HTR2B as a novel biomarker of chronic obstructive pulmonary disease with lung squamous cell carcinoma

Abstract

Chronic obstructive pulmonary disease (COPD) is often associated with lung squamous cell carcinoma (LUSC), which has the same etiology (smoking, inflammation, oxidative stress, microenvironmental changes, and genetics). Smoking, inflammation, and airway remodeling are the most important and classical mechanisms of COPD comorbidity in LUSC patients. Cancer can occur during repeated airway damage and repair (airway remodeling). Changes in the inflammatory and immune microenvironments, which can cause malignant transformation of some cells, are currently being revealed in both LUSC and COPD patients. We obtained the GSE76925 dataset from the Gene Expression Omnibus database. Screening for possible COPD biomarkers was performed using the LASSO regression model and a random forest classifier. The compositional patterns of the immune cell fraction in COPD patients were determined using CIBERSORT. HTR2B expression was analyzed using validation datasets (GSE47460, GSE106986, and GSE1650). HTR2B expression in COPD cell models was determined via real-time quantitative PCR. Epithelial-mesenchymal transition (EMT) marker expression levels were determined after knocking down or overexpressing HTR2B. HTR2B function and mechanism in LUSC were analyzed with the Kaplan‒Meier plotter database. HTR2B expression was inhibited to detect changes in LUSC cell proliferation. A total of 1082 differentially expressed genes (DEGs) were identified in the GSE76925 dataset (371 genes were significantly upregulated, and 711 genes were significantly downregulated). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that the DEGs were mainly enriched in the p53 signaling and β-alanine metabolism pathways. Gene Ontology enrichment analysis indicated that the DEGs were largely related to transcription initiation from the RNA polymerase I promoter and to the regulation of mononuclear cell proliferation. The LASSO regression model and random forest classifier results revealed that HTR2B, DPYS, FRY, and CD19 were key COPD genes. Immune cell infiltration analysis indicated that these genes were closely associated with immune cells. Analysis of the validation sets suggested that HTR2B was upregulated in COPD patients. HTR2B was significantly upregulated in COPD cell models, and its upregulation was associated with increased EMT marker expression. Compared with that in bronchial epithelial cells, HTR2B expression was upregulated in LUSC cells, and inhibiting HTR2B expression led to the inhibition of LUSC cell proliferation. In conclusions, HTR2B might be a new biomarker and therapeutic target in COPD patients with LUSC.

Keywords: Biomarker; COPD; EMT; LUSC; Machine learning.

Figure 1

(A) Flowchart. (B) Heatmap of DEGs in the GSE76925 dataset. (C) Volcano plot of DEGs in the GSE76925 dataset.

Figure 2

(A) KEGG pathway enrichment analysis results for the upregulated DEGs. (B) GO enrichment analysis results for the upregulated DEGs. (C) KEGG pathway enrichment analysis results for the downregulated DEGs. (D) GO enrichment analysis results for the downregulated DEGs.

Figure 3

(A) LASSO coefficient profiles of the risk factors. (B) Eight risk factors selected by LASSO regression analysis. (C) Influence of the number of decision trees on the error rate. X-axis: Number of decision trees; Y-axis: Error rate. The error rate was relatively stable when the number of decision trees was approximately 400. (D) Results of the Gini coefficient method in the random forest classifier. X-axis: Genetic variables; Y-axis: Importance index.

Figure 4

Correlations between key genes and infiltrating immune cells in COPD and normal samples.

Figure 5

(A) HTR2B expression in the GSE76925 dataset. (B) HTR2B expression in the GSE47460 dataset. (C) HTR2B expression in the GSE106986 dataset. (D) HTR2B expression in the GSE1650 dataset. (E) ROC curves for key genes in the GSE76925 dataset. (F) ROC curves for key genes in the GSE47460 dataset. (G) ROC curves for key genes in the GSE106986 dataset. (H) ROC curves for key genes in the GSE1650 dataset. (I) HTR2B expression in CSE-stimulated BEAS-2B cells. (J) HTR2B expression in BEAS-2B cells stimulated with 3% CSE.

Figure 6

(A) Prediction of downstream signaling pathways regulated by HTR2B in COPD. (B) Inhibition of HTR2B expression in BEAS-2B cells. (C–G) Changes in the mRNA expression levels of EMT markers in BEAS-2B cells with different HTR2B expression levels.

Figure 7

(A, B) Prediction of differences in the HTR2B mRNA expression levels between LUSC and normal tissue samples using the Kaplan‒Meier plotter database. (C) Results of survival analysis of LUSC patients stratified by HTR2B expression by using the Kaplan‒Meier plotter. (D) Results of RFS analysis of patients with LUSC stratified by HTR2B expression by using the Kaplan‒Meier plotter. (E–H) Results of IHC staining analysis of HTR2B in the HPA database. (I) Results of survival analysis of patients based on HTR2B expression in the TCGA database. (J) Prediction of downstream signaling pathways regulated by HTR2B in LUSC.

Figure 8

(A) HTR2B expression in BEAS-2B, H226, and H1703 cells. (B, C) Inhibition of HTR2B in H226 and H1703 cells. (D, E) LUSC cell proliferation, as determined by the CCK-8 assay. (F, I) CCND1 expression after short interfering RNA (siRNA)-mediated knockdown of HTR2B, as determined by RT‒qPCR. (G, J) ITGAV expression after siRNA-mediated knockdown of HTR2B, as determined by RT‒qPCR. (H, K) ITGB1 expression after siRNA-mediated knockdown of HTR2B, as determined by RT‒qPCR.

Figure 9

Pattern diagram.