Background: This study is aimed towards an exploration of mutant genes in primary liver cancer (PLC) patients by using bioinformatics and data mining techniques. Methods: Peripheral blood or paraffin-embedded tissues from 8 patients with PLC were analyzed using a 551 cancer-related gene panel on an Illumina NextSeq500 Sequencer (Illumina). Meanwhile, the data of 396 PLC cases were downloaded from The Cancer Genome Atlas (TCGA) database. The common mutated genes were obtained after integrating the mutation information of the above two cohorts, followed by functional enrichment and protein-protein interaction (PPI) analyses. Three well-known databases, including Vogelstein’s list, the Network of Cancer Gene (NCG), and the Catalog of Somatic Mutations in Cancer (COSMIC) database were used to screen driver genes. Furthermore, the Chi-square and logistic analysis were performed to analyze the correlation between the driver genes and clinicopathological characteristics, and Kaplan - Meier (KM) method and multivariate Cox analysis were conducted to evaluate the overall survival outcome. Results: In total, 84 mutation genes were obtained after 8 PLC patients undergoing gene mutation detection with next-generation sequencing (NGS). The top 100 most mutate gene data from PLC patients in TCGA database were downloaded. After integrating the above two cohorts, 17 common mutated genes were identified. Next, 11 driver genes were screened out by analyzing the intersection of the 17 mutation genes and the genes in the three well-known databases. Among them, RB1, TP53, and KRAS gene mutations were connected with clinicopathological characteristics, while all the 11 gene mutations had no relationship with overall survival. Conclusion: This study investigated the mutant genes with significant clinical implications in PLC patients, which may improve the knowledge of gene mutations in PLC molecular pathogenesis.
Lin, D.C., Mayakonda, A., Dinh, H.Q., Huang, P., Lin, L., Liu, X., Ding, L.W., Wang, J., Berman, B.P., Song, E.W., Yin, D. and Koeffler, H.P. (2017) Genomic and Epigenomic Heterogeneity of Hepatocellular Carcinoma. Cancer Research, 77, 2255-2265. https://doi.org/10.1158/0008-5472.CAN-16-2822
Greten, T.F., Lai, C.W., Li, G. and Staveley-O’Carroll, K.F. (2019) Targeted and Immune-Based Therapies for Hepatocellular Carcinoma. Gastroenterology, 156, 510-524. https://doi.org/10.1053/j.gastro.2018.09.051
Wang, H., Lu, Z. and Zhao, X. (2019) Tumorigenesis, Diagnosis, and Therapeutic potential of Exosomes in Liver Cancer. Journal of Hematology & Oncology, 12, Article No. 133. https://doi.org/10.1186/s13045-019-0806-6
Wu, K., Huang, R.S., House, L. and Cho, W.C. (2013) Next-Generation Sequencing for Lung Cancer. Future Oncology, 9, 1323-1336. https://doi.org/10.2217/fon.13.102
Wang, Z., Gerstein, M. and Snyder, M. (2009) RNA-Seq: A Revolutionary Tool for Transcriptomics. Nature Reviews Genetics, 10, 57-63. https://doi.org/10.1038/nrg2484
Gao, J., Ciriello, G., Sander, C. and Schultz, N. (2014) Collection, Integration and Analysis of Cancer Genomic Profiles: From Data to Insight. Current Opinion in Genetics & Development, 24, 92-98. https://doi.org/10.1016/j.gde.2013.12.003
Tomczak, K., Czerwińska, P. and Wiznerowicz, M. (2015) The Cancer Genome Atlas (TCGA): An Immeasurable Source of Knowledge. Contemporary Oncology, 19, A68-A77. https://doi.org/10.5114/wo.2014.47136
Bardou, P., Mariette, J., Escudié, F., Djemiel, C. and Klopp, C. (2014) Jvenn: An Interactive Venn Diagram Viewer. BMC Bioinformatics, 15, Article No. 293. https://doi.org/10.1186/1471-2105-15-293
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering. C. and Jensen, L.J. (2013) STRING v9.1: Protein-Protein Interaction Networks, with Increased Coverage and Integration. Nucleic Acids Research, 41, D808-D815. https://doi.org/10.1093/nar/gks1094
