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Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization
Breast cancer and colorectal cancer are two of the most common malignant tumors worldwide. They cause the leading causes of cancer mortality. Many researches have demonstrated that long noncoding RNAs (lncRNAs) have close linkages with the occurrence and development of the two cancers. Therefore, it...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9895102/ https://www.ncbi.nlm.nih.gov/pubmed/36744179 http://dx.doi.org/10.3389/fgene.2022.1023615 |
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author | Li, Shijun Chang, Miaomiao Tong, Ling Wang, Yuehua Wang, Meng Wang, Fang |
author_facet | Li, Shijun Chang, Miaomiao Tong, Ling Wang, Yuehua Wang, Meng Wang, Fang |
author_sort | Li, Shijun |
collection | PubMed |
description | Breast cancer and colorectal cancer are two of the most common malignant tumors worldwide. They cause the leading causes of cancer mortality. Many researches have demonstrated that long noncoding RNAs (lncRNAs) have close linkages with the occurrence and development of the two cancers. Therefore, it is essential to design an effective way to identify potential lncRNA biomarkers for them. In this study, we developed a computational method (LDA-RWLMF) by integrating random walk with restart and Logistic Matrix Factorization to investigate the roles of lncRNA biomarkers in the prognosis and diagnosis of the two cancers. We first fuse disease semantic and Gaussian association profile similarities and lncRNA functional and Gaussian association profile similarities. Second, we design a negative selection algorithm to extract negative LncRNA-Disease Associations (LDA) based on random walk. Third, we develop a logistic matrix factorization model to predict possible LDAs. We compare our proposed LDA-RWLMF method with four classical LDA prediction methods, that is, LNCSIM1, LNCSIM2, ILNCSIM, and IDSSIM. The results from 5-fold cross validation on the MNDR dataset show that LDA-RWLMF computes the best AUC value of 0.9312, outperforming the above four LDA prediction methods. Finally, we rank all lncRNA biomarkers for the two cancers after determining the performance of LDA-RWLMF, respectively. We find that 48 and 50 lncRNAs have the highest association scores with breast cancer and colorectal cancer among all lncRNAs known to associate with them on the MNDR dataset, respectively. We predict that lncRNAs HULC and HAR1A could be separately potential biomarkers for breast cancer and colorectal cancer and need to biomedical experimental validation. |
format | Online Article Text |
id | pubmed-9895102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98951022023-02-04 Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization Li, Shijun Chang, Miaomiao Tong, Ling Wang, Yuehua Wang, Meng Wang, Fang Front Genet Genetics Breast cancer and colorectal cancer are two of the most common malignant tumors worldwide. They cause the leading causes of cancer mortality. Many researches have demonstrated that long noncoding RNAs (lncRNAs) have close linkages with the occurrence and development of the two cancers. Therefore, it is essential to design an effective way to identify potential lncRNA biomarkers for them. In this study, we developed a computational method (LDA-RWLMF) by integrating random walk with restart and Logistic Matrix Factorization to investigate the roles of lncRNA biomarkers in the prognosis and diagnosis of the two cancers. We first fuse disease semantic and Gaussian association profile similarities and lncRNA functional and Gaussian association profile similarities. Second, we design a negative selection algorithm to extract negative LncRNA-Disease Associations (LDA) based on random walk. Third, we develop a logistic matrix factorization model to predict possible LDAs. We compare our proposed LDA-RWLMF method with four classical LDA prediction methods, that is, LNCSIM1, LNCSIM2, ILNCSIM, and IDSSIM. The results from 5-fold cross validation on the MNDR dataset show that LDA-RWLMF computes the best AUC value of 0.9312, outperforming the above four LDA prediction methods. Finally, we rank all lncRNA biomarkers for the two cancers after determining the performance of LDA-RWLMF, respectively. We find that 48 and 50 lncRNAs have the highest association scores with breast cancer and colorectal cancer among all lncRNAs known to associate with them on the MNDR dataset, respectively. We predict that lncRNAs HULC and HAR1A could be separately potential biomarkers for breast cancer and colorectal cancer and need to biomedical experimental validation. Frontiers Media S.A. 2023-01-20 /pmc/articles/PMC9895102/ /pubmed/36744179 http://dx.doi.org/10.3389/fgene.2022.1023615 Text en Copyright © 2023 Li, Chang, Tong, Wang, Wang and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Li, Shijun Chang, Miaomiao Tong, Ling Wang, Yuehua Wang, Meng Wang, Fang Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization |
title | Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization |
title_full | Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization |
title_fullStr | Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization |
title_full_unstemmed | Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization |
title_short | Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization |
title_sort | screening potential lncrna biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9895102/ https://www.ncbi.nlm.nih.gov/pubmed/36744179 http://dx.doi.org/10.3389/fgene.2022.1023615 |
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