Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning
Abstract
:1. Introduction
2. Results
2.1. Investigating the Prognostic Impact of Tumor Dormancy-Associated Genes across Cancer Types
2.2. Identification of Dormant and Active Cell Clusters in LUAD
2.3. Analysis of the Correlation between O-glycosylation and Tumor Dormancy
2.4. Intercellular Communication between Malignant Cells with Fibroblasts Involved in Tumor Dormancy
2.5. Construction of a Prognostic Model Based on ORDGs
2.6. Validation of the Model and Establishment of the Prognostic Nomogram
2.7. Functional Enrichment Analyses
2.8. Analysis of the Immunological Profile
2.9. Analysis of Mutation Status and Drug Sensitivity
2.10. Validation of the Prognostic Model Genes
3. Discussion
4. Materials and Methods
4.1. Acquisition and Processing of Single-Cell Data
4.2. Data Collection
4.3. Identification of DEGs
4.4. GO and KEGG Analysis
4.5. Gene Set Variation Analysis
4.6. Construction of the Prognostic Model and Validation
4.7. Tumor Microenvironment
4.8. Mutation Analysis
4.9. Prediction of Drug Sensitivity
4.10. Cell Culture
4.11. RNA Extraction and Quantitative Real-Time PCR (qRT-PCR)
4.12. Cell Viability Assay
4.13. Statistical Analysis
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Herbst, R.S.; Morgensztern, D.; Boshoff, C. The Biology and Management of Non-Small Cell Lung Cancer. Nature 2018, 553, 446–454. [Google Scholar] [CrossRef] [PubMed]
- Nooreldeen, R.; Bach, H. Current and Future Development in Lung Cancer Diagnosis. Int. J. Mol. Sci. 2021, 22, 8661. [Google Scholar] [CrossRef] [PubMed]
- Hedley, B.D.; Chambers, A.F. Tumor Dormancy and Metastasis. Adv. Cancer Res. 2009, 102, 67–101. [Google Scholar] [CrossRef] [PubMed]
- Group, N.M.C. Preoperative Chemotherapy for Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis of Individual Participant Data. Lancet 2014, 383, 1561–1571. [Google Scholar] [CrossRef]
- Sosa, M.S.; Bragado, P.; Aguirre-Ghiso, J.A. Mechanisms of Disseminated Cancer Cell Dormancy: An Awakening Field. Nat. Rev. Cancer 2014, 14, 611–622. [Google Scholar] [CrossRef]
- Pantel, K.; Schlimok, G.; Braun, S.; Kutter, D.; Lindemann, F.; Schaller, G.; Funke, I.; Izbicki, J.R.; Riethmüller, G. Differential Expression of Proliferation-Associated Molecules in Individual Micrometastatic Carcinoma Cells. J. Natl. Cancer Inst. 1993, 85, 1419–1424. [Google Scholar] [CrossRef]
- Omokehinde, T.; Johnson, R.W. Dormancy in the Tumor Microenvironment. Adv. Exp. Med. Biol. 2021, 1329, 35–49. [Google Scholar] [CrossRef]
- Recasens, A.; Munoz, L. Targeting Cancer Cell Dormancy. Trends Pharmacol. Sci. 2019, 40, 128–141. [Google Scholar] [CrossRef] [PubMed]
- Damen, M.P.F.; Van Rheenen, J.; Scheele, C.L.G.J. Targeting Dormant Tumor Cells to Prevent Cancer Recurrence. FEBS J. 2021, 288, 6286–6303. [Google Scholar] [CrossRef]
- Di Martino, J.S.; Nobre, A.R.; Mondal, C.; Taha, I.; Farias, E.F.; Fertig, E.J.; Naba, A.; Aguirre-Ghiso, J.A.; Bravo-Cordero, J.J. A Tumor-Derived Type III Collagen-Rich ECM Niche Regulates Tumor Cell Dormancy. Nat. Cancer 2022, 3, 90–107. [Google Scholar] [CrossRef]
