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Introduction

Lung cancer is the leading cause of cancer-associated mortality (accounting for ~13% of all diagnosed cancers), with ~1.8 million new cases in 2012 (1). Between 2011 and 2013, the lung cancer mortality rate increased by 464.84% in China, and this continued to increase (2). Non-small cell lung cancer (NSCLC) constitutes almost 85% of all lung cancers and is usually diagnosed an advanced stage (3). Surgery followed by chemoradiotherapy is the most commonly adopted method for locally advanced NSCLC, but the efficacy is diverse across different patients (4). In previous years, immunotherapy became an optional therapeutic for numerous types of cancers, including melanoma and lung cancer, through sustained activation of the immune system (5,6). Certain immune checkpoint inhibitors have been shown to significantly improve the prognosis of a number of cancers, including cytotoxic T-lymphocyte antigen 4, and nivolumab may prolong the median survival time of patients with melanoma and NSCLC by 2–4 and 6–9 months, respectively (7). However, the morbidity and mortality rate of NSCLC remains high, and additional molecular biology studies are required for more precise treatment and earlier diagnosis.

Next generation RNA sequencing (RNA-Seq) is becoming a popular technology for the quantification of RNA, including mRNA (such as microRNA) and noncoding RNA (such as long noncoding RNA). Variation of genomic structure, including alternative splicing (AS) and single nucleotide polymorphisms, can also be detected by RNA-Seq. Compared with gene microarray, the most evident advantage of RNA-Seq is its ability to detect the expression values of low abundance as well as novel transcripts (8). Previous studies of NSCLC, based on RNA-Seq have been conducted and successfully identified numerous potential targets, involved in the progression or suppression of cancer. Riccardo et al identified the adenosine A3 receptor as a valuable target for NSCLC, based on the combination of gene fusion and differential expression analysis through RNA-Seq (9). In the previous study by Han et al, via RNA-Seq analysis, chromobox 3 and cellular retinoic acid-binding protein 2 were also considered to contribute to the progression of NSCLC (10).

Compared with differential expression analysis at the gene level, exon level analysis may improve analysis of disease stages (11). The present study aimed to identify the potential targets of NSCLC, through the combined analysis of differential expression and AS, and based on the RNA-Seq dataset from the Gene Expression Omnibus (GEO). This may be helpful for the precise treatment of NSCLC, as well as early diagnosis.

Materials and methods

RNA-Seq dataset

The RNA-Seq dataset (GSE68795) in the present study was obtained from GEO (http://www.ncbi.nlm.nih.gov/geo/), which was accumulated by Durrans et al (12). A total of 17 samples were included, containing three immature monocytic myeloid cell (IMMC) samples, two epithelial cell (Epi) samples and two neutrophil (Neu) samples from lung cancer patients, as well as three IMMC samples, three Epi samples and four Neu samples from adjacent normal lung tissues. Illumina HiSeq 2000 (Illumina, Inc., San Diego, CA, USA) was used for the sequencing process. Briefly, total RNA was extracted from flow cytometry sorted cells; TruSeq RNA Sample Preparation kit (Illumina, Inc.) was used for the preparation of cDNA libraries from 15–35 ng RNA; cDNA libraries that passed size and purity check were retained for the following sequencing. Single-end 51 bp short sequences (reads) were generated for the IMMC and Neu samples, while paired-end 102 bp reads were generated for the Epi samples, in lung cancer and adjacent normal lung tissues.

Reads mapping and assembling

Quality control of raw reads was conducted using FastQC software version 1.3, which was developed by Andrews (13), with the default parameters, i.e., reads with a quality score <10 and N >5% were discarded. The remaining reads were mapped to the UCSC genome (version GRCh37/hg19) through TopHat (2.1.0.Linux_x86_64) (14), a fast splice junction mapper for RNA-Seq reads, with no more than 2 mismatches in 25 bp segments. Cufflinks (14) (2.2.1.Linux_x86_64) was used for the assembly of the mapped reads, which allowed for the identification of novel transcripts, and fragments per kilobase of exon per million fragments mapped (FPKM) representation of gene expression value was obtained.

