Cancer Research on Prevention and Treatment    2021, Vol. 48 Issue (12) : 1071-1077     DOI: 10.3971/j.issn.1000-8578.2021.21.0414
Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression
LU Mei1, YANG Xiaojuan1, ZOU Jieya1, GUO Rong2, WANG Xin1, ZHANG Qian1, DENG Xuepeng1, TAO Jianfen1, NIE Jianyun1, YANG Zhuangqing1
1. Department Ⅲ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, China; 2. Department Ⅱ of Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Provincial Cancer Hospital, Kunming 650118, China
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Abstract Objective To screen out significant differential genes for predicting the effect of neoadjuvant chemotherapy (NAC) and select the most suitable breast cancer patients for NAC. Methods A total of 60 breast cancer patients’ samples before and after NAC were collected for high-throughput RNASeq. We selected AHNAK, CIDEA, ADIPOQ and AKAP12 as the candidate genes that related to tumor chemotherapeutic resistance. We analyzed the correlation of AHNAK, CIDEA, ADIPOQ, AKAP12 expression levels with the effect of NAC by logistic regression analysis, constructed a prediction model and demonstrated the model by the nomogram. Results AHNAK, CIDEA, ADIPOQ and AKAP12 expression were upregulated in the residual tumor tissues of non-pCR group after NAC(P<0.05). Compared with pCR group, non-pCR group presented higher expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 (P<0.05). The high expression levels of AHNAK, CIDEA, ADIPOQ and AKAP12 significantly reduced the pCR rate of NAC for breast cancer (P<0.05). Our prediction model which AHNAK, CIDEA, ADIPOQ and AKAP12 were involved in showed a good fitting effect with H1 test (χ2=6.3967, P=0.4945) and the ROC curve (AUC 0.8249, 95%CI: 0.722-0.9271). Conclusion AHNAK, CIDEA, ADIPOQ and AKAP12 may be novel marker genes for NAC effect on breast cancer. The efficacy prediction model based on this result is expected to be a new method to select the optimal patients of breast cancer for neoadjuvant chemotherapy.
Keywords Breast neoplasms      Neoadjuvant chemotherapy      Gene expression      Efficacy prediction model     
ZTFLH:  R737.9  
Fund:Kunming Medical Joint Project-General Project (No: 202001AY070001-241); Graduate Innovation Fund of Kunming Medical College (No: 2020S223)
Issue Date: 13 December 2021
 Cite this article:   
LU Mei,YANG Xiaojuan,ZOU Jieya, et al. Efficacy Prediction Model for Neoadjuvant Chemotherapy on Breast Cancer Based on Differential Genes Expression[J]. Cancer Research on Prevention and Treatment, 2021, 48(12): 1071-1077.
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LU Mei
YANG Xiaojuan
ZOU Jieya
GUO Rong
DENG Xuepeng
TAO Jianfen
NIE Jianyun
YANG Zhuangqing
[1] Siegel RL, Miller KD, Fuchs HE, et al. Cancer Statistics, 2021[J].
CA Cancer Clin, 2021, 0: 7-33.
[2] Early Breast Cancer Trialists’ Collaborative Group (EBCTCG).
Long-term outcomes for neoadjuvant versus adjuvant
chemotherapy in early breast cancer: meta-analysis of individual
patient data from ten randomized trials[J]. Lancet Oncol, 2018,
19(1): 27-39.
[3] Penault-Llorca F, Radosevic-Robin N. Biomarkers of residual
disease after neoadjuvant therapy for breast cancer[J]. Nat Rev
Clin Oncol, 2016, 13(8): 487-503.
[4] von Minckwitz G, Untch M, Blohmer JU, et al. Definition and
impact of pathologiccomplete response on prognosis after
neoadjuvant chemotherapy in various intrinsicbreast cancer
subtypes[J]. J Clin Oncol, 2012, 30(15): 1796-1804.
[5] I-SPY2 Trial Consortium, Yee D, DeMichele AM, et al. Association
of Event-Free and Distant Recurrence-Free Survival With
Individual-Level Pathologic Complete Response in Neoadjuvant
Treatment of Stages 2 and 3 Breast Cancer: Three-Year Follow-up
Analysis for the I-SPY2 Adaptively Randomized Clinical Trial[J].
JAMA Oncol, 2020, 6(9): 1355-1362.
[6] Levasseur N, Sun J, Gondara L, et al. Impact of pathologic
complete response on survival after neoadjuvant chemotherapy in
early-stage breast cancer: a population-based analysis[J]. Cancer
Res Clin Oncol, 2020, 146(2): 529-536.
[7] Ahn S, Kim HJ, Kang E, et al. Genomic profiling of multiple breast
cancer reveals inter-lesional heterogeneity[J]. Br J Cancer, 2020,
122(5): 697-704.
[8] Yokobayashi Y. High-Throughput Analysis and Engineering of
Ribozymes and Deoxyribozymes by Sequencing[J]. Acc Chem
Res, 2020, 53(12): 2903-2912.
