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Construction of an anoikis‒related prognostic signature to predict immunotherapeutic response and prognosis in hepatocellular carcinoma

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Abstract

Purpose

Anoikis resistance is an important inducer of tumor metastasis. The role of anoikis-related genes (ARGs) in hepatocellular carcinoma (HCC) remains unclear.

Methods

A list of ARGs was obtained and regression analysis was employed to assemble an anoikis-related prognostic signature and risk score formula from mRNA data and clinical prognostic data downloaded from The Cancer Genome Atlas database. Quantitative real-time PCR (qRT-PCR) was performed on clinical samples to validate the selected ARGs expressions. Kaplan‒Meier curves, ROC curves, and Cox regression analyses were used to demonstrated the prognostic value of this signature. Biological functional enrichment analysis and immune infiltration analysis were utilized to analyze the differences in potential biological functions, immune cell infiltration, immune functions, and immunotherapeutic responses.

Results

A prognostic signature based on 6 ARGs and corresponding prognostic nomogram were established. Our qRT-PCR results showed a higher expression of 6 ARGs in HCC tissues (p value < 0.05). Kaplan‒Meier curves, ROC curves, and Cox regression analyses demonstrated good prognostic value of the signature in HCC (p value < 0.05). Significant differences between the enriched biological functions and immune landscapes of patients in different risk groups were discovered (p value < 0.05). In addition, patients with higher risk scores possibly had poor therapeutic response to transhepatic arterial chemotherapy and embolization or sorafenib, but their responses to immunotherapy might be more effective.

Conclusion

A successful anoikis-related prognostic signature and corresponding clinical nomogram were established, which might facilitate better predictions of prognosis and therapeutic responses for HCC patients.

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Data availability statement

All the data analyzed in this research were downloaded from the TCGA (The Cancer Genome Atlas) (https://portal.gdc.cancer.gov/), ICGC (International Cancer Genome Consortium) (https://dcc.icgc.org/projects/LIRI-JP), GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo), and GSEA (gene set enrichment analysis) (https://www.gsea-msigdb.org/gsea/index.jsp) databases.

Abbreviations

HCC:

Hepatocellular carcinoma

ECM:

Cell and extracellular matrix

ARGs:

Anoikis-related genes

TCGA:

The Cancer Genome Atlas

ICGC:

International Cancer Genome Consortium

GEO:

Gene Expression Omnibus

qRT-PCR:

Quantitative real-time PCR

GSEA:

Gene set enrichment analysis

TACE:

Transhepatic arterial chemotherapy and embolization

DEARGs:

Differentially expressed ARGs

LASSO:

Least absolute shrinkage and selection operator

K-M:

Kaplan–Meier

ROC:

Receiver operating characteristic

PCA:

Principal component analysis

AUC:

Area under the curve

DEGs:

Differentially expressed genes

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

FDR:

False discovery rate

TIDE:

Tumor immune dysfunction and exclusion

CNV:

Copy number variation

TIMER:

Tumor Immune Estimation Resource

OS:

Overall survival

PFS:

Progression free survival

TNM:

Tumor size/lymph nodes/distant metastasis

DCs:

Dendritic cells

NK cells:

Natural killer cells

IFN:

Interferon

IC50:

Half maximal inhibitory concentration

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Funding

This work was supported by the Shandong Provincial Taishan Scholars Expert Program (Grant No. tstp20221158), the National Natural Science Foundation of China (82172647, 82073200, 81874178), the Fundamental Research Funds for the Central Universities (Shandong University), Shandong Provincial Natural Science Foundation (Grant No. ZR2021ZD26, ZR2021MH194) and the Jinan University Innovation Team Independent Training Fund (2020GXRC023).

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Authors and Affiliations

  1. Department of General Surgery, Qilu Hospital, Shandong University, Jinan, 250100, China

    Chen Xiong, Guoqiang Pan, Guangxiao Meng, Lunjie Yan, Ruizhe Li, Yuchuan Yan, Yafei Yang, Xiao Zhang, Chuncheng Yang, Zhaoru Dong & Tao Li

  2. Institute for Financial Studies, Shandong University, Jinan, 250100, China

    Hanchao Wang

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  1. Chen Xiong
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Contributions

CX, GQP and GXM designed the research process of this study. CX, HCW and LJY. downloaded and processed the data from common databases. CX accomplished the experiments in this study. CX, RZL, CCY, YFY and YCY drew the pictures and made all the tables in this research. CX and XZ accomplished and revised the manuscript. ZRD and TL were responsible for the designment and accomplishment of this research. All authors agreed to submit this manuscript.

Corresponding authors

Correspondence to Zhaoru Dong or Tao Li.

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Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics statement

All patient’s samples in this study were approved by the Ethics Committee of Qilu Hospital of Shandong University.

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Supplementary Information

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432_2023_5428_MOESM1_ESM.jpg

Supplementary file1 Fig. S1: qRT-PCR results showing the expression of 6 ARGs in HCC and paracancerous tissues. (A) E2F1. (B) BRMS1. (C) ITGA5. (D) PTRH2. (E) PTK2. (F) BMF. * p value <0.05; ** p value <0.01 (JPG 1009 KB)

432_2023_5428_MOESM2_ESM.jpg

Supplementary file2 Fig. S2: Verifying the values of the signature to predict HCC patient prognosis via data from the GSE116174 dataset. (A) Kaplan‒Meier curves showing the OS of HCC patients in different risk groups. (B-C) Scatterplots showing the survival conditions and survival times of HCC patients in the GSE116174 dataset. (D) ROC curves showing the AUC values of the prognostic signature at 1, 3, and 5 years in the GSE116174 dataset. (F) A heatmap showing the expression levels of 6 ARGs in tissues of different risk groups in the GSE116174 dataset (JPG 693 KB)

432_2023_5428_MOESM3_ESM.jpg

Supplementary file3 Fig. S3: Univariate and multivariate Cox regression analyses of the risk score. (A): Univariate Cox regression analysis via data from TCGA database. (B): Multivariate Cox regression analysis via data from the TCGA database. (C): Univariate Cox regression analysis via data from the ICGC database. (D): Multivariate Cox regression analysis via data from the ICGC database (JPG 404 KB)

432_2023_5428_MOESM4_ESM.jpg

Supplementary file4 Fig. S4: Boxplots showing the expression of 6 ARGs in patients with different clinical features. * p value <0.05; ** p value <0.01; *** p value <0.001 (JPG 1028 KB)

432_2023_5428_MOESM5_ESM.jpg

Supplementary file5 Fig. S5: Boxplots showing the different IC50 values of 6 types of common chemotherapeutic drugs between patients with different prognostic risks. (A): Cisplatin. (B): Cyclophosphamide. (C): 5-Fluorouracil. (D): Gemcitabine. (E): Oxaliplatin. (F): Temozolomide (JPG 971 KB)

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Xiong, C., Pan, G., Wang, H. et al. Construction of an anoikis‒related prognostic signature to predict immunotherapeutic response and prognosis in hepatocellular carcinoma. J Cancer Res Clin Oncol 149, 16869–16884 (2023). https://doi.org/10.1007/s00432-023-05428-0

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