Biomarkers for Breast Cancer: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Peter Tang and Version 1 by Hsing-Ju Wu.

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.

  • breast cancer
  • biomarker
  • diagnosis
  • prognosis
  • treatment

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][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][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][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][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][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][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][18][19]. They must be validated by human samples to ensure that they reflect the clinical outcome [20,21][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][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][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][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.

Type Biomarkers Clinical Value Clinical Validation/Research Design References

DNA

Immune response-related genes (BTN3A2, CD2 and TRBC1)

may be used to identify patients with a good prognosis in HR−/HER2+ breast cancer.

Measured the tissues from 819 breast cancer patients.

[30]

Immunity genes (APOBEC3G, CCL5, CCR2, CD2, CD27, CD3D, CD52, CORO1A, CXCL9, GZMA, GZMK, HLA-DMA, IL2RG, LCK, PRKCB, PTPRC, and SH2D1A)

immunity gene expression was an important parameter for prognosis.

Tested on 225 breast tumor FFPE tissues.

[31]

T helper type-1 gene signatures (IFNG, STAT1, GRZM, CXCL9)

are correlated with favorable clinical outcome, particularly in ER- tumors.

  

[32,33,34]

[32][33][34]

methylated 14-3-3 σ

as a blood-based biomarker for breast cancer diagnosis.

meta-analysis

[35]

methylated APC and RARβ2

might be valuable serum-based molecular markers for early detection of early-stage breast cancer, low grade tumors and TNBC.

Tested on serum samples from 121 breast cancer patients, 79 patients with benign breast diseases, and 66 healthy controls.

[36]

S100P and HYAL2 hypomethylation

as breast cancer biomarkers for early stage detection.

S100P: Validation I: 235 familial breast cancer cases and 206 controls; Validation II: 189 sporadic breast cancer cases and 189 controls; Validation III: 156 sporadic breast cancer cases and 151 controls.

HYAL2

: first validation round: 338 breast cancer cases and 507 controls; second validation round: 189 breast cancer cases and 189 controls.

[37,38]

[37][38]

long noncoding RNA 299 gene (LINC00299) methylation

for early detection of TNBC in young women.

Examined blood samples of 154 TNBC cases and 159 breast cancer-free matched controls.

[39]

ESR1 mutations

1. ESR1 Y537S mutation promotes resistance to fulvestrant.

2. may have clinical utility in directing further endocrine therapy.

3.

ESR1

mutations are prevalent in ER-positive aromatase inhibitor-treated metastatic breast cancer predicting its prognosis.

1. Testing the blood samples of 195 patients from the PALOMA-3 cohort;

2. In the SoFEA trial, plasma samples of 162 patients were tested; in the PALOMA3 trial, plasma samples of 360 patients were tested.

3. In the BOLERO-2 cohort, 541 plasma samples were examined.

[40,41,42]

[40][41][42]

TP53 mutation

associated with better prognosis in metaplastic breast cancer with increased RFS and OS.

Examined the clinical outcomes data of 52 archived samples.

[43]

a 14-gene prognostic signature (PFKL, P4HA2, GRHPR, SDC3, PPP1R15A, SIAH2, NDRG1, BTG1, TPD52, MAFF, ISG20, LALBA, ERRFI1, and VHL)

could serve as a potential prognostic biomarker for breast cancer.

Clinical data from 1097 cases were obtained from the TCGA database. 113 adjacent normal samples and 1039 breast cancer patients were followed-up for

≥1 month.

[44]

28-CpG based methylation panel

could independently predict the overall survival of breast cancer patients. Patients with high methylation risk were associated with tumor heterogeneity

and poor survival.

The DNA methylation profile of The

Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA)

included a total of 890 breast cancer samples.

A total of 62, 118, 188, 70, and 58 breast cancer samples were included in GSE37754, GSE72245, GSE75067, GSE78754, and GSE72251. 40 normal breast samples and 80 breast cancer samples in GSE666952.

[45]

MicroRNAs

miR-21 and/or miR-221

can be successfully applied as breast cancer biomarkers.

