Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia

Lysosomes have a tight connection to cancer and can eliminate cancer cells. The dismal prognosis of acute myeloid leukemia (AML) patients may thus be improved by a thorough examination of the function of lysosome-related genes (LRGs). By using a variety of machine learning methods including random forest approach, LASSO-COX regression, and extreme gradient boosting (XGBoost), we create a prognostic six-LRGs-related signature (HPS1, BCAN, SLC2A8, DOC2A, CHMP4C, and SLC29A3), which categorized AML patients into two groups with significant survival and tumor microenvironment (TME) differences. Data from the ICGC and TARGET cohorts were used as test cohorts for the validation of the prognostic LRGs-related signature. We also discovered that chemotherapeutic susceptibility was connected to the LRGs-related signature. Finally, we evaluated gene expression levels in the LRGs-related signature between normal and AML samples and confirmed the elevation of CHMP4C expression in 90 clinical samples. In summary, a six-LRGs-related signature was developed to predict the prognosis of AML patients, and more research is necessary to determine whether this signature has therapeutic promise as an anti-AML target.