跳到主要內容

簡易檢索 / 詳目顯示

研究生: 劉佳媛
Chia-Yuan Liu
論文名稱: 基因表現與乳癌亞型的關聯性分析
Association analysis of gene expression with breast cancer subtype
指導教授: 許藝瓊
Yi-Chiung Hsu
口試委員:
學位類別: 碩士
Master
系所名稱: 生醫理工學院 - 生醫科學與工程學系
Department of Biomedical Sciences and Engineering
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 68
中文關鍵詞: 乳癌亞型高表達基因TCGAUALCAN
外文關鍵詞: Breast cancer, Subtypes, Gene expression, TCGA, UALCAN
相關次數: 點閱:28下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 乳癌是女性最常見的癌症,對全球健康構成重大威脅。隨著次世代基因定
    序(NGS,next generation sequencing)的快速發展,我們得以更深入地研究與乳癌相關的基因。特定基因的異常表達與乳癌的發展和預後息息相關。乳癌具有異質性,包含多種不同的亞型,每種亞型在基因表達、生物學特性和對治療的反應上都存在顯著差異。因此,在乳癌研究中考慮亞型因素至關重要,有助於更精準地理解不同亞型的發病機制和尋找更有效的治療策略。
    癌症基因組圖譜(TCGA)大型基因組計畫為研究這些基因提供了大量且完整的數據資源,能以系統性地分析大量乳癌樣本的基因。不僅有助於我們更深入地了解乳癌的分子機制,還能為開發更有效的標靶治療策略奠定基礎。標靶治療通過精準鎖定癌細胞中的特定基因,達到抑制腫瘤生長、擴散的目的,從而提高治療效果並降低副作用。更重要的是,發現新的標靶可以為那些對現有治療方法產生抗藥性的患者提供新的治療選擇,並有助於開發更個性化的治療方案。
    本篇在利用 UALCAN 平台和 TCGA 數據庫。UALCAN 平台支持基於轉錄組數據的基因表達量分析和 Kaplan-Meier 存活曲線生成,能夠快速篩選出在腫瘤組織中表達量顯著高於正常組織的基因。本篇的研究方法包括基因表達分析和存活分析,在分析特定高表達基因在乳癌中的角色,並探討這些基因與患者預後之間的關聯,同時考慮乳癌亞型的影響,以及快速地發現新的診斷標誌物和標靶治療,通過深入研究這些基因從而改善乳癌患者的預後,並為克服現有治療的局限性提供新的機會。最終為乳癌的早期診斷、精準治療和預後改善做出貢獻。


    Breast cancer is the most common cancer among women and poses a significant global health threat. As Next generation sequencing (NGS)improves by leaps and bounds, researchers are now able to gain deeper insights into genes associated with breast cancer. Abnormal expression of specific genes is closely linked to the development and prognosis of the disease. Breast cancer is a heterogeneous disease composed of multiple subtypes, each characterized by distinct gene expression patterns, biological behaviors, and treatment responses. Therefore, considering breast cancer subtypes is crucial for accurately understanding tumorigenesis and for
    identifying more effective therapeutic strategies.The Cancer Genome Atlas (TCGA), a large-scale genomics initiative, offers a comprehensive dataset that enables systematic
    analysis of gene expression across numerous breast cancer samples. This resource not only enhances our understanding of the molecular mechanisms of breast cancer but also lays the foundation for the development of more effective targeted therapies. Targeted therapies work by precisely inhibiting specific genes in cancer cells to suppress tumor growth and metastasis, thereby improving treatment outcomes and minimizing side effects. Moreover, the discovery of novel targets may offer alternative treatment options for patients who develop resistance to existing therapies,
    and it facilitates the advancement of personalized medicine.In this study, we utilized the UALCAN platform and TCGA database. UALCAN supports transcriptome-based
    gene expression analysis and Kaplan–Meier survival curve enabling rapid identification of genes that are significantly overexpressed in tumor tissues compared
    to normal tissues. The research methodology includes gene expression analysis and survival analysis to investigate the role of specific highly expressed genes in breast cancer, examine their association with patient prognosis, and assess the impact of molecular subtypes. The ultimate goal is to identify novel diagnostic biomarkers and therapeutic targets, improve patient outcomes, and provide new opportunities to overcome the limitations of current treatments—thereby contributing to early diagnosis, precision medicine, and enhanced prognosis in breast cancer.