Tested the sera of 50 patients with breast cancer, 25 fibroadenoma, and 25 healthy controls.

[46,47]

[46][47]

six miRNA signature, miR-21, miR-221, miR-210, miR-195, miR-145, and let-7a

for early detection of TNBC.

Examined 85 paired tumor tissues and sera with an equal number of adjacent normal tissue margins and normal sera from healthy women and 15 benign fibroadenomas.

[48]

miR-21

promotes the transformation and development of breast cancer.

Examined on blood samples of 30 female patients with breast tumors and 30 with benign breast lesions

[49]

Exosomal miR-1246 and miR-21

for detection of breast cancer.

Tested the plasma of 16 patients with breast cancer and 16 healthy control subjects.

[50]

five-miRNA signature, miR-1246, miR-1307-3p, miR-4634, miR-6861-5p, and miR-6876-5p

for detection of early stage breast cancer.

Tested 1280 serum samples of breast cancer patients, 2836 serum samples from non-cancer controls, 451 from patients with other types of cancers, and 63 from patients with non-breast benign diseases.

[51]

The eight-marker signature (miR-16, let7d, miR-103, miR-107, miR-148a, let-7i, miR-19b, and miR-22)

for early detection of breast cancer including younger women.

Tested plasma from 127 sporadic breast cancer cases and 80 healthy controls.

[52]

a 9-miRNA profile

for early detection of breast cancer.

Examined 116 blood samples including 36 with breast cancer.

[53]

miR-1204

could be a novel prognostic/diagnostic biomarker for breast cancer patients.

Tested sera from 144 breast cancer patients and 38 healthy controls.

[54]

combination of miR-181b-5p, miR-200b-3p, miR-200c-3p, and miR-203a-3p

could be potential diagnostic biomarkers for inflammatory breast cancer.

Examined tissue specimens of 18 non- inflammatory breast cancer and 17 inflammatory breast cancer patients.

[55]

miR-140 and miR-196a

both miR-140 and miR-196a are promising biomarkers for the diagnosis of breast cancer.

Tested 110 cases of breast cancer and their adjacent non-tumor tissues.

[56]

miR-26a/26b

may be useful markers of the progression of breast cancer.

Examined 29 pairs of fresh breast cancer and

adjacent tissues.

[57]

miR-26b

inhibited TNBC cell proliferation and tumor growth.

-

[58]

miR-182

contributed to cell progression.

45 patients with breast cancer.

[59]

miR-224

inhibited proliferation and migration of breast cancer cells.

Examined serum samples from 45 patients with breast cancer.

[60]

miR-124-3p

reduced breast cancer cell proliferation and metastasis.

Tested 30 breast cancer and normal breast tissues.

[61]

miRNA-17 and miRNA-20b

resistance to taxol in breast cancer patients increased with the loss of miRNA-17 and miRNA-20b.

55 pairs of breast cancer tissues and adjacent normal tissues were examined.

[62]

miR-18a

overexpression directly led to Dicer repression and confers paclitaxel resistance in TNBC.

Tested 20 TNBC patient tissues.

[63]

miR-90b, 130a, 200b, and 452

contribute to chemoresistance.

-

[64]

miRNAs 221 and 222

chemoresistance to fulvestrant, doxorubicin, or trastuzumab.

-

[64]

miRNA 320a

chemoresistance to paclitaxel.

-

[64]

miRNAs let-7, 181a and 145

chemoresistance to doxorubicin, tamoxifen, or epirubicin.

-

[64]

miRNA 125b

chemoresistance to tamoxifen, letrozole, anastrazole or fulvestrant.

-

[64]

miR200c and miR489

downregulation of miR200c and miR489 were correlated with better prognosis.

-

[64]

miR484 and miR4443

upregulation of miR484 and miR4443 were associated with better prognosis.

-

[64]

miR520h and miR125b

upregulation of miR520h and miR125b were correlated with poor prognosis.

-

[64]

miR125b and miR21

could be novel, noninvasive predictive markers for neoadjuvant chemotherapy response and prognosis in breast cancer.

