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Table of Contents

    Topic review

    Biomarkers for Breast Cancer

    Subjects: Oncology
    View times: 25
    Submitted by: Hsing-Ju Wu

    Definition

    Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed.

    1. Introduction

    Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide [1]. It is estimated that there were approximately 2 million new cases and 627,000 breast cancer-related mortalities globally in 2018 [2][3]. Although the five-year relative survival rate for localized breast cancer is relatively high (80–92%), the survival rate dramatically declines to <25% for metastatic breast cancer [4]. Breast cancer is fairly heterogeneous; gene-expression profiling of breast cancer revealed six intrinsic molecular subtypes: luminal A (estrogen receptor (ER)+, progesterone receptor (PR)+, human epidermal growth factor receptor 2 (HER2)−, and Ki67−), luminal B (ER+, PR+, HER+/−, and Ki67+), HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low (low expression of cellular adhesion genes) [5][6][7]. Triple-negative breast cancer (TNBC) belongs to either the basal-like or claudin-low subtypes [7]. Breast cancer subtypes differ in terms of clinical relevance, patterns of gene expression, selection of therapeutic strategies, responses to treatment, and prognosis [5][8][9]. Therefore, knowledge of the specific breast cancer subtype is important in guiding treatment decisions and predicting prognosis.

    Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes, leading to a better prognosis for breast cancer patients [10]. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous well-known limitations including low sensitivity of 25~59% for detecting cancer in dense breasts, which present commonly in younger women, as well as high rates of false-negatives and false positives, and 1–10% overdiagnosis [11][12][13]. Therefore, the effective management of breast cancer during therapy or early detection depends on the availability of reliable noninvasive diagnostic, prognostic, and predictive biomarkers [14][15]. In addition, an increasing number of patients demand personalized or precise treatments; hence, the identification of novel biomarkers for diagnosis and prognosis and the development of new drugs is urgently required.

    Biomarkers for cancer include substances released from the cancer cells themselves or by other tissues in response to tumors as well as physiological markers that can be visualized using imaging technology or detected by molecular technology [16][17]. Biomarkers are objective and quantifiable evaluations of biological states or diseases that can predict tumor behavior, prognosis, or treatment responses, thus playing an important role in the management of breast cancer [18][19]. They must be validated by human samples to ensure that they reflect the clinical outcome [20][21]. Because tumor cells are highly heterogeneous, a single biomarker might not have sufficient sensitivity and specificity to accurately predict cancer progression and metastasis, and a combination of multiple markers is more appealing.

    With the rapid advancement of molecular signaling pathways and genetic signatures, including immunohistochemistry, next-generation sequencing, and targeted multigene, numerous clinically relevant biomarkers in tissue and/or blood (liquid biopsies) have been reported to aid in determining the risk of metastasis, prognosis, recurrence, treatment guidance, and drug resistance in breast cancer. Some of these have been used clinically [19][22][23][24]. However, they lack specificity and sensitivity. Therefore, the identification of novel and effective biomarkers is urgently required. In addition, there is an emerging development of immunotherapies for breast cancer, and it is important to identify reliable biomarkers for predicting who will benefit from immunotherapies.

    2. Types of Biomarkers

    Biomarkers used in cancer range from macromolecules, such as DNA, genetic mutations, RNA, and proteins to whole cells (Table 1 and Table 2). They can circulate in the blood as circulating mRNA, circulating free DNA, and circulating tumor cells, making liquid biopsies attractive for clinical use [17][25][26]. Two types of biomarkers are used for cancer treatment outcome: prognostic biomarkers are associated with clinical outcome and can inform whether a patient should be treated, and predictive biomarkers to guide a treatment that is effective only in a subtype of breast cancer [27][28][29]. Some biomarkers are already available in clinical practice, whereas some biomarkers have been validated in mouse models or clinical trials.

    Table 1. Biomarkers discovered recently for breast cancer.

    Table 2. Immune cells and other non-cancer cells as the biomarkers for breast cancer.

    Cell Types

    Prognosis/Treatment

    References

    T cells (Tregs)

    better prognosis in lymph node negative, primary breast cancer patients including those with stages I–III.

    [32][33][34][101][102][103]

    CD8 T cells

    were predictive for response to checkpoint inhibitors.

    [104]

    B cells

    1. better prognosis in lymph node negative, primary breast cancer patients including those with stages I–III, ER- breast cancer, highly proliferating luminal B breast cancer, and

    2. improved outcome in HR+ breast cancer.

    [101][102][105][106]

    Plasma cells

    better prognosis in ER- breast cancer and highly proliferating luminal B breast cancer.

    [106]

    TILs

    1. The frequency of TILs is usually high in the more aggressive breast cancer subtypes. TIL frequency was found to be a superior prognostic marker;

    2. were predictive for response to checkpoint inhibitors,

    3. was associated with improved responses to trastuzumab or lapatinib in HER2+ breast cancer.

    [33][104][106][107][108]

    Macrophages

    associate with survival in basal-like breast cancer.

