vesicle on Macrophage Polarization and Function. Antioxidants 2022, 11, 464. [Google Scholar] [CrossRef] [PubMed]
  23. Polverino, F.; Mirra, D.; Yang, C.X.; Esposito, R.; Spaziano, G.; Rojas-Quintero, J.; Sgambato, M.; Piegari, E.; Cozzolino, A.; Cione, E.; et al. Similar Programmed Death Ligand 1 (PD-L1) Expression Profile in Patients with Mild COPD and Lung Cancer. Sci. Rep. 2022, 12, 22402. [Google Scholar] [CrossRef] [PubMed]
  24. Franco, R.; Schoneveld, O.; Georgakilas, A.G.; Panayiotidis, M.I. Oxidative Stress, DNA Methylation and Carcinogenesis. Cancer Lett. 2008, 266, 6–11. [Google Scholar] [CrossRef] [PubMed]
  25. Kabesch, M.; Adcock, I.M. Epigenetics in Asthma and COPD. Biochimie 2012, 94, 2231–2241. [Google Scholar] [CrossRef] [PubMed]
  26. Molina-Pinelo, S.; Pastor, M.D.; Suarez, R.; Romero-Romero, B.; Gonzalez De la Pena, M.; Salinas, A.; Garcia-Carbonero, R.; De Miguel, M.J.; Rodriguez-Panadero, F.; Carnero, A.; et al. MicroRNA Clusters: Dysregulation in Lung Adenocarcinoma and COPD. Eur. Respir. J. 2014, 43, 1740–1749. [Google Scholar] [CrossRef] [PubMed]
  27. Mirra, D.; Cione, E.; Spaziano, G.; Esposito, R.; Sorgenti, M.; Granato, E.; Cerqua, I.; Muraca, L.; Iovino, P.; Gallelli, L.; et al. Circulating MicroRNAs Expression Profile in Lung Inflammation: A Preliminary Study. J. Clin. Med. 2022, 11, 5446. [Google Scholar] [CrossRef] [PubMed]
  28. Lu, L.-F.; Liston, A. MicroRNA in the Immune System, MicroRNA as an Immune System. Immunology 2009, 127, 291–298. [Google Scholar] [CrossRef] [PubMed]
  29. Iannone, F.; Montesanto, A.; Cione, E.; Crocco, P.; Caroleo, M.C.; Dato, S.; Rose, G.; Passarino, G. Expression Patterns of Muscle-Specific MiR-133b and MiR-206 Correlate with Nutritional Status and Sarcopenia. Nutrients 2020, 12, 297. [Google Scholar] [CrossRef] [PubMed]
  30. Cannataro, R.; Caroleo, M.C.; Fazio, A.; La Torre, C.; Plastina, P.; Gallelli, L.; Lauria, G.; Cione, E. Ketogenic Diet and MicroRNAs Linked to Antioxidant Biochemical Homeostasis. Antioxidants 2019, 8, 269. [Google Scholar] [CrossRef] [PubMed]
  31. Leidinger, P.; Keller, A.; Borries, A.; Huwer, H.; Rohling, M.; Huebers, J.; Lenhof, H.P.; Meese, E. Specific peripheral miRNA profiles for distinguishing lung cancer from COPD. Lung Cancer 2011, 74, 41–47. [Google Scholar] [CrossRef] [PubMed]
  32. Keller, A.; Fehlmann, T.; Ludwig, N.; Kahraman, M.; Laufer, T.; Backes, C.; Vogelmeier, C.; Diener, C.; Biertz, F.; Herr, C.; et al. COSYCONET Study Group. Genome-wide MicroRNA Expression Profiles in COPD: Early Predictors for Cancer Development. Genom. Proteom. Bioinform. 2018, 16, 162–171. [Google Scholar] [CrossRef] [PubMed]
  33. Zhang, L.; Liao, Y.; Tang, L. MicroRNA-34 Family: A Potential Tumor Suppressor and Therapeutic Candidate in Cancer. J. Exp. Clin. Cancer Res. 2019, 38, 53. [Google Scholar] [CrossRef] [PubMed]
  34. Lu, Y. MiR-223-5p Suppresses OTX1 to Mediate Malignant Progression of Lung Squamous Cell Carcinoma Cells. Comput. Math. Methods Med. 2021, 2021, 6248793. [Google Scholar] [CrossRef] [PubMed]
