Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 24 (2), 1614-1625

Identification of Dynamic Signatures Associated With Smoking-Related Squamous Cell Lung Cancer and Chronic Obstructive Pulmonary Disease

Affiliations

Identification of Dynamic Signatures Associated With Smoking-Related Squamous Cell Lung Cancer and Chronic Obstructive Pulmonary Disease

Xiaoru Sun et al. J Cell Mol Med.

Abstract

Chronic obstructive pulmonary disease (COPD) is a risk factor for the development of lung cancer. The aim of this study was to identify early diagnosis biomarkers for lung squamous cell carcinoma (SQCC) in COPD patients and to determine the potential pathogenetic mechanisms. The GSE12472 data set was downloaded from the Gene Expression Omnibus database. Differentially co-expressed links (DLs) and differentially expressed genes (DEGs) in both COPD and normal tissues, or in both SQCC + COPD and COPD samples were used to construct a dynamic network associated with high-risk genes for the SQCC pathogenetic process. Enrichment analysis was performed based on Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used the gene expression data and the clinical information to identify the co-expression modules based on weighted gene co-expression network analysis (WGCNA). In total, 205 dynamic DEGs, 5034 DLs and one pathway including CDKN1A, TP53, RB1 and MYC were found to have correlations with the pathogenetic progress. The pathogenetic mechanisms shared by both SQCC and COPD are closely related to oxidative stress, the immune response and infection. WGCNA identified 11 co-expression modules, where magenta and black were correlated with the "time to distant metastasis." And the "surgery due to" was closely related to the brown and blue modules. In conclusion, a pathway that includes TP53, CDKN1A, RB1 and MYC may play a vital role in driving COPD towards SQCC. Inflammatory processes and the immune response participate in COPD-related carcinogenesis.

Keywords: TP53; chronic obstructive pulmonary disease; infection; oxidative stress; squamous cell lung cancer.

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Protein‐protein interaction (PPI) network of dynamic differentially expressed genes (DEGs) (FDR < 0.05) constructed by STRING. Interactions at medium confidence (score > 0.4) and evidence from experiments, database searches and text mining were considered. Black circle shows the zoom‐in the significant module of the PPI. Nodes with no or scattered interactions were excluded
Figure 2
Figure 2
Hierarchical clustering analysis of DEGs. Heatmap of the top 35 dynamic DEGs (FDR < 0.01). The red colour in the heatmap denotes higher gene expression, and the white colour in the heatmap denotes the lower gene expression. Target gene symbols for the top 35 DEGs are involved
Figure 3
Figure 3
Scatter plot of canonical pathways based on ingenuity pathway analysis (IPA). Canonical pathway of the dynamic DEG. Ratio is the ratio of numbers of DEGs annotated in this pathway term to the numbers of all genes annotated in this pathway term. The data presented are log‐transformed P‐value (FDR corrected) of pathways found to be enriched in the tested group of genes
Figure 4
Figure 4
The human PPI network of squamous cell lung cancer (SQCC) pathogenic process‐associated genes. All the DEGs and DLs were assembled based on the d‐PPC. The network was visualized using the Cytoscape program. The expression of chronic obstructive pulmonary disease (COPD) is represented by the colour of the circle. Orange represents a higher level of expression, and white represents lower expression. The expression of SQCC + COPD is represented by the size of the circle. The right table is the degree (number of neighbours) of the dynamic DEGs (FDR < 0.01) in the PPI. Among the DEGs, the network of ALDH1A1, MVP, CLDN23 and FLNB was displayed
Figure 5
Figure 5
GO and KEGG enrichment scatter plot of the PPI network. The y‐axis shows significantly enriched GO and pathway terms relative to the network, and the x‐axis shows the enrichment scores of these terms. Dot size represents the number of genes, and the colour indicates the q‐value. BP, biological process; CC, cellular component; GO, gene ontology; MF, molecular function
Figure 6
Figure 6
Weighted gene co‐expression network analysis. A, Sample clustering with no evident outliers. B, Analysis of the network topology showed that it satisfied the scale‐free topology threshold of 0.9 when β = 8. The left panel shows analysis of the scale‐free fit index for various soft‐thresholding powers (β). The right panel shows the mean connectivity analysis of various soft‐thresholding powers. C, Clustering dendrograms of genes based on dissimilarity topological overlap and module colours. The branches of the cluster dendrogram correspond to the 11 different gene modules. Each piece of the leaves on the cluster dendrogram corresponds to a gene. D, Correlations between the gene modules and clinical traits. E, Scatter plots of gene significance (GS) for metastasis vs. module membership (MM) in the magenta, black, blue and brown modules. F, Top 10 hub genes in the magenta, black, blue and brown modules
Figure 7
Figure 7
Hypothetical pathway related to the disease pathogenesis process. The hypothetical pathway may include down‐regulated RB1 and TP53 and up‐regulated MYC, which together repress the activation of CDKN1A. This further prevents cell cycle arrest and apoptosis, in turns driving COPD towards SQCC. Meanwhile, the down‐regulated RB1 hardly promotes cell cycle progress
Figure 8
Figure 8
Validation in hub genes and the hypothesized pathway‐related genes in GSE60486. A, Identification of common genes between the PPI network of GSE12472 and the PPI network of GSE60486 by overlapping them. The hub genes and pathway‐related genes were also in GSE60486. B, Heatmap hierarchical clustering showed the selected genes clustering in the three stages of the SQCC pathogenetic progress

Similar articles

See all similar articles

References

    1. Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 Report: GOLD executive summary. Arch Bronconeumol. 2017;53:128‐149. - PubMed
    1. Guo Y, Zhang T, Wang Z, et al. Body mass index and mortality in chronic obstructive pulmonary disease: A dose‐response meta‐analysis. Medicine. 2016;95:e4225. - PMC - PubMed
    1. GBD . 2013 Mortality and Causes of Death Collaborators. Global, regional, and national age‐sex specific all‐cause and cause‐specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385:117‐171. - PMC - PubMed
    1. Burney PGJ, Patel J, Newson R, et al. Global and regional trends in COPD mortality, 1990–2010. Eur Respir J. 2015;45:1239‐1247. - PMC - PubMed
    1. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin. 2011;61:69‐90. - PubMed
Feedback