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 miR-326 were found to be …