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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Exploring Prognostic Signatures of Hepatocellular Carcinoma and the Potential Implications in Tumor Immune Microenvironment

Author(s): Hongxu Chen, Zhijing Jiang*, Bingshi Yang, Guiling Yan, Xiaochen Wang and Shuning Zang

Volume 25, Issue 6, 2022

Published on: 09 March, 2021

Page: [998 - 1004] Pages: 7

DOI: 10.2174/1386207324666210309100923

Price: $65

Abstract

Objective: The objective of this study is to construct a prognostic model using genetic markers of liver cancer and explore the signature genes associated with the tumor immune microenvironment.

Methods: Cox proportional hazards regression analysis was carried out to screen the significant HR using the dataset of TCGA Liver Cancer (LIHC) gene expression data. Then LASSO (least absolute shrinkage and selection operator) was performed to select the minimal variables with significant HR of genes. Thus, the prognostic model was constructed by the minimal variables with their HR. Time-dependent receiver-operating characteristic (ROC) curve and area under the ROC curve (AUC) value was used to assess the prognostic performance. Then the patients were divided into high and low-risk groups by the median of the model. Survival analysis was performed on the two groups with testing and an independent dataset. Furthermore, enrichment analysis of signature mRNAs and lncRNAs and their co-expression genes was performed. Then, Spearman rank correlation was used to calculate the correlation between immune cells and genes in the prognostic model, and abundance difference of the immune cells in high and low risks groups was tested.

Results: A total of 5989 genes with significant HR were identified. 6 key genes (three mRNAs: DHX37, SMIM7, and MFSD1, three lncRNAs: PIWIL4, KCNE5, and LOC100128398) screened by LASSO were used to construct the model with their HR value respectively. The AUC values of 1 and 5-year overall survival were 0.78 and 0.76 in discovery data and 0.67 and 0.68 in testing data. Survival analysis performed significantly discriminated high and low groups with testing and independent data. Furthermore, many immune cells such as nTreg found a significant correlation with the genes in the prognostic model, and many immune cells showed significantly different abundance in high and low-risk groups.

Conclusion: In the study, we used Univariate Cox analyses and LASSO algorithm with TCGA gene expression data to construct the prognostic model in liver cancer patients. The prognostic model comprised of three mRNAs, including DHX37, SMIM7, MFSD1, and three lncRNAs, including PIWIL4, KCNE5, and LOC100128398. Furthermore, these gene expression levels were associated with the abundance of some immune cells, such as nTreg. Also, many immune cells have significantly different abundance in high and low-risk groups. All these results indicated that the combination with all these six genes could be the potential biomarker for the prognosis of liver cancer.

Keywords: Prognostic model construction, signature, lncRNA, survival analysis, ROC curve, tumor immune microenvironment.

