Background

Lung cancer is one of the most common carcinomas in the world. In 2018, the number of patients newly diagnosed with lung cancer across the globe was 2.09 million, and around 1.76 million patients will die from the disease [1]. Although early diagnosis and treatment of lung cancer have made significant progress, the 5-year relative overall survival (OS) is less than 20% [2]. Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), and NSCLC accounts for approximately 85% of all lung cancer cases [3]. There is a significant and positive correlation between cigarette smoke and lung cancer, and the risk of developing lung cancer in smokers is nearly 10 times higher than that in non-smokers [4, 5]. Nonetheless, it is still unclear how normal lung cells become cancerous in cigarette smokers.

The development of high-throughput sequencing technology has made it possible to identify changes in single bases within the coding sequences of specific genes during lung tumorigenesis. There are plenty of publicly available cancer multi-omic data that we can obtain free from The Cancer Gene Atlas (TCGA; http://cancergenome.nih.gov/) and Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). A meticulous and thorough analysis of these data can identify genes and signaling pathways crucial to lung cancer, which will help for a better understanding of the mechanisms of cancer occurrence and development.

Based on the gene expression profiles, recent studies have identified several genes associated with lung cancer. Spira et al observed that CYP1B1, NEK2 and CENPF were significantly correlated with LUAD [6]. Liu et al suggested that EPHA4, FGFR2, and EGFR may be strongly associated with the development and progression of smoking-related LUAD [7]. Landi et al demonstrated that elevated mRNA levels of NEK2 and TTK have the potential to increase the risk of mortality from smoking-related LUAD [8]. Also, numerous genomic and transcriptional alterations in LUAD appeared to be associated with the patient’s smoking history [9]. However, there is still a shortage of reliable biomarkers for smoking-related LUAD.

In this study, we aimed to identify novel biomarkers for LUAD in smokers. The workflow of our study is presented in Fig. 1. An in vitro carcinogenesis model was established by exposing BEAS-2B cells to cigarette smoke continuously for 30 passages (S30). In the present study, candidate genes were obtained by integrative analysis of differentially expressed genes (DEGs) according to databases and our mRNA sequencing data. Among these, the smoking-related genes observed in S30 cells and LUAD were further validated by quantitative PCR (qPCR), immunofluorescence assays (IF), and western blotting (WB), and analyzed for a possible association with cancer-related pathways and prognosis. Furthermore, the multi-omics data in the TCGA database were used to explore the regulatory mechanisms of these three genes.

Fig. 1
figure 1

Workflow for identification of smoking-related genes in malignant transformation cells and LUAD. LUAD lung adenocarcinoma

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Results

Differentially expressed genes in S30 cells and GEO datasets

Based on the high throughput analysis, a total of 753 differentially expressed genes (DEGs) were identified in cigarette smoke-induced transformed cells (S30) compared with unexposed BEAS-2B cells, including 273 up-regulated and 480 down-regulated genes (Fig. 2a, b). Besides, DEGs in LUAD tissues were screened out from four GEO datasets by differential expression analysis (Fig. 2c–f). Based on the integration analysis, 209 down-regulated genes and 25 up-regulated genes were identified in the GEO datasets (Fig. 2g and Additional file 1: Table S2). A total of 11 down-regulated and 2 up-regulated smoking-related genes were identified by taking the intersection of the DEGs extracted from S30 cells and GEO datasets (Fig. 2f).

Fig. 2
figure 2

Identification of smoking-related genes in lung cancer. a A volcano plot was generated to visualize the distribution of DEGs. b Counts of upregulated or downregulated mRNAs. Volcano plots were generated to visualize the distribution of DEGs between LUAD tissues and adjacent normal tissues from different study cohorts, including GSE27262 (c), GSE19804 (d), GSE19188 (e) and GSE76760 (f). The X-axis of volcano plot indicates the fold change (FC, log-scaled), whereas the Y-axis shows the p-values (log-scaled). Each dot represents a different gene, and the red/green color of the dots categorizes the up-regulated/down-regulated genes under the filtering condition. g Heatmap of DEGs derived from integrated analysis. Each column represents one dataset and each row represents one gene; the gradual color ranged from green to red represents the changing process from down-regulation to up-regulation. h Venn diagram showing the overlap of identified DEGs from GEO datasets and cigarette smoke-induced malignant-transformation-cell model

