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Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers

A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparative...

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Autores principales: Wang, Xuezhu, Dong, Yucheng, Wu, Zilong, Wang, Guanqun, Shi, Yue, Zheng, Yongchang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479194/
https://www.ncbi.nlm.nih.gov/pubmed/34604037
http://dx.doi.org/10.3389/fonc.2021.703461
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author Wang, Xuezhu
Dong, Yucheng
Wu, Zilong
Wang, Guanqun
Shi, Yue
Zheng, Yongchang
author_facet Wang, Xuezhu
Dong, Yucheng
Wu, Zilong
Wang, Guanqun
Shi, Yue
Zheng, Yongchang
author_sort Wang, Xuezhu
collection PubMed
description A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparatively analyzed the circRNA landscape in pan-cancer and pan-normal tissues. Using co-expression and LASSO regularization analyses, as well as a support vector machine, we analyzed 265 pan-cancer and 319 pan-normal tissues in order to identify the circRNAs with the highest ability to distinguish between pan-cancer and pan-normal tissues. We further studied their expression in plasma exosomes from patients with cancer and their relation with cancer mutations and tumor microenvironment landscape. We discovered that circRNA expression was globally reduced in pan-cancer tissues and plasma exosomes from cancer patients than in pan-normal tissues and plasma exosomes from healthy controls. We identified dynein axonemal heavy chain 14 (DNAH14), the top back-spliced gene exclusive to pan-cancer tissues, as the host gene of three pan-cancer tissue-enriched circRNAs. Among these three circRNAs, chr1_224952669_224968874_+ was significantly elevated in plasma exosomes from hepatocellular carcinoma and colorectal cancer patients. It was also related to the cancer mutation chr1:224952669: G>A, a splice acceptor variant, and was increasingly transcription-driven in cancer tissues. Moreover, pan-cancer tissue-enriched and pan-normal tissue-enriched circRNAs were associated with distinct tumor microenvironment patterns. Our machine learning-based analysis provides insights into the aberrant landscape and biogenesis of circRNAs in cancer and highlights cancer mutation-related and DNAH14-derived circRNA, chr1_224952669_224968874_+, as a potential cancer biomarker.
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spelling pubmed-84791942021-09-30 Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers Wang, Xuezhu Dong, Yucheng Wu, Zilong Wang, Guanqun Shi, Yue Zheng, Yongchang Front Oncol Oncology A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparatively analyzed the circRNA landscape in pan-cancer and pan-normal tissues. Using co-expression and LASSO regularization analyses, as well as a support vector machine, we analyzed 265 pan-cancer and 319 pan-normal tissues in order to identify the circRNAs with the highest ability to distinguish between pan-cancer and pan-normal tissues. We further studied their expression in plasma exosomes from patients with cancer and their relation with cancer mutations and tumor microenvironment landscape. We discovered that circRNA expression was globally reduced in pan-cancer tissues and plasma exosomes from cancer patients than in pan-normal tissues and plasma exosomes from healthy controls. We identified dynein axonemal heavy chain 14 (DNAH14), the top back-spliced gene exclusive to pan-cancer tissues, as the host gene of three pan-cancer tissue-enriched circRNAs. Among these three circRNAs, chr1_224952669_224968874_+ was significantly elevated in plasma exosomes from hepatocellular carcinoma and colorectal cancer patients. It was also related to the cancer mutation chr1:224952669: G>A, a splice acceptor variant, and was increasingly transcription-driven in cancer tissues. Moreover, pan-cancer tissue-enriched and pan-normal tissue-enriched circRNAs were associated with distinct tumor microenvironment patterns. Our machine learning-based analysis provides insights into the aberrant landscape and biogenesis of circRNAs in cancer and highlights cancer mutation-related and DNAH14-derived circRNA, chr1_224952669_224968874_+, as a potential cancer biomarker. Frontiers Media S.A. 2021-09-15 /pmc/articles/PMC8479194/ /pubmed/34604037 http://dx.doi.org/10.3389/fonc.2021.703461 Text en Copyright © 2021 Wang, Dong, Wu, Wang, Shi and Zheng 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 Oncology
Wang, Xuezhu
Dong, Yucheng
Wu, Zilong
Wang, Guanqun
Shi, Yue
Zheng, Yongchang
Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers
title Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers
title_full Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers
title_fullStr Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers
title_full_unstemmed Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers
title_short Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers
title_sort machine learning-based comparative analysis of pan-cancer and pan-normal tissues identifies pan-cancer tissue-enriched circrnas related to cancer mutations as potential exosomal biomarkers
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479194/
https://www.ncbi.nlm.nih.gov/pubmed/34604037
http://dx.doi.org/10.3389/fonc.2021.703461
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