Differential expression analysis

Cuffdiff of Cufflinks was used to test the significance of differential expression of genes based on FPKM. Genes with fold change (FC) >2 (upregulated) or <0.5 (downregulated), and false discovery rate (FDR) adjusted for P<0.05, were considered to be differentially expressed.

Differential alternative splicing analysis

The replicate multivariate analysis of transcript splicing (rMATS) (15), developed by Shen et al, was used to screen differential alternative splicing genes across samples. The mapping results (in bam format) were submitted to rMATS. Annotation of genes (in GTF format) was obtained from the UCSC and used for the screening of known splicing sites. Finally, five main alternative splicing types, including skipping exon (SE), retention intron (RI), alternative 5′splice site (A5SS), alternative 3′splice site (A3SS) and mutually exclusive exons (MXE), which satisfied the criteria of FDR <0.1, were screened out as DAS genes.

Functional enrichment analysis

The DE and DAS genes were submitted to the Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.ncifcrf.gov/) (16) for the analysis of enrichment of gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. P<0.05 was considered to indicate a statistically significant difference for the screening of significant GO terms and pathways.

Results

RNA-Seq landscape

The average number of reads obtained from the RNA-Seq dataset was 60242792, and at least 35 million reads were prepared for every sample, as shown in Table I. The clean reads were mapped to the human genome (hg19) by TopHat and 86.50–91.90% unique mapped rates were obtained across all samples. These were used for the following analysis.

Table I. Mapping overview of all the RNA-Sequencing experiments.

Table I.

Mapping overview of all the RNA-Sequencing experiments.

Sample	All reads	Mapped reads	Mapped rates, %	Unique mapped	Unique mapped rates, %
Adj-Epi1	97717750	92115270	94.3	87061215	89.1
Adj-Epi2	63907228	58725212	91.9	55272496	86.5
Adj-Epi3	123019030	116594941	94.8	110088025	89.5
Adj-IMM1	40304691	38809145	96.3	37030002	91.9
Adj-IMM2	36685339	35109464	95.7	33284563	90.7
Adj-IMM3	44679997	42067287	94.2	40025779	89.6
Adj-Neu1	49967221	47183219	94.4	45452825	91.0
Adj-Neu2	50458780	47159700	93.5	44872985	88.9
Adj-Neu3	53402957	50511053	94.6	48090506	90.1
Adj-Neu4	52277703	49603994	94.9	47551180	91.0
Tum-Epi1	106711340	101588228	95.2	95969883	89.9
Tum-Epi2	94931814	90391800	95.2	85487063	90.1
Tum-IMM1	45344289	43038591	94.9	41037840	90.5
Tum-IMM2	42060455	40620197	96.6	38638529	91.9
Tum-IMM3	35570701	33745251	94.9	32229614	90.6
Tum-Neu1	37106424	34076868	91.8	32442881	87.4
Tum-Neu2	49981740	47119502	94.3	44980801	90.0

[i] Epi, epithelial cell; Adj, adjacent; IMM, immature monocyte; Neu, neutrophil; Tum, tumor.

Differential expression analysis

The unique mapped reads were assembled into transcripts by cufflinks, and FPKM values were obtained to quantify the mRNA expression level. Based on the criteria of FC >2 or <0.5, and FDR adjusted P<0.05, umerous genes were revealed to be differential expression in tumor samples compared with normal samples.

In Epi tumor samples, a total of 1,624 DE genes were identified, which contained 915 downregulated and 709 upregulated genes. However, in IMMC tumor samples, 1,593 DE genes (639 downregulated and 954 upregulated genes) were obtained. In Neu tumor samples, 1,672 DE genes were obtained, which consisted of 1,141 downregulated and 1,672 upregulated genes. Furthermore, as shown in Fig. 1, 111 overlapping genes were identified among those three groups of DE genes, and 91 of those overlaps were consistently upregulated or downregulated across all three types of NSCLC tumor cells.

Figure 1. Number of differential expression genes of Epi, IMMC and Neu tumor samples compared with the corresponding normal samples. Epi, epithelial cell; IMMC, immature monocytic myeloid cell; Neu, neutrophil.