    [103][108][109][110]

    MDSCs

    are correlated with poor survival in ER- tumors.

    [109][110]

    Neutrophils

    1. are associated with poor breast cancer survival;

    2. inhibiting leukotriene-generating enzyme arachidonate 5-lipoxygenase (Alox5) abrogates neutrophil pro-metastatic activity and consequently reduces metastasis.

    [108][111]

    NK cells

    were found significantly depleted from peripheral blood compared to pretreatment levels after chemotherapy.

    [102]

    myeloid dendritic cell

    improved outcome in HR+ breast cancer.

    [105]

    astrocytes

    may provide new opportunities for effective anti-metastasis therapies, especially for brain metastasis patients.

    [112]

    This entry is adapted from 10.3390/ijms22020636

    References

    1. Tawab Osman, N.; Khalaf, M.; Ibraheem, S. Assessment of CIP2A and ROCK-I expression and their prognostic value in breast cancer. Pol. J. Pathol. 2020, 71, 87–98.
    2. Global Burden of Disease Cancer Collaboration; Fitzmaurice, C.; Akinyemiju, T.F.; Al Lami, F.H.; Alam, T.; Alizadeh-Navaei, R.; Allen, C.; Alsharif, U.; Alvis-Guzman, N.; Amini, E.; et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2018, 4, 1553–1568.
    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, 394–424.
    4. DeSantis, C.E.; Fedewa, S.A.; Goding Sauer, A.; Kramer, J.L.; Smith, R.A.; Jemal, A. Breast cancer statistics, 2015: Convergence of incidence rates between black and white women. CA Cancer J. Clin. 2016, 66, 31–42.
    5. Perou, C.M.; Sorlie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; Rees, C.A.; Pollack, J.R.; Ross, D.T.; Johnsen, H.; Akslen, L.A.; et al. Molecular portraits of human breast tumours. Nature 2000, 406, 747–752.
    6. Prat, A.; Parker, J.S.; Karginova, O.; Fan, C.; Livasy, C.; Herschkowitz, J.I.; He, X.; Perou, C.M. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010, 12, R68.
    7. Garrido-Castro, A.C.; Lin, N.U.; Polyak, K. Insights into Molecular Classifications of Triple-Negative Breast Cancer: Improving Patient Selection for Treatment. Cancer Discov. 2019, 9, 176–198.
    8. Sorlie, T.; Perou, C.M.; Tibshirani, R.; Aas, T.; Geisler, S.; Johnsen, H.; Hastie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 2001, 98, 10869–10874.
    9. Sotiriou, C.; Neo, S.Y.; McShane, L.M.; Korn, E.L.; Long, P.M.; Jazaeri, A.; Martiat, P.; Fox, S.B.; Harris, A.L.; Liu, E.T. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl. Acad. Sci. USA 2003, 100, 10393–10398.
    10. Pace, L.E.; Keating, N.L. A systematic assessment of benefits and risks to guide breast cancer screening decisions. JAMA 2014, 311, 1327–1335.
    11. Drukteinis, J.S.; Mooney, B.P.; Flowers, C.I.; Gatenby, R.A. Beyond mammography: New frontiers in breast cancer screening. Am. J. Med. 2013, 126, 472–479.
    12. Bleyer, A.; Welch, H.G. Effect of three decades of screening mammography on breast-cancer incidence. N. Engl. J. Med. 2012, 367, 1998–2005.
    13. Oeffinger, K.C.; Fontham, E.T.; Etzioni, R.; Herzig, A.; Michaelson, J.S.; Shih, Y.C.; Walter, L.C.; Church, T.R.; Flowers, C.R.; LaMonte, S.J.; et al. Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update from the American Cancer Society. JAMA 2015, 314, 1599–1614.
    14. Hayes, D.F.; Isaacs, C.; Stearns, V. Prognostic factors in breast cancer: Current and new predictors of metastasis. J. Mammary Gland Biol. Neoplasia 2001, 6, 375–392.
    15. Weigel, M.T.; Dowsett, M. Current and emerging biomarkers in breast cancer: Prognosis and prediction. Endocr. Relat. Cancer 2010, 17, R245–R262.
    16. Loke, S.Y.; Lee, A.S.G. The future of blood-based biomarkers for the early detection of breast cancer. Eur. J. Cancer 2018, 92, 54–68.