  35. Chen, H.; Luo, Y.; Lin, M.; Peng, X.; Liu, M.; Wang, Y.; Li, S.; Yang, D.; Yang, Z. Serum Exosomal miR-16-5p Functions as a Tumor Inhibitor and a New Biomarker for PD-L1 Inhibitor-dependent Immunotherapy in Lung Adenocarcinoma by Regulating PD-L1 Expression. Cancer Med. 2022, 11, 2627–2643. [Google Scholar] [CrossRef] [PubMed]
  36. Tian, Y.; Sun, C.; Zhang, L.; Pan, Y. Clinical Significance of MiRNA—106a in Non-Small Cell Lung Cancer Patients Who Received Cisplatin Combined with Gemcitabine Chemotherapy. Cancer Biol. Med. 2018, 15, 157. [Google Scholar] [CrossRef] [PubMed]
  37. Han, J.; Hu, J.; Sun, F.; Bian, H.; Tang, B.; Fang, X. MicroRNA-20a-5p Suppresses Tumor Angiogenesis of Non-Small Cell Lung Cancer through RRM2-Mediated PI3K/Akt Signaling Pathway. Mol. Cell. Biochem. 2021, 476, 689–698. [Google Scholar] [CrossRef] [PubMed]
  38. Zhang, X.; Li, Y.; Qi, P.; Ma, Z. Biology of MiR-17-92 Cluster and Its Progress in Lung Cancer. Int. J. Med. Sci. 2018, 15, 1443–1448. [Google Scholar] [CrossRef] [PubMed]
  39. Sweat, Y.; Ries, R.J.; Sweat, M.; Su, D.; Shao, F.; Eliason, S.; Amendt, B.A. MiR-17 Acts as a Tumor Suppressor by Negatively Regulating the MiR-17-92 Cluster. Mol. Ther. Nucleic Acids 2021, 26, 1148–1158. [Google Scholar] [CrossRef] [PubMed]
  40. Fathinavid, A.; Ghobadi, M.Z.; Najafi, A.; Masoudi-Nejad, A. Identification of Common MicroRNA between COPD and Non-Small Cell Lung Cancer through Pathway Enrichment Analysis. BMC Genom. Data 2021, 22, 41. [Google Scholar] [CrossRef] [PubMed]
  41. Sokolowski, J.W., Jr.; Burgher, L.W.; Jones, F.L., Jr.; Patterson, J.R.; Selecky, P.A. Guidelines for fiberoptic bronchoscopy in adults. American Thoracic Society guidelines. Medical Section of the American Lung Association. Am. Rev. Respir. Dis. 1987, 136, 1066. [Google Scholar] [CrossRef] [PubMed]
  42. de Torres, J.P.; Marín, J.M.; Casanova, C.; Cote, C.; Carrizo, S.; Cordoba-Lanus, E.; Baz-Dávila, R.; Zulueta, J.J.; Aguirre-Jaime, A.; Saetta, M.; et al. Lung Cancer in Patients with Chronic Obstructive Pulmonary Disease: Incidence and Predicting Factors. Am. J. Respir. Crit. Care Med. 2011, 184, 913–919. [Google Scholar] [CrossRef] [PubMed]
  43. Park, H.Y.; Kang, D.; Shin, S.H.; Yoo, K.-H.; Rhee, C.K.; Suh, G.Y.; Kim, H.; Shim, Y.M.; Guallar, E.; Cho, J.; et al. Chronic Obstructive Pulmonary Disease and Lung Cancer Incidence in Never Smokers: A Cohort Study. Thorax 2020, 75, 506–509. [Google Scholar] [CrossRef] [PubMed]
  44. Lofdahl, J.M. Bronchoalveolar Lavage in COPD: Fluid Recovery Correlates with the Degree of Emphysema. Eur. Respir. J. 2005, 25, 275–281. [Google Scholar] [CrossRef] [PubMed]
  45. Pavel, A.B.; Campbell, J.D.; Liu, G.; Elashoff, D.; Dubinett, S.; Smith, K.; Whitney, D.; Lenburg, M.E.; Spira, A. Alterations in Bronchial Airway MiRNA Expression for Lung Cancer Detection. Cancer Prev. Res. 2017, 10, 651–659. [Google Scholar] [CrossRef] [PubMed]
