TY - JOUR
T1 - Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma
AU - Xiang, Shixin
AU - Li, Jing
AU - Shen, Jing
AU - Zhao, Yueshui
AU - Wu, Xu
AU - Li, Mingxing
AU - Yang, Xiao
AU - Kaboli, Parham Jabbarzadeh
AU - Du, Fukuan
AU - Zheng, Yuan
AU - Wen, Qinglian
AU - Cho, Chi Hin
AU - Yi, Tao
AU - Xiao, Zhangang
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grants 81503093, 81972643, and 81672444), the Joint Funds of the Southwest Medical University & Luzhou (Grants 2016LZXNYD-T01, 2017LZXNYD-Z05, and 2019LXXNYKD-07), and the Science & Technology Department of Sichuan Province (Grant 2018JY0079).
Publisher Copyright:
© Copyright © 2021 Xiang, Li, Shen, Zhao, Wu, Li, Yang, Kaboli, Du, Zheng, Wen, Cho, Yi and Xiao.
PY - 2021/4/7
Y1 - 2021/4/7
N2 - Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis. Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER). Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration. Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.
AB - Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis. Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER). Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration. Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.
KW - ESTIMATE algorithm
KW - Hepatocellular carcinoma
KW - Prognosis
KW - TCGA
KW - tumor microenvironment
UR - http://www.scopus.com/inward/record.url?scp=85104614130&partnerID=8YFLogxK
U2 - 10.3389/fimmu.2021.653836
DO - 10.3389/fimmu.2021.653836
M3 - Journal article
C2 - 33897701
AN - SCOPUS:85104614130
SN - 1664-3224
VL - 12
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 653836
ER -
