In Silico Bioinformatics Followed by Molecular Validation Using Archival FFPE Tissue Biopsies Identifies a Panel of Transcripts Associated with Severe Asthma and Lung Cancer

Abstract

Severe asthma and lung cancer are both heterogeneous pathological diseases affecting the lung tissue. Whilst there are a few studies that suggest an association between asthma and lung cancer, to the best of our knowledge, this is the first study to identify common genes involved in both severe asthma and lung cancer. Publicly available transcriptomic data for 23 epithelial brushings from severe asthmatics and 55 samples of formalin-fixed paraffin-embedded (FFPE) lung cancer tissue at relatively early stages were analyzed by absolute gene set enrichment analysis (GSEA) in comparison to 37 healthy bronchial tissue samples. The key pathways enriched in asthmatic patients included adhesion, extracellular matrix, and epithelial cell proliferation, which contribute to tissue remodeling. In the lung cancer dataset, the main pathways identified were receptor tyrosine kinase signaling, wound healing, and growth factor response, representing the early cancer pathways. Analysis of the enriched genes derived from the pathway analysis identified seven genes expressed in both the asthma and lung cancer sets: BCL3, POSTN, PPARD, STAT1, MYC, CD44, and FOSB. The differential expression of these genes was validated in vitro in the cell lines retrieved from different lung cancer and severe asthma patients using real-time PCR. The effect of the expression of the seven genes identified in the study on the overall survival of lung cancer patients (n = 1925) was assessed using a Kaplan–Meier plot. In vivo validation performed in the archival biopsies obtained from patients diagnosed with both the disease conditions provided interesting insights into the pathogenesis of severe asthma and lung cancer, as indicated by the differential expression pattern of the seven transcripts in the mixed group as compared to the asthmatics and lung cancer samples alone.

LINCATRA: Two-cycle method to amplify RNA for transcriptome analysis from formalin-fixed paraffin-embedded tissue

“…A similar comparison conducted on the FFPE samples from lung cancer patients and epithelial brushings from severe asthma patients using in silico approach identified six transcripts BCL3 , STAT1 , LUM , FOSB , PPARD and CD44 significantly differentially expressing in both cohorts and two genes MYC and POSTN upregulated in lung cancer and severe asthma respectively [ 60 ]. Genes like STAT1 , BCL3 and FOSB were upregulated in asthmatic lung cancer patients as compared to severe asthmatics.…”

“…The primer sequence used is described in Supplemental Table S2 qRT-PCR was performed in a Quant Studio 3 system in the following conditions: Hold stage 50 °C for 2min, 95 °C for 10 min; 40 PCR cycles: 95 °C for 15 s, 60 °C for 1min, 95 °C for 15 s; Melt Curve stage: 60 °C for 1 min, 95 °C for 1 s. The Ct-value of the gene of interest was normalized against the expression of the house keeping gene (18S) from each sample, and the relative gene expression (2-ΔΔCt) was calculated using severe asthma cases as controls. The eight genes identified from bioinformatic analysis ( BCL3 , STAT1 , LUM , POSTN , MYC , FOSB , CD44 and PPARD ) from our previous study [ 60 ] were used to assess the efficiency of amplified RNA in understanding the gene expression using qRT-PCR.…”

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“…An advantage of some of the aforementioned conjugation of in silico and in vitro methods mentioned above is that new patterns and markers can be retrieved from previously archived data that may be obscured by the noise of redundant pathways (Salameh et al, 2022). The image analysis of the microarrays, histographs and tumour sections are closer to being linked than previously thought using these machine learning methods to link the pathophysiological characteristics of those objects to the molecular profiles (Chua et al, 2021).…”