Xiaotao Jiang1,2, Qiaofeng Yan2,3, Linling Xie1,2, Shijie Xu4, Kailin Jiang1,2, Jiahua Huang1,2, Yi Wen1, Yanhua Yan1,2, Junhui Zheng1,2, Shuting Tang1,2, Kechao Nie1,2, Zhihua Zheng1,2, Jinglin Pan5, Peng Liu1,2, Yuancheng Huang1,2, Xingrui Yan1,2, Yushan Zou1,2, Xuan Chen6, Fengbin Liu1,7,8, Peiwu Li1, Kunhai Zhuang1. 1. Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong, China. 2. First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong, China. 3. No. 1 Traditional Chinese Medicine Hospital in Changde, Changde 415000, Hunan, China. 4. Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong, China. 5. Department of Gastroenterology, Hainan Provincial Hospital of Traditional Chinese Medicine, Haikou 570100, Hainan, China. 6. Ningde Hospital of Traditional Chinese Medicine Affiliated to Fujian University of Traditional Chinese Medicine, Ningde 352100, Fujian, China. 7. Baiyun Hospital of The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510470, Guangdong, China. 8. Lingnan Medical Research Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong, China.
Abstract
BACKGROUND: Gastric cancer (GC), an extremely aggressive tumor with a very different prognosis, is the third leading cause of cancer-related mortality. We aimed to construct a ferroptosis-related prognostic model that can be distinguished prognostically. METHODS: The gene expression and the clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). The ferroptosis-related genes were obtained from the FerrDb. Using the "limma" R package and univariate Cox analysis, ferroptosis-related genes with differential expression and prognostic value were identified in the TCGA cohort. Last absolute shrinkage and selection operator (LASSO) Cox regression was applied to shrink ferroptosis-related predictors and construct a prognostic model. Functional enrichment, ESTIMATE algorithm, and single-sample gene set enrichment analysis (ssGSEA) were applied for exploring the potential mechanism. GC patients from the GEO cohort were used for validation. RESULTS: An 8-gene prognostic model was constructed and stratified GC patients from TCGA and meta-GEO cohort into high-risk groups or low-risk groups. GC patients in high-risk groups have significantly poorer OS compared with those in low-risk groups. The risk score was identified as an independent predictor for OS. Functional analysis revealed that the risk score was mainly associated with the biological function of extracellular matrix (ECM) organization and tumor immunity. CONCLUSION: In conclusion, the ferroptosis-related model can be utilized for the clinical prognostic prediction in GC.
BACKGROUND: Gastric cancer (GC), an extremely aggressive tumor with a very different prognosis, is the third leading cause of cancer-related mortality. We aimed to construct a ferroptosis-related prognostic model that can be distinguished prognostically. METHODS: The gene expression and the clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). The ferroptosis-related genes were obtained from the FerrDb. Using the "limma" R package and univariate Cox analysis, ferroptosis-related genes with differential expression and prognostic value were identified in the TCGA cohort. Last absolute shrinkage and selection operator (LASSO) Cox regression was applied to shrink ferroptosis-related predictors and construct a prognostic model. Functional enrichment, ESTIMATE algorithm, and single-sample gene set enrichment analysis (ssGSEA) were applied for exploring the potential mechanism. GC patients from the GEO cohort were used for validation. RESULTS: An 8-gene prognostic model was constructed and stratified GC patients from TCGA and meta-GEO cohort into high-risk groups or low-risk groups. GC patients in high-risk groups have significantly poorer OS compared with those in low-risk groups. The risk score was identified as an independent predictor for OS. Functional analysis revealed that the risk score was mainly associated with the biological function of extracellular matrix (ECM) organization and tumor immunity. CONCLUSION: In conclusion, the ferroptosis-related model can be utilized for the clinical prognostic prediction in GC.
Authors: Freddie Bray; Jacques Ferlay; Isabelle Soerjomataram; Rebecca L Siegel; Lindsey A Torre; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2018-09-12 Impact factor: 508.702