    17. Voith von Voithenberg, L.; Crocetti, E.; Martos, C.; Dimitrova, N.; Giusti, F.; Randi, G.; Rooney, R.; Dyba, T.; Bettio, M.; Negrao Carvalho, R. Cancer registries—Guardians of breast cancer biomarker information: A systematic review. Int. J. Biol. Markers 2019, 34, 194–199.
    18. Hinestrosa, M.C.; Dickersin, K.; Klein, P.; Mayer, M.; Noss, K.; Slamon, D.; Sledge, G.; Visco, F.M. Shaping the future of bi-omarker research in breast cancer to ensure clinical relevance. Nat. Rev. Cancer 2007, 7, 309–315.
    19. Giridhar, K.V.; Liu, M.C. Available and emerging molecular markers in the clinical management of breast cancer. Expert Rev. Mol. Diagn. 2019, 19, 919–928.
    20. Strimbu, K.; Tavel, J.A. What are biomarkers? Curr. Opin. HIV AIDS 2010, 5, 463–466.
    21. Fleming, T.R.; Powers, J.H. Biomarkers and surrogate endpoints in clinical trials. Stat. Med. 2012, 31, 2973–2984.
    22. Nalejska, E.; Maczynska, E.; Lewandowska, M.A. Prognostic and predictive biomarkers: Tools in personalized oncology. Mol. Diagn. Ther. 2014, 18, 273–284.
    23. Kwa, M.; Makris, A.; Esteva, F.J. Clinical utility of gene-expression signatures in early stage breast cancer. Nat. Rev. Clin. Oncol. 2017, 14, 595–610.
    24. Li, G.; Hu, J.; Hu, G. Biomarker Studies in Early Detection and Prognosis of Breast Cancer. Adv. Exp. Med. Biol. 2017, 1026, 27–39.
    25. Eccles, S.A.; Aboagye, E.O.; Ali, S.; Anderson, A.S.; Armes, J.; Berditchevski, F.; Blaydes, J.P.; Brennan, K.; Brown, N.J.; Bryant, H.E.; et al. Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer. Breast Cancer Res. 2013, 15, R92.
    26. Berghuis, A.M.S.; Koffijberg, H.; Prakash, J.; Terstappen, L.W.; IJzerman, M.J. Detecting Blood-Based Biomarkers in Metastatic Breast Cancer: A Systematic Review of Their Current Status and Clinical Utility. Int. J. Mol. Sci. 2017, 18, 363.
    27. Simon, R. Sensitivity, Specificity, PPV, and NPV for Predictive Biomarkers. J. Natl. Cancer Inst. 2015, 107, djv153.
    28. Janes, H.; Pepe, M.S.; McShane, L.M.; Sargent, D.J.; Heagerty, P.J. The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment. J. Natl. Cancer Inst. 2015, 107, djv157.
    29. Fine, J.P.; Pencina, M. On the quantitative assessment of predictive biomarkers. J. Natl Cancer Inst. 2015, 107, djv187.
    30. Han, J.; Choi, Y.L.; Kim, H.; Choi, J.Y.; Lee, S.K.; Lee, J.E.; Choi, J.S.; Park, S.; Choi, J.S.; Kim, Y.D.; et al. MMP11 and CD2 as novel prognostic factors in hormone receptor-negative, HER2-positive breast cancer. Breast Cancer Res. Treat. 2017, 164, 41–56.
    31. Yang, B.; Chou, J.; Tao, Y.; Wu, D.; Wu, X.; Li, X.; Li, Y.; Chu, Y.; Tang, F.; Shi, Y.; et al. An assessment of prognostic immunity markers in breast cancer. NPJ Breast Cancer 2018, 4, 35.
    32. Denkert, C.; Wienert, S.; Poterie, A.; Loibl, S.; Budczies, J.; Badve, S.; Bago-Horvath, Z.; Bane, A.; Bedri, S.; Brock, J.; et al. Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: Results of the ring studies of the international immuno-oncology biomarker working group. Mod. Pathol. 2016, 29, 1155–1164.
    33. Alistar, A.; Chou, J.W.; Nagalla, S.; Black, M.A.; D’Agostino, R., Jr.; Miller, L.D. Dual roles for immune metagenes in breast cancer prognosis and therapy prediction. Genome Med. 2014, 6, 80.
    34. Liu, S.; Foulkes, W.D.; Leung, S.; Gao, D.; Lau, S.; Kos, Z.; Nielsen, T.O. Prognostic significance of FOXP3+ tumor-infiltrating lymphocytes in breast cancer depends on estrogen receptor and human epidermal growth factor receptor-2 expression status and concurrent cytotoxic T-cell infiltration. Breast Cancer Res. 2014, 16, 432.
    35. Ye, M.; Huang, T.; Ying, Y.; Li, J.; Yang, P.; Ni, C.; Zhou, C.; Chen, S. Detection of 14-3-3 sigma (sigma) promoter methylation as a noninvasive biomarker using blood samples for breast cancer diagnosis. Oncotarget 2017, 8, 9230–9242.
