Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Study Workflow
2.2. Breast Cancer Sample Collection, Transcriptomic Data Preparation, and Tumor Budding Evaluation
2.3. Differential Expression Analysis
2.4. Mutual Information (MI)
2.5. The Protein–Protein Interaction Network and Network Diffusion Technique
2.6. Pathway Enrichment Analysis
3. Results
3.1. Potential Biomarkers by Differentially Expressed Genes and Mutual Information
3.2. Effectiveness of Potential Biomarkers in Distinguishing between Low- and High-TB Samples
3.3. Relevant Proteins and Functional Pathway Enrichment Analysis
4. Discussion and Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Hong, R.; Xu, B. Breast cancer: An up-to-date review and future perspectives. Cancer Commun. 2022, 42, 913–936. [Google Scholar] [CrossRef] [PubMed]
- Huang, T.; Bao, H.; Meng, Y.-H.; Zhu, J.-L.; Chu, X.-D.; Chu, X.-L.; Pan, J.-H. Tumour budding is a novel marker in breast cancer: The clinical application and future prospects. Ann. Med. 2022, 54, 1303–1312. [Google Scholar] [CrossRef] [PubMed]
- Voutsadakis, I.A. Prognostic role of tumor budding in breast cancer. World J. Exp. Med. 2018, 8, 12. [Google Scholar] [CrossRef] [PubMed]
- Everett, L.J.; Mav, D.; Phadke, D.P.; Balik-Meisner, M.R.; Shah, R.R. Impact of Aligner, Normalization Method, and Sequencing Depth on TempO-seq Accuracy. Bioinform. Biol. Insights 2022, 16, 11779322221095216. [Google Scholar] [CrossRef] [PubMed]
- Trejo, C.L.; Babić, M.; Imler, E.; Gonzalez, M.; Bibikov, S.I.; Shepard, P.J.; VanSteenhouse, H.C.; Yeakley, J.M.; Seligmann, B.E. Extraction-free whole transcriptome gene expression analysis of FFPE sections and histology-directed subareas of tissue. PLoS ONE 2019, 14, e0212031. [Google Scholar] [CrossRef] [PubMed]
- Turnbull, A.K.; Selli, C.; Martinez-Perez, C.; Fernando, A.; Renshaw, L.; Keys, J.; Figueroa, J.D.; He, X.; Tanioka, M.; Munro, A.F. Unlocking the transcriptomic potential of formalin-fixed paraffin embedded clinical tissues: Comparison of gene expression profiling approaches. BMC Bioinform. 2020, 21, 30. [Google Scholar] [CrossRef] [PubMed]
- Yeakley, J.M.; Shepard, P.J.; Goyena, D.E.; VanSteenhouse, H.C.; McComb, J.D.; Seligmann, B.E. A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling. PLoS ONE 2017, 12, e0178302. [Google Scholar] [CrossRef] [PubMed]
- Bushel, P.R.; Ferguson, S.S.; Ramaiahgari, S.C.; Paules, R.S.; Auerbach, S.S. Comparison of normalization methods for analysis of TempO-Seq Targeted RNA sequencing data. Front. Genet. 2020, 11, 594. [Google Scholar] [CrossRef] [PubMed]
- Vergara, J.R.; Estévez, P.A. A review of feature selection methods based on mutual information. Neural Comput. Appl. 2014, 24, 175–186. [Google Scholar] [CrossRef]
- Song, L.; Langfelder, P.; Horvath, S. Comparison of co-expression measures: Mutual information, correlation, and model based indices. BMC Bioinform. 2012, 13, 328. [Google Scholar] [CrossRef]
- Li, K.; Yu, M.; Liu, L.; Li, T.; Zhai, J. Feature selection method based on weighted mutual information for imbalanced data. Int. J. Softw. Eng. Knowl. Eng. 2018, 28, 1177–1194. [Google Scholar] [CrossRef]
- Shukla, A.K.; Singh, P.; Vardhan, M. A new hybrid feature subset selection framework based on binary genetic algorithm and information theory. Int. J. Comput. Intell. Appl. 2019, 18, 1950020. [Google Scholar] [CrossRef]