  46. Dou, L.; Han, K.; Xiao, M.; Lv, F. MiR-223-5p Suppresses Tumor Growth and Metastasis in Non-Small Cell Lung Cancer by Targeting E2F8. Oncol. Res. 2019, 27, 261–268. [Google Scholar] [CrossRef]
  47. Roffel, M.P.; Bracke, K.R.; Heijink, I.H.; Maes, T. MiR-223: A Key Regulator in the Innate Immune Response in Asthma and COPD. Front. Med. 2020, 7, 196. [Google Scholar] [CrossRef] [PubMed]
  48. Schembri, F.; Sridhar, S.; Perdomo, C.; Gustafson, A.M.; Zhang, X.; Ergun, A.; Lu, J.; Liu, G.; Zhang, X.; Bowers, J.; et al. MicroRNAs as Modulators of Smoking-Induced Gene Expression Changes in Human Airway Epithelium. Proc. Natl. Acad. Sci. USA 2009, 106, 2319–2324. [Google Scholar] [CrossRef] [PubMed]
  49. Wei, K.; Pan, C.; Yao, G.; Liu, B.; Ma, T.; Xia, Y.; Jiang, W.; Chen, L.; Chen, Y. MiR-106b-5p Promotes Proliferation and Inhibits Apoptosis by Regulating BTG3 in Non-Small Cell Lung Cancer. Cell. Physiol. Biochem. 2017, 44, 1545–1558. [Google Scholar] [CrossRef] [PubMed]
  50. Sharma, A.; Kumar, M.; Ahmad, T.; Mabalirajan, U.; Aich, J.; Agrawal, A.; Ghosh, B. Antagonism of Mmu-Mir-106a Attenuates Asthma Features in Allergic Murine Model. J. Appl. Physiol. 2012, 113, 459–464. [Google Scholar] [CrossRef] [PubMed]
  51. Jiang, X.; Wang, J.; Deng, X.; Xiong, F.; Ge, J.; Xiang, B.; Wu, X.; Ma, J.; Zhou, M.; Li, X.; et al. Role of the Tumor Microenvironment in PD-L1/PD-1-Mediated Tumor Immune Escape. Mol. Cancer 2019, 18, 10. [Google Scholar] [CrossRef] [PubMed]
  52. Wei, J.; Jia, A.; Ma, L.; Wang, Y.; Qiu, L.; Xiao, B. MicroRNA-16 Inhibits the Proliferation and Metastasis of Human Lung Cancer Cells by Modulating the Expression of YAP. J. Buon. 2020, 25, 862–868. [Google Scholar] [PubMed]
  53. Zhang, K.; Yang, L.; Wang, J.; Sun, T.; Guo, Y.; Nelson, R.; Tong, T.R.; Pangeni, R.; Salgia, R.; Raz, D.J. Ubiquitin-Specific Protease 22 Is Critical to in Vivo Angiogenesis, Growth and Metastasis of Non-Small Cell Lung Cancer. Cell Commun. Signal. 2019, 17, 167. [Google Scholar] [CrossRef] [PubMed]
  54. Wang, N.; Zhan, T.; Ke, T.; Huang, X.; Ke, D.; Wang, Q.; Li, H. Increased Expression of RRM2 by Human Papillomavirus E7 Oncoprotein Promotes Angiogenesis in Cervical Cancer. Br. J. Cancer 2014, 110, 1034–1044. [Google Scholar] [CrossRef] [PubMed]
  55. Mirra, D.; Esposito, R.; Spaziano, G.; La Torre, C.; Vocca, C.; Tallarico, M.; Cione, E.; Gallelli, L.; D’Agostino, B. Lung MicroRNAs Expression in Lung Cancer and COPD: A Preliminary Study. Biomedicines 2023, 11, 736. [Google Scholar] [CrossRef] [PubMed]
  56. Danov, O.; Wolff, M.; Bartel, S.; Böhlen, S.; Obernolte, H.; Wronski, S.; Jonigk, D.; Hammer, B.; Kovacevic, D.; Reuter, S.; et al. Cigarette Smoke Affects Dendritic Cell Populations, Epithelial Barrier Function, and the Immune Response to Viral Infection With H1N1. Front. Med. 2020, 7, 571003. [Google Scholar] [CrossRef] [PubMed]
  57. Barclay, A.N.; Brown, M.H. The SIRP Family of Receptors and Immune Regulation. Nat. Rev. Immunol. 2006, 6, 457–464. [Google Scholar] [CrossRef] [PubMed]