    中文摘要 .............i Abstract .............. ii 致謝 ................... iii 目錄 .................... iv 圖目錄 .............. vii 表目錄 ................ x 一、 緒論 ....... 1 1-1 研究動機 ....... 1 1-2 研究背景 ............. 1 1-3 乳癌的基因表現與突變 ....... 2 1-4 TCGA(The Cancer Genome Atlas)...... 3 1-5 UALCAN ............ 3 1-6 差異表達基因(DEGs) ........ 4 二 、研究工具 .............. 5 2-1癌症基因組圖譜計畫(TheCancerGenomeAtlas,TCGA) .......... 5 2-2 UALCAN .............. 5 三 、研究方法 ............. 7 3-1數據採集 .............. 7 3-2存活分析 .............. 7 3-3分析流程 ............... 8 四 、結果 .................. 9 4-1 存活分析與基因表達 .............. 9 4-1-1存活分析與COL10A1基因表達 ..... 10 4-1-2存活分析與CST1基因表達................... 11 4-1-3存活分析與MMP11基因表達........ 12 4-1-4存活分析與NEK2基因表達 ........ 13 4-1-5存活分析與UBE2C基因表達 ....... 14 4-1-6存活分析與NUF2基因表達 ......... 15 4-1-7存活分析與CXCL10基因表達 ....... 16 4-1-8存活分析與PBK基因表達 ........ 17 4-1-9存活分析與AURKB基因表達 ........ 18 4-1-10存活分析與CEP55基因表達 ...... 19 4-1-11存活分析與ANLN基因表達 ........ 20 4-2 表達量分析 ................ 21 4-2-1 COL10A1 在乳癌中的表達 ......... 22 4-2-2 CST1在乳癌中的表達 ........ 23 4-2-3 MMP11在乳癌中的表達 ....... 24 4-2-4 NEK2在乳癌中的表達 ........ 25 4-2-5 UBE2C在乳癌中的表達 ......... 26 4-2-6 NUF2在乳癌中的表達 ............ 27 4-2-7 CXCL10在乳癌中的表達 ....... 28 4-2-8 PBK在乳癌中的表達 .......... 29 4-2-9 AURKB在乳癌中的表達 ............... 30 4-2-10 CEP55在乳癌中的表達 ............ 31 4-2-11 ANLN在乳癌中的表達........... 32 4-3與 TP53 突變狀態的關聯性 .......... 33 4-3-1 COL10A1與 TP53 突變狀態的關聯性 ................. 34 4-3-2 CST1與 TP53 突變狀態的關聯性 ........ 34 4-3-3 MMP11與 TP53 突變狀態的關聯性 .... 35 4-3-4 NEK2與 TP53 突變狀態的關聯性 ........ 35 4-3-5 UBE2C與 TP53 突變狀態的關聯性 ..... 36 4-3-6 NUF2與 TP53 突變狀態的關聯性 ...... 36 4-3-7 CXCL10與 TP53 突變狀態的關聯性 .... 37 4-3-8 PBK與 TP53 突變狀態的關聯性 ......... 37 4-3-9 AURKB與 TP53 突變狀態的關聯性 ..... 38 4-3-10 CEP55與 TP53 突變狀態的關聯性 ..... 38 4-3-11 ANLN與 TP53 突變狀態的關聯性 ..... 39 4-4 文獻探討 .................... 40 4-4-1 COL10A1 ................... 40 4-4-2 CST1 ..................... 41 4-4-3 MMP11 ........... 41 4-4-4 NEK2 .................... 42 4-4-5 UBE2C ......... 42 4-4-6 NUF2 ........... 43 4-4-7 CXCL10................... 43 4-4-8 PBK ................ 44 4-4-9 AURKB ............... 44 4-4-10 CEP55 ................... 