Classify Multicategory Outcome in Patients With Lung Adenocarcinoma Using Clinical, Transcriptomic and Clinico-Transcriptomic Data: Machine Learning Versus Multinomial Models

Authors

Fei Deng
Lanlan Shen
He Wang
Lanjing Zhang

Publication Date

1-1-2020

Journal

American Journal of Cancer Research

PMID

33415023

PMCID

PMC7783755

PubMedCentral® Posted Date

12-1-2020

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Lung adenocarcinoma, cause-specific mortality, survival, machine learning, multilabel classification, transcriptomic

Abstract

Classification of multicategory survival-outcome is important for precision oncology. Machine learning (ML) algorithms have been used to accurately classify multi-category survival-outcome of some cancer-types, but not yet that of lung adenocarcinoma. Therefore, we compared the performances of 3 ML models (random forests, support vector machine [SVM], multilayer perceptron) and multinomial logistic regression (Mlogit) models for classifying 4-category survival-outcome of lung adenocarcinoma using the TCGA. Mlogit model overall performed similar to SVM and multilayer perceptron models (micro-average area under curve=0.82), while random forests model was inferior. Surprisingly, transcriptomic data alone and clinico-transcriptomic data appeared sufficient to accurately classify the 4-category survival-outcome in these patients, but no models using clinical data alone performed well. Notably, NDUFS5, P2RY2, PRPF18, CCL24, ZNF813, MYL6, FLJ41941, POU5F1B, and SUV420H1 were the top-ranked genes that were associated with alive without disease and inversely linked to other outcomes. Similarly, BDKRB2, TERC, DNAJA3, MRPL15, SLC16A13, CRHBP and ACSBG2 were associated with alive with progression and GAL3ST3, AD2, RAB41, HDC, and PLEKHG1 associated with dead with disease, respectively, while also inversely linked other outcomes. These cross-linked genes may be used for risk-stratification and future treatment development.