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      A risk model based on pyroptosis subtypes predicts tumor immune microenvironment and guides chemotherapy and immunotherapy in bladder cancer

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          Abstract

          Although immunotherapy has revolutionized bladder cancer (BLCA) therapy, only few patients demonstrate durable clinical benefits due to the heterogeneity. Emerging evidence has linked pyroptosis to shaping tumor microenvironment (TME) and predicting therapy response. However, the relationship between pyroptosis and immunotherapy response in BLCA remains elusive. In this study, we performed a comprehensive bioinformatic analysis to dissect the role of pyroptosis in BLCA. Differentially expressed pyroptosis-related genes (DEPRGs) between tumor and normal tissues were identified using publicly available datasets. Kaplan–Meier analysis was performed to screen for DEPRGs associated with survival. Consensus clustering was used for BLCA subtyping. TME characteristics were evaluated by CIBERSORT, ESTIMATE and immune checkpoint genes (ICGs). Following univariate COX regression and LASSO analyses with pyroptosis-related DEGs, the risk model and nomogram were constructed with TCGA dataset and validated in the GEO dataset. Furthermore, therapeutic responses in high- and low-risk groups were compared using TIDE and GDSC databases. Two pyroptosis-related subtypes (Cluster 1 and 2) were identified based on expression patterns of GSDMA and CHMP4C. Bioinformatic analyses showed that cluster 1 had poor survival, more M0/M1/M2 macrophages, higher immune/stromal/ESTIMATE scores, and higher expression levels of ICGs. A 15-gene signature for predicting prognosis could classify patients into high- and low-risk groups. Furthermore, the correlation of risk scores with TIDE score and IC 50 showed that patients in low-risk group were more sensitive to immunotherapy, whereas patients in high-risk group could better benefit from chemotherapy. Our study identified two novel pyroptosis-related subtypes and constructed a risk model, which can predict the prognosis, improve our understanding the role of PRGs in BLCA, and guide chemotherapy and immunotherapy.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                longhuimin2021@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 December 2022
                12 December 2022
                2022
                : 12
                : 21467
                Affiliations
                Department of Urology, Ningbo Medical Center Li Huili Hospital, 315199 Ningbo, China
                Article
                26110
                10.1038/s41598-022-26110-4
                9744904
                36509838
                d155f816-5578-41e7-b235-3bee46eac1fc
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 June 2022
                : 9 December 2022
                Funding
                Funded by: The Natural Science Foundation of Ningbo
                Award ID: 2021J281
                Award ID: 2021J281
                Award ID: 2021J281
                Award ID: 2021J281
                Award ID: 2021J281
                Award Recipient :
                Categories
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                © The Author(s) 2022

                Uncategorized
                cancer,computational biology and bioinformatics,immunology,urology
                Uncategorized
                cancer, computational biology and bioinformatics, immunology, urology

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