Decoding the role of RPL38 in lung adenocarcinoma: a multi-omics approach

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Ribosomal Protein L (RPL) family members are implicated in cancer, but their specific roles in lung adenocarcinoma (LUAD) are not fully understood.
• Understanding the functions of RPL genes is crucial for developing targeted therapies for LUAD.

Purpose of the Study:

• To identify key RPL genes in LUAD using machine learning.
• To develop a predictive risk model for LUAD based on RPL gene expression.
• To investigate the role and prognostic significance of Ribosomal Protein L38 (RPL38) in LUAD.

Main Methods:

• Machine learning analysis of The Cancer Genome Atlas (TCGA) LUAD data.
• Multi-omics analyses, including pan-cancer cohorts and spatial transcriptomics.
• In vitro (CCK‑8, colony formation, wound healing, Transwell assays) and in vivo (subcutaneous xenograft models) functional experiments.

Main Results:

• A three-gene RPL-based prognostic signature was developed, identifying high-risk LUAD patients with poorer survival and increased immunosuppression.
• RPL38 was found to be upregulated across multiple cancers, including LUAD, and associated with unfavorable outcomes.
• RPL38 ablation inhibited LUAD cell proliferation, migration, and tumor growth, while spatial transcriptomics linked its expression to an immunosuppressive tumor microenvironment.

Conclusions:

• RPL38 exhibits a tumor-promoting role in LUAD.
• This study elucidates RPL38's contribution to LUAD pathogenesis.
• RPL38 presents a potential therapeutic target for LUAD treatment.