  58. Zhu, D.; Pan, C.; Li, L.; Bian, Z.; Lv, Z.; Shi, L.; Zhang, J.; Li, D.; Gu, H.; Zhang, C.-Y.; et al. MicroRNA-17/20a/106a Modulate Macrophage Inflammatory Responses through Targeting Signal-Regulatory Protein α. J. Allergy Clin. Immunol. 2013, 132, 426–436.e8. [Google Scholar] [CrossRef] [PubMed]
  59. Huang, C.; Yu, M.; Yao, X. MicroRNA-17 and the Prognosis of Human Carcinomas: A Systematic Review and Meta-Analysis. BMJ Open 2018, 8, e018070. [Google Scholar] [CrossRef] [PubMed]
  60. Lee, S.-H.; Goswami, S.; Grudo, A.; Song, L.; Bandi, V.; Goodnight-White, S.; Green, L.; Hacken-Bitar, J.; Huh, J.; Bakaeen, F.; et al. Antielastin Autoimmunity in Tobacco Smoking–Induced Emphysema. Nat. Med. 2007, 13, 567–569. [Google Scholar] [CrossRef] [PubMed]
  61. Polverino, F.; Laucho-Contreras, M.; Rojas Quintero, J.; Divo, M.; Pinto-Plata, V.; Sholl, L.; de-Torres, J.P.; Celli, B.R.; Owen, C.A. Increased Expression of A Proliferation-Inducing Ligand (APRIL) in Lung Leukocytes and Alveolar Epithelial Cells in COPD Patients with Non Small Cell Lung Cancer: A Possible Link between COPD and Lung Cancer? Multidiscip. Respir. Med. 2016, 11, 17. [Google Scholar] [CrossRef] [PubMed]
  62. Qiu, C.; Chen, G.; Cui, Q. Towards the Understanding of MicroRNA and Environmental Factor Interactions and Their Relationships to Human Diseases. Sci. Rep. 2012, 2, 318. [Google Scholar] [CrossRef] [PubMed]
  63. Xie, M.; Park, D.; Sica, G.L.; Deng, X. Bcl2-Induced DNA Replication Stress Promotes Lung Carcinogenesis in Response to Space Radiation. Carcinogenesis 2020, 41, 1565–1575. [Google Scholar] [CrossRef] [PubMed]
  64. Liu, C.; Wei, X. Unraveling the Potential of Senescence-Related Genes in Guiding Clinical Therapy of Lung Adenocarcinoma Patients. Funct. Integr. Genom. 2023, 23, 188. [Google Scholar] [CrossRef] [PubMed]
  65. Wu, L.; Wan, S.; Li, J.; Xu, Y.; Lou, X.; Sun, M.; Wang, S. Expression and Prognostic Value of E2F3 Transcription Factor in Non-small Cell Lung Cancer. Oncol. Lett. 2021, 21, 411. [Google Scholar] [CrossRef] [PubMed]
  66. Zhang, J.; He, J.; Xia, J.; Chen, Z.; Chen, X. Delayed Apoptosis by Neutrophils from COPD Patients Is Associated with Altered Bak, Bcl-Xl, and Mcl-1 MRNA Expression. Diagn. Pathol. 2012, 7, 65. [Google Scholar] [CrossRef] [PubMed]
  67. Chanvorachote, P.; Sriratanasak, N.; Nonpanya, N. C-Myc Contributes to Malignancy of Lung Cancer: A Potential Anticancer Drug Target. Anticancer Res. 2020, 40, 609–618. [Google Scholar] [CrossRef]
  68. Zhang, J.; Pan, L.; Zhang, Q.; Zhao, Y.; Wang, W.; Lin, N.; Zhang, S.; Wu, Q. MFN2 Deficiency Affects Calcium Homeostasis in Lung Adenocarcinoma Cells via Downregulation of UCP4. FEBS Open Bio. 2023, 13, 1107–1124. [Google Scholar] [CrossRef] [PubMed]
  69. Luo, Z.; Ye, X.; Shou, F.; Cheng, Y.; Li, F.; Wang, G. RNF115-Mediated Ubiquitination of P53 Regulates Lung Adenocarcinoma Proliferation. Biochem. Biophys. Res. Commun. 2020, 530, 425–431. [Google Scholar] [CrossRef] [PubMed]
