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. 2020 Apr;9(4):2424-2433.
doi: 10.21037/tcr.2020.03.32.

The association between genomic variations and histological grade in hepatocellular carcinoma

Jun Liu  1 , Guangbing Li  1 , Yuan Guo  2 , Ning Fan  2 , Yunjin Zang  2
Affiliations

The association between genomic variations and histological grade in hepatocellular carcinoma

Jun Liu et al. Transl Cancer Res. 2020 Apr.

Abstract

Background: Histological grade (HG) is an important prognostic factor for hepatocellular carcinoma. With the development of precision medicine, diagnosis with a sequencing technology has become increasingly accepted. It is vital to discuss their similarities and differences to bridge or improve the traditional HG diagnosis with the novel sequencing technique.

Methods: A total of 658 tumor samples were collected from 602 Chinese hepatocellular carcinoma patients and sequenced for a panel of pan-cancer genes. Nucleotide usage bias, genomic variation-related scores, driver genes, and biological processes were compared among different HGs. These results were further verified using a cohort dataset from the Western population.

Results: Genomic variation subtypes, such as C>G substitution, maximum somatic allele frequency (MSAF), and TP53, and biological processes including "angiogenesis" and "regulation of homotypic cell-cell adhesion" were found to be significantly associated with HG in both Chinese and Western populations.

Conclusions: The association identified between genomic variation and HG could aid our understanding of HG as an important clinical measure, and potentially be used to predict HG for hepatocellular carcinoma.

Keywords: Histological grade (HG); genomic variation; hepatocellular carcinoma; sequencing.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr.2020.03.32). The work was carried out as part of the employment of the corresponding author at the Affiliated Hospital of Qingdao University. The Affiliated Hospital of Qingdao University was not involved in the manuscript writing, editing, approval, or decision to publish. The other authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The landscape of genomic variations. From top to bottom, the bar plot indicates the tumor mutation burdens (TMBs) and the below heat map indicated the clinicopathological characteristics. HG (histological grades) includes WD (well-differentiated), MD (moderately differentiated), and PD (poorly differentiated). The bottom left bar plot indicates the percentage of genomic variation for each gene in the patients. The bottom right heatmap shows genomic variation types.
Figure 2
Figure 2
Variation distribution for hepatocellular carcinoma (HCC). (A) The upper plot shows the distribution of five types of genomic variation for three groups of HGs (histological grades). The lower plot is the distribution of 12 substitution types, which are grouped into transition and transversion. The x-axis indicates the patient percentage and the y-axis indicated the HGs. (B) The upper and the lower plots show MSAF (maximum somatic allele frequency) and MATH (mutant allele tumor heterogeneity) distributions for the three HG groups, respectively.
Figure 3
Figure 3
Frequency of genomic variation in driver genes. (A) The percentage of patients with five types of genomic variations for hepatocellular carcinoma (HCC) driver genes. (B) The average grade of tumors with/without substitution/indel/truncation mutations in driver genes. *, P<0.05; **, P<0.01; ***, P<0.001. (C) The average grade of tumors with/without amplification in driver genes. SNV, single-nucleotide variant; CNV, copy number variation.
Figure 4
Figure 4
The substitution/indel/truncation overlaps between different histological grades. (A) The substitution/indel/truncation overlaps between three HGs including WD, MD, and PD; (B) the enriched biological processes for well-differentiated tumors; (C) the enriched biological processes for moderately differentiated tumors; (D) the enriched biological processes for poorly differentiated tumors. The length of the blue bar indicates the negative log-transformed false discovery rate (FDR). HGs, histological grades; WD, well-differentiated; MD, moderately differentiated; PD, poorly differentiated.
Figure 5
Figure 5
Biological processes could predict survival accurately. (A) The top enriched biological process in WD-specific genes from the Chinese population was validated in the Western population; (B) the top enriched biological process in MD-specific genes from the Chinese population was validated in the Western population; (C) the top enriched biological process in PD-specific genes from the Chinese population was validated in the Western population; (D) the intersection of enriched biological processed in the Chinese population and the Western population. WD, well-differentiated; MD, moderately differentiated; PD, poorly differentiated.
Figure S1
Figure S1
Workflow for this study.
Figure S2
Figure S2
The gene list of the targeted sequencing.
Figure S3
Figure S3
Gene amplification difference between different grades. (A) The gene amplification overlaps between three HGs for substitution/indel/truncation; (B) the enriched biological processes for well differentiated tumors; (C) the enriched biological processes for moderately differentiated tumors; (D) the enriched biological processes for poorly differentiated tumors. The length of the blue bar indicates the negative log transformed false discover rate (FDR). WD, well differentiated; MD, moderately differentiated; PD, poorly differentiated.
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