Literature DB >> 32009046

[Research on Analysis of Final Diagnosis and Prognostic Factors, and Development of New Therapeutic Drugs for Malignant Tumors (Especially Malignant Pediatric Tumors)].

Takashi Suzuki1.   

Abstract

Outcomes of treatment for malignant pediatric tumors including leukemia are improving by conventional multimodal treatment with strong chemotherapy, surgical resection, radiotherapy, and bone marrow transplantation. However, patients with advanced neuroblastoma, metastatic Ewing's sarcoma family of tumor (ESFT), and metastatic osteosarcoma continue to have an extremely poor prognosis. Therefore novel therapeutic strategies are urgently needed to improve their survival. Apoptotic cell death is a key mechanism for normal cellular homeostasis. Intact apoptotic mechanisms are pivotal for embryonic development, tissue remodeling, immune regulation, and tumor regression. Genetic aberrations disrupting programmed cell death often underpin tumorigenesis and drug resistance. Moreover, it has been suggested that apoptosis or cell differentiation proceeds to spontaneous regression in early stage neuroblastoma. Therefore apoptosis or cell differentiation is a critical event in this cancer. We extracted many compounds from natural plants (Angelica keiskei, Alpinia officiarum, Lycaria puchury-major, Brassica rapa) or synthesized cyclophane pyridine, indirubin derivatives, vitamin K3 derivatives, burchellin derivatives, and GANT61, and examined their effects on apoptosis, cell differentiation, and cell cycle in neuroblastoma and ESFT cell lines compared with normal cells. Some compounds were very effective against these tumor cells. These results suggest that they may be applicable as an efficacious and safe drug for the treatment of malignant pediatric tumors.

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Keywords:  apoptosis; cell differentiation; malignant pediatric tumor; natural plant; prognostic factor; tumor suppressor gene

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Year:  2020        PMID: 32009046     DOI: 10.1248/yakushi.19-00178

Source DB:  PubMed          Journal:  Yakugaku Zasshi        ISSN: 0031-6903            Impact factor:   0.302


  2 in total

1.  Machine Learning-Based Gynecologic Tumor Diagnosis and Its Postoperative Incisional Infection Influence Factor Analysis.

Authors:  Qian Shen; Ling Wang
Journal:  J Healthc Eng       Date:  2021-11-23       Impact factor: 2.682

2.  Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.

Authors:  Vinodkumar Mohanakurup; Syam Machinathu Parambil Gangadharan; Pallavi Goel; Devvret Verma; Sameer Alshehri; Ramgopal Kashyap; Baitullah Malakhil
Journal:  Comput Intell Neurosci       Date:  2022-07-06
  2 in total

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