    36. Swellam, M.; Abdelmaksoud, M.D.; Sayed Mahmoud, M.; Ramadan, A.; Abdel-Moneem, W.; Hefny, M.M. Aberrant methylation of APC and RARbeta2 genes in breast cancer patients. IUBMB Life 2015, 67, 61–68.
    37. Yang, R.; Stocker, S.; Schott, S.; Heil, J.; Marme, F.; Cuk, K.; Chen, B.; Golatta, M.; Zhou, Y.; Sutter, C.; et al. The association between breast cancer and S100P methylation in peripheral blood by multicenter case-control studies. Carcinogenesis 2017, 38, 312–320.
    38. Yang, R.; Pfutze, K.; Zucknick, M.; Sutter, C.; Wappenschmidt, B.; Marme, F.; Qu, B.; Cuk, K.; Engel, C.; Schott, S.; et al. DNA methylation array analyses identified breast cancer-associated HYAL2 methylation in peripheral blood. Int. J. Cancer 2015, 136, 1845–1855.
    39. Manoochehri, M.; Jones, M.; Tomczyk, K.; Fletcher, O.; Schoemaker, M.J.; Swerdlow, A.J.; Borhani, N.; Hamann, U.; Borhani, N.; Hamann, U. DNA methylation of the long intergenic noncoding RNA 299 gene in triple-negative breast cancer: Results from a prospective study. Sci. Rep. 2020, 10, 11762.
    40. Fribbens, C.; O’Leary, B.; Kilburn, L.; Hrebien, S.; Garcia-Murillas, I.; Beaney, M.; Cristofanilli, M.; Andre, F.; Loi, S.; Loibl, S.; et al. Plasma ESR1 Mutations and the Treatment of Estrogen Receptor-Positive Advanced Breast Cancer. J. Clin. Oncol. 2016, 34, 2961–2968.
    41. Chandarlapaty, S.; Chen, D.; He, W.; Sung, P.; Samoila, A.; You, D.; Bhatt, T.; Patel, P.; Voi, M.; Gnant, M.; et al. Prevalence of ESR1 Mutations in Cell-Free DNA and Outcomes in Metastatic Breast Cancer: A Secondary Analysis of the BOLERO-2 Clinical Trial. JAMA Oncol. 2016, 2, 1310–1315.
    42. O’Leary, B.; Cutts, R.J.; Liu, Y.; Hrebien, S.; Huang, X.; Fenwick, K.; Andre, F.; Loibl, S.; Loi, S.; Garcia-Murillas, I.; et al. The Genetic Landscape and Clonal Evolution of Breast Cancer Resistance to Palbociclib plus Fulvestrant in the PALOMA-3 Trial. Cancer Discov. 2018, 8, 1390–1403.
    43. Vallecillo, L.B.; Chang, J.T.; Chen, K.; Moss, T.J.; Shaw, K.R.; Meric-Bernstam, F.; Eterovic, A.K.; Mills, G.B.; Mani, S.; Li, X.; et al. Whole exome sequencing of metaplastic breast cancer (MpBC): Effect of mutation status on survival. J. Clin. Oncol. 2017, 35, 1090.
    44. Wang, J.; Wang, Y.; Xing, P.; Liu, Q.; Zhang, C.; Sui, Y.; Wu, C. Development and validation of a hypoxia-related prognostic signature for breast cancer. Oncol Lett. 2020, 20, 1906–1914.
    45. Liu, X.P.; Hou, J.; Chen, C.; Guan, L.; Hu, H.K.; Li, S. A DNA Methylation-Based Panel for the Prognosis and Dagnosis of Patients With Breast Cancer and Its Mechanisms. Front. Mol. Biosci. 2020, 7, 118.
    46. Adhami, M.; Haghdoost, A.A.; Sadeghi, B.; Malekpour Afshar, R. Candidate miRNAs in human breast cancer biomarkers: A systematic review. Breast Cancer 2018, 25, 198–205.
    47. Motawi, T.M.; Sadik, N.A.; Shaker, O.G.; El Masry, M.R.; Mohareb, F. Study of microRNAs-21/221 as potential breast cancer biomarkers in Egyptian women. Gene 2016, 590, 210–219.
    48. Thakur, S.; Grover, R.K.; Gupta, S.; Yadav, A.K.; Das, B.C. Identification of Specific miRNA Signature in Paired Sera and Tissue Samples of Indian Women with Triple Negative Breast Cancer. PLoS ONE 2016, 11, e0158946.
    49. Abdulhussain, M.M.; Hasan, N.A.; Hussain, A.G. Interrelation of the Circulating and Tissue MicroRNA-21 with Tissue PDCD4 Expression and the Invasiveness of Iraqi Female Breast Tumors. Indian J. Clin. Biochem. 2019, 34, 26–38.
    50. Hannafon, B.N.; Trigoso, Y.D.; Calloway, C.L.; Zhao, Y.D.; Lum, D.H.; Welm, A.L.; Zhao, Z.J.; Blick, K.E.; Dooley, W.C.; Ding, W.Q. Plasma exosome microRNAs are indicative of breast cancer. Breast Cancer Res. 2016, 18, 90.
