Login Register Cart 0
Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Systematic Analysis of Tumor Stem Cell-related Gene Characteristics to Predict the PD-L1 Immunotherapy and Prognosis of Gastric Cancer

Author(s): Chenchen Wang, Ying Chen, Ru Zhou, Ya’nan Yang and Yantian Fang*

Volume 31, Issue 17, 2024

Published on: 02 November, 2023

Page: [2467 - 2482] Pages: 16

DOI: 10.2174/0109298673278775231101064235

Price: $65

Abstract

Aims: We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC).

Background: Tumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.

Objective: We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.

Methods: We downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram.

Results: Early GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy.

Conclusion: We developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.

« Previous
[1]
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[2]
Chen, L.; Lu, L.; Gong, X.; Xu, Y.; Chu, X.; Huang, G. Gastric cancer with bone marrow invasion and disseminated intravascular coagulation: A case report. Oncologie, 2022, 24(3), 599-604.
[http://dx.doi.org/10.32604/oncologie.2022.023310]
[3]
Han, F; Qu, J; Li, F; Zhang, D; Qu, J; Li, G. Serum long non-coding RNA CCAT2 is a potential diagnostic and prognostic marker for gastric cancer. Oncologie, 2021, 23(1), 131-140.
[http://dx.doi.org/10.32604/Oncologie.2021.014153]
[4]
Cao, M.; Li, H.; Sun, D.; He, S.; Yan, X.; Yang, F.; Zhang, S.; Xia, C.; Lei, L.; Peng, J.; Chen, W. Current cancer burden in China: Epidemiology, etiology, and prevention. Cancer Biol. Med., 2022, 19(8), 1121-1138.
[http://dx.doi.org/10.20892/j.issn.2095-3941.2022.0231] [PMID: 36069534]
[5]
Wang, D.; Chen, H.; Hu, Y. Polarized Autologous Macrophages (PAM) can be a tumor vaccine. Oncologie, 2022, 24(3), 441-449.
[http://dx.doi.org/10.32604/oncologie.2022.024898]
[6]
Li, K.; Zhang, A.; Li, X.; Zhang, H.; Zhao, L. Advances in clinical immunotherapy for gastric cancer. Biochimica et Biophysica Acta (BBA)-. Rev. Can., 2021, 1876(2), 188615.
[7]
Fu, Q.; Zhang, X.; Zhang, Y. The presence of human papillomavirus and Epstein-Barr virus in male Chinese lichen sclerosus patients: A single center study. Asian J. Androl., 2016, 18(4), 650-653.
[http://dx.doi.org/10.4103/1008-682X.160261] [PMID: 26289401]
[8]
Kwak, Y.; Seo, A.N.; Lee, H.E.; Lee, H.S. Tumor immune response and immunotherapy in gastric cancer. J. Pathol. Transl. Med., 2020, 54(1), 20-33.
[http://dx.doi.org/10.4132/jptm.2019.10.08] [PMID: 31674166]
[9]
Fu, Y.; Du, P.; Zhao, J.; Hu, C.; Qin, Y.; Huang, G. Gastric cancer stem cells: Mechanisms and therapeutic approaches. Yonsei Med. J., 2018, 59(10), 1150-1158.
[http://dx.doi.org/10.3349/ymj.2018.59.10.1150] [PMID: 30450848]
[10]
Galassi, C.; Musella, M.; Manduca, N.; Maccafeo, E.; Sistigu, A. The immune privilege of cancer stem cells: A key to understanding tumor immune escape and therapy failure. Cells, 2021, 10(9), 2361.
[http://dx.doi.org/10.3390/cells10092361] [PMID: 34572009]
[11]
Chen, X.; Zhang, D.; Jiang, F.; Shen, Y.; Li, X.; Hu, X.; Wei, P.; Shen, X. Prognostic prediction using a stemness index-related signature in a cohort of gastric cancer. Front. Mol. Biosci., 2020, 7, 570702.
[http://dx.doi.org/10.3389/fmolb.2020.570702] [PMID: 33134315]
[12]
Liu, M.; Zhou, R.; Zou, W.; Yang, Z.; Li, Q.; Chen, Z.; jiang, L.; Zhang, J. Machine learning-identified stemness features and constructed stemness-related subtype with prognosis, chemotherapy, and immunotherapy responses for non-small cell lung cancer patients. Stem Cell Res. Ther., 2023, 14(1), 238.
[http://dx.doi.org/10.1186/s13287-023-03406-4] [PMID: 37674202]
[13]
Yi, L.; Huang, P.; Zou, X.; Guo, L.; Gu, Y.; Wen, C.; Wu, G. Integrative stemness characteristics associated with prognosis and the immune microenvironment in esophageal cancer. Pharmacol. Res., 2020, 161, 105144.
