Analysis of Genes Involved in Lung Cancer: Study of 101 Cases Through Massive Sequencing

Lung cancer is one of the most commonly diagnosed cancers worldwide and remains the leading cause of cancer-related death in both men and women. In 2022, approximately 2.5 million new cases of lung cancer and 1.8 million deaths due to the disease were estimated. Historically, lung cancer has been more frequent in men, although the difference between sexes has been decreasing, with tobacco use remaining the main etiological factor. Survival rates vary considerably depending on the stage at diagnosis and other factors, and overall prognosis remains poor, with a relatively low five-year survival rate compared to other types of cancer. In this work, the objective is to present current approaches to lung cancer diagnosis through the study of multiple genetic alterations and biomarkers, mainly detected by next-generation sequencing (NGS), which has significantly transformed cancer diagnostics by enabling highthroughput and cost-effective genomic analysis. In the context of lung cancer, NGS plays a crucial role in improving molecular characterization, guiding targeted therapies, and supporting personalized medicine strategies. Specifically, its relevance lies in the ability to provide a comprehensive genomic profile of the tumor, identify driver mutations, predict treatment response, detect co-occurring alterations, and assist in therapeutic stratification. A real-world case study was conducted including 101 patients diagnosed with lung cancer between 2023 and 2025 in a reference laboratory, whose tumors were analyzed using NGS. The most frequently altered genes identified were KRAS, EGFR, and ALK, together with other less common but clinically actionable alterations, as well as the evaluation of programmed death-ligand 1 (PD-L1) expression by immunohistochemistry. In summary, next-generation sequencing represents a fundamental tool in the diagnostic workflow of lung cancer, enabling comprehensive molecular profiling that supports personalized treatment selection and contributes to improved clinical management of patients.