    51. Shimomura, A.; Shiino, S.; Kawauchi, J.; Takizawa, S.; Sakamoto, H.; Matsuzaki, J.; Ono, M.; Takeshita, F.; Niida, S.; Shimizu, C.; et al. Novel combination of serum microRNA for detecting breast cancer in the early stage. Cancer Sci. 2016, 107, 326–334.
    52. Freres, P.; Wenric, S.; Boukerroucha, M.; Fasquelle, C.; Thiry, J.; Bovy, N.; Struman, I.; Geurts, P.; Collignon, J.; Schroeder, H.; et al. Circulating microRNA-based screening tool for breast cancer. Oncotarget 2016, 7, 5416–5428.
    53. Lyng, M.B.; Kodahl, A.R.; Binder, H.; Ditzel, H.J. Prospective validation of a blood-based 9-miRNA profile for early detection of breast cancer in a cohort of women examined by clinical mammography. Mol. Oncol. 2016, 10, 1621–1626.
    54. Han, S.; Li, P.; Wang, D.; Yan, H. Dysregulation of serum miR-1204 and its potential as a biomarker for the diagnosis and prognosis of breast cancer. Rev. Assoc. Med. Bras. (1992) 2020, 66, 732–736.
    55. Fahim, S.A.; Abdullah, M.S.; Espinoza-Sanchez, N.A.; Hassan, H.; Ibrahim, A.M.; Ahmed, S.H.; Shakir, G.; Badawy, M.A.; Zakhary, N.I.; Greve, B.; et al. Inflammatory Breast Carcinoma: Elevated microRNA miR-181b-5p and Reduced miR-200b-3p, miR-200c-3p, and miR-203a-3p Expression as Potential Biomarkers with Diagnostic Value. Biomolecules 2020, 10, 1059.
    56. Shahabi, A.; Naghili, B.; Ansarin, K.; Montazeri, V.; Zarghami, N. miR-140 and miR-196a as Potential Biomarkers in Breast Cancer Patients. Asian Pac. J. Cancer Prev. 2020, 21, 1913–1918.
    57. Ma, X.; Dong, W.; Su, Z.; Zhao, L.; Miao, Y.; Li, N.; Zhou, H.; Jia, L. Functional roles of sialylation in breast cancer progression through miR-26a/26b targeting ST8SIA4. Cell Death Dis. 2016, 7, e2561.
    58. Zhang, L.; Du, Y.; Xu, S.; Jiang, Y.; Yuan, C.; Zhou, L.; Ma, X.; Bai, Y.; Lu, J.; Ma, J. DEPDC1, negatively regulated by miR-26b, facilitates cell proliferation via the up-regulation of FOXM1 expression in TNBC. Cancer Lett. 2019, 442, 242–251.
    59. Mihelich, B.L.; Dambal, S.; Lin, S.; Nonn, L. miR-182, of the miR-183 cluster family, is packaged in exosomes and is detected in human exosomes from serum, breast cells and prostate cells. Oncol. Lett. 2016, 12, 1197–1203.
    60. Liu, F.; Liu, Y.; Shen, J.; Zhang, G.; Han, J. MicroRNA-224 inhibits proliferation and migration of breast cancer cells by down-regulating Fizzled 5 expression. Oncotarget 2016, 7, 49130–49142.
    61. Yan, G.; Li, Y.; Zhan, L.; Sun, S.; Yuan, J.; Wang, T.; Yin, Y.; Dai, Z.; Zhu, Y.; Jiang, Z.; et al. Decreased miR-124-3p promoted breast cancer proliferation and metastasis by targeting MGAT5. Am. J. Cancer Res. 2019, 9, 585–596.
    62. Ao, X.; Nie, P.; Wu, B.; Xu, W.; Zhang, T.; Wang, S.; Chang, H.; Zou, Z. Decreased expression of microRNA-17 and mi-croRNA-20b promotes breast cancer resistance to taxol therapy by upregulation of NCOA3. Cell Death Dis. 2016, 7, e2463.
    63. Sha, L.Y.; Zhang, Y.; Wang, W.; Sui, X.; Liu, S.K.; Wang, T.; Zhang, H. MiR-18a upregulation decreases Dicer expression and confers paclitaxel resistance in triple negative breast cancer. Eur. Rev. Med. Pharmacol. Sci. 2016, 20, 2201–2208.
    64. Jayaraj, R.; Nayagam, S.G.; Kar, A.; Sathyakumar, S.; Mohammed, H.; Smiti, M.; Sabarimurugan, S.; Kumarasamy, C.; Priyadharshini, T.; Gothandam, K.M.; et al. Clinical Theragnostic Relationship between Drug-Resistance Specific miRNA Expressions, Chemotherapeutic Resistance, and Sensitivity in Breast Cancer: A Systematic Review and Meta-Analysis. Cells 2019, 8, 1250.
    65. Liu, B.; Su, F.; Chen, M.; Li, Y.; Qi, X.; Xiao, J.; Li, X.; Liu, X.; Liang, W.; Zhang, Y.; et al. Serum miR-21 and miR-125b as markers predicting neoadjuvant chemotherapy response and prognosis in stage II/III breast cancer. Hum. Pathol. 2017, 64, 44–52.