45 4-4-11 ANLN ...................... 45 五 、總結 ..... 47 六 、未來工作 ................. 48 七 、參考文獻 ......................... 49 圖目錄 圖 1 流程圖 ........................ 7 圖 2分析流程 ............... 8 圖 3 . COL10A1表達對乳癌患者生存的影響 .................... 10 圖 4 . COL10A1表達和癌症分型對乳癌患者生存的影響 ........... 10 圖 5 . CST1表達對乳癌患者生存的影響 ............... 11 圖 6 .CST1表達和癌症分型對乳癌患者生存的影響 ........... 11 圖 7 . MMP11表達對乳癌患者生存的影響 ............. 12 圖 8 . MMP11表達和癌症分型對乳癌患者生存的影響 .......... 12 圖 9 . NEK2表達對乳癌患者生存的影響 ......... 13 圖 10 . NEK2表達和癌症分型對乳癌患生存的影響 ........ 13 圖 11 . UBE2C表達對乳癌患者生存的影響 ............ 14 圖 12 . UBE2C表達和癌症分型對乳癌患者生存的影響 ...... 14 圖 13. NUF2表達對乳癌患者生存的影響 ................. 15 圖 14 . NUF2表達和癌症分型對乳癌患者生存的影響 ..... 15 圖 15. CXCL10表達對乳癌患者生存的影響 ... 16 圖 16 .CXCL10 表達和癌症分型對乳癌患者生存的影響 .......... 16 圖 17. PBK 表達對乳癌患者生存的影響 ........... 17 圖 18. PBK 表達和癌症分型對乳癌患者生存的影響 ...... 17 圖 19. AURKB 表達對乳癌患者生存的影響 ........ 18 圖 20. AURKB表達和癌症分型對乳癌患者生存的影響 ........ 18 圖 21. CEP55 表達對乳癌患者生存的影響 ........... 19 圖 22. CEP55表達和癌症分型對乳癌患者生存的影響 ............ 19 圖 23. ANLN表達對乳癌患者生存的影響 .................... 20 圖 24. ANLN表達和癌症分型對乳癌患患者生存的影響 ............ 20 圖 25.正常組織vs.癌組織COL10A1基因表達量 ......... 22 圖 26.不同乳癌亞型COL10A1基因表達量.................... 22 圖 27.正常組織vs.癌組織CST1基因表達量 .................. 23 圖 28.不同乳癌亞型CST1基因表達量 ...................... 23 圖 29.正常組織vs.癌組織MMP11基因表達量 ................. 24 圖30.不同乳癌亞型MMP11基因表達量 ......................... 24 圖 31.正常組織vs.癌組織NEK2基因表達量差異 ........... 25 圖 32.不同乳癌亞型NEK2基因表達量差異 ............... 25 圖 33.正常組織vs.癌組織UBE2C基因表達量差異 ......... 26 圖 34.不同乳癌亞型UBE2C基因表達量差異 ..... 26 圖 35.正常組織vs.癌組織NUF2基因表達量差異 ......... 27 圖 36.不同乳癌亞型NUF2基因表達量差異 ....27 圖 37.正常組織vs.癌組織CXCL10基因表達量差異 .... 28 圖 38.不同乳癌亞型CXCL10基因表達量差異 ........ 28 圖 39.正常組織vs.癌組織PBK基因表達量差異 ......... 29 圖 40.不同乳癌亞型PBK基因表達量差異 ................. 29 圖 41.正常組織vs.癌組織AURKB基因表達量差異 ....... 30 圖 42.不同乳癌亞型AURKB基因表達量差異 .......... 30 圖 43.正常組織vs.癌組織CEP55基因表達量 ............ 31 圖 44.不同乳癌亞型CEP55基因表達量差異 .......... 31 圖 45.正常組織vs.癌組織ANLN基因表達量 .......... 32 圖 46.不同乳癌亞型ANLN基因表達量差異 ......... 32 圖 47 .COL10A1與 TP53 突變狀態的關聯性 ......... 34 圖 48 .CST1與 TP53 突變狀態的關聯性 ................... 34 圖 49. MMP11與 TP53 突變狀態的關聯性 ........... 