Graphical Abstract

[1]
Allemani, C.; Matsuda, T.; Di Carlo, V.; Harewood, R.; Matz, M.; Nikšić, M.; Bonaventure, A.; Valkov, M.; Johnson, C.J.; Estève, J.; Ogunbiyi, O.J.; Azevedo, E.S.G.; Chen, W.Q.; Eser, S.; Engholm, G.; Stiller, C.A.; Monnereau, A.; Woods, R.R.; Visser, O.; Lim, G.H.; Aitken, J.; Weir, H.K.; Coleman, M.P. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet, 2018, 391(10125), 1023-1075.
[2]
Maluccio, M.; Covey, A. Recent progress in understanding, diagnosing, and treating hepatocellular carcinoma. CA Cancer J. Clin., 2012, 62(6), 394-399.
[3]
Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2018, 68(6), 394-424.
[4]
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2018. 2018, 68(1), 7-30.
[5]
Sun, J.H.; Luo, Q.; Liu, L.L.; Song, G.B. Liver cancer stem cell markers: Progression and therapeutic implications. World J. Gastroenterol., 2016, 22(13), 3547-3557.
[6]
Anwanwan, D.; Singh, S.K.; Singh, S.; Saikam, V.; Singh, R. Challenges in liver cancer and possible treatment approaches. Biochim. Biophys. Acta Rev. Cancer, 2020, 1873(1), 188314.
[7]
Li, L.; Wang, H. Heterogeneity of liver cancer and personalized therapy. Cancer Lett., 2016, 379(2), 191-197.
[8]
Bruix, J.; Han, K.H.; Gores, G.; Llovet, J.M.; Mazzaferro, V. Liver cancer: Approaching a personalized care. J. Hepatol., 2015, 62(1)(Suppl.), S144-S156.
[9]
Zucman-Rossi, J.; Villanueva, A.; Nault, J.C.; Llovet, J.M. Genetic Landscape and Biomarkers of Hepatocellular Carcinoma. Gastroenterology, 2015, 149(5), 1226-1239.e1224.
[10]
Scaggiante, B.; Kazemi, M.; Pozzato, G.; Dapas, B.; Farra, R.; Grassi, M.; Zanconati, F.; Grassi, G. Novel hepatocellular carcinoma molecules with prognostic and therapeutic potentials. World J. Gastroenterol., 2014, 20(5), 1268-1288.
[11]
Wang, Y.; Song, J.; Bian, H.; Bo, J.; Lv, S.; Pan, W.; Lv, X. Apelin promotes hepatic fibrosis through ERK signaling in LX-2 cells. 2019, 460(1-2), 205-215.
[12]
Xing, J.; Tian, Y.; Ji, W.; Wang, X. Comprehensive evaluation of SPATS2 expression and its prognostic potential in liver cancer. Medicine (Baltimore), 2020, 99(9), e19230.
[13]
Zhai, K.; Yang, Y.; Gao, Z.G.; Ding, J. Interleukin-6-174G>C gene promoter polymorphism and prognosis in patients with cancer. Oncotarget, 2017, 8(27), 44490-44497.
[14]
Wang, Q.; Zhu, Y.; Li, Z.; Bu, Q.; Sun, T.; Wang, H.; Sun, H.; Cao, X. Up-regulation of SPC25 promotes breast cancer. Aging (Albany NY), 2019, 11(15), 5689-5704.
[15]
Buoncervello, M.; Gabriele, L.; Toschi, E. The Janus Face of Tumor Microenvironment Targeted by Immunotherapy. Int. J. Mol. Sci., 2019, 20(17)
[16]
Houthuijzen, J.M.; Jonkers, J. Cancer-associated fibroblasts as key regulators of the breast cancer tumor microenvironment. Cancer Metastasis Rev., 2018, 37(4), 577-597.
[17]
Whiteside, T.L. Exosome and mesenchymal stem cell cross-talk in the tumor microenvironment. Semin. Immunol., 2018, 35, 69-79.
[18]
Syed, S.N.; Frank, A.C.; Raue, R.; Brüne, B. MicroRNA-A Tumor Trojan Horse for Tumor-Associated Macrophages. Cells, 2019, 8(12)
[19]
Altorki, N.K.; Markowitz, G.J.; Gao, D.; Port, J.L.; Saxena, A.; Stiles, B.; McGraw, T.; Mittal, V. The lung microenvironment: an important regulator of tumour growth and metastasis. Nat. Rev. Cancer, 2019, 19(1), 9-31.