- Ren, Q.; Khoo, W.H.; Corr, A.P.; Phan, T.G.; Croucher, P.I.; Stewart, S.A. Gene Expression Predicts Dormant Metastatic Breast Cancer Cell Phenotype. Breast Cancer Res. BCR 2022, 24, 10. [Google Scholar] [CrossRef]
- Ruth, J.R.; Pant, D.K.; Pan, T.-C.; Seidel, H.E.; Baksh, S.C.; Keister, B.A.; Singh, R.; Sterner, C.J.; Bakewell, S.J.; Moody, S.E.; et al. Cellular Dormancy in Minimal Residual Disease Following Targeted Therapy. Breast Cancer Res. BCR 2021, 23, 63. [Google Scholar] [CrossRef]
- Sosa Cuevas, E.; Roubinet, B.; Mouret, S.; Thépaut, M.; de Fraipont, F.; Charles, J.; Fieschi, F.; Landemarre, L.; Chaperot, L.; Aspord, C. The Melanoma Tumor Glyco-Code Impacts Human Dendritic Cells’ Functionality and Dictates Clinical Outcomes. Front. Immunol. 2023, 14, 1120434. [Google Scholar] [CrossRef]
- Leonel, A.V.; Alisson-Silva, F.; Santos, R.C.M.; Silva-Aguiar, R.P.; Gomes, J.C.; Longo, G.M.C.; Faria, B.M.; Siqueira, M.S.; Pereira, M.G.; Vasconcelos-Dos-Santos, A.; et al. Inhibition of O-GlcNAcylation Reduces Cell Viability and Autophagy and Increases Sensitivity to Chemotherapeutic Temozolomide in Glioblastoma. Cancers 2023, 15, 4740. [Google Scholar] [CrossRef]
- Yun, R.; Hong, E.; Kim, J.; Park, B.; Kim, S.J.; Lee, B.; Song, Y.S.; Kim, S.-J.; Park, S.; Kang, J.M. N-Linked Glycosylation Is Essential for Anti-Tumor Activities of KIAA1324 in Gastric Cancer. Cell Death Dis. 2023, 14, 546. [Google Scholar] [CrossRef] [PubMed]
- Stanczak, M.A.; Rodrigues Mantuano, N.; Kirchhammer, N.; Sanin, D.E.; Jacob, F.; Coelho, R.; Everest-Dass, A.V.; Wang, J.; Trefny, M.P.; Monaco, G.; et al. Targeting Cancer Glycosylation Repolarizes Tumor-Associated Macrophages Allowing Effective Immune Checkpoint Blockade. Sci. Transl. Med. 2022, 14, eabj1270. [Google Scholar] [CrossRef] [PubMed]
- Sharma, P.; Zhang, X.; Ly, K.; Kim, J.H.; Wan, Q.; Kim, J.; Lou, M.; Kain, L.; Teyton, L.; Winau, F. Hyperglycosylation of Prosaposin in Tumor Dendritic Cells Drives Immune Escape. Science 2024, 383, 190–200. [Google Scholar] [CrossRef]
- Sreekumar, A.; Lu, M.; Choudhury, B.; Pan, T.; Pant, D.K.; Lawrence-Paul, M.R.; Sterner, C.J.; Belka, G.K.; Toriumi, T.; Benz, B.A.; et al. B3GALT6 Promotes Dormant Breast Cancer Cell Survival and Recurrence by Enabling Heparan Sulfate-Mediated FGF Signaling. Cancer Cell 2024, 42, 52–69.e7. [Google Scholar] [CrossRef] [PubMed]
- Mukherjee, A.; Bravo-Cordero, J.J. Regulation of Dormancy during Tumor Dissemination: The Role of the ECM. Cancer Metastasis Rev. 2023, 42, 99–112. [Google Scholar] [CrossRef] [PubMed]
- Min, H.-Y.; Lee, H.-Y. Cellular Dormancy in Cancer: Mechanisms and Potential Targeting Strategies. Cancer Res. Treat. 2023, 55, 720–736. [Google Scholar] [CrossRef]
- Bischoff, P.; Trinks, A.; Obermayer, B.; Pett, J.P.; Wiederspahn, J.; Uhlitz, F.; Liang, X.; Lehmann, A.; Jurmeister, P.; Elsner, A.; et al. Single-Cell RNA Sequencing Reveals Distinct Tumor Microenvironmental Patterns in Lung Adenocarcinoma. Oncogene 2021, 40, 6748–6758. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Song, J.; Zhang, H.; Liu, X.; Zuo, F.; Zhao, Y.; Zhao, Y.; Yin, X.; Guo, X.; Wu, X.; et al. Immune Checkpoint HLA-E:CD94-NKG2A Mediates Evasion of Circulating Tumor Cells from NK Cell Surveillance. Cancer Cell 2023, 41, 272–287.e9. [Google Scholar] [CrossRef] [PubMed]
