Comparison of the Metastasis Predictive Potential of mRNA and Long Non-Coding RNA Profiling in Systemically Untreated Breast Cancer
Autor: | Thi T. N. Do, Kristina Pilekær Sørensen, Qihua Tan, Ines Block, Martin Jakob Larsen, Mark Burton, Mads Thomassen, Martin Bak, Torben A Kruse, Søren Cold |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Oncology
Cancer Research medicine.medical_specialty Machine learning methods mRNA Biology Article Metastasis lymph node negative Breast cancer Internal medicine Gene expression medicine MRNA Prognostic predictors RC254-282 Low-risk breast cancer Messenger RNA long non-coding RNA Lymph node negative prognostic predictors Neoplasms. Tumors. Oncology. Including cancer and carcinogens systemically untreated patients medicine.disease Lncrna expression Primary tumor Long non-coding RNA Systemically untreated patients low-risk breast cancer machine learning methods |
Zdroj: | Do, T T N, Block, I, Burton, M, Sørensen, K P, Larsen, M J, Bak, M, Cold, S, Thomassen, M, Tan, Q & Kruse, T A 2021, ' Comparison of the Metastasis Predictive Potential of mRNA and Long Non-Coding RNA Profiling in Systemically Untreated Breast Cancer ', Cancers, vol. 13, no. 19, 4907 . https://doi.org/10.3390/cancers13194907 Cancers Volume 13 Issue 19 Cancers, Vol 13, Iss 4907, p 4907 (2021) |
Popis: | Several gene expression signatures based on mRNAs and a few based on long non-coding RNAs (lncRNAs) have been developed to provide prognostic information beyond clinical evaluation in breast cancer (BC). However, the comparison of such signatures for predicting recurrence is very scarce. Therefore, we compared the prognostic utility of mRNAs and lncRNAs in low-risk BC patients using two different classification strategies. Frozen primary tumor samples from 160 lymph node negative and systemically untreated BC patients were included 80 developed recurrence—i.e., regional or distant metastasis while 80 remained recurrence-free (mean follow-up of 20.9 years). Patients were pairwise matched for clinicopathological characteristics. Classification based on differential mRNA or lncRNA expression using seven individual machine learning methods and a voting scheme classified patients into risk-subgroups. Classification by the seven methods with a fixed sensitivity of ≥90% resulted in specificities ranging from 16–40% for mRNA and 38–58% for lncRNA, and after voting, specificities of 38% and 60% respectively. Classifier performance based on an alternative classification approach of balanced accuracy optimization also provided higher specificities for lncRNA than mRNA at comparable sensitivities. Thus, our results suggested that classification followed by voting improved prognostic power using lncRNAs compared to mRNAs regardless of classification strategy. |
Databáze: | OpenAIRE |
Externí odkaz: |