- Zhongxin, W.; Gang, S.; Jing, Z.; Jia, Z. Feature selection algorithm based on mutual information and lasso for microarray data. Open Biotechnol. J. 2016, 10, 278–286. [Google Scholar] [CrossRef]
- Purbolaksono, M.D.; Widiastuti, K.C.; Mubarok, M.S.; Ma’ruf, F.A. Implementation of mutual information and bayes theorem for classification microarray data. J. Phys. Conf. Ser. 2018, 971, 012011. [Google Scholar] [CrossRef]
- Wang, Y.; Wang, J.; Hu, Y.; Shangguan, J.; Song, Q.; Xu, J.; Wang, H.; Xue, M.; Wang, L.; Zhang, Y. Identification of key biomarkers for STAD using filter feature selection approaches. Sci. Rep. 2022, 12, 19854. [Google Scholar] [CrossRef]
- Bersanelli, M.; Mosca, E.; Remondini, D.; Castellani, G.; Milanesi, L. Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules. Sci. Rep. 2016, 6, 34841. [Google Scholar] [CrossRef]
- Janyasupab, P.; Suratanee, A.; Plaimas, K. Network diffusion with centrality measures to identify disease-related genes. Math. Biosci. Eng. 2021, 18, 2909–2929. [Google Scholar] [CrossRef]
- Sagulkoo, P.; Suratanee, A.; Plaimas, K. Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing. Biomolecules 2022, 12, 690. [Google Scholar] [CrossRef]
- Suratanee, A.; Plaimas, K. DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations. Bioinform. Biol. Insights 2015, 9, 175–186. [Google Scholar] [CrossRef]
- Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef]
- Sayers, E.W.; Beck, J.; Bolton, E.E.; Brister, J.R.; Chan, J.; Comeau, D.C.; Connor, R.; DiCuccio, M.; Farrell, C.M.; Feldgarden, M. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2024, 52, D33. [Google Scholar] [CrossRef] [PubMed]
- Koelzer, V.H.; Zlobec, I.; Lugli, A. Tumor budding in colorectal cancer--ready for diagnostic practice? Hum. Pathol. 2016, 47, 4–19. [Google Scholar] [CrossRef] [PubMed]
- Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [PubMed]
- Kursa, M.B. Praznik: High performance information-based feature selection. SoftwareX 2021, 16, 100819. [Google Scholar] [CrossRef]
- Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023, 51, D638–D646. [Google Scholar] [CrossRef] [PubMed]
- Smola, A.J.; Kondor, R. Kernels and regularization on graphs. In Proceedings of the Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, 24–27 August 2003; pp. 144–158. [Google Scholar]
- Zhou, Y.; Zhou, B.; Pache, L.; Chang, M.; Khodabakhshi, A.H.; Tanaseichuk, O.; Benner, C.; Chanda, S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 2019, 10, 1523. [Google Scholar] [CrossRef] [PubMed]
- Beraha, M.; Metelli, A.M.; Papini, M.; Tirinzoni, A.; Restelli, M. Feature selection via mutual information: New theoretical insights. In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019; pp. 1–9. [Google Scholar]
- Singh, P.K.; Mistry, K. Human NEIL1 DNA glycosylase: Structure, function and polymorphisms. Meta Gene 2017, 11, 49–57. [Google Scholar] [CrossRef]
- Post, A.E.M.; Bussink, J.; Sweep, F.; Span, P.N. Changes in DNA Damage Repair Gene Expression and Cell Cycle Gene Expression Do Not Explain Radioresistance in Tamoxifen-Resistant Breast Cancer. Oncol. Res. 2020, 28, 33–40. [Google Scholar] [CrossRef] [PubMed]