35 圖 50. NEK2與 TP53 突變狀態的關聯性........... 35 圖 51. UBE2C與 TP53 突變狀態的關聯性............ 36 圖 52. NUF2與 TP53 突變狀態的關聯性............. 36 圖 53. CXCL10與 TP53 突變狀態的關聯性......... 37 圖 54. PBK與 TP53 突變狀態的關聯性............ 37 圖 55. AURKB與 TP53 突變狀態的關聯性...... 38 圖 56. CEP55與 TP53 突變狀態的關聯性........... 38 圖 57. ANLN與 TP53 突變狀態的關聯性............ 39 表目錄 表 1乳癌分型......... 1 表 2 MeanTumorTPM/MeanNormalTPM>10且生存分析表現顯著(P<0.05)的11個基因........ 9 表 3 COL10A1基在正常組織與乳癌腫瘤中表現統計值 ....... 22 表 4 COL10A1基因在乳癌不同亞型與正常組組織與乳癌腫瘤中表現統計值 ... 22 表 5 CST1基因在正常組織與乳癌腫瘤中表現統計值 ........ 23 表 6 CST1基因在乳癌不同亞型與正常組織與乳癌腫瘤中表現統計值 .............. 23 表 7 MMP11基因在正常組織與乳癌腫瘤中表現統計值 .......... 24 表 8 MMP11基因在乳癌不同亞型與正常組織與乳癌腫瘤中表現統計值 .......... 24 表 9 NEK2基因在正常組織與乳癌腫瘤中表現統計值 ........... 25 表 10 NEK2基因在乳癌不同亞型與正常組織與乳癌腫瘤中表現統計值 ........... 25 表 11 UBE2C基因在正常組織與乳癌腫瘤中表現統計值 ......... 26 表 12 UBE2C基因在乳癌不同亞型與正常組織與乳癌腫瘤中表現統計值 ......... 26 表 13 NUF2 基因在正常組織與乳癌腫瘤中表現統計值...27 表 14NUF2 基因在乳癌不同亞型正常組織與乳癌腫瘤中表現統計值................ 27 表 15 CXCL10基因在正常組織與乳癌腫瘤中表現統計值 ... 28 表 16 CXCL10 基因在乳癌不同亞型正常組織與乳癌腫瘤中表現統計值 .......... 28 表 17 PBK基因在正常組織與乳癌腫瘤中表現統計值 ...... 29 表 18 PBK 基因在乳癌不同亞型正常組織與乳癌腫瘤中表現統計值 ................. 29 表 19 AURKB基因在正常組織與乳癌腫瘤中表現統計值 ...... 30 表 20 AURKB基因在乳癌不同亞型正常組織與乳癌腫瘤中表現統計值 ............ 30 表 21 CEP55基因在正常組織與乳癌腫瘤中表現統計值 ..... 31 表 22 CEP55基因在乳癌不同亞型正常組織與乳癌腫瘤中表現統計值 .............. 31 表 23 ANLN基因在正常組織與乳癌腫瘤中表現統計值 .... 32 表 24 ANLN基因在乳癌不同亞型正常組織與乳癌腫瘤中表現統計值 ............... 32 表 25依據 TP53 突變狀態的 COL10A1基因在乳癌中的表現 ..... 34 表 26 依據 TP53 突變狀態的 CST1 基因在乳癌中的表現......... 34 表 27依據 TP53 突變狀態的MMP11基因在乳癌中的表現 ....... 35 表 28 依據 TP53 突變狀態的NEK2基因在乳癌中的表現 ........ 35 表 29依據 TP53 突變狀態的UBE2C基因在乳癌中的表現 ....... 36 表 30 依據 TP53 突變狀態的NUF2基因在乳癌中的表現 ...... 36 表 31 依據 TP53 突變狀態的CXCL10基因在乳癌中的表現 ..... 37 表 32依據 TP53 突變狀態的PBK基因在乳癌中的表現 ........ 37 表 33 依據 TP53 突變狀態的AURKB基因在乳癌中的表現 ...... 38 表 34依據 TP53 突變狀態的CEP55基因在乳癌中的表現...... 38 表 35 依據 TP53 突變狀態的ANLN基因在乳癌中的表現 ....... 39

    [1] H. Sung et al., "Global cancer statistics 2020: GLOBOCAN estimates of
    incidence and mortality worldwide for 36 cancers in 185 countries," (in English),
    CA-Cancer J. Clin., Article vol. 71, no. 3, pp. 209-249, May 2021, doi:
    10.3322/caac.21660.
