In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Abstract

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

Radiation Type- and Dose-Specific Transcriptional Responses across Healthy and Diseased Mammalian Tissues

“…This finding is consistent with studies suggesting that cell migration and apoptosis in normal and tumorigenic tissues is regulated by many axon guidance molecules [ 97 ]. Notably, tumor-intrinsic activation of genes indispensable for neuronal development and neurological function is a nearly universal phenomenon in cancer, which, depending on the cancer type, can have either a negative or a positive effect in disease initiation and progression [ 98 , 99 ]. To date, it remains a terra incognita as to whether some radiotherapeutic modalities also trigger this phenomenon.…”

Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis

“…This hypothesis has been experimentally substantiated in various types of cancers, including hematological malignancies [40][41][42][43][44]. Nowadays, there are more and more studies involving applying WGCNA in AML-related analysis published in journals of different levels [45][46][47][48][49][50], which proves the recognition of this algorithm to some extent. However, there is no study aimed at finding AML survival-specific biomarkers using the WGCNA methodology based on adult CN-AML data by far.…”

Up-Regulation of TRIM32 Associated With the Poor Prognosis of Acute Myeloid Leukemia by Integrated Bioinformatics Analysis With External Validation

“…Acute myeloid leukemia (AML) is a highly malignant disease and remains the most common form in adults, accounting for about 32% of all adult leukemia cases ( 22 ). The underlying molecular mechanism of the initiation and progression of AML is still poorly understood ( 23 ).…”