An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma

Yang Chen; Qiao-Xin Lin; Yi-Ting Xu; Fang-Jing Qian; Chen-Jing Lin; Wen-Ya Zhao; Jing-Ren Huang; Ling Tian; Dian-Na Gu

doi:10.3389/fonc.2023.1158605

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma

Front Oncol. 2023 Apr 27:13:1158605. doi: 10.3389/fonc.2023.1158605. eCollection 2023.

Authors

Yang Chen¹, Qiao-Xin Lin^{1

2}, Yi-Ting Xu¹, Fang-Jing Qian¹, Chen-Jing Lin³, Wen-Ya Zhao³, Jing-Ren Huang¹, Ling Tian³, Dian-Na Gu²

Affiliations

¹ Department of Clinical Medicine, Wenzhou Medical University, Wenzhou, China.
² Department of Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
³ Department of Central Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.

Materials and methods: We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.

Results: A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.

Conclusion: The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.

Keywords: anoikis; gene signature; hepatocellular carcinoma; immune infiltration; prognostic model.

Grants and funding

This study was supported by the funding of the National Natural Science Foundation of China (Grant No. 81802328 to Gu DN & No. 82073203 to LT) and the Young Talents Program of the First Affiliated Hospital of Wenzhou Medical University (Grant No. qnyc094).