    [2] M. Arnold et al., "Current and future burden of breast cancer: Global statistics
    for 2020 and 2040," (in English), Breast, Article vol. 66, pp. 15-23, Dec 2022,
    doi: 10.1016/j.breast.2022.08.010.
    [3] M. Raica, I. Jung, A. M. Cîmpean, C. Suciu, and A. M. Mureşan, "From
    conventional pathologic diagnosis to the molecular classification of breast
    carcinoma: are we ready for the change?," (in eng), Rom J Morphol Embryol,
    vol. 50, no. 1, pp. 5-13, 2009.
    [4] P. H. Tan et al., "Immunohistochemical detection of Ki67 in breast cancer
    correlates with transcriptional regulation of genes related to apoptosis and cell
    death," (in eng), Mod Pathol, vol. 18, no. 3, pp. 374-81, Mar 2005, doi:
    10.1038/modpathol.3800254.
    [5] L. Clusan, F. Ferriere, G. Flouriot, and F. Pakdel, "A Basic Review on Estrogen
    Receptor Signaling Pathways in Breast Cancer," Int J Mol Sci, vol. 24, no. 7, Apr
    6 2023, doi: 10.3390/ijms24076834.
    [6] E. Orrantia-Borunda, P. Anchondo-Nuñez, L. E. Acuña-Aguilar, F. O. Gómez
    Valles, and C. A. Ramírez-Valdespino, "Subtypes of Breast Cancer," in Breast
    Cancer, H. N. Mayrovitz Ed. Brisbane (AU): Exon Publications
    Copyright: The Authors.; The authors confirm that the materials included in this
    chapter do not violate copyright laws. Where relevant, appropriate permissions
    have been obtained from the original copyright holder(s), and all original sources
    have been appropriately acknowledged or referenced., 2022.
    [7] Y. Feng et al., "Breast cancer development and progression: Risk factors, cancer
    stem cells, signaling pathways, genomics, and molecular pathogenesis," (in eng),
    Genes Dis, vol. 5, no. 2, pp. 77-106, Jun 2018, doi:
    10.1016/j.gendis.2018.05.001.
    [8] D. Zhan et al., "Expanding individualized therapeutic options via
    genoproteomics," Cancer Lett, vol. 560, p. 216123, Apr 28 2023, doi:
    10.1016/j.canlet.2023.216123.
    [9] S. H. Chen et al., "Comprehensive genomic profiling and therapeutic
    implications for Taiwanese patients with treatment-naive breast cancer," Cancer
    49
    Med, vol. 13, no. 12, p. e7384, Jun 2024, doi: 10.1002/cam4.7384.
    [10] K. Tomczak, P. Czerwinska, and M. Wiznerowicz, "The Cancer Genome Atlas
    (TCGA): an immeasurable source of knowledge," Contemp Oncol (Pozn), vol.
    19, no. 1A, pp. A68-77, 2015, doi: 10.5114/wo.2014.47136.
    [11] D. S. Chandrashekar et al., "UALCAN: An update to the integrated cancer data
    analysis platform," Neoplasia, vol. 25, pp. 18-27, Mar 2022, doi:
    10.1016/j.neo.2022.01.001.
    [12] F. Manzoor, C. A. Tsurgeon, and V. Gupta, "Exploring RNA-Seq Data Analysis
    Through Visualization Techniques and Tools: A Systematic Review of
    Opportunities and Limitations for Clinical Applications," (in eng),
    Bioengineering (Basel), vol. 12, no. 1, Jan 12 2025, doi:
    10.3390/bioengineering12010056.
    [13] S. A. Byron, K. R. Van Keuren-Jensen, D. M. Engelthaler, J. D. Carpten, and D.
    W. Craig, "Translating RNA sequencing into clinical diagnostics: opportunities
    and challenges," Nat Rev Genet, vol. 17, no. 5, pp. 257-71, May 2016, doi:
    10.1038/nrg.2016.10.
