de65c746686fcae869279db4108e46f8 pmc_34680205.pdf fe6f517b3590300bb8bcd1ba815746b87b77bdea pmc_34680205.pdf 9c92ac6f2c282fedc6790aac93db374c3dab0ed18e7a7cb1f172bf2bbe139288 pmc_34680205.pdf Title: In Silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer Subject: 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. Keywords: endometrial cancer; prognostic biomarker; uterine cancer; high-risk; bioinformatics; CPTAC; TCGA Author: Eva Coll-de la Rubia, Elena Martinez-Garcia, Gunnar Dittmar, Petr V. Nazarov, Vicente Bebia, Silvia Cabrera, Antonio Gil-Moreno and Eva Colás Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Mon Oct 11 06:25:34 2021 CEST ModDate: Mon Oct 11 07:17:40 2021 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 15 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1979914 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MWHIBA+VnURWPalladioL Type 1 Custom yes yes no 10 0 NHVDEW+URWPalladioL-Ital Type 1 Custom yes yes no 15 0 JRUPOQ+URWPalladioL-Bold Type 1 Custom yes yes no 20 0 TPBCXR+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 AIQCCL+VnURWPalladioL-Bold Type 1 Custom yes yes no 28 0 LCJEVJ+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 59 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes 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no 164 0 OPJILP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 167 0 OPJIKL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 176 0 OPJILN+PalatinoLinotype TrueType WinAnsi yes yes no 179 0 OPJILP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 182 0 OPJIKL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 191 0 OPJILN+PalatinoLinotype TrueType WinAnsi yes yes no 194 0 OPJILP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 197 0 OPJMIA+Arial CID TrueType Identity-H yes yes yes 206 0 OPLGFC+Arial,Bold CID TrueType Identity-H yes yes yes 212 0 OPJIKL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 218 0 OPJILN+PalatinoLinotype TrueType WinAnsi yes yes no 221 0 OPJILP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 224 0 OPJJPI+SimSun CID TrueType Identity-H yes yes yes 227 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2022-03-22 02:40:02 CET RepresentationInformation: pmc_34680205.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2022-01-10 08:31:29 CET Size: 1979914 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 420 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Children: Item: Title: Data Source Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Data Processing and Identification of Differentially Expressed Genes (DEGs) and Proteins (DEPs) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: Survival Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Statistical Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Functional Analysis of DEG/Ps, Interactions, and Tractability Information Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Children: Item: Title: Study Workflow Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Validated Prognostic Biomarkers in EC Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Validated Biomarkers Associated to OS and RFS in EC Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Biological Significance of the Validated Biomarkers Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Info: Title: In Silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer Author: Eva Coll-de la Rubia, Elena Martinez-Garcia, Gunnar Dittmar, Petr V. Nazarov, Vicente Bebia, Silvia Cabrera, Antonio Gil-Moreno and Eva Colás Subject: 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. 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