Research On Molecular Markers Of Acute Myeloid Leukemia Based On Transcriptomics Data

Recurrence and metastasis of cancer causes 90% of patients to die directly.Studying the molecular mechanism of cancer recurrence and metastasis is an important field of cancer research.The research of molecular markers has extremely important theoretical significance and application value for early diagnosis,individualized treatment and prognosis of cancer.Acute myeloid leukemia(AML)is highly heterogeneous and has a high mortality rate.Discovering the molecular markers that determine cancer recurrence and metastasis from the perspective of systems biology by integrating transcriptomics data is of great significance for improving the accuracy of cancer recurrence and prognosis judgment.This study effectively integrated a variety of transcriptomics data,and found GMI molecular markers based on network function module mining algorithms.First,differentially expressed genes were identified by pooling a large number of 861 human AML patients and 75 normal cases.Second,miRWalk was used to identify the functional miRNA-mRNA regulatory module.Third,The GMI signature based random survival forest prognosis model was developed from training data set and evaluated in independent cohort from The Cancer Genome Atlas dataset.At last,univariate and multivariate Cox proportional hazards regression analyses were applied to evaluate the prognostic value of GMI signature.In this study,23 differentially expressed genes and 16 validated target miRNA were named as the GMI signature,independent cohort was separated into two groups with significantly different overall survival according to the RSF model-based scores.The result shows that GMI molecular markers are closely related to leukemia metastasis and recurrence.So the GMI signature based RSF prognosis model can informs AML patient prognosis effectively.