- Kurppa, K.J.; Liu, Y.; To, C.; Zhang, T.; Fan, M.; Vajdi, A.; Knelson, E.H.; Xie, Y.; Lim, K.; Cejas, P.; et al. Treatment-Induced Tumor Dormancy through YAP-Mediated Transcriptional Reprogramming of the Apoptotic Pathway. Cancer Cell 2020, 37, 104–122.e12. [Google Scholar] [CrossRef] [PubMed]
- Bu, D.; Luo, H.; Huo, P.; Wang, Z.; Zhang, S.; He, Z.; Wu, Y.; Zhao, L.; Liu, J.; Guo, J.; et al. KOBAS-i: Intelligent Prioritization and Exploratory Visualization of Biological Functions for Gene Enrichment Analysis. Nucleic Acids Res. 2021, 49, W317–W325. [Google Scholar] [CrossRef]
- Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust Enumeration of Cell Subsets from Tissue Expression Profiles. Nat. Methods 2015, 12, 453–457. [Google Scholar] [CrossRef]
- Carlino, M.S.; Larkin, J.; Long, G.V. Immune Checkpoint Inhibitors in Melanoma. Lancet 2021, 398, 1002–1014. [Google Scholar] [CrossRef]
- Tang, S.; Qin, C.; Hu, H.; Liu, T.; He, Y.; Guo, H.; Yan, H.; Zhang, J.; Tang, S.; Zhou, H. Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer: Progress, Challenges, and Prospects. Cells 2022, 11, 320. [Google Scholar] [CrossRef]
- Wang, J.; Zhao, X.; Wang, Y.; Ren, F.; Sun, D.; Yan, Y.; Kong, X.; Bu, J.; Liu, M.; Xu, S. circRNA-002178 Act as a ceRNA to Promote PDL1/PD1 Expression in Lung Adenocarcinoma. Cell Death Dis. 2020, 11, 32. [Google Scholar] [CrossRef]
- Yang, L.; He, Y.-T.; Dong, S.; Wei, X.-W.; Chen, Z.-H.; Zhang, B.; Chen, W.-D.; Yang, X.-R.; Wang, F.; Shang, X.-M.; et al. Single-Cell Transcriptome Analysis Revealed a Suppressive Tumor Immune Microenvironment in EGFR Mutant Lung Adenocarcinoma. J. Immunother. Cancer 2022, 10, e003534. [Google Scholar] [CrossRef]
- Martincorena, I.; Campbell, P.J. Somatic Mutation in Cancer and Normal Cells. Science 2015, 349, 1483–1489. [Google Scholar] [CrossRef]
- Marcus, L.; Fashoyin-Aje, L.A.; Donoghue, M.; Yuan, M.; Rodriguez, L.; Gallagher, P.S.; Philip, R.; Ghosh, S.; Theoret, M.R.; Beaver, J.A.; et al. FDA Approval Summary: Pembrolizumab for the Treatment of Tumor Mutational Burden-High Solid Tumors. Clin. Cancer Res. 2021, 27, 4685–4689. [Google Scholar] [CrossRef] [PubMed]
- Leal, T.; Kotecha, R.; Ramlau, R.; Zhang, L.; Milanowski, J.; Cobo, M.; Roubec, J.; Petruzelka, L.; Havel, L.; Kalmadi, S.; et al. Tumor Treating Fields Therapy with Standard Systemic Therapy versus Standard Systemic Therapy Alone in Metastatic Non-Small-Cell Lung Cancer Following Progression on or after Platinum-Based Therapy (LUNAR): A Randomised, Open-Label, Pivotal Phase 3 Study. Lancet Oncol. 2023, 24, 1002–1017. [Google Scholar] [CrossRef] [PubMed]
- Ma, S.-C.; Bai, X.; Guo, X.-J.; Liu, L.; Xiao, L.-S.; Lin, Y.; Tan, J.-L.; Cai, X.-T.; Wen, Y.-X.; Ma, H.; et al. Organ-Specific Metastatic Landscape Dissects PD-(L)1 Blockade Efficacy in Advanced Non-Small Cell Lung Cancer: Applicability from Clinical Trials to Real-World Practice. BMC Med. 2022, 20, 120. [Google Scholar] [CrossRef] [PubMed]
- Park, C.-K.; Cho, H.-J.; Choi, Y.-D.; Oh, I.-J.; Kim, Y.-C. A Phase II Trial of Osimertinib in the Second-Line Treatment of Non-Small Cell Lung Cancer with the EGFR T790M Mutation, Detected from Circulating Tumor DNA: LiquidLung-O-Cohort 2. Cancer Res. Treat. 2019, 51, 777–787. [Google Scholar] [CrossRef]
