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      Construction of an Immunogenic Cell Death-Related Gene Signature and Genetic Subtypes for Predicting Prognosis, Immune Microenvironments, and Drug Sensitivity in Hepatocellular Carcinoma

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          Abstract

          Purpose

          Immunogenic cell death (ICD) is a type of regulated cell death that modifies the immune response by releasing DAMPs or danger signals. Herein, we aimed to develop an ICD-related predictive model for patients with hepatocellular carcinoma (HCC) and investigate its applicability for predicting prognostic outcomes and immunotherapeutic responses.

          Methods

          Differentially expressed genes of ICD were identified in the HCC and normal liver samples. A prognostic risk model and a nomogram containing clinicopathological features were created. To validate the effectiveness of the model, an external dataset was used. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, biological function and chemotherapeutic drug sensitivity were evaluated for different genetic subtypes and risk groups.

          Results

          A total of 35 ICD-related genes (ICDRGs) were identified between HCC and normal samples, 11 of which were significantly associated with overall survival (OS) in HCC patients. Four different genetic subtypes were formed and eight ICDRGs were selected to develop a risk prognostic model. The risk scores were shown to be an independent prognostic factor for HCC and positively correlated with pathological severity. Patients in the high-risk group had a higher frequency of TP53 mutations, increased expression of immune checkpoints and human leukocyte antigen genes. The inhibitory concentrations of chemotherapeutic drugs differed in different populations.

          Conclusion

          In this study, we developed an ICDRG risk model and demonstrated its applicability in predicting survival outcomes, immune and chemotherapeutic responses in HCC patients. ICDRGs are expected to be used as novel biomarkers in the medical decision-making of HCC.

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          Most cited references44

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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                Author and article information

                Journal
                J Inflamm Res
                J Inflamm Res
                jir
                Journal of Inflammation Research
                Dove
                1178-7031
                22 April 2024
                2024
                : 17
                : 2427-2444
                Affiliations
                [1 ]Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine , Beijing, People’s Republic of China
                [2 ]Institute of Liver Diseases, Beijing University of Chinese Medicine , Beijing, People’s Republic of China
                Author notes
                Correspondence: Xiaoke Li, Beijing University of Chinese Medicine, Haiyuncang 5, Dongcheng District, Beijing, People’s Republic of China, Tel +86 010-84015503, Email lixiaoke@vip.163.com
                Author information
                http://orcid.org/0000-0003-2788-7353
                Article
                451800
                10.2147/JIR.S451800
                11049185
                38681068
                a123b651-ce54-4d72-98f1-e54c4a6f7517
                © 2024 Li et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 26 November 2023
                : 29 March 2024
                Page count
                Figures: 10, References: 44, Pages: 18
                Funding
                Funded by: the National Natural Science Foundation of China;
                Funded by: the Beijing University of Chinese Medicine Major Project;
                Funded by: the Beijing University of Chinese Medicine Dongzhimen Hospital 2023 Science and Technology Innovation Foundation;
                This study was funded by grants from the National Natural Science Foundation of China (No. 82174341), the Beijing University of Chinese Medicine Major Project (No. 2020-JYB-ZDGG-115), and the Beijing University of Chinese Medicine Dongzhimen Hospital 2023 Science and Technology Innovation Foundation (No.DZMKJCX-2023-008).
                Categories
                Original Research

                Immunology
                immunogenic cell death-related genes,hepatocellular carcinoma,prognosis,immune microenvironments,drug sensitivity

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