Abstract

 

Lung cancer (LC) is the leading cause of cancer death globally, with late-stage detection contributing to poor prognosis and high mortality. Despite efforts to improve therapies, long-term survival rates have not significantly improved. Early detection of LC is crucial for better outcomes. Non-coding RNA molecules known as microRNAs (miRs) control the post-transcriptional regulation of gene expression and hold promise as biomarkers in LC patients' body fluids, such as sputum. This study aimed to develop a non-invasive diagnostic approach for distinguishing adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes of LC using identified candidate miRs from in-silico analysis of RNA sequencing datasets. The study utilized the Cancer Genome Atlas Program (TCGA) datasets and validated the findings through qRT-PCR analysis of sputum samples. The miR-944 and miR326 were found to be significantly upregulated in SCC compared to ADC samples. Combining these miRs achieved excellent discrimination, effectively distinguishing SCC from ADC in LC with an AUC of 0.985. In conclusion, miR-944 and miR-326 show promise as potential biomarkers in sputum for differentiating between SCC and ADC in LC. These findings propose a non-invasive diagnostic approach that could facilitate early detection and improve patient outcomes for these specific subtypes of LC.

 

Keywords: Squamous cell carcinoma; Adenocarcinoma; Sputum; miR-944; miR-326