Prediction Model for Therapeutic Responses in Ovarian Cancer
Patients using Paclitaxel-resistant Immune-related lncRNAs

Xin      Li; Huiqiang      Liu; Fanchen      Wang; Jia      Yuan; Wencai      Guan; Guoxiong      Xu

doi:10.2174/0109298673281438231217151129

Abstract

Background: Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs.

Objective: The objective of this study was to launch a prediction model for therapeutic responses in OC patients.

Methods: The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells.

Results: The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis.

Conclusion: The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.

« Previous

[1]
Kuroki, L.; Guntupalli, S.R. Treatment of epithelial ovarian cancer. BMJ,  2020, 371, m3773.
 [http://dx.doi.org/10.1136/bmj.m3773] [PMID: 33168565]

[2]
Siegel, R.L.; Miller, K.D.; Wagle, N.S.; Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin.,  2023, 73(1), 17-48.
 [http://dx.doi.org/10.3322/caac.21763] [PMID: 36633525]

[3]
Thusgaard, C.F.; Korsholm, M.; Koldby, K.M.; Kruse, T.A.; Thomassen, M.; Jochumsen, K.M. Epithelial ovarian cancer and the use of circulating tumor DNA: A systematic review. Gynecol. Oncol.,  2021, 161(3), 884-895.
 [http://dx.doi.org/10.1016/j.ygyno.2021.04.020] [PMID: 33892886]

[4]
Das, T; Anand, U; Pandey, SK; Ashby, CR, Jr; Assaraf, YG; Chen, ZS Therapeutic strategies to overcome taxane resistance in cancer. Drug resistance updates: Reviews and commentaries in antimicrobial and anticancer chemotherapy. 2021, 55, 100754.
 [http://dx.doi.org/10.1016/j.drup.2021.100754]

[5]
Tymon-Rosario, J.; Adjei, N.N.; Roque, D.M.; Santin, A.D. Microtubule-interfering drugs: Current and future roles in epithelial ovarian cancer treatment. Cancers,  2021, 13(24), 6239.
 [http://dx.doi.org/10.3390/cancers13246239] [PMID: 34944858]

[6]
Baird, R.D.; Tan, D.S.P.; Kaye, S.B. Weekly paclitaxel in the treatment of recurrent ovarian cancer. Nat. Rev. Clin. Oncol.,  2010, 7(10), 575-582.
 [http://dx.doi.org/10.1038/nrclinonc.2010.120] [PMID: 20683437]

[7]
Markman, M.; Mekhail, T.M. Paclitaxel in cancer therapy. Expert Opin. Pharmacother.,  2002, 3(6), 755-766.
 [http://dx.doi.org/10.1517/14656566.3.6.755] [PMID: 12036415]

[8]
Sharma, S.; Salomon, C. Techniques associated with exosome isolation for biomarker development: Liquid biopsies for ovarian cancer detection. Methods Mol. Biol.,  2020, 2055, 181-199.
 [http://dx.doi.org/10.1007/978-1-4939-9773-2_8] [PMID: 31502152]

[9]
Newick, K.; O’Brien, S.; Moon, E.; Albelda, S.M. CAR T cell therapy for solid tumors. Annu. Rev. Med.,  2017, 68(1), 139-152.
 [http://dx.doi.org/10.1146/annurev-med-062315-120245] [PMID: 27860544]

[10]
Lan, H.; Yuan, J.; Zeng, D.; Liu, C.; Guo, X.; Yong, J.; Zeng, X.; Xiao, S. The emerging role of non-coding RNAs in drug resistance of ovarian cancer. Front. Genet.,  2021, 12, 693259.
 [http://dx.doi.org/10.3389/fgene.2021.693259] [PMID: 34512721]

[11]
Braga, E.A.; Fridman, M.V.; Moscovtsev, A.A.; Filippova, E.A.; Dmitriev, A.A.; Kushlinskii, N.E. LncRNAs in ovarian cancer progression, metastasis, and main pathways: ceRNA and alternative mechanisms. Int. J. Mol. Sci.,  2020, 21(22), 8855.
 [http://dx.doi.org/10.3390/ijms21228855] [PMID: 33238475]

[12]
Song, Y.; Qu, H. Identification and validation of a seven m6A-related lncRNAs signature predicting prognosis of ovarian cancer. BMC Cancer,  2022, 22(1), 633.
 [http://dx.doi.org/10.1186/s12885-022-09591-4] [PMID: 35676619]

[13]
Zheng, J.; Guo, J.; Wang, Y.; Zheng, Y.; Zhang, K.; Tong, J. Bioinformatic analyses of the ferroptosis-related lncrnas signature for ovarian cancer. Front. Mol. Biosci.,  2022, 8, 735871.
 [http://dx.doi.org/10.3389/fmolb.2021.735871] [PMID: 35127813]

[14]
Zheng, J.; Guo, J.; Zhu, L.; Zhou, Y.; Tong, J. Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients. J. Ovarian Res.,  2021, 14(1), 124.
 [http://dx.doi.org/10.1186/s13048-021-00881-2] [PMID: 34560889]

[15]
Zhang, Z.; Xu, Z.; Yan, Y. Role of a pyroptosis-related lncRNA signature in risk stratification and immunotherapy of ovarian cancer. Front. Med.,  2022, 8, 793515.
 [http://dx.doi.org/10.3389/fmed.2021.793515] [PMID: 35096881]