- Phan, T.G.; Croucher, P.I. The Dormant Cancer Cell Life Cycle. Nat. Reviews. Cancer 2020, 20, 398–411. [Google Scholar] [CrossRef]
- Gomatou, G.; Syrigos, N.; Vathiotis, I.A.; Kotteas, E.A. Tumor Dormancy: Implications for Invasion and Metastasis. Int. J. Mol. Sci. 2021, 22, 4862. [Google Scholar] [CrossRef]
- Lengel, H.B.; Mastrogiacomo, B.; Connolly, J.G.; Tan, K.S.; Liu, Y.; Fick, C.N.; Dunne, E.G.; He, D.; Lankadasari, M.B.; Satravada, B.A.; et al. Genomic Mapping of Metastatic Organotropism in Lung Adenocarcinoma. Cancer Cell 2023, 41, 970–985.e3. [Google Scholar] [CrossRef]
- Wang, Z.; Wang, Y.; Chang, M.; Wang, Y.; Liu, P.; Wu, J.; Wang, G.; Tang, X.; Hui, X.; Liu, P.; et al. Single-Cell Transcriptomic Analyses Provide Insights into the Cellular Origins and Drivers of Brain Metastasis from Lung Adenocarcinoma. Neuro-Oncology 2023, 25, 1262–1274. [Google Scholar] [CrossRef] [PubMed]
- Linde, N.; Fluegen, G.; Aguirre-Ghiso, J.A. The Relationship Between Dormant Cancer Cells and Their Microenvironment. In Advances in Cancer Research; Elsevier: Amsterdam, The Netherlands, 2016; Volume 132, pp. 45–71. ISBN 978-0-12-804140-6. [Google Scholar]
- Risson, E.; Nobre, A.R.; Maguer-Satta, V.; Aguirre-Ghiso, J.A. The Current Paradigm and Challenges Ahead for the Dormancy of Disseminated Tumor Cells. Nat. Cancer 2020, 1, 672–680. [Google Scholar] [CrossRef]
- Cassim, S.; Chepulis, L.; Keenan, R.; Kidd, J.; Firth, M.; Lawrenson, R. Patient and Carer Perceived Barriers to Early Presentation and Diagnosis of Lung Cancer: A Systematic Review. BMC Cancer 2019, 19, 25. [Google Scholar] [CrossRef]
- Jiang, J.; Qian, B.; Guo, Y.; He, Z. Identification of Subgroups and Development of Prognostic Risk Models along the Glycolysis–Cholesterol Synthesis Axis in Lung Adenocarcinoma. Sci. Rep. 2024, 14, 14704. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Z.; Chen, M.-J.M.; Luo, Y.; Mojumdar, K.; Peng, X.; Chen, H.; Kumar, S.V.; Akbani, R.; Lu, Y.; Liang, H. Tumor-Intrinsic SIRPA Promotes Sensitivity to Checkpoint Inhibition Immunotherapy in Melanoma. Cancer Cell 2022, 40, 1324–1340.e8. [Google Scholar] [CrossRef] [PubMed]
- Morrissey, M.A.; Kern, N.; Vale, R.D. CD47 Ligation Repositions the Inhibitory Receptor SIRPA to Suppress Integrin Activation and Phagocytosis. Immunity 2020, 53, 290–302.e6. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Zhang, X.; Gu, W.; Su, H.; Wang, X.; Wang, X.; Zhang, J.; Xu, M.; Sheng, W. Unlocking the crucial role of cancer-associated fibroblasts in tumor metastasis: Mechanisms and therapeutic prospects. J. Adv. Res. 2024, in press. Available online: https://pubmed.ncbi.nlm.nih.gov/38825314/ (accessed on 24 July 2024). [CrossRef]
- Shimizu, T.; Sugihara, E.; Yamaguchi-Iwai, S.; Tamaki, S.; Koyama, Y.; Kamel, W.; Ueki, A.; Ishikawa, T.; Chiyoda, T.; Osuka, S.; et al. IGF2 Preserves Osteosarcoma Cell Survival by Creating an Autophagic State of Dormancy That Protects Cells against Chemotherapeutic Stress. Cancer Res. 2014, 74, 6531–6541. [Google Scholar] [CrossRef]