    66. Zheng, R.; Pan, L.; Gao, J.; Ye, X.; Chen, L.; Zhang, X.; Tang, W.; Zheng, W. Prognostic value of miR-106b expression in breast cancer patients. J. Surg. Res. 2015, 195, 158–165.
    67. Masuda, T.; Shinden, Y.; Noda, M.; Ueo, H.; Hu, Q.; Yoshikawa, Y.; Tsuruda, Y.; Kuroda, Y.; Ito, S.; Eguchi, H.; et al. Circulating Pre-microRNA-488 in Peripheral Blood Is a Potential Biomarker for Predicting Recurrence in Breast Cancer. Anticancer Res. 2018, 38, 4515–4523.
    68. Miao, Y.; Zheng, W.; Li, N.; Su, Z.; Zhao, L.; Zhou, H.; Jia, L. MicroRNA-130b targets PTEN to mediate drug resistance and proliferation of breast cancer cells via the PI3K/Akt signaling pathway. Sci. Rep. 2017, 7, 41942.
    69. Zhao, M.; Ang, L.; Huang, J.; Wang, J. MicroRNAs regulate the epithelial-mesenchymal transition and influence breast cancer invasion and metastasis. Tumour Biol. 2017, 39, 1010428317691682.
    70. Seo, S.; Moon, Y.; Choi, J.; Yoon, S.; Jung, K.H.; Cheon, J.; Kim, W.; Kim, D.; Lee, C.H.; Kim, S.W.; et al. The GTP binding activity of transglutaminase 2 promotes bone metastasis of breast cancer cells by downregulating microRNA-205. Am. J. Cancer Res. 2019, 9, 597–607.
    71. Wang, L.; Kang, F.B.; Wang, J.; Yang, C.; He, D.W. Downregulation of miR-205 contributes to epithelial-mesenchymal transition and invasion in triple-negative breast cancer by targeting HMGB1-RAGE signaling pathway. Anticancer Drugs 2019, 30, 225–232.
    72. Lin, C.; Gao, B.; Yan, X.; Lei, Z.; Chen, K.; Li, Y.; Zeng, Q.; Chen, Z.; Li, H. MicroRNA 628 suppresses migration and invasion of breast cancer stem cells through targeting SOS1. Onco Targets Ther. 2018, 11, 5419–5428.
    73. Yin, W.B.; Yan, M.G.; Fang, X.; Guo, J.J.; Xiong, W.; Zhang, R.P. Circulating circular RNA hsa_circ_0001785 acts as a diagnostic biomarker for breast cancer detection. Clin. Chim. Acta 2018, 487, 363–368.
    74. Lu, L.; Sun, J.; Shi, P.; Kong, W.; Xu, K.; He, B.; Zhang, S.; Wang, J. Identification of circular RNAs as a promising new class of diagnostic biomarkers for human breast cancer. Oncotarget 2017, 8, 44096–44107.
    75. Tang, Y.Y.; Zhao, P.; Zou, T.N.; Duan, J.J.; Zhi, R.; Yang, S.Y.; Yang, D.C.; Wang, X.L. Circular RNA hsa_circ_0001982 Promotes Breast Cancer Cell Carcinogenesis Through Decreasing miR-143. DNA Cell Biol. 2017, 36, 901–908.
    76. Wang, H.; Xiao, Y.; Wu, L.; Ma, D. Comprehensive circular RNA profiling reveals the regulatory role of the circRNA-000911/miR-449a pathway in breast carcinogenesis. Int. J. Oncol. 2018, 52, 743–754.
    77. Liang, H.F.; Zhang, X.Z.; Liu, B.G.; Jia, G.T.; Li, W.L. Circular RNA circ-ABCB10 promotes breast cancer proliferation and progression through sponging miR-1271. Am. J. Cancer Res. 2017, 7, 1566–1576.
    78. He, R.; Liu, P.; Xie, X.; Zhou, Y.; Liao, Q.; Xiong, W.; Li, X.; Li, G.; Zeng, Z.; Tang, H. circGFRA1 and GFRA1 act as ceRNAs in triple negative breast cancer by regulating miR-34a. J. Exp. Clin. Cancer Res. 2017, 36, 145.
    79. Gao, D.; Zhang, X.; Liu, B.; Meng, D.; Fang, K.; Guo, Z.; Li, L. Screening circular RNA related to chemotherapeutic resistance in breast cancer. Epigenomics 2017, 9, 1175–1188.
    80. Chen, R.; Jiang, C.; Zhu, Q.; You, S.; Li, Y.; Li, S.; Ding, L.; Meng, H.; Yang, Y.; Zha, X.; et al. Combining the tumor abnormal protein test with tests for carcinoembryonic antigens, cancer antigen 15-3, and/or cancer antigen 125 significantly increased their diagnostic sensitivity for breast cancer. Medicine (Baltim.) 2020, 99, e21231.
    81. Ishibashi, Y.; Ohtsu, H.; Ikemura, M.; Kikuchi, Y.; Niwa, T.; Nishioka, K.; Uchida, Y.; Miura, H.; Aikou, S.; Gunji, T.; et al. Serum TFF1 and TFF3 but not TFF2 are higher in women with breast cancer than in women without breast cancer. Sci. Rep. 2017, 7, 4846.
