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      Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma

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          Abstract

          Background

          Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.

          Methods

          Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.

          Results

          A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.

          Conclusion

          A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

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          Most cited references52

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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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            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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              Regularization Paths for Generalized Linear Models via Coordinate Descent

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                Author and article information

                Journal
                Int J Gen Med
                Int J Gen Med
                ijgm
                International Journal of General Medicine
                Dove
                1178-7074
                30 November 2021
                2021
                : 14
                : 9007-9022
                Affiliations
                [1 ]Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University , Jinan, Shandong, 250021, People’s Republic of China
                [2 ]Department of Pediatrics, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, Hubei, 420100, People’s Republic of China
                [3 ]Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University , Jinan, Shandong, 250021, People’s Republic of China
                Author notes
                Correspondence: Mo Shi; Xiangyan Liu Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University , Jinan, Shandong, 250021, People’s Republic of China Email legendsm01@163.com; liuxiangyan1@163.com
                Author information
                http://orcid.org/0000-0002-6998-1543
                Article
                341175
                10.2147/IJGM.S341175
                8643179
                0d52fe3a-e979-4d1a-a393-480d3beb14ec
                © 2021 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
                : 04 October 2021
                : 15 November 2021
                Page count
                Figures: 8, References: 52, Pages: 16
                Funding
                Funded by: the National Natural Science Foundation of China (81902418);
                Funded by: the Natural Science Foundation of Shandong Province (ZR2020MH244);
                This work was supported by grants from the National Natural Science Foundation of China (81902418) and the Natural Science Foundation of Shandong Province (ZR2020MH244).
                Categories
                Original Research

                Medicine
                early-stage lung squamous cell carcinoma,prognosis,risk score,gene signature,innate immune-related genes,immune cell infiltration

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