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      Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets

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          Abstract

          Objectives

          Patients with colorectal cancer (CRC) may be susceptible to the coronavirus disease-2019 (COVID-19). However, anti-CRC/COVID-19 treatment options are currently unavailable. Since niacin is a vitamin with cytoprotective and anti-inflammatory functions, this study aimed to evaluate the possible functional roles and underlying mechanisms of action of niacin as an anti-COVID-19 and -CRC therapy.

          Interventions

          We used a series of network pharmacology-based and computational analyses to understand and characterize the binding capacity, biological functions, pharmacological targets and therapeutic mechanisms of niacin in CRC/COVID-19.

          Measurements and main results

          We revealed the clinical characteristics of CRC patients and COVID-19 patients, including predisposing genes, survival rate and prognosis. Moreover, the results of molecular docking analysis indicated that niacin exerted effective binding capacity in COVID-19. Further, we disclosed the targets, biological functions and signaling pathways of niacin in CRC/COVID-19. The analysis indicated that niacin could help in treating CRC/COVID-19 through cytoprotection, enhancement of immunologic functions, inhibition of inflammatory reactions and regulation of cellular microenvironment. Furthermore, five core pharmacological targets of niacin in CRC/COVID-19 were also identified, including BCL2L1, PTGS2, IL1B, IFNG and SERPINE1.

          Conclusions

          This study, for the first time, revealed the niacin-associated molecular functions and pharmacological targets for treating CRC/COVID-19, as COVID-19 remains a serious pandemic. But the findings were not validated in actual CRC patients infected with COVID-19, so further investigation is needed to confirm the potential use of niacin for treating CRC/COVID-19.

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          Most cited references52

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          Cancer statistics, 2020

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.
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            Cancer statistics, 2019

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2015, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2016, were collected by the National Center for Health Statistics. In 2019, 1,762,450 new cancer cases and 606,880 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2006-2015) was stable in women and declined by approximately 2% per year in men, whereas the cancer death rate (2007-2016) declined annually by 1.4% and 1.8%, respectively. The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the racial gap in cancer mortality is slowly narrowing, socioeconomic inequalities are widening, with the most notable gaps for the most preventable cancers. For example, compared with the most affluent counties, mortality rates in the poorest counties were 2-fold higher for cervical cancer and 40% higher for male lung and liver cancers during 2012-2016. Some states are home to both the wealthiest and the poorest counties, suggesting the opportunity for more equitable dissemination of effective cancer prevention, early detection, and treatment strategies. A broader application of existing cancer control knowledge with an emphasis on disadvantaged groups would undoubtedly accelerate progress against cancer.
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              AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

              AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user. Copyright 2009 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                Journal
                Brief Bioinform
                Brief Bioinform
                bib
                Briefings in Bioinformatics
                Oxford University Press
                1467-5463
                1477-4054
                10 November 2020
                : bbaa300
                Affiliations
                Gyuilin Medical University
                Gyuilin Medical University
                Gyuilin Medical University
                Gyuilin Medical University
                Gyuilin Medical University
                Gyuilin Medical University
                Author notes
                Corresponding author: Min Su, Guangxi Key Laboratory of Tumor Immunology and Microenvironmental Regulation, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin 541004, PR China. Tel: +86-773-5895860; E-mail: college_sumin@ 123456126.com ; Keng Po Lai, Guangxi Key Laboratory of Tumor Immunology and Microenvironmental Regulation, Guilin Medical University, Huan Cheng North 2nd Road 109, Guilin 541004, PR China. Tel.: +86-773-5895812; E-mail: glmu_kengplai@ 123456yeah.net

                Rong Li and Yu Li contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-8237-6658
                Article
                bbaa300
                10.1093/bib/bbaa300
                7717147
                33169132
                af0604ca-dcea-4d20-878a-d7f9f8712ab6
                © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

                History
                : 27 August 2020
                : 6 October 2020
                : 7 October 2020
                Page count
                Pages: 12
                Funding
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 81560134
                Award ID: 81660091
                Funded by: National Natural Science Foundation of Guangxi;
                Award ID: 2019GXNSFBA185015
                Award ID: 2018GXNSFAA281242
                Funded by: Taiwan Province Talented Young Scientist Program of Guangxi;
                Categories
                Case Study
                AcademicSubjects/SCI01060
                Custom metadata
                PAP

                Bioinformatics & Computational biology
                colorectal neoplasms,covid-19,niacin,network pharmacology,computational biology,prognosis

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