Repana, D., Nulsen, J., Dressler, L., Bortolomeazzi, M., Venkata, S.K., Tourna, A., Yakovleva, A., Palmieri, T. and Ciccarelli, F.D. (2019) The Network of Cancer Genes (NCG): A Comprehensive Catalogue of Known and Candidate Cancer Genes from Cancer Sequencing Screens. Genome Biology, 20, Article No. 1. https://doi.org/10.1186/s13059-018-1612-0
Forbes, S.A., Bindal, N., Bamford, S., Cole, C., Kok, C.Y., Beare, D., Jia, M., Shepherd, R., Leung, K., Menzies, A., Teague, J.W., Campbell, P.J., Stratton, M.R. and Futreal, P.A. (2011) COSMIC: Mining Complete Cancer Genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Research, 39, D945-D950. https://doi.org/10.1093/nar/gkq929
Alekseyev, Y.O., Fazeli, R., Yang, S., Basran, R., Maher, T., Miller, N.S. and Remick, D. (2018) A Next-Generation Sequencing Primer—How Does It Work and What Can It Do? Academic Pathology, 5, 1-11. https://doi.org/10.1177/2374289518766521
Morishita, A., Iwama, H., Fujihara, S., Watanabe, M., Fujita, K., Tadokoro, T., Ohura, K., Chiyo, T., Sakamoto, T., Mimura, S., Nomura, T., Tani, J., Yoneyama, H., Okano, K., Suzuki, Y., Himoto, T. and Masaki, T. (2018) Targeted Sequencing of Cancer-Associated Genes in Hepatocellular Carcinoma Using Next-Generation Sequencing. Oncology Letters, 15, 528-532. https://doi.org/10.3892/ol.2017.7334
Lu, J., Yin, J., Dong, R., Yang, T., Yuan, L., Zang, L., Xu, C., Peng, B., Zhao, J. and Du, X. (2015) Targeted Sequencing of Cancer-Associated Genes in Hepatocellular Carcinoma Using Next Generation Sequencing. Molecular Medicine Reports, 12, 4678-4682. https://doi.org/10.3892/mmr.2015.3952
Kan, Z., Zheng, H., Liu, X., Li, S., Barber, T.D., Gong, Z., Gao, H., Hao, K., Willard, M.D., Xu, J., Hauptschein, R., Rejto, P.A., Fernandez, J., Wang, G., Zhang, Q., Wang, B., Chen, R., Wang, J., Lee, N.P., Zhou, W., Lin, Z., Peng, Z., Yi, K., Chen, S., Li, L., Fan, X., Yang, J., Ye, R., Ju, J., Wang, K., Estrella, H., Deng, S., Wei, P., Qiu, M., Wulur, I.H., Liu, J., Ehsani, M.E., Zhang, C., Loboda, A., Sung, W.K., Aggarwal, A., Poon, R.T., Fan, S.T., Wang, J., Hardwick, J., Reinhard, C., Dai, H., Li, Y., Luk, J.M. and Mao, M. (2013) Whole-Genome Sequencing Identifies Recurrent Mutations in Hepatocellular Carcinoma. Genome Research, 23, 1422-1433. https://doi.org/10.1101/gr.154492.113
Janku, F., Kaseb, A.O., Tsimberidou, A.M., Wolff, R.A. and Kurzrock, R. (2014) Identification of Novel Therapeutic Targets in the PI3K/AKT/mTOR Pathway in Hepatocellular Carcinoma Using Targeted Next Generation Sequencing. Oncotarget, 5, 3012-3022. https://doi.org/10.18632/oncotarget.1687
Li, M., Zhao, H., Zhang, X., Wood, L.D., Anders, R.A., Choti, M.A., Pawlik, T.M., Daniel, H.D., Kannangai, R., Offerhaus, G.J., Velculescu, V.E., Wang, L., Zhou, S., Vogelstein, B., Hruban, R.H., Papadopoulos, N., Cai, J., Torbenson, M.S. and Kinzler, K.W. (2011) Inactivating Mutations of the Chromatin Remodeling Gene ARID2 in Hepatocellular Carcinoma. Nature Genetics, 43, 828-829. https://doi.org/10.1038/ng.903
Anjanappa, M., Hao, Y., Simpson, E.R., Bhat-Nakshatri, P., Nelson, J.B., Tersey, S.A., Mirmira, R.G., Cohen-Gadol, A.A., Saadatzadeh, M.R., Li, L., Fang, F., Nephew, K.P., Miller, K.D., Liu, Y. and Nakshatri, H. (2018) A System for Detecting High Impact-Low Frequency Mutations in Primary Tumors and Metastases. Oncogene, 37, 185-196. https://doi.org/10.1038/onc.2017.322
Ikeno, Y., Seo, S., Iwaisako, K., Yoh, T., Nakamoto, Y., Fuji, H., Taura, K., Okajima, H., Kaido, T., Sakaguchi, S. and Uemoto, S. (2018) Preoperative Metabolic Tumor Volume of Intrahepatic Cholangiocarcinoma Measured by 18F-FDG-PET Is Associated with the KRAS Mutation Status and Prognosis. Journal of Translational Medicine, 16, Article No. 95. https://doi.org/10.1186/s12967-018-1475-x
Saliani, M., Jalal, R. and Ahmadian, M.R. (2019) From Basic Researches to New Achievements in Therapeutic Strategies of KRAS-Driven Cancers. Cancer Biology & Medicine, 16, 435-461.
