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      Prognostic model construction and target identification of Si-Wu-Tang against breast cancer

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          Abstract

          The targets and mechanisms of Si-Wu-Tang (SWT) against (Breast cancer) BRCA were identified and a survival model and nomogram was construted by network pharmacology, bioinformatic analysis and in vitro experiments. A total of 72 anti-breast cancer SWT targets were selected, among which eleven genes ( MAOA、SQLE、CACNA2D1、GLI1、RORB、ITGB3、TACR1、NR3C2、CA3、RBP4 and PTK6) were used to construct a novel prognostic model of breast cancer. The anti-breast cancer activity of SWT was related to the modulation of the receptor tyrosine kinases signaling pathways. Moreover, two compounds, mairin and senkyunone were found to bind directly to ITGB3 and RORB proteins. Finally, mRNA and protein expression of ITGB3 and RORB was observed to be significantly down-regulated after incubation of MCF-7 cells with SWT. Overall, our results indicated that mairin and senkyunone were the key ingredients present in SWT, and ITGB3 as well as RORB proteins were the major targets affected by SWT. The prognostic model can be used to predict the outcome of BRCA patients.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)
          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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            Cancer treatment and survivorship statistics, 2019

            Kimberly D Miller, Leticia Nogueira, Angela Mariotto … (2019)
            The number of cancer survivors continues to increase in the United States because of the growth and aging of the population as well as advances in early detection and treatment. To assist the public health community in better serving these individuals, the American Cancer Society and the National Cancer Institute collaborate every 3 years to estimate cancer prevalence in the United States using incidence and survival data from the Surveillance, Epidemiology, and End Results cancer registries; vital statistics from the Centers for Disease Control and Prevention's National Center for Health Statistics; and population projections from the US Census Bureau. Current treatment patterns based on information in the National Cancer Data Base are presented for the most prevalent cancer types. Cancer-related and treatment-related short-term, long-term, and late health effects are also briefly described. More than 16.9 million Americans (8.1 million males and 8.8 million females) with a history of cancer were alive on January 1, 2019; this number is projected to reach more than 22.1 million by January 1, 2030 based on the growth and aging of the population alone. The 3 most prevalent cancers in 2019 are prostate (3,650,030), colon and rectum (776,120), and melanoma of the skin (684,470) among males, and breast (3,861,520), uterine corpus (807,860), and colon and rectum (768,650) among females. More than one-half (56%) of survivors were diagnosed within the past 10 years, and almost two-thirds (64%) are aged 65 years or older. People with a history of cancer have unique medical and psychosocial needs that require proactive assessment and management by follow-up care providers. Although there are growing numbers of tools that can assist patients, caregivers, and clinicians in navigating the various phases of cancer survivorship, further evidence-based resources are needed to optimize care.
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              Network Pharmacology in Research of Chinese Medicine Formula: Methodology, Application and Prospective.

              Ting-Ting Luo, Yuan Ding Lu, Shi-Kai Yan … (2020)
              Chinese medicine (CM) is usually prescribed as CM formula to treat disease. The lack of effective research approach makes it difficult to elucidate the molecular mechanisms of CM formula owing to its complicated chemical compounds. Network pharmacology is increasingly applied in CM formula research in recent years, which is identified suitable for the study of CM formula. In this review, we summarized the methodology of network pharmacology, including network construction, network analysis and network verification. The aim of constructing a network is to achieve the interaction between the bioactive compounds and targets and the interaction between various targets, and then find out and validate the key nodes via network analysis and network verification. Besides, we reviewed the application in CM formula research, mainly including targets discovery, bioactive compounds screening, toxicity evaluation, mechanism research and quality control research. Finally, we proposed prospective in the future and limitations of network pharmacology, expecting to provide new strategy and thinking on study for CM formula.
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                Author and article information

                Contributors
                Piwen Zhao
                Journal
                Journal ID (nlm-ta): Heliyon
                Journal ID (iso-abbrev): Heliyon
                Title: Heliyon
                Publisher: Elsevier
                ISSN (Electronic): 2405-8440
                Publication date PMC-release: 12 October 2023
                Publication date Collection: October 2023
                Publication date (Electronic): 12 October 2023
                Volume: 9
                Issue: 10
                Electronic Location Identifier: e20709
                Affiliations
                [a ]School of Life Sciences, Beijing University of Chinese Medicine, Beijing, 100029, China
                [b ]The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
                [c ]School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
                [d ]The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
                Author notes
                [∗ ]Corresponding author. pw__zhao@ 123456163.com
                [1]

                The authors contributed equally to this work.

                Article
                Publisher Item ID: S2405-8440(23)07917-3 Publisher ID: e20709
                DOI: 10.1016/j.heliyon.2023.e20709
                PMC ID: 10590855
                PubMed ID: 37876445
                SO-VID: dee584f3-68b4-4763-8587-d98ab7fafa54
                Copyright © © 2023 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 9 February 2023
                Date revision received : 28 September 2023
                Date accepted : 4 October 2023
                Categories
                Subject: Research Article

                Keywords: si-wu-tang,breast cancer,network pharmacology,bioinformatics analysis
                Data availability:
                Keywords: si-wu-tang, breast cancer, network pharmacology, bioinformatics analysis

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