Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors

被引:4
作者
Padwal, Mahesh Kumar [1 ,2 ]
Basu, Sandip [2 ,3 ]
Basu, Bhakti [1 ,2 ]
机构
[1] Bhabha Atom Res Ctr, Mol Biol Div, Mumbai 400085, India
[2] Homi Bhabha Natl Inst, Mumbai 400094, India
[3] Bhabha Atom Res Ctr, Tata Mem Hosp Annexe, Radiat Med Ctr, Mumbai 400012, India
关键词
machine learning; gene features; RNA-SEQ; neuroendocrine tumors; hepatic metastasis; primary site; random forest; RNA-SEQ; BREAST-CANCER; SFRP2; GENE; PROGNOSIS; CELL; EXPRESSION; PROGRESSION; SIGNATURES; DIAGNOSIS; PROMOTER;
D O I
10.3390/curroncol30100668
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.
引用
收藏
页码:9244 / 9261
页数:18
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