- Xue, W.; Liu, Y.; Xin, N.; Miao, J.; Du, J.; Wang, Y.; Shi, H.; Wei, Y.; Zhang, H.; Chen, Y.; et al. Nei Endonuclease VIII-Like1 (NEIL1) Inhibits Apoptosis of Human Colorectal Cancer Cells. Biomed. Res. Int. 2020, 2020, 5053975. [Google Scholar] [CrossRef]
- Vo, T.T.; Kong, G.; Kim, C.; Juang, U.; Gwon, S.; Jung, W.; Nguyen, H.; Kim, S.H.; Park, J. Exploring scavenger receptor class F member 2 and the importance of scavenger receptor family in prediagnostic diseases. Toxicol. Res. 2023, 39, 341–353. [Google Scholar] [CrossRef]
- Kim, C.; Kong, G.; Lee, H.; Tran, Q.; Vo, T.T.; Kwon, S.H.; Park, J.; Kim, S.H.; Park, J. Scavenger receptor class F member 2 (SCARF2) as a novel therapeutic target in glioblastoma. Toxicol. Res. 2022, 38, 249–256. [Google Scholar] [CrossRef] [PubMed]
- Lin, F.; Zhou, J.; Li, X.; Wang, X. NOL4L, a novel nuclear protein, promotes cell proliferation and metastasis by enhancing the PI3K/AKT pathway in ovarian cancer. Biochem. Biophys. Res. Commun. 2021, 559, 121–128. [Google Scholar] [CrossRef] [PubMed]
- Kim, Y.R.; Kim, K.U.; Lee, J.H.; Kim, D.W.; Chung, J.H.; Kim, Y.D.; Shin, D.H.; Lee, M.K.; Shin, Y.I.; Lee, S.Y. Cancer Testis Antigen, NOL4, Is an Immunogenic Antigen Specifically Expressed in Small-Cell Lung Cancer. Curr. Oncol. 2021, 28, 1927–1937. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.H.; Shin, D.H.; Lee, S.Y.; Park, J.Y.; Kim, S.Y.; Hwang, C.S.; Lee, H.J.; Na, J.Y.; Kim, J.Y. NOL4 is a novel nuclear marker of small cell carcinoma and other neuroendocrine neoplasms. Histol. Histopathol. 2022, 37, 1091–1098. [Google Scholar] [CrossRef] [PubMed]
- Pavithran, G.; Rathi, B.; Santoshi, S. Translational and structural vaccinomics approach to design a multi-epitope vaccine against NOL4 autologous antigen of small cell lung cancer. Immunol. Res. 2023, 71, 909–928. [Google Scholar] [CrossRef]
- Wei, Y.; Zheng, L.; Yang, X.; Luo, Y.; Yi, C.; Gou, H. Identification of Immune Subtypes and Candidate mRNA Vaccine Antigens in Small Cell Lung Cancer. Oncologist 2023, 28, e1052–e1064. [Google Scholar] [CrossRef] [PubMed]
- Tugaeva, K.V.; Sluchanko, N.N. Steroidogenic Acute Regulatory Protein: Structure, Functioning, and Regulation. Biochemistry 2019, 84, S233–S253. [Google Scholar] [CrossRef]
- Manna, P.R.; Ahmed, A.U.; Molehin, D.; Narasimhan, M.; Pruitt, K.; Reddy, P.H. Hormonal and Genetic Regulatory Events in Breast Cancer and Its Therapeutics: Importance of the Steroidogenic Acute Regulatory Protein. Biomedicines 2022, 10, 1313. [Google Scholar] [CrossRef]
- Manna, P.R.; Ahmed, A.U.; Vartak, D.; Molehin, D.; Pruitt, K. Overexpression of the steroidogenic acute regulatory protein in breast cancer: Regulation by histone deacetylase inhibition. Biochem. Biophys. Res. Commun. 2019, 509, 476–482. [Google Scholar] [CrossRef]
- Manna, P.R.; Ahmed, A.U.; Yang, S.; Narasimhan, M.; Cohen-Tannoudji, J.; Slominski, A.T.; Pruitt, K. Genomic Profiling of the Steroidogenic Acute Regulatory Protein in Breast Cancer: In Silico Assessments and a Mechanistic Perspective. Cancers 2019, 11, 623. [Google Scholar] [CrossRef]
- Bayly-Jones, C.; Bubeck, D.; Dunstone, M.A. The mystery behind membrane insertion: A review of the complement membrane attack complex. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2017, 372, 20160221. [Google Scholar] [CrossRef] [PubMed]
