PMID- 33329723
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240330
IS  - 1664-8021 (Print)
IS  - 1664-8021 (Electronic)
IS  - 1664-8021 (Linking)
VI  - 11
DP  - 2020
TI  - A Novel XGBoost Method to Identify Cancer Tissue-of-Origin Based on Copy Number 
      Variations.
PG  - 585029
LID - 10.3389/fgene.2020.585029 [doi]
LID - 585029
AB  - The discovery of cancer of unknown primary (CUP) is of great significance in 
      designing more effective treatments and improving the diagnostic efficiency in 
      cancer patients. In the study, we develop an appropriate machine learning model 
      for tracing the tissue of origin of CUP with high accuracy after feature 
      engineering and model evaluation. Based on a copy number variation data 
      consisting of 4,566 training cases and 1,262 independent validation cases, an 
      XGBoost classifier is applied to 10 types of cancer. Extremely randomized tree 
      (Extra tree) is used for dimension reduction so that fewer variables replace the 
      original high-dimensional variables. Features with top 300 weights are selected 
      and principal component analysis is applied to eliminate noise. We find that 
      XGBoost classifier achieves the highest overall accuracy of 0.8913 in the 10-fold 
      cross-validation for training samples and 0.7421 on independent validation 
      datasets for predicting tumor tissue of origin. Furthermore, by contrasting 
      various performance indices, such as precision and recall rate, the experimental 
      results show that XGBoost classifier significantly improves the classification 
      performance of various tumors with less prediction error, as compared to other 
      classifiers, such as K-nearest neighbors (KNN), Bayes, support vector machine 
      (SVM), and Adaboost. Our method can infer tissue of origin for the 10 cancer 
      types with acceptable accuracy in both cross-validation and independent 
      validation data. It may be used as an auxiliary diagnostic method to determine 
      the actual clinicopathological status of specific cancer.
CI  - Copyright (c) 2020 Zhang, Feng, Wang, Dong, Yang, Su and Wang.
FAU - Zhang, Yulin
AU  - Zhang Y
AD  - College of Mathematics and Systems Science, Shandong University of Science and 
      Technology, Qingdao, China.
FAU - Feng, Tong
AU  - Feng T
AD  - College of Mathematics and Systems Science, Shandong University of Science and 
      Technology, Qingdao, China.
FAU - Wang, Shudong
AU  - Wang S
AD  - College of Computer and Communication Engineering, China University of Petroleum 
      (East China), Qingdao, China.
FAU - Dong, Ruyi
AU  - Dong R
AD  - Geneis (Beijing) Co., Ltd., Beijing, China.
FAU - Yang, Jialiang
AU  - Yang J
AD  - Geneis (Beijing) Co., Ltd., Beijing, China.
FAU - Su, Jionglong
AU  - Su J
AD  - School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an 
      Jiaotong-Liverpool University, Suzhou, China.
FAU - Wang, Bo
AU  - Wang B
AD  - Geneis (Beijing) Co., Ltd., Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20201120
PL  - Switzerland
TA  - Front Genet
JT  - Frontiers in genetics
JID - 101560621
PMC - PMC7716814
OTO - NOTNLM
OT  - XGBoost
OT  - copy number variations
OT  - extremely randomized tree
OT  - multiclass
OT  - principal component analysis
OT  - tissue-of-origin
EDAT- 2020/12/18 06:00
MHDA- 2020/12/18 06:01
PMCR- 2020/11/20
CRDT- 2020/12/17 05:51
PHST- 2020/07/19 00:00 [received]
PHST- 2020/10/05 00:00 [accepted]
PHST- 2020/12/17 05:51 [entrez]
PHST- 2020/12/18 06:00 [pubmed]
PHST- 2020/12/18 06:01 [medline]
PHST- 2020/11/20 00:00 [pmc-release]
AID - 10.3389/fgene.2020.585029 [doi]
PST - epublish
SO  - Front Genet. 2020 Nov 20;11:585029. doi: 10.3389/fgene.2020.585029. eCollection 
      2020.