    [14] K. R. Kukurba and S. B. Montgomery, "RNA Sequencing and Analysis," (in
    eng), Cold Spring Harb Protoc, vol. 2015, no. 11, pp. 951-69, Apr 13 2015, doi:
    10.1101/pdb.top084970.
    [15] A. Subramanian et al., "Gene set enrichment analysis: a knowledge-based
    approach for interpreting genome-wide expression profiles," (in eng), Proc Natl
    Acad Sci U S A, vol. 102, no. 43, pp. 15545-50, Oct 25 2005, doi:
    10.1073/pnas.0506580102.
    [16] J. Liñares-Blanco, A. Pazos, and C. Fernandez-Lozano, "Machine learning
    analysis of TCGA cancer data," (in eng), PeerJ Comput Sci, vol. 7, p. e584,
    2021, doi: 10.7717/peerj-cs.584.
    [17] D. S. Chandrashekar et al., "UALCAN: A Portal for Facilitating Tumor
    Subgroup Gene Expression and Survival Analyses," Neoplasia, vol. 19, no. 8,
    pp. 649-658, Aug 2017, doi: 10.1016/j.neo.2017.05.002.
    [18] G. Prelich, "Gene overexpression: uses, mechanisms, and interpretation," (in
    eng), Genetics, vol. 190, no. 3, pp. 841-54, Mar 2012, doi:
    10.1534/genetics.111.136911.
    [19] F. S. Nahm, "What the P values really tell us," (in eng), Korean J Pain, vol. 30,
    no. 4, pp. 241-242, Oct 2017, doi: 10.3344/kjp.2017.30.4.241.
    [20] K. Schneider, K. Zelley, K. E. Nichols, A. Schwartz Levine, and J. Garber, "Li
    Fraumeni Syndrome," in GeneReviews(®), M. P. Adam, J. Feldman, G. M.
    Mirzaa, R. A. Pagon, S. E. Wallace, and A. Amemiya Eds. Seattle (WA):
    University of Washington, Seattle
    50
    Copyright © 1993-2025, University of Washington, Seattle. GeneReviews is a
    registered trademark of the University of Washington, Seattle. All rights
    reserved., 1993.
    [21] P. Hainaut and G. P. Pfeifer, "Somatic TP53 Mutations in the Era of Genome
    Sequencing," (in eng), Cold Spring Harb Perspect Med, vol. 6, no. 11, Nov 1
    2016, doi: 10.1101/cshperspect.a026179.
    [22] M. Olivier, M. Hollstein, and P. Hainaut, "TP53 mutations in human cancers:
    origins, consequences, and clinical use," Cold Spring Harb Perspect Biol, vol. 2,
    no. 1, p. a001008, Jan 2010, doi: 10.1101/cshperspect.a001008.
    [23] W. Zhou et al., "High expression COL10A1 promotes breast cancer progression
    and predicts poor prognosis," Heliyon, vol. 8, no. 10, p. e11083, Oct 2022, doi:
    10.1016/j.heliyon.2022.e11083.
    [24] Q. Yi et al., "Oncogenic mechanisms of COL10A1 in cancer and clinical
    challenges (Review)," Oncol Rep, vol. 52, no. 6, Dec 2024, doi:
    10.3892/or.2024.8821.
    [25] D. N. Dai et al., "Elevated expression of CST1 promotes breast cancer
    progression and predicts a poor prognosis," J Mol Med (Berl), vol. 95, no. 8, pp.
    873-886, Aug 2017, doi: 10.1007/s00109-017-1537-1.
    [26] G. Huang, L. Lu, Y. You, J. Li, and K. Zhang, "Knockdown of ENO1 promotes
    autophagy dependent-ferroptosis and suppresses glycolysis in breast cancer cells
    via the regulation of CST1," (in eng), Drug Dev Res, vol. 85, no. 7, p. e70004,
    Nov 2024, doi: 10.1002/ddr.70004.
