The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival.
Xin LiXiaoqi WangRuihao HuangAndres StuckyXuelian ChenLan SunQin WenYunjing ZengHansel FletcherCharles WangYi XuHuynh CaoFengzhu SunShengwen Calvin LiXi ZhangJiang F ZhongPublished in: Cancers (2022)
Currently, most neuroblastoma patients are treated according to the Children's Oncology Group (COG) risk group assignment; however, neuroblastoma's heterogeneity renders only a few predictors for treatment response, resulting in excessive treatment. Here, we sought to couple COG risk classification with tumor intracellular microbiome, which is part of the molecular signature of a tumor. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (M high and M low ) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies. Mechanistically, the classification power of M-scores implies the effect of CREB over-activation, which may influence the critical genes involved in cellular proliferation, anti-apoptosis, and angiogenesis, affecting tumor cell proliferation survival and metastasis. Thus, intracellular microbiota abundance in neuroblastoma regulates intracellular signals to affect patients' survival.
Keyphrases
- end stage renal disease
- machine learning
- chronic kidney disease
- newly diagnosed
- ejection fraction
- risk assessment
- deep learning
- cell death
- gene expression
- signaling pathway
- patient reported outcomes
- body mass index
- artificial intelligence
- genome wide
- dna methylation
- human health
- microbial community
- minimally invasive
- big data
- heavy metals
- cell cycle arrest
- robot assisted
- antibiotic resistance genes