- Pegram, M.D.; Miles, D.; Tsui, C.K.; Zong, Y. HER2-Overexpressing/Amplified Breast Cancer as a Testing Ground for Antibody-Drug Conjugate Drug Development in Solid Tumors. Clin. Cancer Res. 2020, 26, 775–786. [Google Scholar] [CrossRef] [PubMed]
- Xu, Y.; Wang, K.; Yu, Q. FRMD6 inhibits human glioblastoma growth and progression by negatively regulating activity of receptor tyrosine kinases. Oncotarget 2016, 7, 70080–70091. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; Zhao, C.; Quan, F.; Zhang, P.; Shao, Y.; Liu, L. FERM domain-containing protein 6 exerts a tumor-inhibiting role in thyroid cancer by antagonizing oncogenic YAP1. Biofactors 2022, 48, 428–441. [Google Scholar] [CrossRef] [PubMed]
- von Koskull, A.; Hagström, J.; Haglund, C.; Kaprio, T.; Böckelman, C. High-tissue FRMD6 expression predicts better outcomes among colorectal cancer patients. Biomarkers 2024, 29, 127–133. [Google Scholar] [CrossRef] [PubMed]
- Li, G.; Guo, J.; Shen, B.Q.; Yadav, D.B.; Sliwkowski, M.X.; Crocker, L.M.; Lacap, J.A.; Phillips, G.D.L. Mechanisms of Acquired Resistance to Trastuzumab Emtansine in Breast Cancer Cells. Mol. Cancer Ther. 2018, 17, 1441–1453. [Google Scholar] [CrossRef] [PubMed]
- Tomabechi, R.; Kishimoto, H.; Sato, T.; Saito, N.; Kiyomiya, K.; Takada, T.; Higuchi, K.; Shirasaka, Y.; Inoue, K. SLC46A3 is a lysosomal proton-coupled steroid conjugate and bile acid transporter involved in transport of active catabolites of T-DM1. PNAS Nexus 2022, 1, pgac063. [Google Scholar] [CrossRef] [PubMed]
- Kiyomiya, K.; Tomabechi, R.; Saito, N.; Watai, K.; Takada, T.; Shirasaka, Y.; Kishimoto, H.; Higuchi, K.; Inoue, K. Macrolide and Ketolide Antibiotics Inhibit the Cytotoxic Effect of Trastuzumab Emtansine in HER2-Positive Breast Cancer Cells: Implication of a Potential Drug-ADC Interaction in Cancer Chemotherapy. Mol. Pharm. 2023, 20, 6130–6139. [Google Scholar] [CrossRef]
- Wu, T.; Hu, E.; Xu, S.; Chen, M.; Guo, P.; Dai, Z.; Feng, T.; Zhou, L.; Tang, W.; Zhan, L. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2021, 2, 100141. [Google Scholar] [CrossRef]
- Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
- Abdel-Fatah, T.M.; Perry, C.; Arora, A.; Thompson, N.; Doherty, R.; Moseley, P.M.; Green, A.R.; Chan, S.Y.; Ellis, I.O.; Madhusudan, S. Is there a role for base excision repair in estrogen/estrogen receptor-driven breast cancers? Antioxid. Redox Signal. 2014, 21, 2262–2268. [Google Scholar] [CrossRef] [PubMed]
- Parambil, S.T.; Antony, G.R.; Littleflower, A.B.; Subhadradevi, L. The molecular crosstalk of the hippo cascade in breast cancer: A potential central susceptibility. Biochimie 2024, 222, 132–150. [Google Scholar] [CrossRef] [PubMed]
- Linton, K.M.; Hey, Y.; Dibben, S.; Miller, C.J.; Freemont, A.J.; Radford, J.A.; Pepper, S.D. Methods comparison for high-resolution transcriptional analysis of archival material on Affymetrix Plus 2.0 and Exon 1.0 microarrays. Biotechniques 2009, 47, 587–596. [Google Scholar] [CrossRef] [PubMed]
- Shamis, S.A. The Relationship between Hypoxia, Hypoxia Gene Signature and Survival in Patients with Breast Cancer. Ph.D. Thesis, University of Glasgow, Glasgow, UK, 2023. [Google Scholar]