AS in NSCLC tumor samples

Through AS, a mRNA precursor may result in multiple mature mRNAs with different functions. In the present study, the significant DAS genes were identified for the tumor samples compared with the normal ones through rMATS, with the threshold of FDR <0.1. Notably, alternative splicing of genes in Neu tumor samples was not found to be significantly different compared with Neu normal samples. A total of 2,130 and 1,713 DAS genes were identified in Epi and IMMC tumor samples, respectively. In total, 272 overlapping genes were screened out between the two lists of DAS genes.

A total of 5 types of AS events were identified, including A3SS, A5SS, MXE, RI and SE. As shown in Fig. 2, SE is the most prevalent AS event in Epi and IMMC tumor samples, and accounts for 84.00 and 79.28% of all the AS events in Epi and IMMC, respectively. RI is the least prevalent AS event, which only accounts for 1.64 and 2.80% of all the AS events in Epi and IMMC, respectively.

Figure 2. Distribution of different alternative splicing events in Epi and IMMC tumor samples. Epi, epithelial cell; IMMC, immature monocytic myeloid cell; MXE, mutually exclusive exons; RI, retention intron; SE, skipping exon; A3SS, alternative 3′splice site; A5SS, alternative 5′splice site.

GO and pathway analysis

Functional enrichment analysis based on the DAVID identified a number of significantly enriched GO terms and KEGG pathways in DE and DAS genes. The top five most significant GO terms of DE genes of the three groups of NSCLC tumor samples are listed in Table II. As shown in Table II, biological processes associated with the cell and immune system were significantly enriched (P<0.05) in the DE genes of all the three types of NSCLC tumor samples. Enriched pathways of DE genes of Epi, IMMC and Neu tumor samples are listed in Tables III, IV and V, respectively. Similar to the enriched GO terms, those pathways were mainly involved in the dysregulation of cell division and abnormal activity of the immune and inflammatory systems.

Table II. Top five most significantly enriched GO terms of differential expression genes of the three groups of non-small cell lung cancer samples.

Table II.

Top five most significantly enriched GO terms of differential expression genes of the three groups of non-small cell lung cancer samples.

Category	GO name	P-value
Epi samples
BP	Response to wounding	9.39×10−15
CC	Extracellular space	8.69×10−14
BP	Cell adhesion	2.36×10−11
BP	Biological adhesion	2.59×10−11
CC	Cell fraction	6.12×10−11
IMMC samples
BP	Immune response	3.22×10−10
BP	Response to wounding	3.36×10−7
BP	Cell division	2.27×10−6
CC	Plasma membrane part	6.03×10−6
BP	Mitosis	6.98×10−6
Neu samples
BP	Cell adhesion	1.29×10−10
BP	Biological adhesion	1.50×10−10
MF	GTPase regulator activity	1.90×10−10
MF	Nucleoside-triphosphatase	4.24×10−10
CC	Plasma membrane part	5.47×10−10

[i] BP, biological process; MF, molecular function; CC, cellular component; GO, gene ontology.

Table III. Kyoto Encyclopedia of Genes and Genomes pathways of differential expression genes in epithelial cell tumor samples.

Table III.

Kyoto Encyclopedia of Genes and Genomes pathways of differential expression genes in epithelial cell tumor samples.

Pathway name	Gene number	P-value
ECM-receptor interaction	29	1.64×10−9
Complement and coagulation cascades	21	4.45×10−6
Biosynthesis of unsaturated fatty acids	10	1.09×10−4
Dilated cardiomyopathy	20	1.04×10−3
Focal adhesion	34	1.51×10−3
PPAR signaling pathway	16	2.02×10−3
Hematopoietic cell lineage	18	3.08×10−3
Pathways in cancer	48	3.29×10−3
Prion diseases	10	4.92×10−3
Hypertrophic cardiomyopathy	17	6.63×10−3
Arrhythmogenic right ventricular cardiomyopathy	15	1.30×10−2
Cell adhesion molecules	22	1.47×10−2
Ether lipid metabolism	9	1.63×10−2
Systemic lupus erythematosus	17	2.70×10−2
Basal cell carcinoma	11	3.58×10−2
MAPK signaling pathway	36	3.66×10−2
Synthesis and degradation of ketone bodies	4	4.86×10−2
Terpenoid backbone biosynthesis	5	4.99×10−2

[i] ECM, extracellular matrix; PPAR, peroxisome proliferator-activated receptor; MAPK, mitogen-activated protein kinase.