[http://dx.doi.org/10.1016/j.phrs.2020.105144] [PMID: 32810627]
[14]
Zheng, H.; Liu, H.; Li, H.; Dou, W.; Wang, J.; Zhang, J.; Liu, T.; Wu, Y.; Liu, Y.; Wang, X. Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in colorectal cancer. Stem Cell Res. Ther., 2022, 13(1), 244.
[http://dx.doi.org/10.1186/s13287-022-02913-0] [PMID: 35681225]
[15]
Lu, X.; Ying, Y.; Zhang, W.; Li, R.; Wang, W. Identification of stemness subtypes and features to improve endometrial cancer treatment using machine learning. Artif. Cells Nanomed. Biotechnol., 2023, 51(1), 57-73.
[http://dx.doi.org/10.1080/21691401.2023.2172027] [PMID: 36748358]
[16]
Yang, F; Gan, L; Pan, J; Chen, Y; Zhang, H; Huang, L. Integrated single-cell RNA-sequencing analysis of gastric cancer identifies FABP1 as a novel prognostic biomarker. J Oncol., 2022, 2022, 4761403.
[17]
Gulati, G.S.; Sikandar, S.S.; Wesche, D.J.; Manjunath, A.; Bharadwaj, A.; Berger, M.J.; Ilagan, F.; Kuo, A.H.; Hsieh, R.W.; Cai, S.; Zabala, M.; Scheeren, F.A.; Lobo, N.A.; Qian, D.; Yu, F.B.; Dirbas, F.M.; Clarke, M.F.; Newman, A.M. Single-cell transcriptional diversity is a hallmark of developmental potential. Science, 2020, 367(6476), 405-411.
[http://dx.doi.org/10.1126/science.aax0249] [PMID: 31974247]
[18]
Yu, G.; Wang, L.G.; Han, Y.; He, Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5), 284-287.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[19]
Danilova, L.; Ho, W.J.; Zhu, Q.; Vithayathil, T.; De Jesus-Acosta, A.; Azad, N.S.; Laheru, D.A.; Fertig, E.J.; Anders, R.; Jaffee, E.M.; Yarchoan, M. Programmed cell death ligand-1 (PD-L1) and CD8 expression profiling identify an immunologic subtype of pancreatic ductal adenocarcinomas with favorable survival. Cancer Immunol. Res., 2019, 7(6), 886-895.
[http://dx.doi.org/10.1158/2326-6066.CIR-18-0822] [PMID: 31043417]
[20]
Jiang, P.; Gu, S.; Pan, D.; Fu, J.; Sahu, A.; Hu, X.; Li, Z.; Traugh, N.; Bu, X.; Li, B.; Liu, J.; Freeman, G.J.; Brown, M.A.; Wucherpfennig, K.W.; Liu, X.S. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med., 2018, 24(10), 1550-1558.
[http://dx.doi.org/10.1038/s41591-018-0136-1] [PMID: 30127393]
[21]
Ritchie, ME; Phipson, B; Wu, D; Hu, Y; Law, CW; Shi, W limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 2015, 43(7), e47.
[22]
Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9(1), 559.
[http://dx.doi.org/10.1186/1471-2105-9-559] [PMID: 19114008]
[23]
Liao, Y.; Wang, J.; Jaehnig, E.J.; Shi, Z.; Zhang, B. WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res., 2019, 47(W1), W199-W205.
[http://dx.doi.org/10.1093/nar/gkz401] [PMID: 31114916]
[24]
Hastie, T.; Qian, J.; Tay, K. An Introduction to glmnet; CRAN R Repositary, 2021.
[25]
Charoentong, P.; Finotello, F.; Angelova, M.; Mayer, C.; Efremova, M.; Rieder, D.; Hackl, H.; Trajanoski, Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep., 2017, 18(1), 248-262.
[http://dx.doi.org/10.1016/j.celrep.2016.12.019] [PMID: 28052254]
[26]
Balar, A.V.; Galsky, M.D.; Rosenberg, J.E.; Powles, T.; Petrylak, D.P.; Bellmunt, J.; Loriot, Y.; Necchi, A.; Hoffman-Censits, J.; Perez-Gracia, J.L.; Dawson, N.A.; van der Heijden, M.S.; Dreicer, R.; Srinivas, S.; Retz, M.M.; Joseph, R.W.; Drakaki, A.; Vaishampayan, U.N.; Sridhar, S.S.; Quinn, D.I.; Durán, I.; Shaffer, D.R.; Eigl, B.J.; Grivas, P.D.; Yu, E.Y.; Li, S.; Kadel, E.E., III; Boyd, Z.; Bourgon, R.; Hegde, P.S.; Mariathasan, S.; Thåström, A.; Abidoye, O.O.; Fine, G.D.; Bajorin, D.F. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: A single-arm, multicentre, phase 2 trial. Lancet, 2017, 389(10064), 67-76.
[http://dx.doi.org/10.1016/S0140-6736(16)32455-2] [PMID: 27939400]
[27]
