Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer

ilustraciones, diagramas

Autores:: Montoya Rodriguez, Eileen Tatiana

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer
dc.title.translated.spa.fl_str_mv	Determinación de características del microambiente tumoral asociadas con la progresión del cáncer de ovario
title	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer
spellingShingle	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer 620 - Ingeniería y operaciones afines 610 - Medicina y salud::614 - Medicina Forense; incidencia de lesiones, heridas, enfermedades; medicina preventiva pública 610 - Medicina y salud::616 - Enfermedades Microambiente Tumoral Neoplasias Ováricas Técnicas Histológicas Células del Estroma Tumor Microenvironment Ovarian Neoplasms Histological Techniques Stromal Cells Microambiente tumoral Carcinoma seroso de ovario Riesgo de supervivencia Histopatología computacional Tumor microenvironment Serous carcinomas cancer ovary Survival risk Computational histopathology
title_short	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer
title_full	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer
title_fullStr	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer
title_full_unstemmed	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer
title_sort	Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer
dc.creator.fl_str_mv	Montoya Rodriguez, Eileen Tatiana
dc.contributor.advisor.spa.fl_str_mv	Romero Castro, Edgar Eduardo
dc.contributor.author.spa.fl_str_mv	Montoya Rodriguez, Eileen Tatiana
dc.contributor.researcher.spa.fl_str_mv	Salguero Lopez, Jennifer
dc.contributor.researchgroup.spa.fl_str_mv	Cim@Lab
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines 610 - Medicina y salud::614 - Medicina Forense; incidencia de lesiones, heridas, enfermedades; medicina preventiva pública 610 - Medicina y salud::616 - Enfermedades
topic	620 - Ingeniería y operaciones afines 610 - Medicina y salud::614 - Medicina Forense; incidencia de lesiones, heridas, enfermedades; medicina preventiva pública 610 - Medicina y salud::616 - Enfermedades Microambiente Tumoral Neoplasias Ováricas Técnicas Histológicas Células del Estroma Tumor Microenvironment Ovarian Neoplasms Histological Techniques Stromal Cells Microambiente tumoral Carcinoma seroso de ovario Riesgo de supervivencia Histopatología computacional Tumor microenvironment Serous carcinomas cancer ovary Survival risk Computational histopathology
dc.subject.decs.spa.fl_str_mv	Microambiente Tumoral Neoplasias Ováricas Técnicas Histológicas Células del Estroma
dc.subject.decs.eng.fl_str_mv	Tumor Microenvironment Ovarian Neoplasms Histological Techniques Stromal Cells
dc.subject.proposal.spa.fl_str_mv	Microambiente tumoral Carcinoma seroso de ovario Riesgo de supervivencia Histopatología computacional
dc.subject.proposal.eng.fl_str_mv	Tumor microenvironment Serous carcinomas cancer ovary Survival risk Computational histopathology
description	ilustraciones, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-09-18T15:31:29Z
dc.date.available.none.fl_str_mv	2024-09-18T15:31:29Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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url	https://repositorio.unal.edu.co/handle/unal/86841 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	A. Ghoneum, Role of tumor microenvironment in ovarian cancer pathobiology, 27 April 2018. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978268 I.J. Jacobs, «Progress and Challenges in Screening for Early Detection of Ovarian Cancer*, Molecular & Cellular Proteomics» [En línea]. Available: https://doi.org/10.1074/mcp.R400006-MCP200. KM. Boehm. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer. 2022 Jun;3(6):723-733. doi: 10.1038/s43018-022-00388-9. Y. Yang. Tumor Microenvironment in Ovarian Cancer: Function and Therapeutic Strategy. 11 August 2020. https://pubmed.ncbi.nlm.nih.gov/25271436/. C. Lan. Quantitative histology analysis of the ovarian tumour microenvironment, 17 November 2015. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4647219 Xu H., Machine Learning and Artificial Intelligence–driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides, European Urology Focus,Volume 7, Issue 4, 2021, Pages 706-709, ISSN 2405-4569, https://doi.org/10.1016/j.euf.2021.07.006. S. Mittal. Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology, 4 Junio 2018. https://doi.org/10.1073/pnas.1719551115 P. Ribeiro, Tumor microenvironment components: Allies of cancer progression, Pathology - Research and Practice, Volume 216, Issue 1, 2020, 152729, ISSN 0344-0338, https://doi.org/10.1016/j.prp.2019.152729 S. Nawaz, Computational pathology: Exploring the spatial dimension of tumor ecology, Cancer Letters, Volume 380, Issue 1, 2016, Pages 296-303, ISSN 0304-3835, https://doi.org/10.1016/j.canlet.2015.11.018 A. Heindl, Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer, 2016. https://doi.org/10.18632/oncotarget.12106 ZG Liu, Necroptosis, tumor necrosis and tumorigenesis. Cell Stress. 2019 Dec 19;4(1):1-8. doi: 10.15698/cst2020.01.208 Zhao, Y., Stromal cells in the tumor microenvironment: accomplices of tumor progression?. Cell Death Dix 14, 587 (2023). https://doi.org/10.1038/s41419-023-06110-6 American Cancer Society, Inc., Key Statistics for Ovarian Cancer, 12 January 2021. https://www.cancer.org/cancer/ovarian-cancer/about/key-statistics.html F. Ning. Driving Immune Responses in the Ovarian Tumor Microenvironment, 15 January 2021. https://www.frontiersin.org/articles/10.3389/fonc.2020.604084/full International Agency for Research on Cancer, Colombia Cancer Statistics, March 2021. https://gco.iarc.fr/today/data/factsheets/populations/170-colombia-fact-sheets.pdf R. Yigit. Ovarian cancer creates a suppressive microenvironment to escape immune elimination. May 2010. Available: https://doi.org/10.1016/j.ygyno.2010.01.019 H. Sadeghi. Understanding the tumor microenvironment for effective immunotherapy. Med Res Rev. May 2021;41(3):1474-1498. doi: 10.1002/med.21765 S. Sanegre. Integrating the Tumor Microenvironment into Cancer Therapy. Cancers (Basel). Jun 2020;12(6):1677. doi: 10.3390/cancers12061677 S. Tiwari. INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival. Febrary 2021 doi: 10.1126/sciadv.abb8292 J. Breen. Artificial intelligence in ovarian cancer histopathology: a systematic review. August 2023 doi: https://doi.org/10.1038/s41698-023-00432-6 LK. Hema. Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network. November 2022 doi: 10.1155/2022/5968939 CW Wang. Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images. July 2022 doi: 10.1016/j.compmedimag.2022.102093 M Wu. Exploring prognostic indicators in the pathological images of ovarian cancer based on a deep survival network. Jan 2023 doi: 10.3389/fgene.2022.1069673 Tiwari S. INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival. Sci Adv. 2021 Feb 3;7(6):eabb8292. doi: 10.1126/sciadv.abb8292 CR Stoltzfus. CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues. Cell Rep. 2020 Apr 21;31(3):107523. doi: 10.1016/j.celrep.2020.107523 CM Schürch. Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front. Cell. 2020 Sep 3;182(5):1341-1359.e19. doi: 10.1016/j.cell.2020.07.005 AK Casasent. Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing. Cell. 2018 Jan 11;172(1-2):205-217.e12. doi: 10.1016/j.cell.2017.12.007 MA Lisio. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci. 2019 Feb 22;20(4):952. doi: 10.3390/ijms20040952 S Huh. Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry. J Proteome Res. 2022 Sep 2;21(9):2146-2159. doi: 10.1021/acs.jproteome.2c00218 R. Zhou. Survival prediction of ovarian serous carcinoma based on machine learning combined with pathological images and clinical information. 2024 April. AIP Advances 14, 045324. doi: 10.1063/5.0196414 CW. Wang. A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker. Cancers (Basel). 2022 Mar 24;14(7):1651. doi: 10.3390/cancers14071651 A. Gockley. Outcomes of Women With High-Grade and Low-Grade Advanced-Stage Serous Epithelial Ovarian Cancer. Obstet Gynecol. 2017 Mar;129(3):439-447. doi: 10.1097/AOG.0000000000001867 K. Yoshihara: "Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets." PLoS One. 2010 Mar 10;5(3). doi: 10.1371/journal.pone.0009615 The Cancer Genome Atlas Research Network: "Integrated genomic analyses of ovarian carcinoma." Nature. 2011 Jun 29;474(7353):609-15. doi: 10.1038/nature10166
