Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures
Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been...
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| Published in | Aging (Albany, NY.) Vol. 13; no. 24; pp. 26118 - 26136 |
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| Abstract | Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA. |
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| AbstractList | Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA. Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA.Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA. |
| Author | Xian, Lei Zhang, Yuhong Zhu, Minqi Mo, Junxian |
| Author_xml | – sequence: 1 givenname: Yuhong surname: Zhang fullname: Zhang, Yuhong organization: Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China – sequence: 2 givenname: Minqi surname: Zhu fullname: Zhu, Minqi organization: Guangxi Medical University, Nanning, China – sequence: 3 givenname: Junxian surname: Mo fullname: Mo, Junxian organization: Guangxi Medical University, Nanning, China – sequence: 4 givenname: Lei surname: Xian fullname: Xian, Lei organization: Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China |
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| CitedBy_id | crossref_primary_10_1016_j_surg_2024_03_024 crossref_primary_10_1155_2022_4834791 crossref_primary_10_3389_fgene_2022_921902 crossref_primary_10_1016_j_prp_2024_155154 |
| Cites_doi | 10.1245/s10434-018-6651-y 10.1097/CM9.0000000000000427 10.1158/1078-0432.CCR-14-1559 10.1111/nyas.13677 10.1053/j.gastro.2017.08.023 10.1007/s12026-019-09079-7 10.1016/j.jtcvs.2018.03.146 10.1158/2159-8290.CD-18-0598 10.1158/0008-5472.CAN-17-2268 10.3748/wjg.v21.i26.7933 10.1111/j.1523-5378.2011.00855.x 10.7314/apjcp.2014.15.5.2359 10.3322/caac.21590 10.1038/nrclinonc.2015.209 10.1093/bioinformatics/btq170 10.3748/wjg.v16.i30.3793 10.1038/onc.2016.34 10.3389/fimmu.2018.01718 10.1186/s12967-019-1917-0 10.12659/msm.902615 10.3389/fbioe.2019.00270 10.1097/MNM.0000000000000837 10.1186/1471-2105-14-7 10.1159/000488120 10.1186/1479-5876-12-84 10.18632/oncotarget.16051 10.1038/s41467-018-07767-w 10.1007/s00262-020-02514-x 10.1016/S0140-6736(17)31462-9 10.1053/j.gastro.2005.12.032 10.1016/S0140-6736(12)60643-6 10.1097/SLA.0000000000002410 10.1093/dote/doy115 10.1093/nar/gkv007 10.1016/j.dld.2018.08.002 10.1586/17474124.2016.1140036 10.1089/omi.2011.0118 10.1016/j.cell.2011.02.013 10.1093/nar/gkv1507 10.1016/j.immuni.2010.01.004 |
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| Keywords | esophageal cancer immune cell subtypes tumor microenvironment prognostic model bioinformatics |
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| Title | Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures |
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