Cargando…
CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients
BACKGROUND: Circadian rhythm regulates complex physiological activities in organisms. A strong link between circadian dysfunction and cancer has been identified. However, the factors of dysregulation and functional significance of circadian rhythm genes in cancer have received little attention. METH...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9996877/ https://www.ncbi.nlm.nih.gov/pubmed/36895015 http://dx.doi.org/10.1186/s12967-023-04013-w |
_version_ | 1784903143332511744 |
---|---|
author | Liu, Yuwei Guo, Shuang Sun, Yue Zhang, Caiyu Gan, Jing Ning, Shangwei Wang, Junwei |
author_facet | Liu, Yuwei Guo, Shuang Sun, Yue Zhang, Caiyu Gan, Jing Ning, Shangwei Wang, Junwei |
author_sort | Liu, Yuwei |
collection | PubMed |
description | BACKGROUND: Circadian rhythm regulates complex physiological activities in organisms. A strong link between circadian dysfunction and cancer has been identified. However, the factors of dysregulation and functional significance of circadian rhythm genes in cancer have received little attention. METHODS: In 18 cancer types from The Cancer Genome Atlas (TCGA), the differential expression and genetic variation of 48 circadian rhythm genes (CRGs) were examined. The circadian rhythm score (CRS) model was created using the ssGSEA method, and patients were divided into high and low groups based on the CRS. The Kaplan–Meier curve was created to assess the patient survival rate. Cibersort and estimate methods were used to identify the infiltration characteristics of immune cells between different CRS subgroups. Gene Expression Omnibus (GEO) dataset is used as verification queue and model stability evaluation queue. The CRS model's ability to predict chemotherapy and immunotherapy was assessed. Wilcoxon rank-sum test was used to compare the differences of CRS among different patients. We use CRS to identify potential "clock-drugs" by the connective map method. RESULTS: Transcriptomic and genomic analyses of 48 CRGs revealed that most core clock genes are up-regulated, while clock control genes are down-regulated. Furthermore, we show that copy number variation may affect CRGs aberrations. Based on CRS, patients can be classified into two groups with significant differences in survival and immune cell infiltration. Further studies showed that patients with low CRS were more sensitive to chemotherapy and immunotherapy. Additionally, we identified 10 compounds (e.g. flubendazole, MLN-4924, ingenol) that are positively associated with CRS, and have the potential to modulate circadian rhythms. CONCLUSIONS: CRS can be utilized as a clinical indicator to predict patient prognosis and responsiveness to therapy, and identify potential "clock-drugs". SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04013-w. |
format | Online Article Text |
id | pubmed-9996877 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99968772023-03-10 CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients Liu, Yuwei Guo, Shuang Sun, Yue Zhang, Caiyu Gan, Jing Ning, Shangwei Wang, Junwei J Transl Med Research BACKGROUND: Circadian rhythm regulates complex physiological activities in organisms. A strong link between circadian dysfunction and cancer has been identified. However, the factors of dysregulation and functional significance of circadian rhythm genes in cancer have received little attention. METHODS: In 18 cancer types from The Cancer Genome Atlas (TCGA), the differential expression and genetic variation of 48 circadian rhythm genes (CRGs) were examined. The circadian rhythm score (CRS) model was created using the ssGSEA method, and patients were divided into high and low groups based on the CRS. The Kaplan–Meier curve was created to assess the patient survival rate. Cibersort and estimate methods were used to identify the infiltration characteristics of immune cells between different CRS subgroups. Gene Expression Omnibus (GEO) dataset is used as verification queue and model stability evaluation queue. The CRS model's ability to predict chemotherapy and immunotherapy was assessed. Wilcoxon rank-sum test was used to compare the differences of CRS among different patients. We use CRS to identify potential "clock-drugs" by the connective map method. RESULTS: Transcriptomic and genomic analyses of 48 CRGs revealed that most core clock genes are up-regulated, while clock control genes are down-regulated. Furthermore, we show that copy number variation may affect CRGs aberrations. Based on CRS, patients can be classified into two groups with significant differences in survival and immune cell infiltration. Further studies showed that patients with low CRS were more sensitive to chemotherapy and immunotherapy. Additionally, we identified 10 compounds (e.g. flubendazole, MLN-4924, ingenol) that are positively associated with CRS, and have the potential to modulate circadian rhythms. CONCLUSIONS: CRS can be utilized as a clinical indicator to predict patient prognosis and responsiveness to therapy, and identify potential "clock-drugs". SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04013-w. BioMed Central 2023-03-09 /pmc/articles/PMC9996877/ /pubmed/36895015 http://dx.doi.org/10.1186/s12967-023-04013-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Yuwei Guo, Shuang Sun, Yue Zhang, Caiyu Gan, Jing Ning, Shangwei Wang, Junwei CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients |
title | CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients |
title_full | CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients |
title_fullStr | CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients |
title_full_unstemmed | CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients |
title_short | CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients |
title_sort | crs: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9996877/ https://www.ncbi.nlm.nih.gov/pubmed/36895015 http://dx.doi.org/10.1186/s12967-023-04013-w |
work_keys_str_mv | AT liuyuwei crsacircadianrhythmscoremodelforpredictingprognosisandtreatmentresponseincancerpatients AT guoshuang crsacircadianrhythmscoremodelforpredictingprognosisandtreatmentresponseincancerpatients AT sunyue crsacircadianrhythmscoremodelforpredictingprognosisandtreatmentresponseincancerpatients AT zhangcaiyu crsacircadianrhythmscoremodelforpredictingprognosisandtreatmentresponseincancerpatients AT ganjing crsacircadianrhythmscoremodelforpredictingprognosisandtreatmentresponseincancerpatients AT ningshangwei crsacircadianrhythmscoremodelforpredictingprognosisandtreatmentresponseincancerpatients AT wangjunwei crsacircadianrhythmscoremodelforpredictingprognosisandtreatmentresponseincancerpatients |