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dc.format.extent.spa.fl_str_mv	xi, 34 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Medicina - Maestría en Ingeniería Biomédica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Medicina
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Romero Castro, Edgar Eduardod49b2499bdf2c07e42f8b4dc9715ef18Montoya Rodriguez, Eileen Tatiana389c71de38824b10814ac08c98d23cd3Salguero Lopez, JenniferCim@Lab2024-09-18T15:31:29Z2024-09-18T15:31:29Z2024https://repositorio.unal.edu.co/handle/unal/86841Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasThe tumor microenvironment (TME) encompasses the dynamic interactions between a tumor and surrounding tissues, playing a crucial role in cancer progression. Histological images provide valuable information about the characteristics of the TME. Computational pathology techniques have made significant advances in automating the identification and classification of these interactions; however, certain limitations still persist. The complexity and heterogeneity of the microenvironment make it difficult to identify and classify cellular interactions accurately. Additionally, large volumes of manually annotated data are required to train robust algorithms, and variability in sample staining can affect the consistency of results. Finally, the integration of histological data with other types of data remains a considerable technical and analytical challenge. In this study, we propose a novel approach to define the TME as the interaction zones between tumor, necrotic, and stroma tissues. These zones were classified using a Support Vector Machine (SVM) with an average classification accuracy of 80 %. To establish the relevance of the TME in ovarian cancer, we used its association with survival outcomes using Cox regression modeling. The cases were categorized into high and low-risk groups based on survival time. The results demonstrated a significant correlation using hand-crafted features extracted from the TME, the Cox regression exhibited a notable hazard ratio of 2, 59 (95 % CI: 1, 06 − 6, 3, p = 0,03), indicating a statistically significant impact between TME and survival rate. This methodology suggests that TME organization could serve as a predictive marker in serous carcinoma of the ovary, providing valuable insights into the role of the tumor microenvironment in disease development.El microambiente tumoral (TME) abarca las interacciones din´amicas entre un tumor y los tejidos circundantes, desempe˜nando un papel crucial en la progresi´on del c´ancer. Las im´agenes histol´ogicas proporcionan informaci´on valiosa sobre las caracter´ısticas del TME. Las t´ecnicas de patolog´ıa computacional han logrado avances significativos en la automatizaci´on de la identificaci´on y clasificaci´on de estas interacciones; sin embargo, a´un persisten ciertas limitaciones, como la complejidad y heterogeneidad del microambiente, lo que dificulta la identificaci´on y clasificaci´on de las interacciones celulares. Adem´as, se requiere de grandes vol´umenes de datos anotados manualmente para entrenar algoritmos robustos, y la variabilidad en la tinci´on de muestras puede afectar la consistencia de los resultados. Por ´ultimo, la integraci´on de datos histol´ogicos con otros tipos de datos sigue siendo un desaf´ıo t´ecnico y anal´ıtico considerable. En este estudio, proponemos un enfoque novedoso para definir el TME como las zonas de interacci´on entre los tejidos tumorales, necr´oticos y del estroma. Estas zonas se clasificaron utilizando una m´aquina de vectores de soporte (SVM) con una precisi´on de clasificaci´on promedio de 80 %. Para establecer la relevancia del TME en el c´ancer de ovario, utilizamos su asociaci´on con los resultados de supervivencia mediante la regresi´on de Cox. Los casos se clasificaron en grupos de alto y bajo riesgo seg´un el tiempo de supervivencia. Los resultados demostraron una correlaci´on