Conditional transcriptional relationships may serve as cancer prognostic markers

Abstract Background While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation valu...

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Autores principales:	Hui Yu, Limei Wang, Danqian Chen, Jin Li, Yan Guo
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Cancer prognosis Correlation by Individual Level Product Conditional transcriptional relationships Internal medicine RC31-1245 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/4952c9b05ab34d2db76bfecfb25007b1
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spelling	oai:doaj.org-article:4952c9b05ab34d2db76bfecfb25007b12021-12-05T12:05:23ZConditional transcriptional relationships may serve as cancer prognostic markers10.1186/s12920-021-00958-31755-8794https://doaj.org/article/4952c9b05ab34d2db76bfecfb25007b12021-12-01T00:00:00Zhttps://doi.org/10.1186/s12920-021-00958-3https://doaj.org/toc/1755-8794Abstract Background While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods. Methods Here, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach. Results By integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer gene ESPL1, and the transcriptional synchrony between ESPL1 and PTTG1 tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types. Conclusions Combining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.Hui YuLimei WangDanqian ChenJin LiYan GuoBMCarticleCancer prognosisCorrelation by Individual Level ProductConditional transcriptional relationshipsInternal medicineRC31-1245GeneticsQH426-470ENBMC Medical Genomics, Vol 14, Iss S2, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cancer prognosis Correlation by Individual Level Product Conditional transcriptional relationships Internal medicine RC31-1245 Genetics QH426-470
spellingShingle	Cancer prognosis Correlation by Individual Level Product Conditional transcriptional relationships Internal medicine RC31-1245 Genetics QH426-470 Hui Yu Limei Wang Danqian Chen Jin Li Yan Guo Conditional transcriptional relationships may serve as cancer prognostic markers
description	Abstract Background While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods. Methods Here, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach. Results By integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer gene ESPL1, and the transcriptional synchrony between ESPL1 and PTTG1 tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types. Conclusions Combining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.
format	article
author	Hui Yu Limei Wang Danqian Chen Jin Li Yan Guo
author_facet	Hui Yu Limei Wang Danqian Chen Jin Li Yan Guo
author_sort	Hui Yu
title	Conditional transcriptional relationships may serve as cancer prognostic markers
title_short	Conditional transcriptional relationships may serve as cancer prognostic markers
title_full	Conditional transcriptional relationships may serve as cancer prognostic markers
title_fullStr	Conditional transcriptional relationships may serve as cancer prognostic markers
title_full_unstemmed	Conditional transcriptional relationships may serve as cancer prognostic markers
title_sort	conditional transcriptional relationships may serve as cancer prognostic markers
publisher	BMC
publishDate	2021
url	https://doaj.org/article/4952c9b05ab34d2db76bfecfb25007b1
work_keys_str_mv	AT huiyu conditionaltranscriptionalrelationshipsmayserveascancerprognosticmarkers AT limeiwang conditionaltranscriptionalrelationshipsmayserveascancerprognosticmarkers AT danqianchen conditionaltranscriptionalrelationshipsmayserveascancerprognosticmarkers AT jinli conditionaltranscriptionalrelationshipsmayserveascancerprognosticmarkers AT yanguo conditionaltranscriptionalrelationshipsmayserveascancerprognosticmarkers
_version_	1718372250411859968

Conditional transcriptional relationships may serve as cancer prognostic markers

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