Rui Shen1, Bo Liu1, Xuesen Li1, Tengbo Yu2, Kuishuai Xu2, Jinfeng Ma3. 1. Department of Spinal Surgery, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China. 2. Department of Sport Medicine, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China. 3. Department of Spinal Surgery, the Affiliated Hospital of Qingdao University, Qingdao, 266000, China. majinfengqddx@yeah.net.
Abstract
BACKGROUND: Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. METHODS: We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. RESULTS: A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis (P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. CONCLUSIONS: In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.
BACKGROUND:Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. METHODS: We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. RESULTS: A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis (P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcomapatients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. CONCLUSIONS: In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcomapatients and provide potential novel immunotherapy targets.
Authors: Paul Shannon; Andrew Markiel; Owen Ozier; Nitin S Baliga; Jonathan T Wang; Daniel Ramage; Nada Amin; Benno Schwikowski; Trey Ideker Journal: Genome Res Date: 2003-11 Impact factor: 9.043
Authors: Jorge R Toro; Lois B Travis; Hongyu Julian Wu; Kangmin Zhu; Christopher D M Fletcher; Susan S Devesa Journal: Int J Cancer Date: 2006-12-15 Impact factor: 7.396
Authors: Jan Kosla; Daniela Paňková; Jiří Plachý; Ondřej Tolde; Kristýna Bicanová; Michal Dvořák; Daniel Rösel; Jan Brábek Journal: Cell Commun Signal Date: 2013-07-30 Impact factor: 5.712
Authors: Haoyu Ren; Alexandr V Bazhin; Elise Pretzsch; Sven Jacob; Haochen Yu; Jiang Zhu; Markus Albertsmeier; Lars H Lindner; Thomas Knösel; Jens Werner; Martin K Angele; Florian Bösch Journal: Mol Ther Oncolytics Date: 2021-12-09 Impact factor: 7.200