Table IV. Kyoto Encyclopedia of Genes and Genomes pathways of differential expression genes in immature monocytic myeloid cell tumor samples.

Table IV.

Kyoto Encyclopedia of Genes and Genomes pathways of differential expression genes in immature monocytic myeloid cell tumor samples.

Pathway name	Gene number	P-value
Hematopoietic cell lineage	25	7.50×10−7
DNA replication	13	7.02×10−5
Cell cycle	26	2.25×10−4
Cytokine-cytokine receptor interaction	42	7.78×10−4
Cell adhesion molecules	25	1.26×10−3
ECM-receptor interaction	18	1.95×10−3
Purine metabolism	25	9.05×10−3
Chemokine signaling pathway	29	9.52×10−3
Pantothenate and CoA biosynthesis	6	1.02×10−2
Aminoacyl-tRNA biosynthesis	10	1.29×10−2
Bladder cancer	10	1.51×10−2
PPAR signaling pathway	13	2.75×10−2
Pyrimidine metabolism	16	3.30×10−2
Viral myocarditis	13	3.36×10−2
Axon guidance	20	3.44×10−2
Pathways in cancer	42	3.80×10−2
Focal adhesion	28	3.92×10−2

[i] PPAR, peroxisome proliferator-activated receptor; ECM, extracellular matrix; CoA, coenzyme A; tRNA, transfer RNA.

Table V. Kyoto Encyclopedia of Genes and Genomes pathways of differential expression genes in neutrophil tumor samples.

Table V.

Kyoto Encyclopedia of Genes and Genomes pathways of differential expression genes in neutrophil tumor samples.

Pathway name	Gene number	P-value
Focal adhesion	61	4.81×10−7
Regulation of actin cytoskeleton	62	2.59×10−6
Fc gamma R-mediated phagocytosis	34	5.94×10−6
Chemokine signaling pathway	53	2.67×10−5
Leukocyte transendothelial migration	37	5.92×10−5
ECM-receptor interaction	29	7.03×10−5
Axon guidance	37	4.28×10−4
Pathogenic Escherichia coli infection	20	1.01×10−3
Hypertrophic cardiomyopathy	26	1.40×10−3
Adherens junction	24	1.71×10−3
Pathways in cancer	73	2.62×10−3
Dilated cardiomyopathy	26	4.57×10−3
Valine, leucine and isoleucine degradation	15	7.22×10−3
Glioma	19	8.70×10−3
Natural killer cell mediated cytotoxicity	32	0.018518
Non-small cell lung cancer	16	2.06×10−2
Gap junction	23	2.30×10−2
Aldosterone-regulated sodium reabsorption	13	2.45×10−2
Endocytosis	41	2.55×10−2
MAPK signaling pathway	56	2.78×10−2
Progesterone-mediated oocyte maturation	22	2.95×10−2
Arrhythmogenic right ventricular cardiomyopathy	20	2.97×10−2
ErbB signaling pathway	22	3.32×10−2
Fc epsilon RI signaling pathway	20	3.81×10−2
p53 signaling pathway	18	3.84×10−2
Colorectal cancer	21	4.23×10−2
Cell adhesion molecules	30	4.58×10−2
Glycerolipid metabolism	13	4.84×10−2
B cell receptor signaling pathway	19	4.89×10−2

[i] ECM, extracellular matrix; MAPK, mitogen-activated protein kinase.

For DAS genes in Epi and IMMC tumor samples, their enriched GO terms are shown in Fig. 3, which was obtained by submitting the DAVID result to the WEGO website (http://wego.genomics.org.cn/cgi-bin/wego/index.pl), and their enriched KEGG pathways are shown in Fig. 4. As expected, those DAS genes were also involved in cell and immune abnormality. The insulin signaling pathway and oxidative phosphorylation, which were associated with substance metabolism and were not revealed by DE genes, were also found to be significantly enriched (P<0.05) in Epi and IMMC tumor samples, respectively. DE and DAS genes may be complementary in the understanding of mechanisms of NSCLC.

