Genetic and microenvironmental differences in non-smoking lung adenocarcinoma patients compared with smoking patients

Abstract

Background: Non-smoking-related lung adenocarcinoma (LUAD) has its own characteristics. Genetic and microenvironmental differences in smoking and non-smoking LUAD patients were analyzed to elucidate the oncogenesis of non-smoking-related LUAD, which will improve our understanding of the underlying molecular mechanism and be of clinical use in the future.

Methods: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) databases were used for clinical and genomic information. Various bioinformatics tools were used to analyze differences in somatic mutations, RNA and microRNA (miRNA) expression, immune infiltration, and stemness indices. GO, KEGG, and GSVA analyses were performed with R. A merged protein-protein interaction (PPI) network was constructed and analyzed. A miRNA-differentially expressed gene network was constructed with miRNet. qRT-PCR was used for validation of 4 most significantly differently expressed genes and 2 miRNAs in tumor samples obtained from 20 pairs of non-smoking and smoking patients.

Results: Five hundred and one patients with LUAD were obtained, including 210 in the non-smoking group and 292 in the smoking group. A total of 174 significantly altered somatic mutations were detected, including mutations in tumor protein p53 and epidermal growth factor receptor, which were downregulated in non-smoking-related LUAD. At the RNA level, 231 significantly differentially expressed genes were obtained; 124 were upregulated and 107 downregulated in the non-smoking group. GSVA analysis revealed 42 significant pathways. Other functional and enrichment analyses of somatic mutations and RNA expression levels revealed that these genes were significantly enriched in receptor activity regulation and receptor binding. Differences in microenvironments including immune infiltration (e.g., CD8⁺ T cells and resting mast cells) and stemness indices were also found between groups. A 79-pair interaction was found between differentially expressed genes and miRNAs, of which miR-335-5p and miR-34a-5p were located in the center. Twenty-one genes, including vitronectin, neurotensin, and neuronatin, were differentially expressed in both non-smoking LUAD patients and DMSO-treated A549 cells. And the different expression of neurotensin, neuronatin, trefoil factor family2, regenerating family member 4, miR-377-5p, miR-34a were verified with the same tendency in our own samples.

Conclusions: Non-smoking LUAD patients, compared to smokers, have different characteristics in terms of somatic mutation, gene, and miRNA expression and the microenvironment, indicating a diverse mechanism of oncogenesis.

Keywords: Lung adenocarcinoma; genome; microenvironment; non-smoking.

Figure 1

Flow diagram of whole design.

Figure 2

Survival analysis: survival time analysis of 509 patients with smoking status.

Figure 3

Somatic mutation waterfall map grouped by smoking status, the red band below corresponded to the smoking group, and the blue was the non-smoking group.

Figure 4

Differentially expressed genes in non-Smoking lung adenocarcinoma in TCGA-LUAD dataset. Volcanic map reviewed genes differentially expressed between the smoker and non-smoker groups. Blue dots represented significantly down-regulated genes, red dots represented significantly up-regulated genes, and black dots represented genes that are not differentially expressed. TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma.

Figure 5

Heatmap of gene set variation analysis for and RNA-Seq data (GSVA).

Figure 6

Seventy-nine microRNAs-differentially expressed gene (miRNA-DEG) pairs consisting of 19 miRNAs and 36 mRNAs by miRNet.

Figure 7

Validation of comparatively more significantly different mRNAs and miRNAs expression by qRT-PCR. *, P<0.05; **, P<0.01; ***, P<0.005.

Figure 8

Differences of microenvironment in non-smoking lung adenocarcinoma. (A) Comparation of each leukocyte fraction between smokers and nonsmokers; (B) differential genes related to antimicrobials; (C) Differential genes related to cytokines; (D) differential genes related to natural killer (NK) cell cytotoxicity; (E) differential gene related to cytokine receptors; (F) differential gene related to PD-L1. PD-L1, programmed death-ligand 1. *, P<0.05; **, P<0.01; ***, P<0.005; ****, P<0.001.

Figure 9

Differences of stemness between smokers and non-smokers in LUAD. LUAD, lung adenocarcinoma.

Figure S1

The summary of the LUAD patients’ somatic mutation data, (A) displayed number of variants in each sample as a stacked bar plot and variant types as a boxplot summarized; (B) classified SNPs into transitions and transversions, (a) showed the overall distribution of the six different transformations, (b) classified the SNPs as transitions (Ti) and transversions (Tv), showing their proportion, (c) stacked bar graph of the percent conversion in each sample.

Figure S2

Exclusive/co-occurrence event analysis on top 20 differently mutated genes.

Figure S3

Protein-protein interaction (PPI) network of differently expressed genes between smokers and non-smokers in TCGA-LUAD. The size and gradient color of the Node were adjusted by degree, and the thickness and gradient color of edge are adjusted by combined score.

Figure S4

Differentially expressed genes differentially expressed genes in non-smoking lung adenocarcinoma associated with smoking in GEO datasets. GEO, Gene Expression Omnibus.

Figure S5

Differentially expressed genes between A549 cells treated with DMSO and cigarette smoke condensate (CSC) in GSE69770 datasets.

Figure S6

Flip graph of the functional enrichment analysis of differential mutation and expression genes between smokers and non-smokers group which was more focused on overlapping of genes between different gene sets (A); bubble chart for all significantly different KEGG pathways (B).

Figure S7

Differentially expressed miRNA in non-smoking lung adenocarcinoma in TCGA-LUAD dataset.