significativa utilizando caracter´ısticas hechas a mano extra´ıdas del TME, la regresi´on de Cox mostr´o un ´ındice de riesgo notable de 2, 59 (95 % CI: 1, 06−6, 3, p = 0,03), lo que indica un impacto estad´ısticamente significativo entre el TME y la tasa de supervivencia. Esta metodolog´ıa sugiere que la organizaci´on TME podr´ıa servir como marcador predictivo en el carcinoma seroso de ovario, proporcionando informaci´on valiosa sobre el papel del microambiente tumoral en el desarrollo de la enfermedad (Texto tomado de la fuente).MaestríaMagister en Ingeniería BiomédicaManual annotation was performed on 30 whole slide images (WSIs) by a pathologist to classify tissues into three distinct classes: stroma (green), tumor (red), and necrotic (blue). From each WSI, Consecutive patches of size 224 × 224 pixels were selected for further analysis. These patches were chosen because they provide an optimal balance between computational efficiency and detailed tissue representation. The dataset was constructed by randomly selecting 20,000 patches per tissue type, resulting in a total of 60,000 patches, not overlaping. This dataset was then divided into training and test subsets using a 70-30 split: 70 % (21 patients) for training and 30 % (9 patients) for testing. Additionally, a 10-fold cross-validation was performed to ensure robustness and generalization of the models.Patologia computacionalxi, 34 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Ingeniería BiomédicaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines610 - Medicina y salud::614 - Medicina Forense; incidencia de lesiones, heridas, enfermedades; medicina preventiva pública610 - Medicina y salud::616 - EnfermedadesMicroambiente TumoralNeoplasias OváricasTécnicas HistológicasCélulas del EstromaTumor MicroenvironmentOvarian NeoplasmsHistological TechniquesStromal CellsMicroambiente tumoralCarcinoma seroso de ovarioRiesgo de supervivenciaHistopatología computacionalTumor microenvironmentSerous carcinomas cancer ovarySurvival riskComputational histopathologyFinding out tumor microenvironment characteristics associated with the progression of ovarian cancerDeterminación de características del microambiente tumoral asociadas con la progresión del cáncer de ovarioTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMA. Ghoneum, Role of tumor microenvironment in ovarian cancer pathobiology, 27 April 2018. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978268I.J. Jacobs, «Progress and Challenges in Screening for Early Detection of Ovarian Cancer*, Molecular & Cellular Proteomics» [En línea]. Available: https://doi.org/10.1074/mcp.R400006-MCP200.KM. Boehm. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer. 2022 Jun;3(6):723-733. doi: 10.1038/s43018-022-00388-9.Y. Yang. Tumor Microenvironment in Ovarian Cancer: Function and Therapeutic Strategy. 11 August 2020. https://pubmed.ncbi.nlm.nih.gov/25271436/.C. Lan. Quantitative histology analysis of the ovarian tumour microenvironment, 17 November 2015. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4647219Xu H., Machine Learning and Artificial Intelligence–driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides, European Urology Focus,Volume 7, Issue 4, 2021, Pages 706-709, ISSN 2405-4569, https://doi.org/10.1016/j.euf.2021.07.006.S. Mittal. Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology, 4 Junio 2018. https://doi.org/10.1073/pnas.1719551115P. Ribeiro, Tumor microenvironment components: Allies of cancer progression, Pathology - Research and Practice, Volume 216, Issue 1, 2020, 152729, ISSN 0344-0338, https://doi.org/10.1016/j.prp.2019.152729S. Nawaz, Computational pathology: Exploring the spatial dimension of tumor ecology, Cancer Letters, Volume 380, Issue 1, 2016, Pages 296-303, ISSN 0304-3835, https://doi.org/10.1016/j.canlet.2015.11.018A. Heindl, Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer, 2016. https://doi.org/10.18632/oncotarget.12106ZG Liu, Necroptosis, tumor necrosis and tumorigenesis. Cell Stress. 