- Rajbhandari, N.; Lin, W.-C.; Wehde, B.L.; Triplett, A.A.; Wagner, K.-U. Autocrine IGF1 Signaling Mediates Pancreatic Tumor Cell Dormancy in the Absence of Oncogenic Drivers. Cell Rep. 2017, 18, 2243–2255. [Google Scholar] [CrossRef]
- Bresnahan, E.; Bravo-Cordero, J.J. Sweet Dreams: Glycosylation Controls Tumor Cell Dormancy. Trends Cancer 2024, 10, 180–181. [Google Scholar] [CrossRef]
- Dashzeveg, N.K.; Jia, Y.; Zhang, Y.; Gerratana, L.; Patel, P.; Shajahan, A.; Dandar, T.; Ramos, E.K.; Almubarak, H.F.; Adorno-Cruz, V.; et al. Dynamic Glycoprotein Hyposialylation Promotes Chemotherapy Evasion and Metastatic Seeding of Quiescent Circulating Tumor Cell Clusters in Breast Cancer. Cancer Discov. 2023, 13, 2050–2071. [Google Scholar] [CrossRef] [PubMed]
- Xi, X.; Wang, J.; Qin, Y.; Huang, W.; You, Y.; Zhan, J. Glycosylated modification of MUC1 maybe a new target to promote drug sensitivity and efficacy for breast cancer chemotherapy. Cell Death Dis. 2022, 13, 708. Available online: https://pubmed.ncbi.nlm.nih.gov/35970845/ (accessed on 15 August 2024). [CrossRef]
- Lin, M.-C.; Chuang, Y.-T.; Wu, H.-Y.; Hsu, C.-L.; Lin, N.-Y.; Huang, M.-C.; Lou, P.-J. Targeting Tumor O-Glycosylation Modulates Cancer-Immune-Cell Crosstalk and Enhances Anti-PD-1 Immunotherapy in Head and Neck Cancer. Mol. Oncol. 2024, 18, 350–368. [Google Scholar] [CrossRef]
- Peng, C.; Xu, Y.; Wu, J.; Wu, D.; Zhou, L.; Xia, X. TME-Related Biomimetic Strategies Against Cancer. Int. J. Nanomed. 2024, 19, 109–135. [Google Scholar] [CrossRef]
- Lam, Y.K.; Yu, J.; Huang, H.; Ding, X.; Wong, A.M.; Leung, H.H.; Chan, A.W.; Ng, K.K.; Xu, M.; Wang, X.; et al. TP53 R249S Mutation in Hepatic Organoids Captures the Predisposing Cancer Risk. Hepatology 2023, 78, 727–740. [Google Scholar] [CrossRef] [PubMed]
- Mou, L.; Pu, Z.; Luo, Y.; Quan, R.; So, Y.; Jiang, H. Construction of a Lipid Metabolism-Related Risk Model for Hepatocellular Carcinoma by Single Cell and Machine Learning Analysis. Front. Immunol. 2023, 14, 1036562. [Google Scholar] [CrossRef] [PubMed]
- Levatić, J.; Salvadores, M.; Fuster-Tormo, F.; Supek, F. Mutational Signatures Are Markers of Drug Sensitivity of Cancer Cells. Nat. Commun. 2022, 13, 2926. [Google Scholar] [CrossRef] [PubMed]
- Le, X.; Nilsson, M.; Goldman, J.; Reck, M.; Nakagawa, K.; Kato, T.; Ares, L.P.; Frimodt-Moller, B.; Wolff, K.; Visseren-Grul, C.; et al. Dual EGFR-VEGF Pathway Inhibition: A Promising Strategy for Patients With EGFR-Mutant NSCLC. J. Thorac. Oncol. 2021, 16, 205–215. [Google Scholar] [CrossRef]
- Remon, J.; Saw, S.P.L.; Cortiula, F.; Singh, P.K.; Menis, J.; Mountzios, G.; Hendriks, L.E.L. Perioperative Treatment Strategies in EGFR-Mutant Early-Stage NSCLC: Current Evidence and Future Challenges. J. Thorac. Oncol. 2024, 19, 199–215. [Google Scholar] [CrossRef]
- Butler, A.; Hoffman, P.; Smibert, P.; Papalexi, E.; Satija, R. Integrating Single-Cell Transcriptomic Data across Different Conditions, Technologies, and Species. Nat. Biotechnol. 2018, 36, 411–420. [Google Scholar] [CrossRef]
- 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.; et al. Single-Cell Transcriptional Diversity Is a Hallmark of Developmental Potential. Science 2020, 367, 405–411. [Google Scholar] [CrossRef]
