Integrated whole-exome and bulk transcriptome sequencing delineates the dynamic evolution from preneoplasia to invasive lung adenocarcinoma featured with ground-glass nodules

Abstract

Objective: The genomic and molecular ecology involved in the stepwise continuum progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) remains unclear and requires further elucidation. We aimed to characterize gene mutations and expression landscapes, and explore the association between differentially expressed genes (DEGs) and significantly mutated genes (SMGs) during the dynamic evolution from AIS to IAC.

Methods: Thirty-five patients with ground-glass nodules (GGNs) lung adenocarcinomas were enrolled. Whole-exome sequencing (WES) and transcriptome sequencing (RNA-Seq) were conducted on all patients, encompassing both tumor samples and corresponding noncancerous tissues. Data obtained from WES and RNA-Seq were subsequently analyzed.

Results: The findings from WES delineated that the predominant mutations were observed in EGFR (49%) and ANKRD36C (17%). SMGs, including EGFR and RBM10, were associated with the dynamic evolution from AIS to IAC. Meanwhile, DEGs, including GPR143, CCR9, ADAMTS16, and others were associated with the entire process of invasive LUAD. We found that the signaling pathways related to cell migration and invasion were upregulated, and the signaling pathways of angiogenesis were downregulated across the pathological stages. Furthermore, we found that the messenger RNA (mRNA) levels of FAM83A, MAL2, DEPTOR, and others were significantly correlated with CNVs. Gene set enrichment analysis (GSEA) showed that heme metabolism and cholesterol homeostasis pathways were significantly upregulated in patients with EGFR/RBM10 co-mutations, and these patients may have poorer overall survival than those with EGFR mutations. Based on the six calculation methods for the immune infiltration score, NK/CD8⁺ T cells decreased, and Treg/B cells increased with the progression of early LUAD.

Conclusions: Our findings offer valuable insights into the unique genomic and molecular features of LUAD, facilitating the identification and advancement of precision medicine strategies targeting the invasive progression of LUAD from AIS to IAC.

Keywords: RNA‐Seq; ground‐glass nodule; lung adenocarcinoma; tumor immune environment; whole‐exome sequencing.

FIGURE 1

Mutational signature in LUAD of different histologic subtypes. (A) Representative IHC images for AIS, MIA, and IAC tissues. (B) Relative contribution of the indicated mutation types to the point mutation spectrum for each tissue type. (C) Relative contribution of each indicated trinucleotide change to the three mutational signatures that were identified by NMF analysis of the somatic mutation catalogs. (D) Relative contribution of each mutational signature for each tissue type. (E) Statistical chart of each mutational signature for each tissue type. (F) Heatmap showing the cosine similarity of the mutational signature with the COSMIC signatures.

FIGURE 2

Overview of the LUAD data and mutational landscape. (A) Mutations with high somatic mutational frequencies in LUAD of different histologic subtypes. (B) Mutations with high driver gene frequencies. (C) The proportion of EGFR and RBM10 gene mutation in different histologic subtypes. (D) Hot spots of mutations in the EGFR and RBM10 gene. (E) TMB in different histologic subtypes.

FIGURE 3

Enrichment analysis related to SMGs. (A) Results of GO, KEGG, and Reactome enrichment of SMGs in AIS. (B) Results of GO, KEGG, and Reactome enrichment of SMGs in MIA. (C) Results of GO, KEGG, and reactome enrichment of SMGs in IAC.

FIGURE 4

The transcriptomic landscape between tumor and normal tissues. (A) Heatmap of differentially expressed genes (DEGs) with log2 fold‐change >1 and p < 0.05. Hierarchical clustering analysis was performed on DEGs identified in tumor and normal tissues. Expression levels are represented by color, where blue indicates low expression and pink‐orange indicates high expression. (B) Volcano plot of DEGs with log2 fold‐change >1 and p < 0.05, and enrichment results of DEGs in AIS. (C) Volcano plot of DEGs with log2 fold‐change >1 and p < 0.05, and enrichment results of DEGs in MIA. (D) Volcano plot of DEGs with log2 fold‐change >1 and p < 0.05, and enrichment results of DEGs in IAC.

FIGURE 5

Comparison of DEGs concordance of pathological subtypes. (A) Venn diagrams of DEGs between pathological subtypes. (B) Reactome enrichment analysis of between pathological subtypes. (C) The protein–protein interaction (PPI) network of the shared DEGs. (D, E) The results of the MCODE plugin clustering analysis. The nodes represent proteins, and the edges represent interactions.

FIGURE 6

Upregulated DEGs and associated biological progresses in malignant transformation. (A) Trend chart of DEGs by STEM analysis. (B) Heatmap of upregulated DEGs between tumor and normal tissue in different histologic subtypes. (C) Wikipathway and reactome enrichment analysis of upregulated DEGs. (D) Heatmap of upregulated DEGs with a gradual ascent.

FIGURE 7

Correlation of mRNA expression with CNV status. (A) Overview of the top 30 genes with CNVs. (B) Correlation between mRNA expression and CNVs. (C) The top ten genes with the highest correlation scores between mRNA and CNVs. (D) Scatter diagram of the correlation between mRNA and CNVs. (E) Western blotting results for top 4 proteins in tumor tissues of twelve patients with different pathological stages.

FIGURE 8

Correlation of mRNA expression with EGFR/RBM10 co‐mutations. (A) Volcano plot of DEGs between EGFR/RBM10 co‐mutations and EGFR mutations only. (B) GSEA enrichment analysis of DEGs. (C) The overall survival rates between EGFR/RBM10 co‐mutations and EGFR mutation only by TCGA database. (D) Results of CMap analysis using eXtreme Sum method. The top ranked ten compounds with highest reversal potency are illustrated in the right panel.

FIGURE 9

Immune cell signatures across different pathological stages of malignant pulmonary nodule. (A) The heatmap shows the immune infiltration scores calculated by six methods based on RNA‐Seq data of different pathological stages of malignant pulmonary nodules. (B) Scatter plot of NK cells immune scores for each pathological subtype. (C) Scatter plot of CD8+ T cells immune scores for each pathological subtype. (D) Scatter plot of Tregs immune scores for each pathological subtype. (E) Scatter plot of B cells immune scores for each pathological subtype.