Examined 118 stage II/III breast cancer patients and 30 healthy adult women.

[65]

miR-106b

is a putative plasma marker for risk assessment in patients with breast cancer.

Examined the tissue and plasma samples from 173 patients with primary breast cancer and 50 women with fibroadenoma.

[66]

pre-miR-488

could be a novel prognostic biomarker for predicting recurrence in breast cancer patients.

Tested the blood from 356 female patients with breast cancer without distant metastases, preoperative therapy or previous treatment for various cancers, 330 invasive ductal carcinomas (IDC),

26 were ductal carcinomas in situ (DCIS), and 11 healthy volunteers.

[67]

miR-130b

contributes to MDR through PI3K/Akt signaling pathway.

Tested 29 pairs of breast cancer tissues and their adjacent noncancerous tissues.

[68]

miR-9

inhibit metastasis.

-

[69]

miR-205

inhibit metastasis.

Tested on 40 pairs of TNBC and their adjacent normal breast tissues.

[70,71]

[70][71]

miR-628

inhibit metastasis.

-

[72]

cicRNAs

hsa_circ_0001785

the potential diagnostic biomarker for breast cancer.

Examined the plasma of 57 breast cancer patients and 17 age-matched healthy individuals.

[73]

Combination of hsa_circ_006054, hsa_circ_100219, and hsa_circ_406697

may be diagnostic biomarker for breast cancer.

Tested 51 breast cancer and adjacent normal tissues.

[74]

hsa_circ_0001982

hsa_circ_0001982 knockdown suppressed breast cancer cell proliferation and invasion and induced apoptosis by targeting miR-143.

Examined 29 breast cancer tissues and adjacent normal tissues.

[75]

circRNA-000911

enhanced expression of circRNA-000911 suppressed cell proliferation, migration and invasion, and promoted the apoptosis of breast cancer cells.

Human circRNA microarray analysis.

[76]

circ-ABCB10

circ-ABCB10 knockdown suppressed the proliferation and increased apoptosis of breast cancer cells.

Tested 36 cancer and adjacent noncancerous tissues.

[77]

circGFRA1

Knockdown of circGFRA1 inhibited proliferation and promoted apoptosis in TNBC.

Examined 51 TNBC tissues and their paired adjacent normal tissues.

[78]

circ_0006528

may play a role in breast cancer chemoresistance.

-

[79]

Protein

4-test combination of TAP + CEA + CA125 + CA15-3

higher sensitivity than the traditional test, i.e., CEA, CA125, or CA15-3 and may be auxiliary used in early screening.

Tested on blood of 261 women with operable benign breast disease and 348 with breast cancer.

[80]

TFF1, TFF2 and TFF3

for breast cancer screening.

Examined sera in 94 breast cancer patients and 84 health check females, and breast cancer tissues.

[81]

Pleiotrophin (PTN)

PTN could be a potential biomarker for the presence of breast cancer.

Tested sera in 105 breast cancer patients and 40 healthy volunteers using ELISA. In addition, PTN expression was examined in 80 BC tissues in a nested case-control study by immunohistochemistry.

[82]

Combination of miR-127-3p and HE4

Greatly improved the sensitivity of breast cancer diagnosis and may be a candidate biomarker for early detection and diagnosis of breast cancer.

Examined plasma in 102 patients with breast cancer, and 87 patients with benign breast tumors and 90 healthy volunteers as control.

[83]

Combination of VEGF and CA 15-3

showed the highest usefulness in the diagnosis of early breast cancer.

Tested plasma in 100 breast cancer patients, and 50 patients with benign breast tumors, and 50 healthy women as control.

[84]

Combination of AGR3 and AGR2

showed the potential usability of AGR3 and AGR2 as biomarkers for blood-based early detection of human breast cancer.

Examined 190 breast carcinomas and 39

normal breast tissues; 40 breast cancer and 40 healthy serum samples.