    82. Ma, J.; Kong, Y.; Nan, H.; Qu, S.; Fu, X.; Jiang, L.; Wang, W.; Guo, H.; Zhao, S.; He, J.; et al. Pleiotrophin as a potential biomarker in breast cancer patients. Clin. Chim. Acta 2017, 466, 6–12.
    83. Lu, M.; Ju, S.; Shen, X.; Wang, X.; Jing, R.; Yang, C.; Chu, H.; Cong, H. Combined detection of plasma miR-127-3p and HE4 improves the diagnostic efficacy of breast cancer. Cancer Biomark. 2017, 18, 143–148.
    84. Lawicki, S.; Zajkowska, M.; Glazewska, E.K.; Bedkowska, G.E.; Szmitkowski, M. Plasma levels and diagnostic utility of VEGF, MMP-9, and TIMP-1 in the diagnosis of patients with breast cancer. Onco Targets Ther. 2016, 9, 911–919.
    85. Garczyk, S.; von Stillfried, S.; Antonopoulos, W.; Hartmann, A.; Schrauder, M.G.; Fasching, P.A.; Anzeneder, T.; Tannapfel, A.; Ergonenc, Y.; Knuchel, R.; et al. AGR3 in breast cancer: Prognostic impact and suitable serum-based biomarker for early cancer detection. PLoS ONE 2015, 10, e0122106.
    86. Giussani, M.; Landoni, E.; Merlino, G.; Turdo, F.; Veneroni, S.; Paolini, B.; Cappelletti, V.; Miceli, R.; Orlandi, R.; Triulzi, T.; et al. Extracellular matrix proteins as diagnostic markers of breast carcinoma. J. Cell. Physiol. 2018, 233, 6280–6290.
    87. Yigitbasi, T.; Calibasi-Kocal, G.; Buyukuslu, N.; Atahan, M.K.; Kupeli, H.; Yigit, S.; Tarcan, E.; Baskin, Y. An efficient biomarker panel for diagnosis of breast cancer using surface-enhanced laser desorption ionization time-of-flight mass spectrometry. Biomed. Rep. 2018, 8, 269–274.
    88. Zuo, X.; Chen, L.; Liu, L.; Zhang, Z.; Zhang, X.; Yu, Q.; Feng, L.; Zhao, X.; Qin, T. Identification of a panel of complex auto-antigens (LGALS3, PHB2, MUC1, and GK2) in combination with CA15-3 for the diagnosis of early-stage breast cancer. Tumour Biol. 2016, 37, 1309–1317.
    89. Kostianets, O.; Shyyan, M.; Antoniuk, S.V.; Filonenko, V.; Kiyamova, R. Panel of SEREX-defined antigens for breast cancer autoantibodies profile detection. Biomarkers 2017, 22, 149–156.
    90. Henderson, M.C.; Hollingsworth, A.B.; Gordon, K.; Silver, M.; Mulpuri, R.; Letsios, E.; Reese, D.E. Integration of Serum Protein Biomarker and Tumor Associated Autoantibody Expression Data Increases the Ability of a Blood-Based Proteomic Assay to Identify Breast Cancer. PLoS ONE 2016, 11, e0157692.
    91. Husing, A.; Fortner, R.T.; Kuhn, T.; Overvad, K.; Tjonneland, A.; Olsen, A.; Boutron-Ruault, M.C.; Severi, G.; Fournier, A.; Boeing, H.; et al. Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort. Clin. Cancer Res. 2017, 23, 4181–4189.
    92. Zhang, Z.; Sun, T.; Chen, Y.; Gong, S.; Sun, X.; Zou, F.; Yang, L.; Chen, L.L. CCL25/CCR9 Signal Promotes Migration and Invasion in Hepatocellular and Breast Cancer Cell Lines. DNA Cell Biol. 2016, 35, 348–357.
    93. Kitamura, T.; Pollard, J.W. Therapeutic potential of chemokine signal inhibition for metastatic breast cancer. Pharmacol. Res. 2015, 100, 266–270.
    94. Zhao, C.; Zheng, S.; Yan, Z.; Deng, Z.; Wang, R.; Zhang, B. CCL18 promotes the invasion and metastasis of breast cancer through Annexin A2. Oncol. Rep. 2020, 43, 571–580.
    95. Kim, S.J.; Ju, J.S.; Kang, M.H.; Eun, J.W.; Kim, Y.H.; Raninga, P.V.; Khanna, K.K.; Gyorffy, B.; Pack, C.G.; Han, H.D.; et al. RNA-binding protein NONO contributes to cancer cell growth and confers drug resistance as a theranostic target in TNBC. Theranostics 2020, 10, 7974–7992.
    96. Fan, J.; Tea, M.K.; Yang, C.; Ma, L.; Meng, Q.H.; Hu, T.Y.; Singer, C.F.; Ferrari, M. Profiling of Cross-Functional Peptidases Regulated Circulating Peptides in BRCA1 Mutant Breast Cancer. J. Proteome Res. 2016, 15, 1534–1545.
    97. Le Cornet, C.; Walter, B.; Sookthai, D.; Johnson, T.S.; Kuhn, T.; Herpel, E.; Kaaks, R.; Fortner, R.T. Circulating 27-hydroxycholesterol and breast cancer tissue expression of CYP27A1, CYP7B1, LXR-beta, and ERbeta: Results from the EPIC-Heidelberg cohort. Breast Cancer Res. 2020, 22, 23.
