Roundabout signaling pathway involved in the pathogenesis of COPD by integrative bioinformatics analysis

Focusing on tumor and it's microenvironmental immune members for head and neck cancer patients

OBJECTIVE

Immune-related gene expression levels in the tumor microenvironment (TM) of head and neck squamous cell carcinoma (HNSCC) patients was compared.

MATERIALS AND METHODS

The CD163, CD274, CD86, FUT4, FOXP3, and ITGAX levels of HNSCC patients in their tumor tissues (n =76) and surrounding tissues adjacent to the tumor (n =76) were determined using quantitative real-time PCR (qRT-PCR). Changes in these genes were also evaluated by associating with demographical data of the patients.

RESULTS

CD163, CD274, FUT4, and FOXP3 gene expression levels were significantly higher in tumor tissue than in surrounding tissue. FUT4 fold change was statistically higher in patients with lymph node involvement. CD86 expression was statistically lower in smokers of 50 boxes per year or more. CD163, CD274, and FUT4 expressions were increased in response to the presence of extranodal extension (ENE).

CONCLUSIONS

These preliminary results demonstrate the alterations in expression levels of immunologic markers are associated with the clinical presentations of HNSCC.

AVAILABILITY OF DATA AND MATERIALS

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Systematic analysis of various RNA transcripts and construction of biological regulatory networks at the post-transcriptional level for chronic obstructive pulmonary disease

BACKGROUND

Although chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs.

METHODS

All lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels.

RESULTS

Among the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD.

CONCLUSIONS

We identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

Analysis of endoplasmic reticulum stress-related gene signature for the prognosis and pattern in diffuse large B cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma in adults. This study aimed to determine the prognostic significance of endoplasmic reticulum (ER) stress-related genes in DLBCL. ER stress-related genes were obtained from the molecular signatures database. Gene expression data and clinical outcomes from the gene expression omnibus and TCGA datasets were collected, and differentially expressed genes (DEGs) were screened out. Gene ontology enrichment analysis, the kyoto encyclopaedia of genes and genomes pathway analysis, and geneset enrichment analysis were used to analyse the possible biological function of ER stress-related DEGs in DLBCL. Protein-protein interaction network construction using the STRING online and hub genes were identified by cytoHubba on Cytoscape software. The significant prognosis-related genes were screened, and the differential expression was validated. The immune microenvironment assessment of significant genes were evaluated. Next, the nomogram was built using univariate and multivariate Cox regression analysis. 26 ER stress-related DEGs were screened. Functional enrichment analysis showed them to be involved in the regulation of the endoplasmic reticulum mainly. NUPR1 and TRIB3 were identified as the most significant prognostic-related genes by comparison with the GSE10846, GSE11318, and TCGA datasets. NUPR1 was correlated with a good prognosis and immune infiltration in DLBCL; on the other hand, high expression of TRIB3 significantly correlated with a poor prognosis, which was an independent prognostic factor for DLBCL. In summary, we identified NUPR1 and TRIB3 as critical ER stress-related genes in DLBCL. NUPR1 might be involved in immune infiltration in DLBCL, and TRIB3 might serve as a potential therapeutic target and prognostic factor in DLBCL.

LncRNA H19 promotes tumor angiogenesis in smokers by targeting anti-angiogenic miRNAs

A key concept in drug discovery is the identification of candidate therapeutic targets such as long noncoding RNAs (lncRNAs) because of their extensive involvement in neoplasms, and impressionability by smoking. Induced by exposure to cigarette smoke, lncRNA H19 targets and inactivates miR-29, miR-30a, miR-107, miR-140, miR-148b, miR-199a and miR-200, which control the rate of angiogenesis by inhibiting BiP, DLL4, FGF7, HIF1A, HIF1B, HIF2A, PDGFB, PDGFRA, VEGFA, VEGFB, VEGFC, VEGFR1, VEGFR2 and VEGFR3. Nevertheless, these miRNAs are often dysregulated in bladder cancer, breast cancer, colorectal cancer, glioma, gastric adenocarcinoma, hepatocellular carcinoma, meningioma, non-small-cell lung carcinoma, oral squamous cell carcinoma, ovarian cancer, prostate adenocarcinoma and renal cell carcinoma. As such, the present perspective article seeks to establish an evidence-based hypothetical model of how a smoking-related lncRNA known as H19 might aggravate angiogenesis by interfering with miRNAs that would otherwise regulate angiogenesis in a nonsmoking individual.

Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease

Objective

This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis.

Methods

The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results.

Results

80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells.

Conclusion

STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.

Identification of Dysregulated Mechanisms and Candidate Gene Markers in Chronic Obstructive Pulmonary Disease

Purpose

This study aimed to identify candidate gene markers that may facilitate chronic obstructive pulmonary disease (COPD) diagnosis and treatment.

Methods

The GSE47460 and GSE151052 datasets were analyzed to identify differentially expressed mRNAs (DEmRs) between COPD patients and controls. DEmRs that were differentially expressed in the same direction in both datasets were analyzed for functional enrichment and for coexpression. Genes from the largest three modules were tested for their ability to diagnose COPD based on the area under the receiver operating characteristic curve (AUC). Genes with AUC > 0.7 in both datasets were used to perform regression based on the "least absolute shrinkage and selection operator" in order to identify feature genes. We also identified differentially expressed miRNAs (DEmiRs) between COPD patients and controls using the GSE38974 dataset, then constructed a regulatory network. We also examined associations between feature genes and immune cell infiltration in COPD, and we identified methylation markers of COPD using the GSE63704 dataset.

Results

A total of 1350 genes differentially regulated in the same direction in the GSE47460 and GSE151052 datasets were found. The genes were significantly enriched in immune-related biological functions. Of 186 modules identified using MEGENA, the largest were C1_ 6, C1_ 3, and C1_ 2. Of the 22 candidate genes screened based on AUC, 11 feature genes emerged from analysis of a subset of GSE47460 data, which we validated using another subset of GSE47460 data as well as the independent GSE151052 dataset. Feature genes correlated significantly with infiltration by immune cells. The feature genes GPC4 and RS1 were predicted to be regulated by miR-374a-3p. We identified 117 candidate methylation markers of COPD, including PRRG4.

Conclusion

The feature genes we identified may be potential diagnostic markers and therapeutic targets in COPD. These findings provide new leads for exploring disease mechanisms and targeted treatments.

Bioinformatics analyses of the pathogenesis and new biomarkers of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is one of the major cause of global death. The purpose of our analysis was to detect a more reliable biomarker and small-molecule drug candidates and to identify the precise mechanisms involved in COPD.

METHODS

Three data sets were downloaded from the Gene Expression Omnibus database and analysed by Gene Expression Omnibus 2R. Functional enrichment analyses were performed by Metascape. We use the STRING data to build a protein-protein interaction network. The targets of differentially expressed microRNA (DE miRNA) were predicted by the miRWalk database. Small-molecule drugs were predicted on connectivity map.

RESULTS

A total of 181 differentially expressed genes and 35 DE miRNAs were confirmed. The protein-protein interaction network including all integrated differentially expressed genes was constructed, and 4 modules were filtrated. The module genes were relative to immune, inflammatory and oxidative stress functions according to a pathway analysis. The top 20 key genes were screened. Among the DE miRNAs found to be regulating key genes, miR-194-3p, MiR-502-5p, MiR-5088-5p, MiR-3127-5p, and miR-23a-5p might be the most significant due to their high number of connecting nodes in COPD. In addition, cephaeline, emetine, gabapentin, and amrinone were found to be potential drugs to treat COPD patients.

CONCLUSION

Our study suggests that miR-194-3p, miR-502-5p, and miR-23a-5p might participate in the nosogenesis of COPD. In addition, 4 potential small-molecule drugs were considered potentially useful for treating COPD patients.

