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Establishment of a Genomic-Clinicopathologic Nomogram for Predicting Early Recurrence of Hepatocellular Carcinoma After R0 Resection

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Journal of Gastrointestinal Surgery Aims and scope

Abstract

Background

A high rate of postoperative recurrence, especially early recurrence (ER) occurring within 1 year, seriously impedes patients with hepatocellular carcinoma (HCC) from achieving long-term survival. This study aimed to establish a genomic-clinicopathologic nomogram for precisely predicting ER in HCC patients after R0 resection.

Methods

Two reliable datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected as the training and validation cohorts, respectively. The prognostic genes related to ER were screened out by univariate Cox regression analysis and differential expression analysis. The gene-based prognostic index was constructed using LASSO and Cox regression analyses, and its independent prognostic value was assessed by Kaplan-Meier and multivariate Cox analyses. Gene set enrichment analysis (GSEA) was performed to explore the biological pathways related to the prognostic index. Finally, the nomogram integrating all the independent prognostic factors was established and comprehensively evaluated by calibration plots, the C-index, receiver operating characteristic curves, and decision curve analysis.

Results

Nine dysregulated and prognostic genes related to ER (ZNF131, TATDN2, TXN, DDX55, KPNA2, ZNF30, TIMELESS, SFRP1, and COLEC11) were identified (all P < 0.05). The prognostic index model based on the 9 genes was successfully constructed using the TCGA cohort and showed a certain capability to discriminate the ER group from the non-ER group (P < 0.05) and good independent prognostic value in terms of predicting poor early recurrence-free survival (P < 0.05). Eight biological pathways significantly related to ER were identified by GSEA, such as “cell cycle”, “homologous recombination” and “p53 signaling pathway.” The genomic-clinicopathologic nomogram integrating the 9-gene-based prognostic index and TNM stage displayed significantly higher predictive accuracy and clinical application value than that of TNM stage model both in the training and validation cohorts (all P < 0.05).

Conclusions

The novel genomic-clinicopathologic nomogram may be a convenient and powerful tool for accurately predicting ER in HCC patients after R0 resection.

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Funding

This work was supported by the National Natural Science Foundation of China (grant number: 81570079).

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Authors and Affiliations

  1. Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, Hubei Key Laboratory of Medical Technology on Transplantation, Wuhan, 430071, Hubei, People’s Republic of China

    Bin Yu, Han Liang, Qifa Ye & Yanfeng Wang

  2. TThe 3rd Xiangya Hospital of Central South University, Research Center of National Health Ministry on Transplantation Medicine Engineering and Technology, Changsha, 410013, Hunan, People’s Republic of China

    Qifa Ye

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  1. Bin Yu
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Contributions

Bin Yu designed this study, analyzed the data, and wrote the paper. Han Liang downloaded the data and prepared the data. Qifa Ye revised the paper. Yanfeng Wang designed this study, revised the paper, and provided supervision.

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Correspondence to Yanfeng Wang.

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Supplementary Figure 1

Representative IHC images of the nine genes in the HPA database. (a) Protein levels of ZNF131 in normal liver tissues (https://www.proteinatlas.org/ENSG00000172262-ZNF131/tissue/liver#img) and HCC tissues (https://www.proteinatlas.org/ENSG00000172262-ZNF131/ pathology/liver+cancer#img). (b) Protein levels of TATDN2 in normal liver tissues (https://www.proteinatlas.org/ENSG00000157014-TATDN2/tissue/ liver#img) and HCC tissues (https://www.proteinatlas.org/ENSG00000157014- TATDN2/pathology/liver+cancer#img). (c) Protein levels of TXN in normal liver tissues (https://www.proteinatlas.org/ENSG00000136810-TXN/tissue/liver# img) and HCC tissues (https://www.proteinatlas.org/ENSG00000136810- TXN/pathology/liver+cancer#img). (d) Protein levels of DDX55 in normal liver tissues (https://www.proteinatlas.org/ENSG00000111364-DDX55/tissue/liver# img) and HCC tissues (https://www.proteinatlas.org/ENSG00000111364- DDX55/pathology/liver+cancer#img). (e) Protein levels of KPNA2 in normal liver tissues (https://www.proteinatlas.org/ENSG00000182481-KPNA2/tissue/ liver#img) and HCC tissues (https://www.proteinatlas.org/ENSG00000182481- KPNA2/pathology/liver+cancer#img). (f) Protein levels of ZNF30 in normal liver tissues (https://www.proteinatlas.org/ENSG00000168661-ZNF30/ tissue/liver#img) and HCC tissues (https://www.proteinatlas.org/ ENSG00000168661-ZNF30/pathology/liver+cancer#img). (g) Protein levels of TIMELESS in normal liver tissues (https://www.proteinatlas.org/ ENSG00000111602-TIMELESS/tissue/liver#img) and HCC tissues (https://www.proteinatlas.org/ENSG00000111602-TIMELESS/pathology/liver+cancer#img). (h) Protein levels of SFRP1 in normal liver tissues (https://www.proteinatlas.org/ENSG00000104332-SFRP1/tissue/liver#img) and HCC tissues (https://www.proteinatlas.org/ENSG00000104332-SFRP1/ pathology/liver+cancer#img). (i) Protein levels of COLEC11 in normal liver tissues (https://www.proteinatlas.org/ENSG00000118004-COLEC11/tissue /liver#img) and HCC tissues (https://www.proteinatlas.org/ ENSG00000118004-COLEC11/pathology/liver+cancer#img). In the HPA database, the expression level score is based on the staining intensity (negative, weak, moderate or strong) and fraction of stained cells (<25%, 25–75% or >75%), including not detected (N) (negative or weak with <25%), low (L) (weak with 25–75%/>75%, or moderate with <25%), medium (M) (moderate with 25–75%/>75%, or strong with <25%), and high (H) (strong with 25–75%/>75%). (PNG 2618 kb).

High Resolution Image (TIFF 17268 kb).

Supplementary Figure 2

Gene set enrichment analysis using the TCGA-LIHC dataset. A total of 8 KEGG signaling pathways were significantly enriched in high-risk group defined by gene-based prognostic index, including “cell cycle” (a), “DNA replication” (b), “base excision repair” (c), “spliceosome” (d), “nucleotide excision repair” (e), “RNA degradation” (f), “homologous recombination” (g) and “p53 signaling pathway” (h). NES: normalized enrichment score, NOM p: normalized p-value, FDR q: false discovery rate q-value. (PNG 1043 kb).

High Resolution Image (TIFF 5731 kb).

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Yu, B., Liang, H., Ye, Q. et al. Establishment of a Genomic-Clinicopathologic Nomogram for Predicting Early Recurrence of Hepatocellular Carcinoma After R0 Resection. J Gastrointest Surg 25, 112–124 (2021). https://doi.org/10.1007/s11605-020-04554-1

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