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      Identification of prognostic immune-related genes in the tumor microenvironment of endometrial cancer

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          Abstract

          Endometrial cancer (EC) is one of the most common gynecologic malignancies. To identify potential prognostic biomarkers for EC, we analyzed the relationship between the EC tumor microenvironment and gene expression profiles. Using the ESTIMATE R tool, we found that immune and stromal scores correlated with clinical data and the prognosis of EC patients. Based on the immune and stromal scores, 387 intersection differentially expressed genes were identified. Eight immune-related genes were then identified using two machine learning algorithms. Functional enrichment analysis revealed that these genes were mainly associated with T cell activation and response. Kaplan-Meier survival analysis showed that expression of TMEM150B, CACNA2D2, TRPM5, NOL4, CTSW, and SIGLEC1 significantly correlated with overall survival times of EC patients. In addition, using the TIMER algorithm, we found that expression of TMEM150B, SIGLEC1, and CTSW correlated positively with the tumor infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, and dendritic cells. These findings indicate that the composition of the tumor microenvironment affects the clinical outcomes of EC patients, and suggests that it may provide a basis for development of novel prognostic biomarkers and immunotherapies for EC patients.

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          Evaluation of variable selection methods for random forests and omics data sets

          Abstract Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE).  In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.
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            Endometrial Carcinoma Diagnosis: Use of FIGO Grading and Genomic Subcategories in Clinical Practice: Recommendations of the International Society of Gynecological Pathologists

            In this review, we sought to address 2 important issues in the diagnosis of endometrial carcinoma: how to grade endometrial endometrioid carcinomas and how to incorporate the 4 genomic subcategories of endometrial carcinoma, as identified through The Cancer Genome Atlas, into clinical practice. The current International Federation of Gynecology and Obstetrics grading scheme provides prognostic information that can be used to guide the extent of surgery and use of adjuvant chemotherapy or radiation therapy. We recommend moving toward a binary scheme to grade endometrial endometrioid carcinomas by considering International Federation of Gynecology and Obstetrics defined grades 1 and 2 tumors as “low grade” and grade 3 tumors as “high grade.” The current evidence base does not support the use of a 3-tiered grading system, although this is considered standard by International Federation of Gynecology and Obstetrics, the American College of Obstetricians and Gynecologists, and the College of American Pathologists. As for the 4 genomic subtypes of endometrial carcinoma (copy number low/p53 wild-type, copy number high/p53 abnormal, polymerase E mutant, and mismatch repair deficient), which only recently have been identified, there is accumulating evidence showing these categories can be reproducibly diagnosed and accurately assessed based on biopsy/curettage specimens as well as hysterectomy specimens. Furthermore, this subclassification system can be adapted for current clinical practice and is of prognostic significance independent of conventional variables used for risk assessment in patients with endometrial carcinoma (eg, stage). It is too soon to recommend the routine use of genomic classification in this setting; however, with further evidence, this system may become the basis for the subclassification of all endometrial carcinomas, supplanting (partially or completely) histotype, and grade. These recommendations were developed from the International Society of Gynecological Pathologists Endometrial Carcinoma project.
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              Applications of Immunogenomics to Cancer.

              Cancer immunogenomics originally was framed by research supporting the hypothesis that cancer mutations generated novel peptides seen as "non-self" by the immune system. The search for these "neoantigens" has been facilitated by the combination of new sequencing technologies, specialized computational analyses, and HLA binding predictions that evaluate somatic alterations in a cancer genome and interpret their ability to produce an immune-stimulatory peptide. The resulting information can characterize a tumor's neoantigen load, its cadre of infiltrating immune cell types, the T or B cell receptor repertoire, and direct the design of a personalized therapeutic.
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                Author and article information

                Journal
                Aging (Albany NY)
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                29 February 2020
                19 February 2020
                : 12
                : 4
                : 3371-3387
                Affiliations
                [1 ]Department of Gynecology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
                Author notes
                Correspondence to: Jing Wan; email: wanjing@mail.sysu.edu.cn
                Correspondence to: Xiaomao Li; email: lixmao@mail.sysu.edu.cn
                Article
                102817 102817
                10.18632/aging.102817
                7066904
                32074080
                d1f61c92-70d1-482d-9319-a4cf551d4766
                Copyright © 2020 Chen et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 October 2019
                : 27 January 2020
                Categories
                Research Paper

                Cell biology
                endometrial cancer,tumor microenvironment,prognosis,immune score,tcga
                Cell biology
                endometrial cancer, tumor microenvironment, prognosis, immune score, tcga

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