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: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
BMC
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/4952c9b05ab34d2db76bfecfb25007b1 |
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| Sumario: | 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. |
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