  70. Gao, C.; Xiao, F.; Zhang, L.; Sun, Y.; Wang, L.; Liu, X.; Sun, H.; Xie, Z.; Liang, Y.; Xu, Q.; et al. SENP1 Inhibition Suppresses the Growth of Lung Cancer Cells through Activation of A20-Mediated Ferroptosis. Ann. Transl. Med. 2022, 10, 224. [Google Scholar] [CrossRef] [PubMed]
  71. Tu, Y.; Yao, S.; Chen, Q.; Li, W.; Song, Y.; Zhang, P. 5-Hydroxytryptamine Activates a 5-HT/c-Myc/SLC6A4 Signaling Loop in Non–Small Cell Lung Cancer. Biochim. Biophys. Acta (BBA) Gen. Subj. 2022, 1866, 130093. [Google Scholar] [CrossRef] [PubMed]
  72. Xue, Y.; Jiang, K.; Ou, L.; Shen, M.; Yang, Y.; Lu, J.; Xu, W. Targeting Sphingosine Kinase 1/2 by a Novel Dual Inhibitor SKI-349 Suppresses Non-Small Cell Lung Cancer Cell Growth. Cell Death Dis. 2022, 13, 602. [Google Scholar] [CrossRef]
  73. Zhang, W.; Sun, Y.; Bai, L.; Zhi, L.; Yang, Y.; Zhao, Q.; Chen, C.; Qi, Y.; Gao, W.; He, W.; et al. RBMS1 Regulates Lung Cancer Ferroptosis through Translational Control of SLC7A11. J. Clin. Investig. 2021, 131, e152067. [Google Scholar] [CrossRef] [PubMed]
  74. Hassanein, M.; Hoeksema, M.D.; Shiota, M.; Qian, J.; Harris, B.K.; Chen, H.; Clark, J.E.; Alborn, W.E.; Eisenberg, R.; Massion, P.P. SLC1A5 Mediates Glutamine Transport Required for Lung Cancer Cell Growth and Survival. Clin. Cancer Res. 2013, 19, 560–570. [Google Scholar] [CrossRef] [PubMed]
  75. Li, H.; Zhao, S.; Chen, X.; Feng, G.; Chen, Z.; Fan, S. MiR-145 Modulates the Radiosensitivity of Non-Small Cell Lung Cancer Cells by Suppression of TMOD3. Carcinogenesis 2022, 43, 288–296. [Google Scholar] [CrossRef] [PubMed]
  76. Lu, Z.; Li, S.; Zhao, S.; Fa, X. Upregulated miR-17 Regulates Hypoxia-Mediated Human Pulmonary Artery Smooth Muscle Cell Proliferation and Apoptosis by Targeting Mitofusin 2. Med. Sci. Monit. 2016, 22, 3301–3308. [Google Scholar] [CrossRef] [PubMed]
  77. Christoffersen, N.R.; Shalgi, R.; Frankel, L.B.; Leucci, E.; Lees, M.; Klausen, M.; Pilpel, Y.; Nielsen, F.C.; Oren, M.; Lund, A.H. p53-independent upregulation of miR-34a during oncogene-induced senescence represses MYC. Cell Death Differ. 2010, 17, 236–245. [Google Scholar] [CrossRef] [PubMed]
Table 1. Demographic characteristics of the enrolled patients. Data are means ± SD unless specified. The statistical tests used in these analyses were one-way ANOVA followed by the Tukey Multiple Comparison Test. FEV1: forced expiratory volume in the first second; FVC, forced vital capacity; LABA: long-acting beta-agonists; SABA: short-acting beta-agonists; LAMA: long-acting muscarinic agents.
Table 1. Demographic characteristics of the enrolled patients. Data are means ± SD unless specified. The statistical tests used in these analyses were one-way ANOVA followed by the Tukey Multiple Comparison Test. FEV1: forced expiratory volume in the first second; FVC, forced vital capacity; LABA: long-acting beta-agonists; SABA: short-acting beta-agonists; LAMA: long-acting muscarinic agents.
