Identification of Multi-Transcriptomic Prognostic Biomarkers to Explore Natural Therapeutics for Lung Cancer integrating Machine Learning

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, underscoring the urgent need for novel therapeutic strategies. Cyclin-dependent kinase 1 (CDK1), a central cell-cycle regulator, has emerged as an oncogenic driver and potential target in lung adenocarcinoma. This study aimed to integrate transcriptomics, machine learning (ML), and advanced in silico approaches to identify natural productderived potential inhibitors targeting CDK1. To identify robust differentially expressed genes, first we considered four microarray datasets (GSE19804, GSE10072, GSE18842, GSE10799). Protein-protein network interaction and topological analysis highlighted CDK1 as a primary key hub gene (pKHG) enriched in cell-cycle and p53 pathways. Target validation confirmed CDK1 overexpression, prognostic significance, immune infiltration links, and mutation associations. In addition, the collected naturally sourced phytochemical library of 9,667 was reduced through ML and cheminformatics-based bioactivity (pIC50) prediction to discover potential lead molecules against CDK1. Then, the selected top lead molecules were considered for further evaluation via molecular docking, molecular dynamics simulations, ADMET analysis, and binding free-energy calculations (MM-GBSA). Among the selected phytochemicals, licoflavanone (-8.25 kcal/mol), 3-hydroxyglabrol (-8.22 kcal/mol), and wighteone (-7.34 kcal/mol) showed the highest binding affinity score (GLIDE_XP score) and stable molecular interactions. Furthermore, MD simulations confirmed the conformational stability of ligand-protein complexes, supporting their potential as CDK1 inhibitors. This omics-to-in-silico pipeline identifies CDK1 as a robust therapeutic target and highlights natural product-derived inhibitors with favorable pharmacological and physicochemical properties. Therefore, these findings present a viable framework for accelerating precision drug discovery, with experimental validation underway.