Molecular Systems Architecture of Interactome in the Acute Myeloid Leukemia Microenvironment

Analysis of prognostic biomarker models and immune microenvironment in acute myeloid leukemia by integrative bioinformatics

BACKGROUND

Acute myeloid leukemia (AML) is a hematological cancer driven on by aberrant myeloid precursor cell proliferation and differentiation. A prognostic model was created in this study to direct therapeutic care.

METHODS

Differentially expressed genes (DEGs) were investigated using the RNA-seq data from the TCGA-LAML and GTEx. Weighted Gene Coexpression Network Analysis (WGCNA) examines the genes involved in cancer. Find the intersection genes and construct the PPI network to discover hub genes and remove prognosis-related genes. A nomogram was produced for predicting the prognosis of AML patients using the risk prognosis model that was constructed using COX and Lasso regression analysis. GO, KEGG, and ssGSEA analysis were used to look into its biological function. TIDE score predicts immunotherapy response.

RESULTS

Differentially expressed gene analysis revealed 1004 genes, WGCNA analysis revealed 19,575 tumor-related genes, and 941 intersection genes in total. Twelve prognostic genes were found using the PPI network and prognostic analysis. To build a risk rating model, RPS3A and PSMA2 were examined using COX and Lasso regression analysis. The risk score was used to divide the patients into two groups, and Kaplan-Meier analysis indicated that the two groups had different overall survival rates. Univariate and multivariate COX studies demonstrated that risk score is an independent prognostic factor. According to the TIDE study, the immunotherapy response was better in the low-risk group than in the high-risk group.

CONCLUSIONS

We eventually selected out two molecules to construct prediction models that might be used as biomarkers for predicting AML immunotherapy and prognosis.

Attenuation of Aging-Related Oxidative Stress Pathways by Phytonutrients: A Computational Systems Biology Analysis

Aging results from gradual accumulation of damage to the cellular functions caused by biochemical processes such as oxidative stress, inflammation-driven prolonged cellular senescence state, immune system malfunction, psychological stress, and epigenetic changes due to exposure to environmental toxins. Plant-derived bioactive molecules have been shown to ameliorate the damage from oxidative stress. This research seeks to uncover the mechanisms of action of how phytochemicals from fruit/berry/vegetable (FBV) juice powder mitigate oxidative stress. The study uses a computational systems biology approach to (1) identify biomolecular pathways of oxidative stress; (2) identify phytochemicals from FBV juice powder and their specific action on oxidative stress mechanisms; and (3) quantitatively estimate the effects of FBV juice powder bioactive compounds on oxidative stress. The compounds in FBV affected two oxidative stress molecular pathways: (1) reactive oxygen species (ROS) production and (2) antioxidant enzyme production. Six bioactive compounds including cyanidin, delphinidin, ellagic acid, kaempherol, malvidin, and rutin in FBV significantly lowered production of ROS and increased the production of antioxidant enzymes such as catalase, heme oxygenase-1, superoxide dismutase, and glutathione peroxidase. FBV juice powder provides a combination of bioactive compounds that attenuate aging by affecting multiple pathways of oxidative stress.

Mechanistic Understanding of D-Glucaric Acid to Support Liver Detoxification Essential to Muscle Health Using a Computational Systems Biology Approach

Liver and muscle health are intimately connected. Nutritional strategies that support liver detoxification are beneficial to muscle recovery. Computational-in silico-molecular systems' biology analysis of supplementation of calcium and potassium glucarate salts and their metabolite D-glucaric acid (GA) reveals their positive effect on mitigation of liver detoxification via four specific molecular pathways: (1) ROS production, (2) deconjugation, (3) apoptosis of hepatocytes, and (4) β-glucuronidase synthesis. GA improves liver detoxification by downregulating hepatocyte apoptosis, reducing glucuronide deconjugates levels, reducing ROS production, and inhibiting β-Glucuronidase enzyme that reduces re-absorption of toxins in hepatocytes. Results from this in silico study provide an integrative molecular mechanistic systems explanation for the mitigation of liver toxicity by GA.

The genesis and evolution of acute myeloid leukemia stem cells in the microenvironment: From biology to therapeutic targeting

Acute myeloid leukemia (AML) is a hematological malignancy characterized by cytogenetic and genomic alterations. Up to now, combination chemotherapy remains the standard treatment for leukemia. However, many individuals diagnosed with AML develop chemotherapeutic resistance and relapse. Recently, it has been pointed out that leukemic stem cells (LSCs) are the fundamental cause of drug resistance and AML relapse. LSCs only account for a small subpopulation of all leukemic cells, but possess stem cell properties, including a self-renewal capacity and a multi-directional differentiation potential. LSCs reside in a mostly quiescent state and are insensitive to chemotherapeutic agents. When LSCs reside in a bone marrow microenvironment (BMM) favorable to their survival, they engage into a steady, continuous clonal evolution to better adapt to the action of chemotherapy. Most chemotherapeutic drugs can only eliminate LSC-derived clones, reducing the number of leukemic cells in the BM to a normal range in order to achieve complete remission (CR). LSCs hidden in the BM niche can hardly be targeted or eradicated, leading to drug resistance and AML relapse. Understanding the relationship between LSCs, the BMM, and the generation and evolution laws of LSCs can facilitate the development of effective therapeutic targets and increase the efficiency of LSCs elimination in AML.