    [27] N. Eiro et al., "MMP11 expression in intratumoral inflammatory cells in breast
    cancer," (in eng), Histopathology, vol. 75, no. 6, pp. 916-930, Dec 2019, doi:
    10.1111/his.13956.
    [28] T. Kokuryo, Y. Yokoyama, J. Yamaguchi, N. Tsunoda, T. Ebata, and M. Nagino,
    "NEK2 Is an Effective Target for Cancer Therapy With Potential to Induce
    Regression of Multiple Human Malignancies," (in eng), Anticancer Res, vol. 39,
    no. 5, pp. 2251-2258, May 2019, doi: 10.21873/anticanres.13341.
    [29] M. Cusan and L. Wang, "NEK2, a promising target in TP53 mutant cancer," (in
    eng), Blood Sci, vol. 4, no. 2, pp. 97-98, Apr 2022, doi:
    10.1097/bs9.0000000000000106.
    [30] L. Yuan et al., "Pan-Cancer Bioinformatics Analysis of Gene UBE2C," (in eng),
    Front Genet, vol. 13, p. 893358, 2022, doi: 10.3389/fgene.2022.893358.
    [31] Z. N. Lu, J. Song, T. H. Sun, and G. Sun, "UBE2C affects breast cancer
    proliferation through the AKT/mTOR signaling pathway," (in eng), Chin Med J
    (Engl), vol. 134, no. 20, pp. 2465-2474, Oct 7 2021, doi:
    10.1097/cm9.0000000000001708.
    51
    [32] Y. Deng et al., "NUF2 Promotes Breast Cancer Development as a New Tumor
    Stem Cell Indicator," (in eng), Int J Mol Sci, vol. 24, no. 4, Feb 20 2023, doi:
    10.3390/ijms24044226.
    [33] J. Sun, J. Chen, Z. Wang, Y. Deng, L. Liu, and X. Liu, "[Expression of NUF2 in
    breast cancer and its clinical significance]," (in chi), Nan Fang Yi Ke Da Xue Xue
    Bao, vol. 39, no. 5, pp. 591-597, May 30 2019, doi: 10.12122/j.issn.1673
    4254.2019.05.15.
    [34] N. Karin and H. Razon, "Chemokines beyond chemo-attraction: CXCL10 and its
    significant role in cancer and autoimmunity," (in eng), Cytokine, vol. 109, pp.
    24-28, Sep 2018, doi: 10.1016/j.cyto.2018.02.012.
    [35] X. Wu, A. Sun, W. Yu, C. Hong, and Z. Liu, "CXCL10 mediates breast cancer
    tamoxifen resistance and promotes estrogen-dependent and independent
    proliferation," (in eng), Mol Cell Endocrinol, vol. 512, p. 110866, Jul 15 2020,
    doi: 10.1016/j.mce.2020.110866.
    [36] H. Wen et al., "An Integrative Pan-Cancer Analysis of PBK in Human Tumors,"
    (in eng), Front Mol Biosci, vol. 8, p. 755911, 2021, doi:
    10.3389/fmolb.2021.755911.
    [37] Z. Han, L. Li, Y. Huang, H. Zhao, and Y. Luo, "PBK/TOPK: A Therapeutic
    Target Worthy of Attention," (in eng), Cells, vol. 10, no. 2, Feb 11 2021, doi:
    10.3390/cells10020371.
    [38] S. Pellizzari, H. Athwal, A. C. Bonvissuto, and A. Parsyan, "Role of AURKB
    Inhibition in Reducing Proliferation and Enhancing Effects of Radiotherapy in
    Triple-Negative Breast Cancer," (in eng), Breast Cancer (Dove Med Press), vol.
    16, pp. 341-346, 2024, doi: 10.2147/bctt.S444965.
    [39] G. S. Li et al., "CEP55: an immune-related predictive and prognostic molecular
    biomarker for multiple cancers," (in eng), BMC Pulm Med, vol. 23, no. 1, p. 166,
    May 12 2023, doi: 10.1186/s12890-023-02452-1.
    [40] M. Kalimutho et al., "CEP55 is a determinant of cell fate during perturbed
    mitosis in breast cancer," (in eng), EMBO Mol Med, vol. 10, no. 9, Sep 2018,
    doi: 10.15252/emmm.201708566.
