Transcript-Level Biomarkers of Early Lung Carcinogenesis in Bronchial Lesions

Abstract

Premalignant lesions within the bronchial epithelium signify the initial phases of squamous cell lung carcinoma, posing challenges for detection via conventional methods. Instead of focusing solely on gene expression, in this study, we explore transcriptomic alterations linked to lesion progression, with an emphasis on protein-coding transcripts. We reanalyzed a publicly available RNA-Seq dataset on airway epithelial cells from 82 smokers with and without premalignant lesions. Transcript and gene abundance were quantified using kallisto, while differential expression and transcript usage analysis was performed utilizing sleuth and RATs packages. Functional characterization involved overrepresentation analysis via clusterProfiler, weighted coexpression network analysis (WGCNA), and network analysis via Enrichr-KG. We detected 5906 differentially expressed transcripts and 4626 genes, exhibiting significant enrichment within pathways associated with oxidative phosphorylation and mitochondrial function. Remarkably, transcript-level WGCNA revealed a single module correlated with dysplasia status, notably enriched in cilium-related biological processes. Notable hub transcripts included RABL2B (ENST00000395590), DNAH1 (ENST00000420323), EFHC1 (ENST00000635996), and VWA3A (ENST00000563389) along with transcription factors such as FOXJ1 and ZNF474 as potential regulators. Our findings underscore the value of transcript-level analysis in uncovering novel insights into premalignant bronchial lesion biology, including identification of potential biomarkers associated with early lung carcinogenesis.

Keywords: WGCNA; biomarker; bronchial premalignant lesion; cilium; transcriptomics.

Figure 1

Functional enrichment analysis of the transcripts (A) and genes (B) differentially expressed between normal and PML samples. Gene sets were obtained from the WikiPathways resource [19], and the 15 most enriched gene sets are visualized. GeneRatio is the fraction of differentially expressed genes in the pathway of interest, contributing to the enrichment signal. False-discovery rate (FDR) is the adjusted p-value of the enrichment significance.

Figure 2

Correlation between WGCNA modules and clinical traits for transcript-level (A) and gene-level (B) analyses. Number of molecular features for each module is given in parentheses. Each cell contains Pearson’s correlation between module eigengene (first principal component) and the clinical trait. The p-value for the null hypothesis (correlation coefficient of zero) is given in parentheses.

Figure 3

Functional enrichment analysis of transcripts belonging to the “yellow” module. Tree plots for significantly enriched Gene Ontology terms for biological processes (A) and cellular components (B). The circle size is proportional to the number of genes annotated as belonging to the term, and the circle color encodes the adjusted p-value of enrichment significance. Gene-concept networks for top five significantly enriched Gene Ontology terms for biological processes (C) and cellular components (D).

Figure 4

Network produced by Enrichr-KG, which links the top enriched terms with the hubs from the “yellow” module. Transcripts and genes with at least two connections to the neighboring nodes are visualized.