Identifying miRNA Modules and Related Pathways of Chronic Obstructive Pulmonary Disease Associated Emphysema by Weighted Gene Co-Expression Network Analysis

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a heterogeneous chronic inflammatory disease characterized by progressive airflow limitation that causes high morbidity and mortality. MicroRNA, a short-chain noncoding RNA, regulates gene expression at the transcriptional level. microRNA modules with a role in the pathogenesis of COPD may serve as COPD biomarkers.

METHODS

We downloaded the GSE33336 microarray data set from the Gene Expression Omnibus (GEO) database, the data are derived from 29 lung samples of patients with emphysema undergoing curative resection for lung cancer. We used weighted gene co-expression network analysis (WGCNA) to construct co-expression modules and detect trait-related microRNA modules. We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to predict the biological function of the interest modules, and we screened out candidate hub microRNAs based on their module membership (MM) value and top proteins on the results of the protein-protein interaction (PPI) network.

RESULTS

Three microRNA modules (royal blue, light yellow and grey60) were highly associated with COPD. Axon guidance, proteoglycans in cancer and mitogen-activated protein kinases (MAPK) signaling pathway were common pathways in these three modules. Keratin18 (KRT18) was the top protein in our study. miR-452, miR-149, miR-133a, miR-181a and miR-421 in hub microRNAs may play a role in COPD.

CONCLUSION

These findings provide evidence for the role of miRNAs in COPD and identify biomarker candidates.

Verification of the role of spiperone in the treatment of COPD through bioinformatics analysis

BACKGROUND

Aim of this study is investigates the influence of spiperone on hydrolase activity pathway in chronic obstructive pulmonary disease (COPD).

PATIENTS AND METHODS

Differentially expressed genes (DEGs) were calculated by the limma package from microarray data GSE20257, and analysed via gene set enrichment analysis (GSEA) for identifying COPD related pathways. The regulation of hydrolase activity pathway related drugs was predicted by connectivity Map analysis (CMap). Western blotting and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to investigate the effect of spiperone on regulation of hydrolase activity pathway in vitro experiment.

RESULTS

A total of 378 DEGs were identified by the limma package. GSEA suggested that the regulation of hydrolase activity pathway was involved in the development of COPD. CMap of hub genes of regulation of hydrolase activity pathwayshown the most significant compound was spiperone. Results of vitro experiment verify that cigarette smoke extract (CSE) can increase the expression of fibronectin 1 (FN1) and epidermal growth factor (EGF), coinsided with decrease the expression of chemokine (C-X3-C motif) ligand 1 (CX3CL1), chemokoine (C-C motif) ligand 20 (CCL20), complement component 3 (C3) and slithomolog 2 (SLIT2) in BESA-2B cells and U937 cells. Spiperone can reverse the effect of CSE in BESA-2B cells and U937 cells.

CONCLUSION

Regulation of hydrolase activity pathway was involved in the occurrence of COPD, spiperone was a potential drug for the treatment of COPD by affecting the regulation of hydrolase activity pathway. This study had provided new insights into the potential pathogenesis and treatment of COPD.

Identification of ROBO2 as a Potential Locus Associated with Inhaled Corticosteroid Response in Childhood Asthma

Inhaled corticosteroids (ICS) are the most common asthma controller medication. An important contribution of genetic factors in ICS response has been evidenced. Here, we aimed to identify novel genetic markers involved in ICS response in asthma. A genome-wide association study (GWAS) of the change in lung function after 6 weeks of ICS treatment was performed in 166 asthma patients from the SLOVENIA study. Patients with an improvement in lung function ≥8% were considered as ICS responders. Suggestively associated variants (p-value ≤ 5 × 10⁻⁶) were evaluated in an independent study (n = 175). Validation of the association with asthma exacerbations despite ICS use was attempted in European (n = 2681) and admixed (n = 1347) populations. Variants previously associated with ICS response were also assessed for replication. As a result, the SNP rs1166980 from the ROBO2 gene was suggestively associated with the change in lung function (OR for G allele: 7.01, 95% CI: 3.29–14.93, p = 4.61 × 10⁻⁷), although this was not validated in CAMP. ROBO2 showed gene-level evidence of replication with asthma exacerbations despite ICS use in Europeans (minimum p-value = 1.44 × 10⁻⁵), but not in admixed individuals. The association of PDE10A-T with ICS response described by a previous study was validated. This study suggests that ROBO2 could be a potential novel locus for ICS response in Europeans.