Hugo, W.; Zaretsky, J.M.; Sun, L.; Song, C.; Moreno, B.H.; Hu-Lieskovan, S.; Berent-Maoz, B.; Pang, J.; Chmielowski, B.; Cherry, G.; Seja, E.; Lomeli, S.; Kong, X.; Kelley, M.C.; Sosman, J.A.; Johnson, D.B.; Ribas, A.; Lo, R.S. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell, 2016, 165(1), 35-44.
[http://dx.doi.org/10.1016/j.cell.2016.02.065] [PMID: 26997480]
[28]
Hass, R.; von der Ohe, J.; Ungefroren, H. Impact of the tumor microenvironment on tumor heterogeneity and consequences for cancer cell plasticity and stemness. Cancers, 2020, 12(12), 3716.
[http://dx.doi.org/10.3390/cancers12123716] [PMID: 33322354]
[29]
Sarvaria, A.; Madrigal, J.A.; Saudemont, A. B cell regulation in cancer and anti-tumor immunity. Cell. Mol. Immunol., 2017, 14(8), 662-674.
[http://dx.doi.org/10.1038/cmi.2017.35] [PMID: 28626234]
[30]
Miranda, A.; Hamilton, P.T.; Zhang, A.W.; Pattnaik, S.; Becht, E.; Mezheyeuski, A.; Bruun, J.; Micke, P.; de Reynies, A.; Nelson, B.H. Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proc. Natl. Acad. Sci., 2019, 116(18), 9020-9029.
[http://dx.doi.org/10.1073/pnas.1818210116] [PMID: 30996127]
[31]
Zhang, Z.; Wang, Z.X.; Chen, Y.X.; Wu, H.X.; Yin, L.; Zhao, Q.; Luo, H.Y.; Zeng, Z.L.; Qiu, M.Z.; Xu, R.H. Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response. Genome Med., 2022, 14(1), 45.
[http://dx.doi.org/10.1186/s13073-022-01050-w] [PMID: 35488273]
[32]
Chen, P.; Hsu, W.H.; Han, J.; Xia, Y.; DePinho, R.A. Cancer stemness meets immunity: From mechanism to therapy. Cell Rep., 2021, 34(1), 108597.
[http://dx.doi.org/10.1016/j.celrep.2020.108597] [PMID: 33406434]
[33]
Wang, J.; Ren, H.; Wu, W.; Zeng, Q.; Chen, J.; Han, J.; Lin, M.; Zhang, C.; He, Y.; Li, M. Immune infiltration, cancer stemness, and targeted therapy in gastrointestinal stromal tumor. Front. Immunol., 2021, 12, 691713.
[http://dx.doi.org/10.3389/fimmu.2021.691713] [PMID: 34925310]
[34]
Wang, W; Xu, C; Ren, Y; Wang, S; Liao, C; Fu, X A novel cancer stemness-related signature for predicting prognosis in patients with colon adenocarcinoma. Stem Cells Int, 2021, 2021, 7036059.
[http://dx.doi.org/10.1155/2021/7036059]
[35]
Anselmi, M.; Fontana, F.; Marzagalli, M.; Gagliano, N.; Sommariva, M.; Limonta, P. Melanoma stem cells educate neutrophils to support cancer progression. Cancers, 2022, 14(14), 3391.
[http://dx.doi.org/10.3390/cancers14143391] [PMID: 35884452]
[36]
Gener, P; Seras-Franzoso, J; Callejo, PG; Andrade, F; Rafael, D; Martínez, F Dynamism, sensitivity, and consequences of mesenchymal and stem-like phenotype of cancer cells. Stem Cells Int., 2018, 2018, 4516454.
[http://dx.doi.org/10.1155/2018/4516454]
[37]
Dai, W.; Li, Y.; Mo, S.; Feng, Y.; Zhang, L.; Xu, Y.; Li, Q.; Cai, G. A robust gene signature for the prediction of early relapse in stage I–III colon cancer. Mol. Oncol., 2018, 12(4), 463-475.
[http://dx.doi.org/10.1002/1878-0261.12175] [PMID: 29377588]
[38]
De Francesco, E.M.; Maggiolini, M.; Tanowitz, H.B.; Sotgia, F.; Lisanti, M.P. Targeting hypoxic cancer stem cells (CSCs) with Doxycycline: Implications for optimizing anti-angiogenic therapy. Oncotarget, 2017, 8(34), 56126-56142.
[http://dx.doi.org/10.18632/oncotarget.18445] [PMID: 28915578]
[39]
Li, X.; Cao, Y.; Yu, X.; Jin, F.; Li, Y. A novel autophagy-related genes prognostic risk model and validation of autophagy-related oncogene VPS35 in breast cancer. Cancer Cell Int., 2021, 21(1), 265.
[http://dx.doi.org/10.1186/s12935-021-01970-4] [PMID: 34001111]
[40]
Wu, Q.; Fu, C.; Li, M.; Li, J.; Li, Z.; Qi, L.; Ci, X.; Ma, G.; Gao, A.; Fu, X.; A, J.; An, N.; Liu, M.; Li, Y.; King, J.L.; Fu, L.; Zhang, B.; Dong, J.T. CINP is a novel cofactor of KLF5 required for its role in the promotion of cell proliferation, survival and tumor growth. Int. J. Cancer, 2019, 144(3), 582-594.
[http://dx.doi.org/10.1002/ijc.31908] [PMID: 30289973]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy