Combined scRNAseq and Bulk RNAseq Analysis to Reveal the Dual Roles of Oxidative Stress-Related Genes in Acute Myeloid Leukemia

Abstract

Background. Oxidative stress (OS) can either lead to leukemogenesis or induce tumor cell death by inflammation and immune response accompanying the process of OS through chemotherapy. However, previous studies mainly focus on the level of OS state and the salient factors leading to tumorigenesis and progression of acute myeloid leukemia (AML), and nothing has been done to distinguish the OS-related genes with different functions. Method. First, we downloaded single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) data from public databases and evaluated the oxidative stress functions between leukemia cells and normal cells by the ssGSEA algorithm. Then, we used machine learning methods to screen out OS gene set A related to the occurrence and prognosis of AML and OS gene set B related to treatment in leukemia stem cells (LSCs) like population (HSC-like). Furthermore, we screened out the hub genes in the above two gene sets and used them to identify molecular subclasses and construct a model for predicting therapy response. Results. Leukemia cells have different OS functions compared to normal cells and significant OS functional changes before and after chemotherapy. Two different clusters in gene set A were identified, which showed different biological properties and clinical relevance. The sensitive model for predicting therapy response based on gene set B demonstrated predictive accuracy by ROC and internal validation. Conclusion. We combined scRNAseq and bulk RNAseq data to construct two different transcriptomic profiles to reveal the different roles of OS-related genes involved in AML oncogenesis and chemotherapy resistance, which might provide important insights into the mechanism of OS-related genes in the pathogenesis and drug resistance of AML.

Molecular subtypes predict therapeutic responses and identifying and validating diagnostic signatures based on machine learning in chronic myeloid leukemia

“…With the development of next-generation sequencing (NGS) technologies, studies based on the levels of gene expression and regulation have shed light on the pathogenesis and malignant phenotypes of many diseases [7][8][9][10]. The incorporation of NGS into the diagnosis, risk stratification and therapeutic evaluation of CML patients is of great importance [11].…”