Junyu Huo1,2, Liqun Wu3, Yunjin Zang1, Hongjing Dong1, Xiaoqiang Liu2, Fu He1,2, Xiao Zhang4. 1. Liver Disease Center, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266003, China. 2. Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, China. 3. Liver Disease Center, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266003, China. wulq5810@126.com. 4. Linyi Central Hospital, Linyi, 276400, China.
Abstract
BACKGROUND: In recent years, the relationship between tumor-associated macrophages (TAMs) and solid tumors has become a research hotspot. This study aims to explore the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma (HCC) to provide new methods of treatment for HCC. METHODS: The study selected 343 HCC patients with complete survival information (survival time > = 1 month) in the Cancer Genome Atlas (TCGA) as study subjects. Kaplan-Meier survival analysis assisted in determining the relationship between macrophage infiltration and overall survival (OS), and Pearson correlation tests were used to identify metabolic reprogramming genes (MRGs) associated with tumor macrophage abundance. Lasso regression algorithms were used on prognosis-related MRGs identified by Kaplan-Meier survival analysis and univariate Cox regression analysis to construct a risk score; another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) was used to verify prognostic signature externally. RESULTS: A risk score composed of 8 metabolic genes could accurately predict the OS of a training cohort (TCGA) and a testing cohort (ICGC). The risk score could be widely used for people with different clinical characteristics, and it is a predictor that is independent of other clinical factors that affect prognosis. As expected, compared with the low-risk group, the high-risk group exhibited an obviously higher macrophage abundance, together with a positive correlation between the risk score and the expression levels of three commonly used immune checkpoints (PD1, PDL1, and CTLA4). CONCLUSION: Our study constructed and validated a novel eight-gene signature for predicting HCC patient OS, which may contribute to clinical treatment decisions.
BACKGROUND: In recent years, the relationship between tumor-associated macrophages (TAMs) and solid tumors has become a research hotspot. This study aims to explore the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma (HCC) to provide new methods of treatment for HCC. METHODS: The study selected 343 HCCpatients with complete survival information (survival time > = 1 month) in the Cancer Genome Atlas (TCGA) as study subjects. Kaplan-Meier survival analysis assisted in determining the relationship between macrophage infiltration and overall survival (OS), and Pearson correlation tests were used to identify metabolic reprogramming genes (MRGs) associated with tumor macrophage abundance. Lasso regression algorithms were used on prognosis-related MRGs identified by Kaplan-Meier survival analysis and univariate Cox regression analysis to construct a risk score; another independent cohort (including 228 HCCpatients) from the International Cancer Genome Consortium (ICGC) was used to verify prognostic signature externally. RESULTS: A risk score composed of 8 metabolic genes could accurately predict the OS of a training cohort (TCGA) and a testing cohort (ICGC). The risk score could be widely used for people with different clinical characteristics, and it is a predictor that is independent of other clinical factors that affect prognosis. As expected, compared with the low-risk group, the high-risk group exhibited an obviously higher macrophage abundance, together with a positive correlation between the risk score and the expression levels of three commonly used immune checkpoints (PD1, PDL1, and CTLA4). CONCLUSION: Our study constructed and validated a novel eight-gene signature for predicting HCCpatient OS, which may contribute to clinical treatment decisions.
Authors: Amaia Zabala-Letona; Amaia Arruabarrena-Aristorena; Natalia Martín-Martín; Sonia Fernandez-Ruiz; James D Sutherland; Michelle Clasquin; Julen Tomas-Cortazar; Jose Jimenez; Ines Torres; Phong Quang; Pilar Ximenez-Embun; Ruzica Bago; Aitziber Ugalde-Olano; Ana Loizaga-Iriarte; Isabel Lacasa-Viscasillas; Miguel Unda; Verónica Torrano; Diana Cabrera; Sebastiaan M van Liempd; Ylenia Cendon; Elena Castro; Stuart Murray; Ajinkya Revandkar; Andrea Alimonti; Yinan Zhang; Amelia Barnett; Gina Lein; David Pirman; Ana R Cortazar; Leire Arreal; Ludmila Prudkin; Ianire Astobiza; Lorea Valcarcel-Jimenez; Patricia Zuñiga-García; Itziar Fernandez-Dominguez; Marco Piva; Alfredo Caro-Maldonado; Pilar Sánchez-Mosquera; Mireia Castillo-Martín; Violeta Serra; Naiara Beraza; Antonio Gentilella; George Thomas; Mikel Azkargorta; Felix Elortza; Rosa Farràs; David Olmos; Alejo Efeyan; Juan Anguita; Javier Muñoz; Juan M Falcón-Pérez; Rosa Barrio; Teresa Macarulla; Jose M Mato; Maria L Martinez-Chantar; Carlos Cordon-Cardo; Ana M Aransay; Kevin Marks; José Baselga; Josep Tabernero; Paolo Nuciforo; Brendan D Manning; Katya Marjon; Arkaitz Carracedo Journal: Nature Date: 2017-06-28 Impact factor: 49.962