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Identification of smoking-related genes in lung cancer

Further analysis indicated that 7 of the 13 genes are associated with smoking history (p < 0.05) (Additional file 1: Table S3). Notably, the NME1 expression level in current smokers and reformed smoker for \(\le \)15 years was significantly higher compared to life-long non-smokers (p < 0.01). Conversely, the expression levels of SLIT2 and GYPC in current smokers were markedly lower than those in life-long non-smokers (p < 0.01) (Table 1). As illustrated in Fig. 3, these findings were further validated in two GEO datasets (GSE13213 and GSE41271) with a smoking history.

Fig. 3
figure 3

Validation of the association between mRNA expressions with smoking history in GEO datasets. a GYPC, NME and SLIT2 expression levels were significantly different in smokers vs. non-smokers in GSE13213 dataset. b NME and SLIT2 expression levels were significantly different in smokers vs. non-smokers in GSE41271 dataset. b The three genes expression levels showed no significantly different in smokers vs. non-smokers in GSE41271 dataset

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Table 1 Association of mRNA expression with LUAD patient tobacco smoking history in the TCGA database
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Validation of mRNA and protein expression in S30 cells and lung cancer cell lines

The mRNA expression levels of GYPC and SLIT2 were found to be dependent on smoke-exposure time and were significantly down-regulated in S30 cells (Fig. 4a, c). On the other hand, the NME1 expression level was significantly up-regulated in S30 cells (Fig. 4b). Compared with normal BEAS-2B cells, the expression levels of GYPC and SLIT2 in four human lung adenocarcinoma cell lines (PC9, A549, H1975 and H1299) were up-regulated, while NME1 expression was down-regulated (Fig. 4d). The protein expression levels of GYPC, NME1 and SLIT2 were further validated. Immunofluorescence staining showed that NME1 protein expression was increased in S30 cells compared with normal BEAS-2B cells, while GYPC and SLIT2 expression was decreased (Fig. 4e–g). Western Blot analysis further confirmed the downregulation of GYPC and SLIT2 and the upregulation of NME1in cigarette smoke-exposed S30 cells compared with unexposed BEAS-2B cells (Fig. 4h).

Fig. 4
figure 4

Validation of mRNA and protein expression levels of the three genes. The mRNA expression levels of GYPC (a), NME1 (b) and SLIT2 (c) in cigarette smoke-exposed cells. 2B, normal BEAS-2B cells serve as a control. S5 to S30, BEAS-2B cells exposed to cigarette smoke for different passages. d Heatmap of GYPC, NME1 and SLIT2 in BEAS-2B and four LUAD cell lines. Each column represents one cell line, and each row represents one gene; the gradual color ranged from blue to red represents the changing process from down-regulation to up-regulation. e Immunofluorescence staining of GYPC. f Immunofluorescence staining of NME1. g Immunofluorescence staining of SLIT2. h Western blotting results. S5 to S30, BEAS-2B cells exposed to cigarette smoke for different passages.*p < 0.05, versus normal BEAS-2B cells; **p < 0.01, versus normal BEAS-2B cells

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Association of mRNA expression with pathological characteristics

We further investigated the association between the expression levels of the three genes (NME1, SLIT2 and GYPC) and pathological features (Table 2). For invasion depth, the expression levels of GYPC and SLIT2 were significantly decreased in LUAD tissues at the T2 stage compared with T1 stage tissues, while the expression level of NME1 significantly increased. When lymph node metastasis was considered, the mRNA expression level of NME1 was greater in N1 and N2 vs. N0. Besides, the mRNA expression level of NME1 was upregulated in TNM stage III vs. stage I. It is worth mentioning that the expression levels of GYPC and NME1 were significantly different between males and females, and NME1 and SLIT2 were substantially different between elder patients (\(\ge \)60 years old) and patients aged less than 60 years old.