[85]

COL11A1, COMP, and COL10A1

may be useful in diagnostic assessment for breast cancers

Discovery dataset:

50 healthy donors, 42 patients with benign breast disease, and 52 patients with invasive breast cancer;

validation cohort:

52 healthy donors, 49 benign breast disease, and 66 invasive breast cancer.

[86]

CA15-3 included in the diagnostic panel constituted of 4 protein peaks [m/z 3972, 6850, 8115 (Bc2), and 8949 (Bc3)]

distinguished invasive ductal carcinoma from healthy controls and benign breast diseases.

Tested the sera from 62 patients with invasive ductal carcinoma, and 47 non-cancerous individuals (16 healthy

controls and 31 patients with benign breast diseases).

[87]

Serum autoantigens (LGALS3, PHB2, MUC1 and GK2) in combination with CA 15-3

had better diagnostic values compared with anti-CA 15-3 alone for early-stage breast cancer.

Examined the sera from 100 breast cancer patients and 50 healthy subjects.

[88]

A combination of six antigens, RAD50, PARD3, SPP1, NY-BR-62, and NY-CO-58

could discriminate breast cancer patients from healthy controls.

Tested the sera of 112 patients with breast cancer and 35 patients

with no neoplasm (control group);

Cancer and non-cancerous breast tissue samples were obtained from 17 female patients with primary breast carcinomas and 7 patients with fibrocystic disease.

[89]

A combination of serum protein biomarkers and tumor associated autoantibodies

the benefit of the integration of SPB and TAAb for detecting breast cancer.

Using a retrospective cohort of sera from 18 participants with no breast diseases, 92 participants with benign breast diseases, and 100 participants with breast cancers.

[90]

Sex hormones: estradiol, testosterone, and SHBG

Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification.

Tested blood of 1217 breast cancer cases (430 pre- and 787 postmenopausal) and 1976 matched

Controls.

[91]

CCL25/CCR9 chemokine signaling

promotes migration and invasion in different cell lines by selective regulating several EMT markers.

-

[92]

CCL18-PITPNM3 chemokine signaling

promotes the invasion and metastasis of breast cancer through the PI3K/Akt/GSK3β/Snail pathway.

-

[93,94]

[93][94]

TNBC-specific RBP, NONO

NONO is highly expressed in TNBC and is associated with poor patient outcomes, a potential therapeutic target in TNBC.

Tested on tissue microarray.

[95]

Peptides KNG1K438-R457 and C3fS1304-R1320

differentiate BRCA1 mutant breast cancer from sporadic B breast cancer and cancer-free BRCA1 mutant carriers.

Examined on serum samples from 55 carriers of hereditary BRCA1 mutations, of whom 28 were diagnosed with breast cancer, and 27 remained cancer-free, 39 were diagnosed with sporadic breast cancer, and 38 were healthy controls.

[96]

Lipid

27-hydroxycholesterol

may offer a novel breast cancer risk strategy.

Tested on sera of 530 incident invasive breast cancer cases and 1036 control participants from Heidelberg cohort of EPIC.

[97,98]

[97][98]

Exosome

fibronectin

This liquid biopsy to detect fibronectin on circulating extracellular vesicles could be a promising method to detect early breast cancer.

Tested on plasma samples from 70 disease-free individuals, 240 breast cancer patients, 40 breast cancer patients after surgical resection, 55 patients with benign breast tumor, and 80 patients with non-cancerous diseases (thyroiditis, gastritis, hepatitis B, and rheumatoid arthritis.

[99]

Del-1

is a promising marker for identification of patients with early-stage breast cancer and distinguish breast cancer from benign breast tumors and noncancerous diseases.

Measured in plasma samples from 81 healthy controls, 269 patients with breast cancer, 50 breast cancer patients after surgical resection, 64 patients with benign breast tumors, and 98 patients with noncancerous diseases.

[100]

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]

[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]

[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]

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

Macrophages

associate with survival in basal-like breast cancer.

[103,108,109,110]

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

MDSCs

are correlated with poor survival in ER- tumors.

[109,110]

[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]

[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]

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.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Video Production Service
Feedback