[20]
Meurette, O.; Mehlen, P. Notch Signaling in the Tumor Microenvironment. Cancer Cell, 2018, 34(4), 536-548.
[21]
Roma-Rodrigues, C.; Mendes, R.; Baptista, P.V. Targeting Tumor Microenvironment for Cancer Therapy. 2019, 20(4)
[22]
Jarosz-Biej, M.; Smolarczyk, R.; Cichoń, T.; Kułach, N. Tumor Microenvironment as A “Game Changer” in Cancer Radiotherapy. Int. J. Mol. Sci., 2019, 20(13)
[23]
Gurin, D.; Slavik, M.; Hermanova, M. The tumor immune microenvironment and its implications for clinical outcome in patients with oropharyngeal squamous cell carcinoma., 2020.
[24]
Chandler, C.; Liu, T.; Buckanovich, R.; Coffman, L.G. The double edge sword of fibrosis in cancer. Transl. Res., 2019, 209, 55-67.
[25]
Chen, C.; Liu, J.M.; Luo, Y.P. MicroRNAs in tumor immunity: functional regulation in tumor-associated macrophages. J. Zhejiang Univ. Sci. B, 2020, 21(1), 12-28.
[26]
Eissmann, M.F.; Buchert, M.; Ernst, M. IL33 and Mast Cells-The Key Regulators of Immune Responses in Gastrointestinal Cancers? Front. Immunol., 2020, 11, 1389.
[27]
Miao, Y.R.; Zhang, Q.; Lei, Q.; Luo, M.; Xie, G.Y.; Wang, H.; Guo, A.Y. ImmuCellAI: A Unique Method for Comprehensive T-Cell Subsets Abundance Prediction and its Application in Cancer Immunotherapy. Adv. Sci. (Weinh.), 2020, 7(7), 1902880.
[28]
Zhou, Y.; Zhou, B.; Pache, L.; Chang, M. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. 2019, 35(17), 3199-3202.
[29]
Fagerberg, L.; Hallström, B.M.; Oksvold, P.; Kampf, C.; Djureinovic, D.; Odeberg, J.; Habuka, M.; Tahmasebpoor, S.; Danielsson, A.; Edlund, K.; Asplund, A.; Sjöstedt, E.; Lundberg, E.; Szigyarto, C.A.; Skogs, M.; Takanen, J.O.; Berling, H.; Tegel, H.; Mulder, J.; Nilsson, P.; Schwenk, J.M.; Lindskog, C.; Danielsson, F.; Mardinoglu, A.; Sivertsson, A.; von Feilitzen, K.; Forsberg, M.; Zwahlen, M.; Olsson, I.; Navani, S.; Huss, M.; Nielsen, J.; Ponten, F.; Uhlén, M. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics, 2014, 13(2), 397-406.
[30]
Zhang, Y.; Liu, T.; Chen, L.; Yang, J.; Yin, J.; Zhang, Y.; Yun, Z.; Xu, H.; Ning, L.; Guo, F.; Jiang, Y.; Lin, H.; Wang, D.; Huang, Y.; Huang, J. RIscoper: a tool for RNA-RNA interaction extraction from the literature. Bioinformatics, 2019, 35(17), 3199-3202.
[31]
Dong, M.B.; Wang, G.; Chow, R.D.; Ye, L.; Zhu, L.; Dai, X.; Park, J.J.; Kim, H.R.; Errami, Y.; Guzman, C.D.; Zhou, X.; Chen, K.Y.; Renauer, P.A.; Du, Y.; Shen, J.; Lam, S.Z.; Zhou, J.J.; Lannin, D.R.; Herbst, R.S.; Chen, S. Systematic Immunotherapy Target Discovery Using Genome-Scale In Vivo CRISPR Screens in CD8 T Cells. Cell, 2019, 178(5), 1189-1204.e1123.
[32]
Denisov, E.V.; Skryabin, N.A.; Gerashchenko, T.S.; Tashireva, L.A.; Wilhelm, J.; Buldakov, M.A.; Sleptcov, A.A.; Lebedev, I.N.; Vtorushin, S.V.; Zavyalova, M.V.; Cherdyntseva, N.V.; Perelmuter, V.M. Clinically relevant morphological structures in breast cancer represent transcriptionally distinct tumor cell populations with varied degrees of epithelial-mesenchymal transition and CD44(+)CD24(-) stemness. Oncotarget, 2017, 8(37), 61163-61180.
[33]