Healthy Never-SmokersHealthy SmokersCOPDNSCLCp-Value
Total Participants (N)9111112
Age (SD)70.5 (15.2)52.5 (8.3)63.5 (6)75 (16.9)NS
Gender (M/F)5/46/55/66/6NS
Smoking history (pack/years)NA3528.331<0.001
Smoking habit
(current/former smoker)		NA8/311/06/60.0133
FEV1 (% predicted)96% (3.2)91% (12)66% (14)88% (11)0.0438
FEV/FVC76.1 (2.5)75 (1.4)59 (1.7)74 (3)<0.0001
Comorbidities
Hypertension (%)5 (55.5%)5 (45.4%)3 (27.2%)5 (41.6%)0.0152
Other cardiovascular diseases (%)2 (22.2%)03 (27.2%)1 (12.5%)NS
Diabetes mellitus (%)1 (16.7%)001 (0.8%)NS
Medications
Inhaled corticosteroids (N, %)0000NS
LABA/SABA/LAMA (N, %)0011 (100%)3 (25%)<0.0001
Table 2. Bioinformatics tools for in silico analysis. Number of validated genes for each miRNA analyzed in miR Target Link 2.0 and Diana Tools databases.
Table 2. Bioinformatics tools for in silico analysis. Number of validated genes for each miRNA analyzed in miR Target Link 2.0 and Diana Tools databases.
Number of Target Genes
miRNAmiR Target Link 2.0DIANA Tools
hsa-miR-223-5p55110
hsa-miR-16-5p2279455
hsa-miR-20a-5p1659611
hsa-miR-17-5p1817136
hsa-miR-34a-5p968324
hsa-miR-106a-5p1166435
Table 3. miRNA gene interaction and possible biochemical pathways involved.
Table 3. miRNA gene interaction and possible biochemical pathways involved.
Biochemical PathwaysmiRNAValidated Target Genes
DNA replication—apoptosishsa-miR-16-5p
hsa-miR-17-5p
hsa-miR-20a-5p
hsa-miR-34a-5p		BCL2
DNA damage telomere stress—senescence pathwayshsa-miR-16-5p
hsa-miR-17-5p
hsa-miR-20a-5p
hsa-miR-34a-5p		CYCS
Uncontrolled tumor growth and invasionhsa-miR-16-5p
hsa-miR-17-5p
hsa-miR-20a-5p
hsa-miR-34a-5p		E2F3
Apoptosis—Bcl2 pathway—drug resistancehsa-miR-16-5p
hsa-miR-17-5p
hsa-miR-20a-5p
hsa-miR-34a-5p		MCL1
Promotion of tumor growth—drug resistance—poor survivalhsa-miR-16-5p
hsa-miR-17-5p
hsa-miR-34a-5p
hsa-miR-106a-5p		MFN2
Cancer cell growth and survival—drug resistance—poor survivalhsa-miR-16-5p
hsa-miR-17-5p
hsa-miR-20a-5p
hsa-miR-34a-5p		MYC
p53 pathway—proliferation and energy metabolismhsa-miR-20a-5p
hsa-miR-16-5p
hsa-miR-106a-5p
hsa-miR-223-5p		RNF115
Cell cycle deregulation and cell proliferation—drug resistancehsa-miR-20a-5p
hsa-miR-16-5p
hsa-miR-34a-5p
hsa-miR-223-5p		SENP1
Cellular transformation and growthhsa-miR-16-5p
hsa-miR-20a-5p
hsa-miR-16-5p
hsa-miR-34a-5p
hsa-miR-106a-5p		SLC1A5
C-MYC pathway—poor survivalhsa-miR-16-5p
hsa-miR-20a-5p
hsa-miR-16-5p
hsa-miR-106a-5p		SLC6A4
Ferroptosis—tumor progressionhsa-miR-20a-5p
hsa-miR-16-5p
hsa-miR-106a-5p
hsa-miR-223-5p		SLC7A11
Cancer progression—EGFR/PI3K/AKT or MAPK/ERK signaling pathwayshsa-miR-20a-5p
hsa-miR-16-5p
hsa-miR-34a-5p
hsa-miR-106a-5p		TMOD3
Table 4. Abbreviations, gene names, methods, and tissues on which the selected miRNAs were validated as targets from the miR Target Link Human database.
Table 4. Abbreviations, gene names, methods, and tissues on which the selected miRNAs were validated as targets from the miR Target Link Human database.