- Shahjaman, M.; Manir Hossain Mollah, M.; Rezanur Rahman, M.; Islam, S.M.S.; Nurul Haque Mollah, M. Robust identification of differentially expressed genes from RNA-seq data. Genomics 2020, 112, 2000–2010. [Google Scholar] [CrossRef] [PubMed]
- Strybel, U.; Marczak, L.; Zeman, M.; Polanski, K.; Mielańczyk, Ł.; Klymenko, O.; Samelak-Czajka, A.; Jackowiak, P.; Smolarz, M.; Chekan, M.; et al. Molecular Composition of Serum Exosomes Could Discriminate Rectal Cancer Patients with Different Responses to Neoadjuvant Radiotherapy. Cancers 2022, 14, 993. [Google Scholar] [CrossRef] [PubMed]
- Haldrup, J.; Strand, S.H.; Cieza-Borrella, C.; Jakobsson, M.E.; Riedel, M.; Norgaard, M.; Hedensted, S.; Dagnaes-Hansen, F.; Ulhoi, B.P.; Eeles, R.; et al. FRMD6 has tumor suppressor functions in prostate cancer. Oncogene 2021, 40, 763–776. [Google Scholar] [CrossRef] [PubMed]
- Wang, T.; Guo, H.; Zhang, L.; Yu, M.; Li, Q.; Zhang, J.; Tang, Y.; Zhang, H.; Zhan, J. FERM domain-containing protein FRMD6 activates the mTOR signaling pathway and promotes lung cancer progression. Front. Med. 2023, 17, 714–728. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Q.; Zheng, B.; Meng, S.; Xu, Y.; Guo, J.; Chen, L.J.; Xiao, J.; Zhang, W.; Tan, Z.R.; Tang, J.; et al. Increased expression of SLC46A3 to oppose the progression of hepatocellular carcinoma and its effect on sorafenib therapy. Biomed. Pharmacother. 2019, 114, 108864. [Google Scholar] [CrossRef] [PubMed]
- Sheikholeslami, S.; Azizi, F.; Ghasemi, A.; Alibakhshi, A.; Parsa, H.; Tavangar, S.M.; Shivaee, S.; Yeganeh, M.Z.; Hedayati, M.; Teimoori-Toolabi, L. NOL4 is Downregulated and Hyper-Methylated in Papillary Thyroid Carcinoma Suggesting Its Role as a Tumor Suppressor Gene. Int. J. Endocrinol. Metab. 2020, 18, 108510. [Google Scholar] [CrossRef]
- Bayat, A.; Iqbal, S.; Borredy, K.; Amiel, J.; Zweier, C.; Barcia, G.; Kraus, C.; Weyhreter, H.; Bassuk, A.G.; Chopra, M.; et al. PRICKLE2 revisited—Further evidence implicating PRICKLE2 in neurodevelopmental disorders. Eur. J. Hum. Genet. 2021, 29, 1235–1244. [Google Scholar] [CrossRef]
- Zhu, L.; Liu, Y.; Tang, H.; Wang, P. Circular RNA Circ_0001777 Suppresses Lung Adenocarcinoma Progression In Vitro and In Vivo. Biochem. Genet. 2023, 61, 704–724. [Google Scholar] [CrossRef]
- Qian, H.; Cui, N.; Zhou, Q.; Zhang, S. Identification of miRNA biomarkers for stomach adenocarcinoma. BMC Bioinform. 2022, 23, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Kim, H.; Ryu, J.; Lee, C. Genome-wide identification of expression quantitative trait loci for human telomerase. Medicine 2016, 95, e5209. [Google Scholar] [CrossRef] [PubMed]
- Aloe, L.; Rocco, M.L.; Balzamino, B.O.; Micera, A. Nerve growth factor: Role in growth, differentiation and controlling cancer cell development. J. Exp. Clin. Cancer Res. 2016, 35, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Fernández-Nogueira, P.; Bragado, P.; Almendro, V.; Ametller, E.; Rios, J.; Choudhury, S.; Mancino, M.; Gascón, P. Differential expression of neurogenes among breast cancer subtypes identifies high risk patients. Oncotarget 2015, 7, 5313–5326. [Google Scholar] [CrossRef] [PubMed]
