Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach

• Published in: The EPMA journal 2024-03, Vol.15 (1), p.67-97
• Main Authors: Yang, Jingru, Ouedraogo, Serge Yannick, Wang, Jingjing, Li, Zhijun, Feng, Xiaoxia, Ye, Zhen, Zheng, Shu, Li, Na, Zhan, Xianquan
• Format: Article
• Language: English
• Subjects: Biomarkers; Biomedical and Life Sciences; Biomedicine; Cancer; Care and treatment; Development and progression; Gene expression; Genes; Genetic aspects; Health aspects; Lung cancer; Medical research; Medicine, Experimental; Medicine/Public Health; Metastasis; Respiratory agents; Squamous cell carcinoma; Ubiquitin
• ISSN: 1878-5077; 1878-5085; 1878-5085
• DOI: 10.1007/s13167-024-00352-w

Relevance The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome’s regulatory function is closely associated with the disease’s pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Methods This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan–Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters. Results This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin–proteasome system for degradation. Conclusions Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such a