He, L., Fan, X., Li, Y., Chen, M., Cui, B., Chen, G., Dai, Y., Zhou, D., Hu, X. and Lin, H. (2019) Overexpression of Zinc Finger Protein 384 (ZNF 384), A Poor Prognostic Predictor, Promotes Cell Growth by Upregulating the Expression of Cyclin D1 in Hepatocellular Carcinoma. Cell Death & Disease, 10, Article No. 444. https://doi.org/10.1038/s41419-019-1681-3
Chen, S.L., Liu, L.L., Wang, C.H., Lu, S.X., Yang, X., He, Y.F., Zhang, C.Z. and Yun, J.P. (2020) Loss of RDM1 Enhances Hepatocellular Carcinoma Progression via p53 and Ras/Raf/ERK Pathways. Molecular Oncology, 14, 373-386. https://doi.org/10.1002/1878-0261.12593
Springer, S.U., Chen, C.H., Rodriguez, Pena. M.D.C., Li, L., Douville, C., Wang, Y., Cohen, J.D., Taheri, D., Silliman, N., Schaefer, J., Ptak, J., Dobbyn, L., Papoli, M., Kinde, I., Afsari, B., Tregnago, A.C., Bezerra, S.M., VandenBussche, C., Fujita, K., Ertoy, D., Cunha, I.W., Yu, L., Bivalacqua, T.J., Grollman, A.P., Diaz, L.A., Karchin, R., Danilova, L., Huang, C.Y., Shun, C.T., Turesky, R.J., Yun, B.H., Rosenquist, T.A., Pu, Y.S., Hruban, R.H., Tomasetti, C., Papadopoulos, N., Kinzler, K.W., Vogelstein, B., Dickman, K.G. and Netto, G.J. (2018) Non-Invasive Detection of Urothelial Cancer through the Analysis of Driver Gene Mutations and Aneuploidy. Elife, 7, Article No. e32143. https://doi.org/10.7554/eLife.32143
Merid, S.K., Goranskaya, D. and Alexeyenko, A. (2014) Distinguishing between Driver and Passenger Mutations in Individual Cancer Genomes by Network Enrichment Analysis. BMC Bioinformatics, 15, Article No. 308. https://doi.org/10.1186/1471-2105-15-308
Tian, R., Basu, M.K. and Capriotti, E. (2014) ContrastRank: A New Method for Ranking Putative Cancer Driver Genes and Classification of Tumor Samples. Bioinformatics, 17, i572-i857. https://doi.org/10.1093/bioinformatics/btu466
Shangguan, H., Tan, S.Y. and Zhang, J.R. (2015) Bioinformatics Analysis of Gene Expression Profiles in Hepatocellular Carcinoma. European Review for Medical and Pharmacological Sciences, 19, 2054-2061.
El-Ayadi, M., Egervari, K., Merkler, D., McKee, T.A., Gumy-Pause, F., Stichel, D., Capper, D., Pietsch, T., Ansari, M. and Bueren. A.O. (2018) Concurrent IDH1 and SMARCB1 Mutations in Pediatric Medulloblastoma: A Case Report. Frontiers in Neurology, 9, Article No. 398. https://doi.org/10.3389/fneur.2018.00398
Chaudhary, K., Poirion, O.B., Lu, L., Huang, S., Ching, T. and Garmire, L.X. (2019) Multimodal Meta-Analysis of 1,494 Hepatocellular Carcinoma Samples Reveals Significant Impact of Consensus Driver Genes on Phenotypes. Clinical Cancer Research, 25, 463-472. https://doi.org/10.1158/1078-0432.ccr-18-0088
Hill, M.A., Alexander, W.B., Guo, B., Kato, Y., Patra, K., O’Dell, M.R., McCall, M.N., Whitney-Miller, C.L., Bardeesy, N. and Hezel, A.F. (2018) Kras and Tp53 Mutations Cause Cholangiocyte- and Hepatocyte-Derived Cholangiocarcinoma. Cancer Research, 78, 4445-4451. https://doi.org/10.1158/0008-5472.CAN-17-1123
Levi, S., Urbano-Ispizua, A., Gill, R., Thomas, D.M., Gilbertson, J., Foster, C. and Marshall, C.J. (1991) Multiple K-ras Codon 12 Mutations in Cholangiocarcinomas Demonstrated with a Sensitive Polymerase Chain Reaction Technique. Cancer Research, 51, 3497-3502.