AbbreviationGene NameMethodsTissuesReferences (PMID)
BCL2BCL2 Apoptosis RegulatorLuciferase reporter assay, qRT-PCR, Western blot, reporter assay, proteomics analysis, immunohistochemistry, microarray, sequencing, HITS-CLIP, immunoblot, immunoprecipitationCervix cells, gastric cells,
bone cells, marrow cells,
spleen, liver, kidney, lymph node, tracheal/bronchial epithelial cells, breast cells, ovary cells, embryonic kidney cells, gastric cancer cells, B cells, mesothelial cell, glioma cells		17877811
18449891
18362358
17351108
17707831
20643754
20876285
19269153
16166262
19903841
20371350
23907579
22473208
24148817
25435430
26397135
26722459
CYCSCytochrome C, SomaticProteomics,
PAR-CLIP, PCR array		Breast cells,
brain and liver		18668040
23446348
28097098
E2F3E2F Transcription Factor 3CLASH. HITS-CLIP,
luciferase reporter assay, Western blot		Human embryonic kidney cells, B cells, stem cells23622248
22473208
17252019
MCL1MCL1 Apoptosis Regulator, BCL2 Family MemberHITS-CLIP, microarray, immunohistochemistry, luciferase reporter assay, qRT-PCR, Western blot, PCR arrayHuman embryonic kidney cells, leukemic cells, liver22473208
18362358
23594563
28097098
MFN2 Mitofusin 2 Proteomics, luciferase reporter assay, Western blot, CLASHBreast cells, lungs, human embryonic kidney cells18668040
27640178
23622248
MYC MYC Proto-Oncogene, BHLH Transcription Factor TRAP, Western blot,
CLASH, Luciferase reporter assay, Western blot, reporter assay; Western blot, qRT-PCR, microarray; sequencing.		Bone cells, mouse embryonic fibroblasts, breast cells, kidney cells, cervical cells, human fibroblasts, oral epithelium, stem cells, lymphoblastoid cells, bladder cells24510096
18695042
23622248
19696787
21294122
21297663
22159222
20371350
25572695
RNF115 Ring Finger Protein 115 HITS-CLIPKidney cells, cervical cells, neuroblastoma cells, mouse embryonic fibroblasts, glial cells23313552
23824327
SENP1 SUMO Specific Peptidase 1 HITS-CLIP, MIRT025428,
PAR-CLIP		Human embryonic kidney cells22473208
20371350
21572407
SLC1A5 Solute Carrier Family 12 Member 6 HITS-CLIPNeuronal mouse cells, mouse primary embryonic fibroblasts23313552
SLC6A4 Solute Carrier Family 6 Member 4 qRT-PCR, Western blot, HITS-CLIPLungs, brain22940131
23313552
23313552
SLC7A11 Solute Carrier Family 7 Member 11 PAR-CLIP
HITS-CLIP		Cervix cells, mouse neural progenitor cells, human retinal epithelial cells, primary mouse embryo fibroblasts, human embryonic stem cells, kidney cells23313552
22012620
20371350
21572407
TMOD3 Tropomodulin 3 HITS-CLIP
Proteomics		Cervical cells,
colorectal cells		23313552
21566225
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Mirra, D.; Esposito, R.; Spaziano, G.; Sportiello, L.; Panico, F.; Squillante, A.; Falciani, M.; Cerqua, I.; Gallelli, L.; Cione, E.; et al. MicroRNA Monitoring in Human Alveolar Macrophages from Patients with Smoking-Related Lung Diseases: A Preliminary Study. Biomedicines 2024, 12, 1050. https://doi.org/10.3390/biomedicines12051050

AMA Style

Mirra D, Esposito R, Spaziano G, Sportiello L, Panico F, Squillante A, Falciani M, Cerqua I, Gallelli L, Cione E, et al. MicroRNA Monitoring in Human Alveolar Macrophages from Patients with Smoking-Related Lung Diseases: A Preliminary Study. Biomedicines. 2024; 12(5):1050. https://doi.org/10.3390/biomedicines12051050

Chicago/Turabian Style

Mirra, Davida, Renata Esposito, Giuseppe Spaziano, Liberata Sportiello, Francesca Panico, Antonio Squillante, Maddalena Falciani, Ida Cerqua, Luca Gallelli, Erika Cione, and et al. 2024. "MicroRNA Monitoring in Human Alveolar Macrophages from Patients with Smoking-Related Lung Diseases: A Preliminary Study" Biomedicines 12, no. 5: 1050. https://doi.org/10.3390/biomedicines12051050

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