- Qiu, X.; Mao, Q.; Tang, Y.; Wang, L.; Chawla, R.; Pliner, H.A.; Trapnell, C. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 2017, 14, 979–982. Available online: https://pubmed.ncbi.nlm.nih.gov/28825705/ (accessed on 8 July 2024). [CrossRef]
- Fang, Z.; Tian, Y.; Sui, C.; Guo, Y.; Hu, X.; Lai, Y.; Liao, Z.; Li, J.; Feng, G.; Jin, L.; et al. Single-Cell Transcriptomics of Proliferative Phase Endometrium: Systems Analysis of Cell-Cell Communication Network Using CellChat. Front Cell Dev. Biol. 2022, 10, 919731. Available online: https://pubmed.ncbi.nlm.nih.gov/35938159/ (accessed on 8 July 2024). [CrossRef]
- Jin, S.; Guerrero-Juarez, C.F.; Zhang, L.; Chang, I.; Ramos, R.; Kuan, C.-H.; Myung, P.; Plikus, M.V.; Nie, Q. Inference and Analysis of Cell-Cell Communication Using CellChat. Nat. Commun. 2021, 12, 1088. [Google Scholar] [CrossRef]
- Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef]
- Hänzelmann, S.; Castelo, R.; Guinney, J. GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data. BMC Bioinform. 2013, 14, 7. [Google Scholar] [CrossRef]
- Maeser, D.; Gruener, R.F.; Huang, R.S. oncoPredict: An R Package for Predicting in Vivo or Cancer Patient Drug Response and Biomarkers from Cell Line Screening Data. Brief. Bioinform. 2021, 22, bbab260. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Yao, E.; Liu, Y.; Zhang, Y.; Ding, M.; Liu, J.; Chen, X.; Fan, S. FUT2 Facilitates Autophagy and Suppresses Apoptosis via P53 and JNK Signaling in Lung Adenocarcinoma Cells. Cells 2022, 11, 4031. [Google Scholar] [CrossRef] [PubMed]
- Ganesan, R.; Bhasin, S.S.; Bakhtiary, M.; Krishnan, U.; Cheemarla, N.R.; Thomas, B.E.; Bhasin, M.K.; Sukhatme, V.P. Taxane Chemotherapy Induces Stromal Injury That Leads to Breast Cancer Dormancy Escape. PLoS Biol. 2023, 21, e3002275. [Google Scholar] [CrossRef] [PubMed]
- Barkan, D.; El Touny, L.H.; Michalowski, A.M.; Smith, J.A.; Chu, I.; Davis, A.S.; Webster, J.D.; Hoover, S.; Simpson, R.M.; Gauldie, J.; et al. Metastatic growth from dormant cells induced by a col-I-enriched fibrotic environment. Cancer Res. 2010, 70, 5706–5716. Available online: https://pubmed.ncbi.nlm.nih.gov/20570886/ (accessed on 19 July 2024). [CrossRef]
Gene | Forward Primer (5′-3′) | Reverse Primer (5′-3′) |
---|---|---|
PTTG1IP | GTCTGGACTACCCAGTTACAAGC | CGCCTCAAAGTTCACCCAA |
EFNB2 | TATGCAGAACTGCGATTTCCAA | TGGGTATAGTACCAGTCCTTGTC |
TNFRSF11A | AGATCGCTCCTCCATGTACCA | GCCTTGCCTGTATCACAAACTTT |
GAPDH | GAACATCATCCCTGCCTCTACT | CCTGCTTCACCACCTTCTTG |
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Dong, C.; Liu, Y.; Chong, S.; Zeng, J.; Bian, Z.; Chen, X.; Fan, S. Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning. Int. J. Mol. Sci. 2024, 25, 9502. https://doi.org/10.3390/ijms25179502
Dong C, Liu Y, Chong S, Zeng J, Bian Z, Chen X, Fan S. Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning. International Journal of Molecular Sciences. 2024; 25(17):9502. https://doi.org/10.3390/ijms25179502
Chicago/Turabian StyleDong, Chenfei, Yang Liu, Suli Chong, Jiayue Zeng, Ziming Bian, Xiaoming Chen, and Sairong Fan. 2024. "Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning" International Journal of Molecular Sciences 25, no. 17: 9502. https://doi.org/10.3390/ijms25179502