Rodrigues-Peres, R.M. de, S.C.B.; Anurag, M.; Lei, J.T.; Conz, L.; Gonçalves, R.; Cardoso Filho, C.; Ramalho, S.; de Paiva, G.R.; Derchain, S.F.M.; Lopes-Cendes, I.; Ellis, M.J.; Sarian, L.O. Copy number alterations associated with clinical features in an underrepresented population with breast cancer. Mol. Genet. Genomic Med., 2019, 7(7), e00750.
[34]
Ceder, M.M.; Lekholm, E.; Klaesson, A.; Tripathi, R.; Schweizer, N.; Weldai, L.; Patil, S.; Fredriksson, R. Glucose Availability Alters Gene and Protein Expression of Several Newly Classified and Putative Solute Carriers in Mice Cortex Cell Culture and D. melanogaster. Front. Cell Dev. Biol., 2020, 8, 579.
[35]
Zheng, J.; Liu, X.; Wang, P.; Xue, Y.; Ma, J.; Qu, C.; Liu, Y. CRNDE Promotes Malignant Progression of Glioma by Attenuating miR-384/PIWIL4/STAT3 Axis. Mol. Ther., 2016, 24(7), 1199-1215.
[36]
Wang, Z.; Liu, N.; Shi, S.; Liu, S.; Lin, H. The Role of PIWIL4, an Argonaute Family Protein, in Breast Cancer. J. Biol. Chem., 2016, 291(20), 10646-10658.
[37]
Ning, L.; Cui, T.; Zheng, B.; Wang, N.; Luo, J.; Yang, B.; Du, M.; Cheng, J.; Dou, Y.; Wang, D. MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation. Nucleic Acids Res., 2020.
[38]
Lin, Y.; Liu, T.; Cui, T.; Wang, Z.; Zhang, Y.; Tan, P.; Huang, Y.; Yu, J.; Wang, D. RNAInter in 2020: RNA interactome repository with increased coverage and annotation. Nucleic Acids Res., 2020, 48(D1), D189-d197.
[39]
Li, Y.; Wang, C.; Miao, Z.; Bi, X.; Wu, D.; Jin, N.; Wang, L.; Wu, H.; Qian, K.; Li, C.; Zhang, T.; Zhang, C.; Yi, Y.; Lai, H.; Hu, Y.; Cheng, L.; Leung, K.S.; Li, X.; Zhang, F.; Li, K.; Li, X.; Wang, D. ViRBase: a resource for virus-host ncRNA-associated interactions., 2015. 43(Database issue), D578-582.
[40]
Abbott, G.W. KCNE4 and KCNE5: K(+) channel regulation and cardiac arrhythmogenesis. Gene, 2016, 593(2), 249-260.
[41]
Huang, Y.; Wang, J.; Zhao, Y.; Wang, H.; Liu, T.; Li, Y.; Cui, T.; Li, W.; Feng, Y.; Luo, J.; Gong, J.; Ning, L.; Zhang, Y.; Wang, D.; Zhang, Y. cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function. Nucleic Acids Res., 2020.
[42]
Szymczak, S.; Dose, J.; Torres, G.G.; Heinsen, F.A.; Venkatesh, G.; Datlinger, P.; Nygaard, M.; Mengel-From, J.; Flachsbart, F.; Klapper, W.; Christensen, K.; Lieb, W.; Schreiber, S.; Häsler, R.; Bock, C.; Franke, A.; Nebel, A. DNA methylation QTL analysis identifies new regulators of human longevity. Hum. Mol. Genet., 2020, 29(7), 1154-1167.
[43]
Jin, N.; Li, Y.; Zhang, L.; Yang, H.; Hu, Z.; Zhang, L.; Hu, C.; Li, C.; Qian, K.; Zhang, C.; Huang, Y.; Li, K.; Lin, H.; Wang, D.
[44]
Terabe, M.; Berzofsky, J.A. Tissue-Specific Roles of NKT Cells in Tumor Immunity. Front. Immunol., 2018, 9, 1838.
[45]
Paluskievicz, C.M.; Cao, X.; Abdi, R.; Zheng, P.; Liu, Y.; Bromberg, J.S. T Regulatory Cells and Priming the Suppressive Tumor Microenvironment. Front. Immunol., 2019, 10, 2453.
[46]
Downs-Canner, S.; Berkey, S.; Delgoffe, G.M.; Edwards, R.P.; Curiel, T.; Odunsi, K.; Bartlett, D.L.; Obermajer, N. Suppressive IL-17A(+)Foxp3(+) and ex-Th17 IL-17A(neg)Foxp3(+) T(reg) cells are a source of tumour-associated T(reg) cells. Nat. Commun., 2017, 8, 14649.

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