Table 2 Association of mRNA expression levels of GYPC, NME1 and SLIT2 with the pathological features
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Gene Ontology enrichment analysis

Based on the UALCAN online tool, a total of 1182, 1771 and 1822 genes significantly correlated with GYPC, NME1 and SLIT2 were extracted respectively. Gene ontology enrichment analysis was performed to demonstrate the potential biological functions of these related genes using DAVID. The results showed that these three genes were functionally associated with several critical biological processes. For GYPC, the genes co-expressed with it were remarkably enriched in apoptotic signaling pathway and extracellular matrix organization, as well as cell adhesion Fig. 5a). When NME1 was considered, the related genes were found to be enriched in cell proliferation, DNA repair and cell cycle, as well as Wnt signaling pathway Fig. 5d). Besides, the genes correlated with SLIT2 were significantly associated with extracellular matrix organization, JAK-STAT cascade, and cell adhesion Fig. 5g). In addition, GSEA enrichment analysis confirmed the three genes signatures, including calcium mediated signaling and regulation of cell-cell adhesion for GYPC Fig. 5b, c), RNA catabolic process and regulation of cell cycle phase transition for NME1 Fig. 5e, f), as well as cell matrix adhesion and TGF-\(\beta \) receptor signaling pathway for SLIT2 Fig. 5h, i).

Fig. 5
figure 5

The GO enrichment results of GYPC, NME1 and SLIT2. aThe bubble chart shows the GO terms related to GYPC; GSEA used to validate the gene signatures of GYPC, including (b) calcium mediated signaling and c regulation of cell-cell adhesion. d The bubble chart shows the GO terms related to NME1; GSEA used to validate the gene signatures of NME1, including e RNA catabolic process and f regulation of cell cycle phase transition. g The bubble chart shows the GO terms related to SLIT2; GSEA used to validate the gene signatures of SLIT2, including (H) cell matrix adhesion and i TGF-\(\beta \) receptor signaling pathway

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KEGG pathway enrichment analysis

To better illustrate the functional role of these three genes in lung cancer, KEGG pathway analysis was performed for the related genes using DAVID online tool. The results showed that these three genes were involved in multiple pathways in lung cancer. When considering GYPC, the related genes were involved in JAK-STAT, PI3K-Akt, and Ras/Rap1 signaling pathways, as well as focal adhesion and cell adhesion molecules (Fig. 6a). Besides, genes related to NME1 were found to be enriched in base excision repair, mismatch repair, and cell cycle (Fig. 6d). Similarly, the SLIT2 related genes were significantly associated with TGF-\(\beta \) receptor, VEGF, MAPK and JAK-STAT signaling pathways, as well as lung small lung cancer and focal adhesion (Fig. 6g). The further GSEA enrichment confirmed these pathway signatures of GYPC (Fig. 6b, c), NME1 (Fig. 6e, f), and SLIT2 (Fig. 6h, i).

Fig. 6
figure 6

The KEGG enrichment results of GYPC, NME1 and SLIT2. aThe bubble chart shows the KEGG pathways related to GYPC; GSEA used to validate the gene signatures of GYPC, including b JAK-STAT signaling pathway and c cell molecular adhesion. d The bubble chart shows the KEGG pathways related to NME1; GSEA used to validate the gene signatures of NME1, including e cell cycle and f base excision repair. g The bubble chart shows the GO terms related to SLIT2; GSEA used to validate the gene signatures of SLIT2, including h focal adhesion and i TGF-\(\beta \) signaling pathway

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Survival analysis

A total of 7 data cohorts, including 1221 LUAD patients, were used to establish univariate Cox models. The univariate Cox model analyses of TCGA dataset suggested that higher GYPC expression has a favorite prognosis (HR < 1, p < 0.05) (Fig. 7a); The analysis in GSE13213 and GSE30219 datasets revealed that higher NME1 expression was a risk factor for LUAD patients prognosis (HR < 1, p < 0.05) (Fig. 7b). In addition, the analysis in GSE13213 and GSE41271 indicated that increased SLIT2 expression is associated with a better prognosis (HR < 1, p < 0.05) (Fig. 7c). The KM survival analysis is consistent with the univariate Cox analysis (Fig. 8).

Fig. 7
figure 7

Forest plots based on univariate survival analysis in patients with LUAD. Univariate COX results of GYPC (a), NME1 (b) and SLIT2 (c) in patients with LUAD. LUAD lung adenocarcinoma, HR hazard ratio, CI confidence interval, TCGA The Cancer Genome Atlas

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Fig. 8
figure 8

Kaplan–Meier survival analysis of LUAD patients by GYPC, NME1 and SLIT2 expression in different datasets. a Kaplan–Meier survival curve of overall survival (OS) based on GYPC expression in TCGA-LUAD dataset; b Kaplan–Meier survival curve of OS based on NME1 expression in GSE13213 dataset; c Kaplan–Meier survival curve of OS based on NME1 expression in GSE30219 dataset; d Kaplan–Meier survival curve of OS based on SLIT2 expression in GSE13213 dataset; e Kaplan–Meier survival curve of OS based on SLIT2 expression in GSE41271 dataset

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Gene dysregulation is mediated by methylation and gene amplification in lung cancer

To understand possible regulation mechanisms of dysregulation of these three genes, we analyzed the public multi-omics datasets in the TCGA database. Based on the UALCAN online tool, the promoter regions of GYPC and SLIT2 were found hyper-methylated significantly, as well as the NME1 promoter was hypo-methylated (Fig. 9a–c). Also, further person correlation analysis suggested that these three genes mRNA expression levels were remarkedly negatively correlated with promoter methylation levels (r = − 0.455, − 0.208 and − 0.263, all p < 0.001, Fig. 9d–f). Besides, the up-regulated NME1 expression was found significantly positively associated with increased gene amplification (r = 0.349, p < 0.001, Fig. 10b, e). Unexpectedly, the expression of GYPC was found negatively correlated to gene amplification (r = −0.147, p < 0.001, Fig. 10a, d). However, there is no significant correlation between SLIT2 expression and gene amplification (r = −0.010, p = 0.822, (Fig. 10c, f).

Fig. 9
figure 9

Correlation analysis between promoter methylation and mRNA expression levels in lung cancer. The promoter methylation levels of these three genes obtained from UALCAN online tool, including GYPC (a), NME1 (b), and SLIT2 (c). Pearson correlation analysis shows a significant positive correlation between gene expression and promoter methylation levels in LUAD, including GYPC (d), NME1 (e), and SLIT2 (f), the red line represents linear regression of data

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Fig. 10
figure 10

Correlation analysis between gene copy numbers and mRNA expression levels in lung cancer. Gene expression levels of GYPC (a), NME1 (b), and SLIT2 (c) in LUAD tissues with different amplification states. Pearson correlation analysis shows a significant positive correlation between gene expression levels and copy numbers in LUAD, including GYPC (d), NME1 (e), and SLIT2 (f), the red line represents linear regression of data

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Discussion

Cigarette smoking is the primary risk factor for lung cancer development, and it is a significant contributor to the morbidity and mortality of LUAD patients [10, 11]. Recent studies have shown that histologically similar lung tumors have different molecular mechanisms of carcinogenesis because of different smoking status [12]. Thus, the mechanism of lung cancer in smokers and non-smokers needs to be assessed differently.

With the rapid development of sequencing and data analysis technologies, some essential genes related to LUAD have been identified through bioinformatics analysis. For example, SPP1 has been identified as a prognostic biomarker in four LUAD datasets in the GEO database, which was also validated by the TCGA database [13]. Fan et al suggested 12 significant biomarkers that could distinguish lung cancer patients with different risks from the GEO database [14]. Gan et al identified the aberrantly expressed miR-375 gene involved in LUAD through the comparison of miRNA expression profiles in cancerous tissues based on the analysis and validation from TCGA and GEO datasets and published studies[15]. In the present study, three smoking-related signature genes, namely GYPC, NME1 and SLIT2, were identified by an integrated analysis on the LUAD datasets in the GEO database and the high throughput sequencing data of cigarette smoke-induced malignant transformed BEAS-2B cells. In particular, NME1 was reported increased by cigarette smoking in oral squamous cell carcinoma (OSCC) [16], and the lower expression of SLIT2 were found in the lungs of cigarette smoke-induced emphysema mice [17].