Identification of Inflammation-Related Biomarker Lp-PLA2 for Patients With COPD by Comprehensive Analysis

Purpose

Chronic obstructive pulmonary disease (COPD) is a complex and persistent lung disease and lack of biomarkers. The aim of this study is to screen and verify effective biomarkers for medical practice.

Methods

Differential expressed genes analysis and weighted co-expression network analysis were used to explore potential biomarker. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) analysis were used to explore potential mechanism. CIBERSORTx website was used to evaluate tissue-infiltrating immune cells. Enzyme-linked immunosorbent assay (ELISA) was used to assess the concentrations of the Lp-PLA2 in serum.

Results

Ten genes were selected via combined DEGs and WGCNA. Furthermore, PLA2G7 was choose based on validation from independent datasets. Immune infiltrate and enrichment analysis suggest PLA2G7 may regulate immune pathway via macrophages. Next, Lp-PLA2(coded by PLA2G7 gene) level was upregulated in COPD patients, increased along with The Global Average of COPD (GOLD) stage. In additional, Lp-PLA2 level was significant correlate with FEV1/FVC, BMI, FFMI, CAT score, mMRC score and 6MWD of COPD patients. Finally, the predictive efficiency of Lp-PLA2 level (AUC:0.796) and derived nomogram model (AUC:0.884) in exercise tolerance was notably superior to that of the sit-to-stand test and traditional clinical features.

Conclusion

Lp-PLA2 is a promising biomarker for COPD patients and is suitable for assessing exercise tolerance in clinical practice.

Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples

Chronic obstructive pulmonary disease (COPD) kills over three million people worldwide every year. Despite its high global impact, the knowledge about the underlying molecular mechanisms is still limited. In this study, we aimed to extend the available knowledge by identifying a small set of COPD-associated genes. We analysed different publicly available gene expression datasets containing whole lung tissue (WLT) and airway epithelium (AE) samples from over 400 human subjects for differentially expressed genes (DEGs). We reduced the resulting sets of 436 and 663 DEGs using a novel computational approach that utilises a random depth-first search to identify genes which improve the distinction between COPD patients and controls along the first principle component of the data. Our method identified small sets of 10 and 15 genes in the WLT and AE, respectively. These sets of genes significantly (p < 10^–20) distinguish COPD patients from controls with high fidelity. The final sets revealed novel genes like cysteine rich protein 1 (CRIP1) or secretoglobin family 3A member 2 (SCGB3A2) that may underlie fundamental molecular mechanisms of COPD in these tissues.

Pre-adolescence DNA methylation is associated with lung function trajectories from pre-adolescence to adulthood

BACKGROUND

The pattern of lung function development from pre-adolescence to adulthood plays a significant role in the pathogenesis of respiratory diseases. Inconsistent findings in genetic studies on lung function trajectories, the importance of DNA methylation (DNA-M), and the critical role of adolescence in lung function development motivated the present study of pre-adolescent DNA-M with lung function trajectories. This study investigated epigenome-wide associations of DNA-M at cytosine-phosphate-guanine dinucleotide sites (CpGs) at childhood with lung function trajectories from childhood to young adulthood.

METHODS

DNA-M was measured in peripheral blood at age 10 years in the Isle of Wight (IOW) birth cohort. Spirometry was conducted at ages 10, 18, and 26 years. A training/testing-based method was used to screen CpGs. Multivariable logistic regressions were applied to assess the association of DNA-M with lung function trajectories from pre-adolescence to adulthood. To detect differentially methylated regions (DMRs) among CpGs, DMR enrichment analysis was conducted. Findings were further tested in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Pathway analyses were performed on the mapped genes of the identified CpGs and DMRs. Biological relevance of the identified CpGs was assessed with gene expression. All analyses were stratified by sex.

