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      Role of Kinetochore Scaffold 1 (KNL1) in Tumorigenesis and Tumor Immune Microenvironment in Pan-Cancer: Bioinformatics Analyses and Validation of Expression

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          Abstract

          Purpose

          Kinetochore scaffold 1 (KNL1), a crucial protein during cell mitosis participating in cell division, was widely expressed in multiple kinds of cancers. However, the expression profile, the effect on cell biological function, tumor immune microenvironment, and predictive value of clinical prognosis in pan-cancer of KNL1 still require a comprehensive inquiry.

          Methods

          The mRNA and protein expression profile of KNL1 was validated in pan-cancer using different databases. Six algorithms were used to explore the correlation between KNL1 and immune infiltration and the relationship between KNL1 and tumor mutation burden (TMB), microsatellite instability (MSI), and TIDE score were calculated. The diagnostic and clinical prognostic predictive ability of KNL1 was assessed. Differentially expressed genes (DEGs) of KNL1 were screened out and function enrichment analyses were performed in pancreatic adenocarcinoma (PAAD), stomach adenocarcinoma (STAD), and bladder urothelial carcinoma (BLCA). Finally, 8 cases of pancreatic adenocarcinoma tissues and paired adjacent tissues were collected for immunohistochemical (IHC) staining and the histological score (H-score) was calculated. Real-time PCR was performed in gastric cancer and bladder cancer cell lines.

          Results

          KNL1 was abnormally upregulated in more than half of cancers across different databases. IHC and real-time PCR verified the up-regulated expression in cancer tissues in PAAD, gastric cancer, and BLCA. The satisfactory diagnostic value of KNL1 was indicated in 30 cancers and high KNL1 expression was associated with poorer overall survival (OS) in 12 cancers. The prognostic role of KNL1 as a predictive biomarker of PAAD was clarified. KNL1 played an active part in the cell cycle and cell proliferation. Moreover, KNL1 was likely to mold the Th2-dominant suppressive tumor immune microenvironment and was associated with TMB, MSI, and immune checkpoint-related genes in pan-cancer.

          Conclusion

          Our study elucidated the anomalous expression of KNL1 and revealed that KNL1 was a promising prognostic biomarker in pan-cancer.

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          Most cited references55

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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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              Robust enumeration of cell subsets from tissue expression profiles

              We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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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
                31 October 2023
                2023
                : 16
                : 4883-4906
                Affiliations
                [1 ]Department of Oncology, the Affiliated Hospital of Qingdao University , Qingdao, People’s Republic of China
                [2 ]Department of Urology, Qingdao Municipal Hospital, Qingdao University , Qingdao, People’s Republic of China
                [3 ]Department of Gastrointestinal Surgery, the Affiliated Hospital of Qingdao University , Qingdao, People’s Republic of China
                Author notes
                Correspondence: Weiwei Qi, Email qwwqdfy@126.com
                [*]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0009-0002-0995-409X
                Article
                424245
                10.2147/IJGM.S424245
                10625436
                f1f11af2-c755-43d3-b063-960b3b63f2c2
                © 2023 Ding 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
                : 28 June 2023
                : 24 October 2023
                Page count
                Figures: 8, References: 55, Pages: 24
                Categories
                Original Research

                Medicine
                knl1,bioinformatics,pan-cancer,immune infiltration,prognosis,clinical prognostic model
                Medicine
                knl1, bioinformatics, pan-cancer, immune infiltration, prognosis, clinical prognostic model

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