Glycophorin C (GYPC) is an integral membrane glycoprotein. A recent study suggested GYPC can be used as a biomarker of breast cancer [18]. Increased GYPC gene expression was also reported to correlate with a worse outcome in childhood acute lymphoblastic leukemia [19]. However, there is limited evidence for the function of GYPC in the majority of solid tumors, especially in lung cancer. Our present study suggested that increased expression of GYPC was associated with a favorable outcome in LUAD patients. GSEA enrichment analysis indicated that GYPC was significantly associated with the JAK/STAT and cell adhesion signaling pathways which are essential to lung cancer progression and migration.

The NME/NM23 nucleoside diphosphate kinase 1 (NM23-H1, NME1) is a metastasis-associated gene the expression of which was correlated with various tumors. The expression level of NME1 was found to be inversely proportional to the metastasis potential of several cancers, including breast cancer [20], gastric cancer [21], melanoma [22] and colon adenocarcinoma [23]. These results indicate that NME1 may act as a metastasis suppressor in these tumors. More importantly, elevated expression of NME1 has a better prognosis outcome in patients with breast cancer [24] and melanoma [25]. Instead, higher NME1 expression was significantly associated with poor prognosis in patients with neuroblastoma [26] and osteosarcoma [27], as well as cervical cancer [28]. In this study, increased mRNA expression of NME1 was found to be related to LUAD invasion depth and lymph node metastasis. It is noteworthy that decreased expression of NME1 was associated with an improved prognosis in LUAD patients. Studies have shown that lymphatic metastasis is directly associated with distant recurrence and poor overall survival (OS) in non-small cell lung cancer patients [29]. We further found that NME1 was involved in the cell cycle pathway, and the defect of cell cycle regulation has been reported to contribute to uncontrolled cancer cell proliferation [30]. Thus, we suggested that NME1 plays a different role in LUAD than many other cancers, and it may serve as a potential biomarker for LUAD.

SLIT2, a secreted glycoprotein of the SLIT family [31], is involved in the epithelial-mesenchymal transition (EMT) process [32], which permits cancer cells to acquire migratory, invasive, and stem-like properties [33]. The knockdown of the SLIT2 gene promoted the growth of gastric cancer cells and metastasis through activation of the AKT/\(\beta \)-catenin-mediated signaling pathway [34]. Another study demonstrated that decreased expression of SLIT2 is associated with a poor prognosis and brain-specific metastasis in breast cancer patients [35]. The results in the present study showed that SLIT2 was down-regulated in cigarette smoke-exposed cells and LUAD, and increased expression of SLIT2 was associated with a better outcome in LUAD patients. Furthermore, SLIT2 was significantly enriched in the TGF-\(\beta \) signaling and focal adhesion pathways by enrichment analysis. Since both signaling pathways contribute to EMT activation, we suggested that SLIT2 might serve as a potential tumor metastasis indicator.

The study of epigenetics provides an important clue for understanding the genesis and development of lung cancer [36]. Recent studies suggested that during the genesis of lung cancer, the promoter methylation levels of genes associated with multiple cellular functions are increased [37]. Our analysis determined that these genes expression levels were significantly negatively correlated with the promoter methylation status, which indicated that they may be gnomically regulated by DNA methylation. Besides, we analyzed the correlation between gene expression levels and copy numbers, which is another regulation mechanism at genome level. Copy number variation (CNV) is generally considered to be any genomic alteration greater than 50 base pairs in length [38], and it has been shown to play an essential role in human cancer. The loss of tumor suppressor genes and the gain of proto-oncogenes can contribute to cancer development [39, 40]. In this study, only NME1 was found to have a significant positive correlation among expression and copy numbers, indicating that NME1 was regulated not only by promoter methylation, but by copy numbers.

Conclusions

In conclusion, our results indicated that GYPC, NME1 and SLIT2 may play a vital role in the development of smoking-related LUAD, which will be helpful in predicting the prognosis of LUAD patients. Mechanically, these three genes may be regulated by DNA methylation. Further in vitro and in vivo studies are needed to gain insights into the underlying molecular mechanisms of these three genes in LUAD.

