Abstract
The global incidence of hepatocellular carcinoma (HCC) has increased threefold in the last 30 years. In the United States, individuals with ancestry from Asia, Africa and Latin America have a significantly higher risk of developing HCC. However, the molecular mechanisms by which HCC disparities occur remain mostly understudied. Herein, we employed advanced bioinformatics analysis tools to identify genomic drivers that could explain the differences seen among HCC patients of distinct ethnicities (geographic origins). Data from TCGA and open-source software tools HiSTAT, StringTie, and Ballgown were used to map next-generation sequencing (NGS) reads from DNA and RNA, assemble transcripts, and quantify gene abundance. Differential genes/transcripts were mapped to known biomarkers and targets of systemic HCC therapeutics. Four overlapping transcripts were identified between each ethnicity group: FCN2, FCN3, COLEC10, and GDF2. However, we also found that multiple genes are expressed in an ethnicity-specific manner. Our models also revealed that both current and emerging biomarkers fail to capture heterogeneity between patients of different ethnicities. Finally, we have determined that first-line treatment, such as Sorafenib, may be better suited for Asian patients, while Lenvatinib may exhibit better efficacy for Caucasian patients. In conclusion, we have outlined that the pathways involved in early hepatocarcinogenesis may occur in an ethnicity-specific manner and that these distinct phenotypes should be taken into account for biomarker and therapeutic development.
Competing Interest Statement
AGED Diagnostics.
Footnotes
Potential conflict of interest: Nothing to report.
Financial Support: This work was supported by AGED Diagnostics.
Resources: The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from: the GTEx Portal on 09/04/2020 and/or dbGaP accession number phs000424.vN.pN on 09/04/2020.
Abbreviations
- HBV
- hepatitis B virus
- HCC
- hepatocellular carcinoma
- HCV
- hepatitis C virus
- NAFLD
- Nonalcoholic fatty liver disease
- NASH
- Nonalcoholic steatohepatitis
- LIHC
- Liver Hepatocellular Carcinoma
- PNPLA3
- polymorphisms in adiponutrin 3
- GCKR
- Glucokinase Regulator
- TCGA
- The Cancer Genome Atlas
- RNA-Seq
- RNA-sequencing
- CGC
- Cancer Genomics Cloud
- AJCC
- American Joint Committee on Cancer
- SPP1
- osteopontin
- GOLM1
- Golgi Membrane Protein
- MDK
- midkine
- DKK1
- Dickkopf-1
- GPC3
- glycoprotein-3
- F2
- coagulation factor II
- DCPS
- Decapping Enzyme Scavenger
- FUCA1
- alpha-1-fucosidase
- AFP
- alpha fetoprotein