Figure 3. Significantly enriched gene ontology terms of alternative splicing genes in (A) epithelial cell and (B) immature monocytic myeloid cell tumor samples. BP, biological process; MF, molecular function; CC, cellular component.

Figure 4. Significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways of alternative splicing genes in (A) epithelial cell and (B) immature monocytic myeloid cell tumor samples. PPAR, peroxisome proliferator-activated receptor; IgA, immunoglobulin A; Jak-STAT, Janus kinase-signal transducers and activators of transcription; NOD, non-obese diabetic; TCA, tricarboxylic acid.

Discussion

Lung cancer remains the leading cause of cancer-associated mortality in recent years, and its main type is NSCLC (17). An increasing number of studies about NSCLC have been emerging and numerous potential biomarkers for the early diagnosis or treatment of NSCLC have been identified subsequent to the development of molecular biology technologies. However, the mechanisms of NSCLC remain largely unknown, and additional studies are required to improve its prognosis. In the present study, through re-analysis of the RNA-Seq dataset from the GEO database, the significant DE and DAS genes were screened out in three types of NSCLC tumor samples (Epi, IMMC and Neu), compared with the corresponding normal samples, and their enriched GO terms and KEGG pathways were obtained. The present study aimed to provide results to help with the development of therapeutic methods and drugs for NSCLC.

The rapid growth of RNA-Seq provides the chance to observe variation in the genome at the exon level, and screening of alternative splicing is one of its important applications (18). AS is an important mechanism that could result in proteins with different functions from one mRNA precursor, and it has been shown to affect the status of numerous diseases. AS events involving exons 15–19 of d'Origine nantais tyrosine kinase have been screened out in lung cancer (19). In addition, the T cell factor 4K AS isoform was found to improve the prognosis of NSCLC by suppressing the proliferation and metastasis of tumor cells (20). In the present study, significant alternative splicing was not observed in any genes in the Neu tumor samples compared with the normal samples. Numerous AS genes were identified in Epi and IMMC tumor samples, which may indicate the differences of those three types of cells in the inducement of NSCLC.

In accordance with previous studies, SE is the most prevalent AS event, while RI is the least prevalent AS event in Epi and IMMC tumor samples (21,22). A total of 272 genes demonstrated alternative splicing in Epi and IMMC tumor samples, and this may be an indication of the important roles of those genes in the progression of NSCLC. Compared with AS analysis, numerous DE genes were identified in all three types of tumor samples, and 111 overlapping genes were screened out. One of the significant differences between the DE genes and DAS genes is the involvement of substance metabolism-associated pathways of DAS genes, including the insulin signaling pathway and oxidative phosphorylation. Insulin-like growth factor (IGF-1R) performs important roles through its downstream signaling in a number of solid tumors, including NSCLC (23). Furthermore, IGF-1R affects cisplatin and radiation resistance in lung cancer (24). In addition, oxidative stress/activity is affected by IGF-1R in numerous diseases, resulting in the dysregulation of biosystems, such as in cancer or neurodegeneration (25,26). DE and DAS analysis revealed a number of common GO terms and pathways, and the primary terms and pathways were cell or immune-associated. These have been investigated in numerous studies and were confirmed to play important roles in the development of numerous types of cancers, including lung cancer, gastric cancer and liver cancer (27–29).

Based on the integrated analysis of DE and DAS, alternative splicing was identified by leucine-rich repeat kinase 2 (LRRK2) in Epi and IMMC tumor samples, and was consistently downregulated in all three types of tumor cells. LRRK2 is a member of the leucine-rich repeat kinase family and encodes a protein with an ankryin repeat region. LRRK2 has been shown to play important roles in neurodegeneration diseases, including Parkinson's disease (30–32). The role of LRRK2 in cancer development remains unclear; however, certain indirect associations were identified (33). For example, through disrupting the activation of the proto-oncogene MET, LRRK2 may prevent tumor cell proliferation (34). Therefore, it may be a novel target for NSCLC.

In conclusion, through the combined analysis of DE and DAS, potential targets for NSCLC were screened out. Furthermore, DE and DAS may be complementary for understanding the mechanisms of NSCLC. The present study may be helpful in the diagnosis and treatment of NSCLC.

References