RESULTS

High and low trajectories of FVC, FEV1, and FEV1/FVC in each sex were identified. At PBonferroni < 0.05, DNA-M at 96 distinct CpGs (41 in males) showed associations with FVC, FEV1, and FEV1/FVC trajectories in IOW cohort. These 95 CpGs (cg24000797 was disqualified) were further tested in ALSPAC; 44 CpGs (19 in males) of these 95 showed the same directions of association as in the IOW cohort; and three CpGs (two in males) were replicated. DNA-M at two and four CpGs showed significant associations with the corresponding gene expression in males and females, respectively. At PFDR < 0.05, 23 and 10 DMRs were identified in males and females, respectively. Pathways were identified; some of those were linked to lung function and chronic obstructive lung diseases.

CONCLUSION

The identified CpGs at pre-adolescence have the potential to serve as candidate markers for lung function trajectory prediction and chronic lung diseases.

PTPLAD2 and USP49 Involved in the Pathogenesis of Smoke-Induced COPD by Integrative Bioinformatics Analysis

Purpose

Chronic obstructive pulmonary disease (COPD) is a typical chronic disease, but its molecular pathogenesis remains unclear. This study aimed to investigate the expression of biomarkers during COPD development.

Methods

Markers significantly associated with COPD were screened using bioinformatics tools. qRT-PCR and Western blot were used to explore the expression of PTPLAD2 and USP49 in BEAS-2B cells. CCK-8 assay was used to determine the influence of PTPLAD2 and USP49 in BEAS-2B on cell proliferation.

Results

In this study, 86 DEGs were identified in GSE76925. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that the phosphoinositide 3-kinase-Akt signaling pathway, ECM-receptor interaction, mRNA process, and viral transcription were all involved in the development of COPD. In addition, 14 hub genes were identified by WGCNA. PTPLAD2 and USP49 shared DEGs and hub genes and their expression levels were significantly reduced after CSE-treatment in BEAS-2B cells.

Conclusion

Our results suggest that PTPLAD2 and USP49 may be useful biomarkers of COPD.

Identification and validation of smoking-related genes in lung adenocarcinoma using an in vitro carcinogenesis model and bioinformatics analysis

Background

Lung cancer is one of the most common carcinomas in the world, and lung adenocarcinoma (LUAD) is the most lethal and most common subtype of lung cancer. Cigarette smoking is the most leading risk factor of lung cancer, but it is still unclear how normal lung cells become cancerous in cigarette smokers. This study aims to identify potential smoking-related biomarkers associated with the progression and prognosis of LUAD, as well as their regulation mechanism using an in vitro carcinogenesis model and bioinformatics analysis.

Results

Based on the integration analysis of four Gene Expression Omnibus (GEO) datasets and our mRNA sequencing analysis, 2 up-regulated and 11 down-regulated genes were identified in both S30 cells and LUAD. By analyzing the LUAD dataset in The Cancer Gene Analysis (TCGA) database, 3 of the 13 genes, viz., glycophorin C (GYPC), NME/NM23 nucleoside diphosphate kinase 1 (NME1) and slit guidance ligand 2 (SLIT2), were found to be significantly correlated with LUAD patients’ smoking history. The expression levels of GYPC, NME1 and SLIT2 in S30 cells and lung cancer cell lines were validated by quantitative PCR, immunofluorescence, and western blot assays. Besides, these three genes are associated with tumor invasion depth, and elevated expression of NME1 was correlated with lymph node metastasis. The enrichment analysis suggested that these genes were highly correlated to tumorigenesis and metastasis-related biological processes and pathways. Moreover, the increased expression levels of GYPC and SLIT2, as well as decreased expression of NME1 were associated with a favorable prognosis in LUAD patients. Furthermore, based on the multi-omics data in the TCGA database, these genes were found to be regulated by DNA methylation.

Conclusion

In conclusion, our observations indicated that the differential expression of GYPC, NME1 and SLIT2 may be regulated by DNA methylation, and they are associated with cigarette smoke-induced LUAD, as well as serve as prognostic factors in LUAD patients.