- Chakravarthy, R.; Mnich, K.; Gorman, A.M. Nerve growth factor (NGF)-mediated regulation of p75NTR expression contributes to chemotherapeutic resistance in triple negative breast cancer cells. Biochem. Biophys. Res. Commun. 2016, 478, 1541–1547. [Google Scholar] [CrossRef] [PubMed]
- Wu, R.; Li, K.; Yuan, M.; Luo, K.Q. Nerve growth factor receptor increases the tumor growth and metastatic potential of triple-negative breast cancer cells. Oncogene 2021, 40, 2165–2181. [Google Scholar] [CrossRef] [PubMed]
- Bashir, N.; Ishfaq, M.; Mazhar, K.; Khan, J.S.; Shahid, R. Upregulation of CD271 transcriptome in breast cancer promotes cell survival via NFκB pathway. Mol. Biol. Rep. 2021, 49, 487–495. [Google Scholar] [CrossRef] [PubMed]
- Hasan, N.; Pangeni, G.; Ray, T.A.; Fransen, K.M.; Noel, J.; Borghuis, B.G.; McCall, M.A.; Gregg, R.G. LRIT3 is Required for Nyctalopin Expression and Normal ON and OFF Pathway Signaling in the Retina. eNeuro 2020, 7. [Google Scholar] [CrossRef]
- Tadini-Buoninsegni, F.; Smeazzetto, S. Mechanisms of charge transfer in human copper ATPases ATP7A and ATP7B. IUBMB Life 2017, 69, 218–225. [Google Scholar] [CrossRef]
- Shi, B.; Zhang, W.; Wang, T.; Cui, Z. The therapeutic and prognostic role of cuproptosis-related genes in triple negative breast cancer. BMC Bioinform. 2023, 24, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Zhang, D.; Lu, W.; Zhuo, Z.; Wang, Y.; Zhang, W.; Zhang, M. Comprehensive analysis of a cuproptosis-related ceRNA network implicates a potential endocrine therapy resistance mechanism in ER-positive breast cancer. BMC Med. Genom. 2023, 16, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Yu, Z.; Cao, W.; Ren, Y.; Zhang, Q.; Liu, J. ATPase copper transporter A, negatively regulated by miR-148a-3p, contributes to cisplatin resistance in breast cancer cells. Clin. Transl. Med. 2020, 10, 57–73. [Google Scholar] [CrossRef] [PubMed]
- Shanbhag, V.; Jasmer-McDonald, K.; Zhu, S.; Martin, A.L.; Gudekar, N.; Khan, A.; Ladomersky, E.; Singh, K.; Weisman, G.A.; Petris, M.J. ATP7A delivers copper to the lysyl oxidase family of enzymes and promotes tumorigenesis and metastasis. Proc. Natl. Acad. Sci. USA 2019, 116, 6836–6841. [Google Scholar] [CrossRef] [PubMed]
- Hauge, H.; Fjelland, K.E.; Sioud, M.; Aasheim, H.-C. Evidence for the involvement of FAM110C protein in cell spreading and migration. Cell. Signal. 2009, 21, 1866–1873. [Google Scholar] [CrossRef] [PubMed]
- Li, M.; Zhong, D.; Li, G. Regulatory role of local tissue signal Del-1 in cancer and inflammation: A review. Cell. Mol. Biol. Lett. 2021, 26, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Lee, S.J.; Lee, J.; Kim, W.W.; Jung, J.H.; Park, H.Y.; Park, J.-Y.; Chae, Y.S. Del-1 Expression as a Potential Biomarker in Triple-Negative Early Breast Cancer. Oncology 2018, 94, 243–256. [Google Scholar] [CrossRef] [PubMed]
- Moon, P.-G.; Lee, J.-E.; Cho, Y.-E.; Lee, S.J.; Jung, J.H.; Chae, Y.S.; Bae, H.-I.; Kim, Y.-B.; Kim, I.-S.; Park, H.Y.; et al. Identification of Developmental Endothelial Locus-1 on Circulating Extracellular Vesicles as a Novel Biomarker for Early Breast Cancer Detection. Clin. Cancer Res. 2016, 22, 1757–1766. [Google Scholar] [CrossRef] [PubMed]
- Lee, S.J.; Lee, J.; Jung, J.H.; Park, H.Y.; Moon, P.-G.; Chae, Y.S.; Baek, M.-C. Exosomal Del-1 as a Potent Diagnostic Marker for Breast Cancer: Prospective Cohort Study. Clin. Breast Cancer 2021, 21, E748–E756. [Google Scholar] [CrossRef]
