A novel immune checkpoint-related gene signature for hepatocellular carcinoma to predict clinical outcomes and therapeutic response

Siyuan Tian; Yinan Hu; Chunmei Yang; Jiahao Yu; Jingyi Liu; Guoyun Xuan; Yansheng Liu; Keshuai Sun; Miao Zhang; Shuoyi Ma; Yulong Shang; Xia Zhou; Ying Han; Siyuan Tian; Yinan Hu; Chunmei Yang; Jiahao Yu; Jingyi Liu; Guoyun Xuan; Yansheng Liu; Keshuai Sun; Miao Zhang; Shuoyi Ma; Yulong Shang; Xia Zhou; Ying Han

doi:10.3934/mbe.2022220

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 5: 4719-4736. doi: 10.3934/mbe.2022220

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Research article

A novel immune checkpoint-related gene signature for hepatocellular carcinoma to predict clinical outcomes and therapeutic response

1.
State Key Laboratory of Cancer Biology, Xijing Hospital of Digestive Diseases, The Fourth Military Medical University, Xi'an 710032, China
2.
Department of Radiation Oncology, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China
3.
Department of Gastroenterology, The Air Force Hospital from Eastern Theater of PLA, Nanjing 210002, China

Academic Editor: Ulf Schmitz

Received: 04 January 2022 Revised: 01 March 2022 Accepted: 01 March 2022 Published: 10 March 2022

Immune checkpoint genes (ICGs) have recently been proven to perform instrumental functions in the maintenance of immune homeostasis and represent a promising therapeutic strategy; however, their expression patterns and prognostic values are not fully elucidated in hepatocellular carcinoma (HCC). In this investigation, we focused on establishing and validating a prognostic gene signature to facilitate decision-making in clinical practice. Clinical information, as well as transcriptome data, was obtained from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox method were employed to build a multi-gene signature in the TCGA database, while the ICGC database was used for validation. Subsequently, utilizing the six-gene signature, we were able to categorize patients into high- and low-risk groups. In two cohorts, survival analysis findings revealed a dismal outlook for the high-risk group. The receiver operating characteristic curves were utilized to estimate the gene signature's prediction ability. Moreover, correlation analysis showed high-risk group was linked to advanced pathological stage, infiltration of immune cells and therapeutic response. In summary, this unique gene profile might serve not only as a useful prognostic indicator but also as a marker of therapy responsiveness in HCC.
- HCC,
- immune checkpoint,
- gene signature,
- survival analysis,
- therapeutic response
Citation: Siyuan Tian, Yinan Hu, Chunmei Yang, Jiahao Yu, Jingyi Liu, Guoyun Xuan, Yansheng Liu, Keshuai Sun, Miao Zhang, Shuoyi Ma, Yulong Shang, Xia Zhou, Ying Han. A novel immune checkpoint-related gene signature for hepatocellular carcinoma to predict clinical outcomes and therapeutic response[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4719-4736. doi: 10.3934/mbe.2022220

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Abstract

Immune checkpoint genes (ICGs) have recently been proven to perform instrumental functions in the maintenance of immune homeostasis and represent a promising therapeutic strategy; however, their expression patterns and prognostic values are not fully elucidated in hepatocellular carcinoma (HCC). In this investigation, we focused on establishing and validating a prognostic gene signature to facilitate decision-making in clinical practice. Clinical information, as well as transcriptome data, was obtained from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox method were employed to build a multi-gene signature in the TCGA database, while the ICGC database was used for validation. Subsequently, utilizing the six-gene signature, we were able to categorize patients into high- and low-risk groups. In two cohorts, survival analysis findings revealed a dismal outlook for the high-risk group. The receiver operating characteristic curves were utilized to estimate the gene signature's prediction ability. Moreover, correlation analysis showed high-risk group was linked to advanced pathological stage, infiltration of immune cells and therapeutic response. In summary, this unique gene profile might serve not only as a useful prognostic indicator but also as a marker of therapy responsiveness in HCC.

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