TY - JOUR
T1 - In silico approach for validating and unveiling new applications for prognostic biomarkers of endometrial cancer
AU - la Rubia, Eva Coll De
AU - Martinez-Garcia, Elena
AU - Dittmar, Gunnar
AU - Nazarov, Petr V.
AU - Bebia, Vicente
AU - Cabrera, Silvia
AU - Gil-Moreno, Antonio
AU - Colás, Eva
N1 - Funding Information:
This research was funded by grants from the Instituto de Salud Carlos III (ISCIII) grant number PI17/02155, PI20/00644, and the IFI19/00029 to E.C.-d.l.R., the Ministerio de ciencia, Innovaci?n y Universidades through a RETOS Colaboraci?n (RTC-2017-6261-1), both co-financed by the European Regional Development Fund (FEDER); from Fundaci?n Cient?fica Asociaci?n Espa?ola Contra el C?ncer (AECC) grant number GCTRA1804MATI and CIBERONC network grant number CB16/12/00328; and Grups Consolidats de la Generalitat de Catalunya (2017SGR1661). E.C. is supported by an Investigator Grant from AECC (INVES20051COLA). E.M.-G. was supported by Televie grant F5/20/5-TLV/DD.
Funding Information:
Funding: This research was funded by grants from the Instituto de Salud Carlos III (ISCIII) grant number PI17/02155, PI20/00644, and the IFI19/00029 to E.C.-d.l.R., the Ministerio de ciencia, Inno-vación y Universidades through a RETOS Colaboración (RTC-2017-6261-1), both co-financed by the European Regional Development Fund (FEDER); from Fundación Científica Asociación Española Contra el Cáncer (AECC) grant number GCTRA1804MATI and CIBERONC network grant number CB16/12/00328; and Grups Consolidats de la Generalitat de Catalunya (2017SGR1661). E.C. is supported by an Investigator Grant from AECC (INVES20051COLA). E.M.-G. was supported by Televie grant F5/20/5-TLV/DD.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/10/9
Y1 - 2021/10/9
N2 - Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.
AB - Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.
KW - Bioinformatics
KW - CPTAC
KW - Endometrial cancer
KW - High-risk
KW - Prognostic biomarker
KW - TCGA
KW - Uterine cancer
UR - http://www.scopus.com/inward/record.url?scp=85116736757&partnerID=8YFLogxK
UR - https://www.ncbi.nlm.nih.gov/pubmed/34680205
U2 - 10.3390/cancers13205052
DO - 10.3390/cancers13205052
M3 - Article
C2 - 34680205
AN - SCOPUS:85116736757
SN - 2072-6694
VL - 13
JO - Cancers
JF - Cancers
IS - 20
M1 - 5052
ER -