Last Posted: Oct 21, 2021
- Medical guidelines for Li-Fraumeni syndrome 2019, version 1.1.
Kumamoto Tadashi et al. International journal of clinical oncology 2021
- Exploration of Immune-Related Gene Expression in Osteosarcoma and Association With Outcomes.
Liu Wangmi et al. JAMA network open 2021 4(8) e2119132
- Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.
Pereira Helcio Mendonça et al. The British journal of radiology 2021 20201391
- 12 Survival-related differentially expressed genes based on the TARGET-osteosarcoma database.
Rothzerg Emel et al. Experimental biology and medicine (Maywood, N.J.) 2021 15353702211007410
- Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm.
Jiang Jiuzhou et al. Scientific reports 2021 11(1) 5542
- Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis.
Li Runmin et al. Experimental biology and medicine (Maywood, N.J.) 2021 Feb 1535370221992015
- Germline TP53 Testing in Breast Cancers: Why, When and How?
Evans D Gareth et al. Cancers 2020 Dec 12(12)
- Retrospective investigation of hereditary syndromes in patients with medulloblastoma in a single institution.
Wang Ying et al. Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery 2020 Sep
- A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.
Zhong Jingyu et al. European radiology 2020 Sep
- Elevated long noncoding RNA HAGLROS expression correlates with clinical progression and prognosis in osteosarcoma.
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- A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma.
Wu Guangzhi et al. BMC cancer 2020 May 20(1) 456
- Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study.
Huang Bingsheng et al. BMC cancer 2020 Apr 20(1) 322
- Omic approaches to pediatric bone sarcomas.
Tirtei Elisa et al. Pediatric blood & cancer 2019 Nov e28072
- Machine learning-based prediction of response to PARP inhibition across cancer types.
KE Hill et al, MedRXIV, September 27, 2019
- Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18 F-FDG Textural Features Machine Learning Approaches with PCA.
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- The Childhood Cancer Data Initiative: Transforming the Pediatric Cancer Landscape through Sharing Data
DR Lowy, NCI, August 15, 2019
- Co-Expression Network Analysis Identified Gene Signatures in Osteosarcoma as a Predictive Tool for Lung Metastasis and Survival.
Zhang Honghua et al. Journal of Cancer 2019 10(16) 3706-3716
- Clinical genomic sequencing of pediatric and adult osteosarcoma reveals distinct molecular subsets with potentially targetable alterations.
Suehara Yoshiyuki et al. Clinical cancer research : an official journal of the American Association for Cancer Research 2019 Jun
- Current understanding of pharmacogenetic implications of DNA damaging drugs used in osteosarcoma treatment.
Hattinger Claudia Maria et al. Expert opinion on drug metabolism & toxicology 2019 Mar
- Possible roles of genetic variations in chemotherapy related cardiotoxicity in pediatric acute lymphoblastic leukemia and osteosarcoma.
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Rare Disease PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other information that address the public health impact and translation of genomic and other precision health discoveries into improved health outcomes related to rare diseases...more
Selected Rare Diseases
- Alpha-1 Antitrypsin Deficiency
- Amyotrophic Lateral Sclerosis
- Brugada Syndrome
- Cerebral Palsy
- Cystic Fibrosis
- Duchenne Muscular Dystrophy
- Erythema Multiforme
- Familial Mediterranean Fever
- Fragile X Syndrome
- Gaucher Disease
- Graves Disease
- Huntington Disease
- Myasthenia Gravis
- Retinitis Pigmentosa
- Severe Combined Immunodeficiency
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.