- Lee, J.; Jeong, J.-H.; Jung, J.H.; Kim, W.W.; Lee, S.J.; Park, J.-Y.; Park, J.Y.; Kang, S.H.; Kim, E.A.; Park, J.H.; et al. Overcoming Tamoxifen Resistance by Regulation of Del-1 in Breast Cancer. Oncology 2019, 97, 180–188. [Google Scholar] [CrossRef]
- Nommick, A.; Boutin, C.; Rosnet, O.; Schirmer, C.; Bazellières, E.; Thomé, V.; Loiseau, E.; Viallat, A.; Kodjabachian, L. Lrrcc1 and Ccdc61 are conserved effectors of multiciliated cell function. J. Cell Sci. 2022, 135, 258960. [Google Scholar] [CrossRef] [PubMed]
- Chen, D.; Yu, W.; Aitken, L.; Gunn-Moore, F. Willin/FRMD6: A Multi-Functional Neuronal Protein Associated with Alzheimer’s Disease. Cells 2021, 10, 3024. [Google Scholar] [CrossRef] [PubMed]
- Sun, M.; Shen, Y.; Jia, G.; Deng, Z.; Shi, F.; Jing, Y.; Xia, S. Activation of the HNRNPA2B1/miR-93-5p/FRMD6 axis facilitates prostate cancer progression in an m6A-dependent manner. J. Cancer 2023, 14, 1242–1256. [Google Scholar] [CrossRef] [PubMed]
BCA Case | Age (y) | Tumor Size (cm) | OS (y) | DFS (y) | Tumor Staging | Pathological Differentiation | TB Level |
---|---|---|---|---|---|---|---|
B168 | 33 | 7.3 | 11.3 | 11.3 | IIIB | PD | low |
TNBC-G06 | 67 | 4.5 | 5.3 | 5.1 | IIA | PD | low |
B041 | 26 | 5.2 | 5.9 | 2.3 | IIIA | PD | high |
B031 | 50 | 3 | 4 | 4.1 | IIIC | PD | high |
B166 | 55 | 4 | 6.6 | 1.5 | IIIA | PD | high |
TNBC-G01 | 67 | 2.4 | 5.6 | 5.3 | IIIA | MD | high |
TNBC-G03 | 61 | 4 | 0.6 | 0.6 | IIA | MD | high |
TNBC-G19 | 51 | 4.3 | 5.1 | 5.1 | IIIB | PD | high |
TNBC-P04 | 54 | 3.5 | 3.2 | 3.2 | IIA | PD | high |
TNBC-P06 | 50 | 3 | 5.2 | 4 | IIIC | MD | high |
PC-B-099 | 50 | 6.5 | 2 | 1.3 | IIIA | PD | high |
PC-B-130 | 44 | 6 | 0.8 | 0.6 | IIIC | PD | high |
TNBC-P09 | 59 | 2.5 | 2.5 | 1.2 | IIA | PD | high |
B165 | 49 | 4 | 11.5 | 1.8 | III | PD | low |
IBC-11B | 60 | 4.5 | 15.1 | 15.1 | III | PD | high |
Genes | baseMean | logFC | lfcSE | Stat | p-Value | Adjusted p-Value |
---|---|---|---|---|---|---|
NOL4 | 200.02 | −5.14 | 1.1013 | −4.6685 | 3.03 × 10−6 | 0.002490 |
STAR | 36.16 | −5.00 | 1.1492 | −4.3530 | 1.34 × 10−5 | 0.005210 |
C8G | 216.37 | −4.84 | 0.9440 | −5.1302 | 2.89 × 10−7 | 0.000580 |
NEIL1 | 588.06 | 3.16 | 0.8325 | 3.8001 | 0.000145 | 0.029210 |
SLC46A3 | 244.48 | 2.97 | 0.6544 | 4.5456 | 5.48 × 10−6 | 0.003100 |
FRMD6 | 441.97 | 2.00 | 0.4376 | 4.5627 | 5.05 × 10−6 | 0.003100 |
SCARF2 | 159.33 | 3.28 | 0.8192 | 4.0070 | 6.15 × 10−5 | 0.015180 |
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Janyasupab, P.; Singhanat, K.; Warnnissorn, M.; Thuwajit, P.; Suratanee, A.; Plaimas, K.; Thuwajit, C. Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis. Biomolecules 2024, 14, 896. https://doi.org/10.3390/biom14080896
Janyasupab P, Singhanat K, Warnnissorn M, Thuwajit P, Suratanee A, Plaimas K, Thuwajit C. Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis. Biomolecules. 2024; 14(8):896. https://doi.org/10.3390/biom14080896
Chicago/Turabian StyleJanyasupab, Panisa, Kodchanan Singhanat, Malee Warnnissorn, Peti Thuwajit, Apichat Suratanee, Kitiporn Plaimas, and Chanitra Thuwajit. 2024. "Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis" Biomolecules 14, no. 8: 896. https://doi.org/10.3390/biom14080896