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      Spindle and Kinetochore-associated Family Genes are Prognostic and Predictive Biomarkers in Hepatocellular Carcinoma

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          Abstract

          Background and Aims

          Hepatocellular carcinoma (HCC) is one of the most frequent malignant tumors. Spindle and kinetochore-associated (SKA) family genes are essential for the maintenance of the metaphase plate and spindle checkpoint silencing during mitosis. Recent studies have indicated that dysregulation of SKA family genes induces tumorigenesis, tumor progression, and chemoresistance via modulation of cell cycle and DNA replication. However, the differential transcription of SKAs in the context of HCC and its prognostic significance has not been demonstrated.

          Methods

          Bioinformatics analyses were performed using TCGA, ONCOMINE, HCCDB, Kaplan-Meier plotter, STRING, GEPIA databases. qRT-PCR, western blot, and functional assays were utilized for in vitro experiments.

          Results

          We found remarkable upregulation of transcripts of SKA family genes in HCC samples compared with normal liver samples on bioinformatics analyses and in vitro validation. Interaction analysis and enrichment analysis showed that SKA family members were mainly related to microtubule motor activity, mitosis, and cell cycle. Immuno-infiltration analysis showed a correlation of all SKA family genes with various immune cell subsets, especially T helper 2 (Th2) cells. Transcriptional levels of SKA family members were positively associated with histologic grade, T stage, and α-fetoprotein in HCC patients. Receiver operating characteristic curve analysis demonstrated a strong predictive ability of SKA1/2/3 for HCC. Increased expression of these SKAs was associated with unfavorable overall survival, progression-free survival, and disease-specific survival. On Cox proportional hazards regression analyses, SKA1 upregulation and pathological staging were independent predictors of overall survival and disease-specific survival of HCC patients. Finally, clinical tissue microarray validation and in vitro functional assays revealed SKA1 acts an important regulatory role in tumor malignant behavior.

          Conclusions

          SKA family members may potentially serve as diagnostic and prognostic markers in the context of HCC. The correlation between SKAs and immune cell infiltration provides a promising research direction for SKA-targeted immunotherapeutics for HCC.

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          Most cited references56

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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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            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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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Journal
                J Clin Transl Hepatol
                J Clin Transl Hepatol
                JCTH
                Journal of Clinical and Translational Hepatology
                XIA & HE Publishing Inc.
                2225-0719
                2310-8819
                28 August 2022
                4 January 2022
                : 10
                : 4
                : 627-641
                Affiliations
                Department of Orthopedics, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, China
                Author notes
                [* ] Correspondence to: Tongwei Chu, Department of Orthopedics, Xinqiao Hospital, Third Military Medical University (Army Medical University), No.83 Xinqiao Main Street, Shapingba District, Chongqing 400037, China. ORCID: https://orcid.org/0000-0003-0309-7082. Tel: +86-13708388336, E-mail: chtw@ 123456sina.com

                This research was funded by the General project of Chongqing Natural Science Foundation of China (No. cstc2020jcyj-msxmX0688), National Key Research and Development Plan Project (2016YFC1101504), and the Clinical Research Project of the Second Affiliated Hospital of the Army Military Medical University (2018XLC2016).

                The authors have no conflict of interests related to this publication.

                Development of the idea, design of the research and drafting of the manuscript (CC), analysis of the data (YZ, XH, SY, JY, ZW), obtainment of copies of the studies, revision of the writing and project supervision (TC). All authors read and approved the submitted version.

                Author information
                https://orcid.org/0000-0003-0309-7082
                Article
                JCTH.2021.00216
                10.14218/JCTH.2021.00216
                9396317
                6398067d-4df7-4e48-ad4e-ff5d52c6f388
                © 2022 Authors.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 June 2021
                : 7 September 2021
                : 8 October 2021
                Categories
                Original Article

                spindle and kinetochore-associated genes,liver hepatocellular carcinoma,prognostic value,immune infiltration,bioinformatics analysis

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