Bioinformatic Profiling Identifies a Fatty Acid Metabolism-Related Gene Risk Signature for Malignancy, Prognosis, and Immune Phenotype of Glioma
المؤلفون المشاركون
Yao, Yu
Qi, Ying
Chen, Di
Lu, Qiqi
Ji, Chunxia
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-04
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Cancer cells commonly have metabolic abnormalities.
Aside from altered glucose and amino acid metabolism, cancers cells often share the attribute of fatty acid metabolic alterations.
However, fatty acid metabolism related-gene set has not been systematically investigated in gliomas.
Here, we provide a bioinformatic profiling of the fatty acid catabolic metabolism-related gene risk signature for the malignancy, prognosis and immune phenotype of glioma.
In this study, a cohort of 325 patients with whole genome RNA-seq expression data from the Chinese Glioma Genome Atlas (CGGA) dataset was used as training set, while another cohort of 667 patients from The Cancer Genome Atlas (TCGA) dataset was used as validating set.
After confirmed that fatty acid catabolic metabolism-related gene set could distinguish clinicopathological features of gliomas, we used LASSO regression analysis to develop a fatty-acid metabolism-related gene risk signature for glioma.
This 8-gene risk signature was found to be a good predictor of clinical and molecular features involved in the malignancy of gliomas.
We also identified that this 8-gene risk signature had high prognostic values in patients with gliomas.
Correlation analysis showed that our risk signature was closely associated with the immune cells involved in the microenvironment of glioma.
Furthermore, the fatty acid catabolic metabolism-related gene risk signature was also found to be significantly correlated with immune checkpoint members B7-H3 and Tim-3.
In summary, we have identified a fatty acid metabolism-related gene risk signature for malignancy, prognosis, and immune phenotype of glioma; and our study might contribute to better understanding of metabolic pathways and further developing of novel therapeutic approaches for gliomas.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Qi, Ying& Chen, Di& Lu, Qiqi& Yao, Yu& Ji, Chunxia. 2019. Bioinformatic Profiling Identifies a Fatty Acid Metabolism-Related Gene Risk Signature for Malignancy, Prognosis, and Immune Phenotype of Glioma. Disease Markers،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1147147
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Qi, Ying…[et al.]. Bioinformatic Profiling Identifies a Fatty Acid Metabolism-Related Gene Risk Signature for Malignancy, Prognosis, and Immune Phenotype of Glioma. Disease Markers No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1147147
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Qi, Ying& Chen, Di& Lu, Qiqi& Yao, Yu& Ji, Chunxia. Bioinformatic Profiling Identifies a Fatty Acid Metabolism-Related Gene Risk Signature for Malignancy, Prognosis, and Immune Phenotype of Glioma. Disease Markers. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1147147
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1147147
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر