Analysis of Ferritinophagy-Related Genes Associated with the Prognosis and Regulatory Mechanisms in Non-Small Cell Lung Cancer

Yuan Hao ¹ Xin Wang ¹ Zerong Ni ¹ Yuhui Ma ² Jing Wang ³ WEN Su ^4*

• ¹ Clinical Trials Center,Cancer Hospital Affiliated to Shanxi Medical University,Shanxi Province Cancer Hospital,Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
• ² Department of Cancer Center, Shanxi Bethune Hospital,Shanxi Academy of Medical Sciences Tongji Shanxi Hospital,Tongji Medical College Huazhong University Science of and Technology, Taiyuan, China
• ³ Pathology Department,Cancer Hospital Affiliated to Shanxi Medical University,Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
• ⁴ Immunology Department,Cancer Hospital Affiliated to Shanxi Medical University,Shanxi Province Cancer Hospital,Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China

Lung cancer remains a major global health issue, with non-small cell lung cancer (NSCLC) constituting approximately 85% of cases. Ferritinophagy, a pivotal autophagic process in ferroptosis, plays an essential role in tumor initiation and progression. However, the specific contributions of ferritinophagy-related genes (FRGs) to NSCLC pathogenesis remain incompletely understood. In this study, weighted gene co-expression network analysis (WGCNA) was employed to identify key modular genes associated with FRG scores. Genes overlapping between these modules and differentially expressed genes (DEGs) were selected for further investigation. Prognostic genes were identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, with subsequent validation using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) on both clinical samples and the TCGA-NSCLC dataset. A nomogram incorporating clinicopathological features and risk scores was developed to predict patient outcomes.Further analyses focused on functional enrichment, drug sensitivity, and the immune microenvironment. Cross-referencing 2,142 key modular genes with 2,764 DEGs revealed 600 candidate genes. Univariate Cox regression and LASSO analysis of these candidates identified eight prognostic genes: KLK8, MFI2, B3GNT3, MYRF, CREG2, GLB1L3, AHNAK2, and NLRP10. Two distinct risk groups exhibited significant survival differences. Both the risk score and pathological N stage were found to be independent prognostic factors, forming the basis for the nomogram. Notable correlations were observed between certain immune cells, prognostic genes, and immune responses, affecting the efficacy of immunotherapy and drug sensitivity. qRT-PCR confirmed that, except for NLRP10, all prognostic genes exhibited expression patterns consistent with TCGA-NSCLC data.This study highlights the significant role of FRGs in NSCLC prognosis and regulation, offering novel insights for personalized treatment strategies.