    [41] L. Zhang et al., "Clinical implication and immunological landscape analyses of
    ANLN in pan-cancer: A new target for cancer research," (in eng), Cancer Med,
    vol. 12, no. 4, pp. 4907-4920, Feb 2023, doi: 10.1002/cam4.5177.
    [42] A. Maryam and Y. R. Chin, "ANLN Enhances Triple-Negative Breast Cancer
    Stemness Through TWIST1 and BMP2 and Promotes its Spheroid Growth," (in
    eng), Front Mol Biosci, vol. 8, p. 700973, 2021, doi:
    10.3389/fmolb.2021.700973.
    [43] E. Ruiz et al., "An Integrative Multi-Omics Analysis of The Molecular Links
    52
    between Aging and Aggressiveness in Thyroid Cancers," (in eng), Aging Dis,
    vol. 14, no. 3, pp. 992-1012, Jun 1 2023, doi: 10.14336/ad.2022.1021.
    [44] L. Tian et al., "Whole transcriptome scanning and validation of negatively
    related genes in UC-MSCs," (in eng), Heliyon, vol. 10, no. 6, p. e27996, Mar 30
    2024, doi: 10.1016/j.heliyon.2024.e27996.
    [45] R. Patra, N. C. Das, and S. Mukherjee, "Exploring the Differential Expression
    and Prognostic Significance of the COL11A1 Gene in Human Colorectal
    Carcinoma: An Integrated Bioinformatics Approach," (in eng), Front Genet, vol.
    12, p. 608313, 2021, doi: 10.3389/fgene.2021.608313.
    [46] L. Zhang et al., "MST1 interactomes profiling across cell death in esophageal
    squamous cell carcinoma," (in eng), Med Rev (2021), vol. 4, no. 6, pp. 531-543,
    Dec 2024, doi: 10.1515/mr-2024-0031.
    [47] H. Zhang et al., "An Extracellular Matrix-Based Signature Associated With
    Immune Microenvironment Predicts the Prognosis and Therapeutic Responses of
    Patients With Oesophageal Squamous Cell Carcinoma," (in eng), Front Mol
    Biosci, vol. 8, p. 598427, 2021, doi: 10.3389/fmolb.2021.598427.
    [48] H. Wu et al., "FHND004 inhibits malignant proliferation of multiple myeloma
    by targeting PDZ-binding kinase in MAPK pathway," (in eng), Aging (Albany
    NY), vol. 16, no. 5, pp. 4811-4831, Mar 7 2024, doi: 10.18632/aging.205634.
    [49] T. Budhbaware, J. Rathored, and S. Shende, "Molecular methods in cancer
    diagnostics: a short review," (in eng), Ann Med, vol. 56, no. 1, p. 2353893, Dec
    2024, doi: 10.1080/07853890.2024.2353893.
    [50] S. Sundaresan, S. K. Lavanya, and M. Manickam, "Emerging Molecular
    Technology in Cancer Testing," (in eng), Ejifcc, vol. 35, no. 3, pp. 142-153, Oct
    2024.
    [51] Y. J. Kim et al., "UBE2C Overexpression Aggravates Patient Outcome by
    Promoting Estrogen-Dependent/Independent Cell Proliferation in Early Hormone
    Receptor-Positive and HER2-Negative Breast Cancer," (in eng), Front Oncol,
    vol. 9, p. 1574, 2019, doi: 10.3389/fonc.2019.01574.
    [52] R. Pereira, J. Oliveira, and M. Sousa, "Bioinformatics and Computational Tools
    for Next-Generation Sequencing Analysis in Clinical Genetics," (in eng), J Clin
    Med, vol. 9, no. 1, Jan 3 2020, doi: 10.3390/jcm9010132.
    [53] Y. W. Li et al., "Molecular Characterization and Classification of HER2-Positive
    Breast Cancer Inform Tailored Therapeutic Strategies," (in eng), Cancer Res,
    vol. 84, no. 21, pp. 3669-3683, Nov 4 2024, doi: 10.1158/0008-5472.Can-23
    4066.

    QR CODE
    :::