Methods

BEAS-2B cell culture and cigarette smoke exposure

Human bronchial epithelial cells (BEAS-2B) and human LUAD cell lines (PC9, A549, H1975 and H1299) were purchased from the American Type Culture Collection (ATCC, USA). They were maintained in basal LHC-8 nutrient medium (Thermo Fisher Scientific, Waltham, MA, USA) or Dulbecco’s Modified Eagle Medium (DMEM, Invitrogen, MA, USA) in an incubator maintaining at 37 °C and humidified atmosphere of 5% CO\(_{2}\). The in vitro model for malignant transformation was established by exposing BEAS-2B cells to cigarette smoke continuously for 5, 10, 20 and 30 passages (S5, S10, S20 and S30, separately), and this has been described in detail previously [41, 42].

RNA isolation and high-throughput sequencing

Total RNA was isolated from normal BEAS-2B and S30 cells with TRIzol RNA isolation reagent (Invitrogen, MA, USA) according to the manufacturer’s protocol. Three biological replicates per group were used for mRNA sequencing analysis. A total of 1.5 \(\mu \)g RNA per sample was used as input material for the RNA library construction. The mRNA sequencing procedure has been described in detail in our previous article, and the raw data has been deposited in the Sequence Read Archive (SRA) database (https://trace.ncbi.nlm.nih.gov/Traces/sra/) with identifier SRP181756 [43]. Genes were computed by summing the fragments per kilo-base of exon per million fragments mapped (FPKM) of transcripts in each gene group.

Human database extraction

Four datasets, GSE27262 [44], GSE19188 [45], GSE76760 [46] and GSE19804 [47], were downloaded from the Gene Expression Omnibus (GEO) database for differential expression analysis (Additional file 1: Table S1). All datasets met the following two criteria: (1) tissue samples obtained from human LUAD and adjacent normal tissues; and (2) each set included at least 50 samples. An additional six other datasets with survival information were downloaded for survival analysis (Additional file 1: Table S1). In these datasets, only LUAD and normal samples were retrieved and analyzed. The RNA-seq by the expected maximization (RSEM) data and the corresponding clinical information of LUAD in The Cancer Gene Atlas (TCGA) database were obtained from Xena (https://xena.ucsc.edu). The RSEM gene expression measurements for LUAD cases were transformed by using log\(_{2}\) (RSEM + 1).

Differential expression and integration analysis

For our RNA sequencing data, differential expression analysis of normal BEAS-2B and S30 cells was performed using the “DESeq2” R package [48]. For GEO and TCGA datasets, the “Limma” package was subsequently employed for identifying differentially expressed genes (DEGs) in each dataset [49]. |log2FC\(|>\)1 and a p-value < 0.05 were considered statistically significant for the DEGs. Gene integration for the DEGs identified from the four datasets was conducted using another R package “RobustRankAggreg” [50]. The expression levels of integrated genes in four GEO datasets were visualized with the R package “pheatmap” (https://cran.r-project.org/web/packages/pheatmap/index.html). Furthermore, a Venn diagram was generated by the “VennDiagram” R package (https://cran.r-project.org/web/packages/VennDiagram/index.html) to visualize the genes with the consistent change in S30 cells and LUAD samples.

Real-time quantitative PCR

The total RNA of cells (including Beas-2B, S5, S10, S20 and S30 cells, as well as 4 LUAD cell lines) was isolated using TRIzol reagent (Invitrogen, MA, USA) according to the manufacturer’s protocol. A total amount of 1.5 \(\mu \)g of total RNA from each sample was reversely transcribed into complementary DNA (cDNA) using Revert Aid First Strand Complementary DNA Synthesis Kit (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. Quantitative PCR (qPCR) was performed using NovoScript\(^{\textregistered }\)SYBR Two-Step qRT-PCR Kit (novoprotein, China) on QuantStudioTM 6 Flex qRT-PCR system (Applied Biosystems, Foster City, CA, USA). GAPDH was used as a reference. The primer pairs used for qPCR in this study were listed in Table 3.