2019 Dec 19;4(1):1-8. doi: 10.15698/cst2020.01.208Zhao, Y., Stromal cells in the tumor microenvironment: accomplices of tumor progression?. Cell Death Dix 14, 587 (2023). https://doi.org/10.1038/s41419-023-06110-6American Cancer Society, Inc., Key Statistics for Ovarian Cancer, 12 January 2021. https://www.cancer.org/cancer/ovarian-cancer/about/key-statistics.htmlF. Ning. Driving Immune Responses in the Ovarian Tumor Microenvironment, 15 January 2021. https://www.frontiersin.org/articles/10.3389/fonc.2020.604084/fullInternational Agency for Research on Cancer, Colombia Cancer Statistics, March 2021. https://gco.iarc.fr/today/data/factsheets/populations/170-colombia-fact-sheets.pdfR. Yigit. Ovarian cancer creates a suppressive microenvironment to escape immune elimination. May 2010. Available: https://doi.org/10.1016/j.ygyno.2010.01.019H. Sadeghi. Understanding the tumor microenvironment for effective immunotherapy. Med Res Rev. May 2021;41(3):1474-1498. doi: 10.1002/med.21765S. Sanegre. Integrating the Tumor Microenvironment into Cancer Therapy. Cancers (Basel). Jun 2020;12(6):1677. doi: 10.3390/cancers12061677S. Tiwari. INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival. Febrary 2021 doi: 10.1126/sciadv.abb8292J. Breen. Artificial intelligence in ovarian cancer histopathology: a systematic review. August 2023 doi: https://doi.org/10.1038/s41698-023-00432-6LK. Hema. Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network. November 2022 doi: 10.1155/2022/5968939CW Wang. Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images. July 2022 doi: 10.1016/j.compmedimag.2022.102093M Wu. Exploring prognostic indicators in the pathological images of ovarian cancer based on a deep survival network. Jan 2023 doi: 10.3389/fgene.2022.1069673Tiwari S. INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival. Sci Adv. 2021 Feb 3;7(6):eabb8292. doi: 10.1126/sciadv.abb8292CR Stoltzfus. CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues. Cell Rep. 2020 Apr 21;31(3):107523. doi: 10.1016/j.celrep.2020.107523CM Schürch. Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front. Cell. 2020 Sep 3;182(5):1341-1359.e19. doi: 10.1016/j.cell.2020.07.005AK Casasent. Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing. Cell. 2018 Jan 11;172(1-2):205-217.e12. doi: 10.1016/j.cell.2017.12.007MA Lisio. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci. 2019 Feb 22;20(4):952. doi: 10.3390/ijms20040952S Huh. Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry. J Proteome Res. 2022 Sep 2;21(9):2146-2159. doi: 10.1021/acs.jproteome.2c00218R. Zhou. Survival prediction of ovarian serous carcinoma based on machine learning combined with pathological images and clinical information. 2024 April. AIP Advances 14, 045324. doi: 10.1063/5.0196414CW. Wang. A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker. Cancers (Basel). 2022 Mar 24;14(7):1651. doi: 10.3390/cancers14071651A. Gockley. Outcomes of Women With High-Grade and Low-Grade Advanced-Stage Serous Epithelial Ovarian Cancer. Obstet Gynecol. 2017 Mar;129(3):439-447. doi: 10.1097/AOG.0000000000001867K. Yoshihara: "Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets." PLoS One. 2010 Mar 10;5(3). doi: 10.1371/journal.pone.0009615The Cancer Genome Atlas Research Network: "Integrated genomic analyses of ovarian carcinoma." Nature. 2011 Jun 29;474(7353):609-15. doi: 10.1038/nature10166EstudiantesLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86841/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1014260389_2024.pdf1014260389_2024.pdfTesis de Maestria en Ingenieria Biomedicaapplication/pdf3899206https://repositorio.unal.edu.co/bitstream/unal/86841/2/1014260389_2024.pdf89bacc33950716a692a2284c040368a8MD52THUMBNAIL1014260389_2024.pdf.jpg1014260389_2024.pdf.jpgGenerated Thumbnailimage/jpeg4525https://repositorio.unal.edu.co/bitstream/unal/86841/3/1014260389_2024.pdf.jpg7e71be74fbabf46294ed4a397fc10437MD53unal/86841oai:repositorio.unal.edu.co:unal/868412024-09-18 23:50:29.799Repositorio Institucional Universidad Nacional de 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Finding out tumor microenvironment characteristics associated with the progression of ovarian cancer

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