[9] Prudncio P, Rebelo K, Grosso AR, et al. Analysis of Mammalian
Native Elongating Transcript sequencing (mNET-seq) highthroughput
data[J]. Methods, 2020, 178: 89-95.
[10] Nottingham RM, Wu DC, Qin Y, et al. RNA-seq of human
reference RNA samples using a thermostable group Ⅱ intron
reverse transcriptase[J]. RNA, 2016, 22(4): 597-613.
[11] Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to
cancer therapies[J]. Nature Rev Clin Oncol, 2018, 15(2): 81-94.
[12] Zhao J, Zhang H, Lei T, et al. Drug resistance gene expr‍ession
and chemotherapy sensitivity detection in Chinese women with different molecular subtypes of breast cancer[J]. Cancer Biol
Med, 2020, 17(4): 1014-1025.
[13] Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage
years[J]. Nat Rev Genet, 2019, 20(11): 631-656.
[14] Loibl S, Treue D, Budczies J, et al. Mutational Diversity and
Therapy Response in Breast Cancer: A Sequencing Analysis in the
Neoadjuvant GeparSepto Trial[J]. Clin Cancer Res, 2019, 25(13):
[15] Virtanen S, Schulte R, Stingl J, et al. High-throughput surface
marker screen on primary human breast tissues reveals further
cellular heterogeneity[J]. Breast Cancer Res, 2021, 23(1): 66.
[16] Yamada A, Yu P, Lin W, et al. A RNA-Sequencing approach for
the identification of novel long non-coding RNA biomarkers in
colorectal cancer[J]. Sci Rep, 2018, 8(1): 575.
[17] Salehi S, Kabeer F, Ceglia N, et al. Clonal fitness inferred from
time-series modelling of single-cell cancer genomes[J]. Nature,
2021, 595(7868) :585-590.
[18] Garcia-Martinez L, Zhang Y, Nakata Y, et al. Epigenetic
mechanisms in breast cancer therapy and resistance[J]. Nat
Commun, 2021, 12(1): 1786.
[19] Kim C, Gao R, Sei E, et al. Chemoresistance Evolution in Triple-
Negative Breast Cancer Delineated by Single-Cell Sequencing[J].
Cell, 2018, 173(4): 879-893. e13.
[20] Pasculli B, Barbano R, Parrella P. Epigenetics of breast
cancer: biology and clinical implication in the era of precision
medicine[J]. Semin Cancer Biol, 2018, 51: 22-35.
[21] Davis T, van Niekerk G, Peres J, et al. Doxorubicin resistance in
breast cancer: A novel role for the human protein AHNAK[J].
Biochem Pharmacol, 2018, 148: 174-183.
[22] Shtivelman E, Cohen FE, Bishop JM. A human gene (AHNAK)
encoding an unusually large protein with a 1.2-microns polyionic
rod structure[J]. Proc Natl Acad Sci U S A, 1992, 89(12):
[23] Roper N, Brown AL, Wei JS, et al. Clonal Evolution and
Heterogeneity of Osimertinib Acquired Resistance Mechanisms in
EGFR Mutant Lung Cancer[J]. Cell Rep Med, 2020, 1(1): 100007.
[24] Xue D, Lu H, Xu HY, et al. Long noncoding RNA MALAT1
enhances the docetaxel resistance of prostate cancer cells via
miR-145-5p-mediated regulation of AKAP12[J]. J Cell Mol Med,
2018, 22(6): 3223-3237.
[25] Chen FJ, Yin Y, Chua BT, et al. CIDE family proteins control
lipid homeostasis and the development of metabolic diseases[J].
Traffic, 2020, 21(1): 94-105.
[26] Christodoulatos GS, Spyrou N, Kadillari J, et al. The Role
of Adipokines in Breast Cancer: Current Evidence and
Perspectives[J]. Curr Obes Rep, 2019, 8(4): 413-433.
[27] Gantov M, Pagnotta P, Lotufo C, et al. Beige adipocytes contribute
to breast cancer progression[J]. Oncol Rep, 2021, 45(1): 317-328.
[28] Sun G, Zhang X, Liu Z, et al. The Adiponectin-AdipoR1 Axis
Mediates Tumor Progression and Tyrosine Kinase Inhibitor
Resistance in Metastatic Renal Cell Carcinoma[J]. Neoplasia,
2019, 21(9): 921-931.
[29] Tsai M, Lo S, Audeh W, et al. Association of 70-Gene Signature
Assay Findings With Physicians’ Treatment Guidance for Patients
With Early Breast Cancer Classified as Intermediate Risk by the
21-Gene Assay[J]. JAMA Oncol, 2018, 4(1): e173470.
[30] Groenendijk FH, Treece T, Yoder E, et al. Estrogen receptor
variants in ER-positive basal-type breast cancers responding to
therapy like ER-negative breast cancers[J]. NPJ Breast Cancer,
2019, 5: 15.

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