AUTHOR=Chen Yang , Lu Yunfei , Huang Changzhi , Wu Jingyu , Shao Yu , Wang Zhenling , Zhang Hongqiang , Fu Zan TITLE=Subtypes analysis and prognostic model construction based on lysosome-related genes in colon adenocarcinoma JOURNAL=Frontiers in Genetics VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1149995 DOI=10.3389/fgene.2023.1149995 ISSN=1664-8021 ABSTRACT=

Background: Lysosomes are essential for the development and recurrence of cancer. The relationship between a single lysosome-related gene and cancer has previously been studied, but the relationship between the lysosome-related genes (LRGs) and colon adenocarcinoma (COAD) remains unknown. This research examined the role of lysosome-related genes in colon adenocarcinoma.

Methods: 28 lysosome-related genes associated with prognosis (PLRGs) were found by fusing the gene set that is differently expressed between tumor and non-tumor in colon adenocarcinoma with the gene set that is related to lysosomes. Using consensus unsupervised clustering of PLRGs, the colon adenocarcinoma cohort was divided into two subtypes. Prognostic and tumor microenvironment (TME) comparisons between the two subtypes were then made. The PLRGs_score was constructed using the least absolute shrinkage and selection operator regression (LASSO) method to quantify each patient’s prognosis and provide advice for treatment. Lastly, Western Blot and immunohistochemistry (IHC) were used to identify MOGS expression at the protein level in colon adenocarcinoma tissues.

Results: PLRGs had more somatic mutations and changes in genetic level, and the outcomes of the two subtypes differed significantly in terms of prognosis, tumor microenvironment, and enrichment pathways. Then, PLRGs_score was established based on two clusters of differential genes in the cancer genome atlas (TCGA) database, and external verification was performed using the gene expression omnibus (GEO) database. Then, we developed a highly accurate nomogram to enhance the clinical applicability of the PLRGs_score. Finally, a higher PLRGs_score was associated with a poorer overall survival (OS), a lower tumor mutation burden (TMB), a lower cancer stem cell (CSC) index, more microsatellite stability (MSS), and a higher clinical stage. MOGS was substantially elevated at the protein level in colon adenocarcinoma as additional confirmation.

Conclusion: Overall, based on PLRGs, we identified two subtypes that varied significantly in terms of prognosis and tumor microenvironment. Then, in order to forecast patient prognosis and make treatment suggestions, we developed a diagnostic model with major significance for prognosis, clinical relevance, and immunotherapy. Moreover, we were the first to demonstrate that MOGS is highly expressed in colon adenocarcinoma.