    98. Lu, D.L.; Le Cornet, C.; Sookthai, D.; Johnson, T.S.; Kaaks, R.; Fortner, R.T. Circulating 27-Hydroxycholesterol and Breast Cancer Risk: Results From the EPIC-Heidelberg Cohort. J. Natl. Cancer Inst. 2019, 111, 365–371.
    99. Moon, P.G.; Lee, J.E.; Cho, Y.E.; Lee, S.J.; Chae, Y.S.; Jung, J.H.; Kim, I.S.; Park, H.Y.; Baek, M.C. Fibronectin on circulating extracellular vesicles as a liquid biopsy to detect breast cancer. Oncotarget 2016, 7, 40189–40199.
    100. 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 Detec-tion. Clin. Cancer Res. 2016, 22, 1757–1766.
    101. Mao, Y.; Qu, Q.; Chen, X.; Huang, O.; Wu, J.; Shen, K. The Prognostic Value of Tumor-Infiltrating Lymphocytes in Breast Cancer: A Systematic Review and Meta-Analysis. PLoS ONE 2016, 11, e0152500.
    102. Verma, R.; Foster, R.E.; Horgan, K.; Mounsey, K.; Nixon, H.; Smalle, N.; Hughes, T.A.; Carter, C.R. Lymphocyte depletion and repopulation after chemotherapy for primary breast cancer. Breast Cancer Res. 2016, 18, 10.
    103. Garcia-Martinez, E.; Gil, G.L.; Benito, A.C.; Gonzalez-Billalabeitia, E.; Conesa, M.A.; Garcia Garcia, T.; Garcia-Garre, E.; Vicente, V.; Ayala de la Pena, F. Tumor-infiltrating immune cell profiles and their change after neoadjuvant chemotherapy predict re-sponse and prognosis of breast cancer. Breast Cancer Res. 2014, 16, 488.
    104. Loi, S.; Adams, S.; Schmid, P.; Cortés, J.; Cescon, D.W.; Winer, E.P.; Toppmeyer, D.L.; Rugo, H.S.; De Laurentiis, M.; Nanda, R.; et al. Relationship between Tumor Infiltrating Lymphocyte (TIL) Levels and Response to Pembrolizumab (Pembro) in Metastatic Triple-Negative Breast Cancer (mTNBC): Results from KEYNOTE-086. In Proceedings of the ESMO Annul Meeting, Madrid, Spain, 8–12 September 2017; Elsevier Inc.: Amsterdam, The Netherlands, 2017.
    105. Kwon, M.J. Emerging immune gene signatures as prognostic or predictive biomarkers in breast cancer. Arch. Pharm. Res. 2019, 42, 947–961.
    106. Nagalla, S.; Chou, J.W.; Willingham, M.C.; Ruiz, J.; Vaughn, J.P.; Dubey, P.; Lash, T.L.; Hamilton-Dutoit, S.J.; Bergh, J.; Sotiriou, C.; et al. Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis. Genome Biol. 2013, 14, R34.
    107. Salgado, R.; Denkert, C.; Demaria, S.; Sirtaine, N.; Klauschen, F.; Pruneri, G.; Wienert, S.; Van den Eynden, G.; Baehner, F.L.; Penault-Llorca, F.; et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: Recommendations by an International TILs Working Group 2014. Ann. Oncol. 2015, 26, 259–271.
    108. Mohammed, Z.M.; Going, J.J.; Edwards, J.; Elsberger, B.; McMillan, D.C. The relationship between lymphocyte subsets and clinico-pathological determinants of survival in patients with primary operable invasive ductal breast cancer. Br. J. Cancer 2013, 109, 1676–1684.
    109. Diaz-Montero, C.M.; Salem, M.L.; Nishimura, M.I.; Garrett-Mayer, E.; Cole, D.J.; Montero, A.J. Increased circulating mye-loid-derived suppressor cells correlate with clinical cancer stage, metastatic tumor burden, and doxorubicin-cyclophosphamide chemotherapy. Cancer Immunol. Immunother. 2009, 58, 49–59.
    110. Ali, H.R.; Chlon, L.; Pharoah, P.D.; Markowetz, F.; Caldas, C. Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study. PLoS Med. 2016, 13, e1002194.
    111. Wculek, S.K.; Malanchi, I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature 2015, 528, 413–417.
    112. Zhang, L.; Zhang, S.; Yao, J.; Lowery, F.J.; Zhang, Q.; Huang, W.C.; Li, P.; Li, M.; Wang, X.; Zhang, C.; et al. Microenvironment-induced PTEN loss by exosomal microRNA primes brain metastasis outgrowth. Nature 2015, 527, 100–104.
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