Table 3 Primers used in this study
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Immunofluorescence analysis

The S30 and unexposed BEAS-2B cells were fixed in PBS containing 4.0% paraformaldehyde without methanol. The cells were washed and permeabilized with 0.2% Triton X-100 and blocked with 5% goat serum for 1 hour at room temperature. Diluted antibodies for human NME1 (11086-2-AP, Proteintech, Chicago, IL, USA), SLIT2 (20217-1-AP, Proteintech, Chicago, IL, USA) or GYPC (ab108619, Abcam, Cambridge, MA, USA) were added drop by drop and the slides were kept in a wet box at 4 °C overnight. Following incubated with FITC-conjugated goat anti-rabbit IgG for 1 hour at room temperature, the slides were washed, and the nuclei were counter-stained with 4,6-diamidino-2-phenylindole (DAPI). Photographs were taken and visualized using an FV1200MPE multiphoton laser scanning microscope (FV1200, OLYMPUS, Japan). The acquisition parameters were held constant for all the experiments.

Western blot analysis

Total protein was extracted with RIPA buffer, and 20 μg of extracted total proteins were separated on SDS-PAGE gel and transferred onto a PVDF membrane (Millipore, Billerica, MA). After blocking with 5% bovine serum album (BSA, solarbio, China), the membrane was incubated at 4 °C overnight with various primary antibodies, including NME1, SLIT2 and GYPC. The HRP-labeled secondary antibody was used according to the host species of the primary antibody. Western blots were developed using electrochemiluminescence (ECL) substrate and visualized using the GeneTools GBox (Syngene, Frederick, MD, USA) system. The intensity of each spot was quantified using NIH ImageJ software (NIH, Bethesda, MD, USA).

Analysis of patient smoking and pathological features

The mRNA expression levels of integrated genes in LUAD patients with different smoking histories were examined to identify the genes related to smoking. All samples in the TCGA LUAD and GEO datasets were divided into different groups according to the patient’s clinical features, including gender, age and invasion depth, as well as the status of lymph node metastasis, distant metastasis and tumor-node-metastasis (TNM) stage to find an association with mRNA expression levels.

Enrichment analysis

To investigate the potential biological function of these three smoking-related genes, genes related to them were extracted from UALCAN (http://ualcan.path.uab.edu/index.html) with the Pearson coefficient \(\ge \) 0.3 [51]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID online tool (https://david.ncifcrf.gov/). Besides, the gene set enrichment analysis (GSEA, http://software.broadinstitute.org/gsea/index.jsp)[52] was used to verify the biological processes and KEGG pathways related to these genes. For the GSEA parameters, “1000”, “gene_set”, “weighted”, and “Pearson” were selected as “Number of permutations”, “Permutation type”, “Enrichment statistic”, and “Metric for ranking genes”, respectively.

Survival analysis

The univariate Cox regression models were used to calculate the hazard ratios (HRs) and the 95% confidence intervals (CIs) based on GYPC, NME1 and SLIT2 expression levels in LUAD patients in six GEO datasets (GSE13213, GSE26939, GSE30219, GSE41271, GSE42127 and GSE14814) and the TCGA LUAD dataset. The Cox analysis was performed using the “survival” (https://cran.r-project.org/web/packages/survival/index.html) package, and the “survminer” (https://github.com/kassambara/survminer) package was used to generate Kaplan-Meier (KM) survival curves. All LUAD patients were divided into high and low expression groups according to the median values of mRNA expression level.

Copy number and methylation analysis

To further understand the regulatory mechanisms of these three genes, we used the multi-omics data in the TCGA database for further correlation analysis. The promoter methylation levels of these three genes in TCGA LUAD dataset were performed using UALCAN . Besides, the methylation and copy number variation (CNV) data in the TCGA LUAD dataset was obtained from LinkedOmics (http://www.linkedomics.orglogin.php), and Pearson coefficient was calculated to demonstrate the relationship between them and mRNA expression levels.

Statistical analysis

In this study, heat map, survival, and differential expression analyses were performed with the R software packages. Experimental data were analyzed using GraphPad Prism 7 (GraphPad Software Inc., La Jolla, CA, USA). Data were reported as the mean ± standard deviation (SD) of three independent experiments. Data were analyzed using Student’s t-test to compare between two groups. p-values < 0.05 were considered significant.