[16]
Li, H.; Liu, Z.Y.; Chen, Y.C.; Zhang, X.Y.; Wu, N.; Wang, J. Identification and validation of an immune-related lncRNAs signature to predict the overall survival of ovarian cancer. Front. Oncol.,  2022, 12, 999654.
 [http://dx.doi.org/10.3389/fonc.2022.999654] [PMID: 36313727]

[17]
Lheureux, S.; Gourley, C.; Vergote, I.; Oza, A.M. Epithelial ovarian cancer. Lancet,  2019, 393(10177), 1240-1253.
 [http://dx.doi.org/10.1016/S0140-6736(18)32552-2] [PMID: 30910306]

[18]
Torre, L.A.; Trabert, B.; DeSantis, C.E.; Miller, K.D.; Samimi, G.; Runowicz, C.D.; Gaudet, M.M.; Jemal, A.; Siegel, R.L. Ovarian cancer statistics, 2018. CA Cancer J. Clin.,  2018, 68(4), 284-296.
 [http://dx.doi.org/10.3322/caac.21456] [PMID: 29809280]

[19]
Rodolakis, I.; Pergialiotis, V.; Liontos, M.; Haidopoulos, D.; Loutradis, D.; Rodolakis, A.; Bamias, A.; Thomakos, N. Chemotherapy response score in ovarian cancer patients: An overview of its clinical utility. J. Clin. Med.,  2023, 12(6), 2155.
 [http://dx.doi.org/10.3390/jcm12062155] [PMID: 36983157]

[20]
Atallah, G.A.; Kampan, N.C.; Chew, K.T.; Mohd Mokhtar, N.; Md Zin, R.R.; Shafiee, M.N.B.; Abd Aziz, N.H.B. Predicting prognosis and platinum resistance in ovarian cancer: Role of immunohistochemistry biomarkers. Int. J. Mol. Sci.,  2023, 24(3), 1973.
 [http://dx.doi.org/10.3390/ijms24031973] [PMID: 36768291]

[21]
Jin, Y.; Cao, J.; Cheng, H.; Hu, X. LncRNA POU6F2-AS2 contributes to malignant phenotypes and paclitaxel resistance by promoting SKP2 expression in stomach adenocarcinoma. J. Chemother.,  2023, 35(7), 638-652.
 [http://dx.doi.org/10.1080/1120009X.2023.2177807] [PMID: 36797828]

[22]
Zhao, H.; Wang, A.; Zhang, Z. LncRNA SDHAP1 confers paclitaxel resistance of ovarian cancer by regulating EIF4G2 expression via miR-4465. J. Biochem.,  2020, 168(2), 171-181.
 [http://dx.doi.org/10.1093/jb/mvaa036] [PMID: 32211849]

[23]
Chen, W.; Yan, L.; Long, B.; Lin, L. Identification of immune-related lncRNAs for predicting prognosis and immune landscape characteristics of uveal melanoma. J. Oncol.,  2022, 2022, 1-12.
 [http://dx.doi.org/10.1155/2022/7680657] [PMID: 36405245]

[24]
Xing, X.L.; Xing, C.; Huang, Z.; Yao, Z.Y.; Liu, Y.W. Immune-related lncRNAs to construct novel signatures and predict the prognosis of rectal cancer. Front. Oncol.,  2021, 11, 661846.
 [http://dx.doi.org/10.3389/fonc.2021.661846] [PMID: 34485113]

[25]
Cioffi, R.; Bergamini, A.; Rabaiotti, E.; Petrone, M.; Pella, F.; Ferrari, D.; Mangili, G.; Candiani, M. Neoadjuvant chemotherapy in high-risk ovarian cancer patients: Role of age. Tumori,  2019, 105(2), 168-173.
 [http://dx.doi.org/10.1177/0300891618792468] [PMID: 30157707]

[26]
Tajik, P.; van de Vrie, R.; Zafarmand, M.H.; Coens, C.; Buist, M.R.; Vergote, I. The FIGO stage IVA versus IVB of ovarian cancer: Prognostic value and predictive value for neoadjuvant chemotherapy. International journal of gynecological cancer. 2018, 28(3), 453-458.
 [http://dx.doi.org/10.1097/IGC.0000000000001186]

[27]
Nasioudis, D.; Ko, E.M.; Haggerty, A.F.; Giuntoli, R.L., II; Burger, R.A.; Morgan, M.A.; Latif, N.A. Isolated distant lymph node metastases in ovarian cancer. Should a new substage be created? Gynecol. Oncol. Rep.,  2019, 28, 86-90.
 [http://dx.doi.org/10.1016/j.gore.2019.03.008] [PMID: 30976643]

[28]
Liang, W.; Wang, L.; Li, H.; Liu, C.; Wu, M.; Li, J. The added value of CA125 normalization before interval debulking surgery to the chemotherapy response score for the prognostication of ovarian cancer patients receiving neoadjuvant chemotherapy for advanced disease. J. Cancer,  2021, 12(3), 946-953.
 [http://dx.doi.org/10.7150/jca.52711] [PMID: 33403051]

[29]
Klotz, D.M.; Link, T.; Wimberger, P.; Kuhlmann, J.D. A predictive and prognostic model for surgical outcome and prognosis in ovarian cancer computed by clinico-pathological and serological parameters (CA125, HE4, mesothelin). Clin. Chem. Lab. Med., 2023.
 [http://dx.doi.org/10.1515/cclm-2023-0314]

[30]
Métairie, M.; Benoit, L.; Koual, M.; Bentivegna, E.; Wohrer, H.; Bolze, P.A.; Kerbage, Y.; Raimond, E.; Akladios, C.; Carcopino, X.; Canlorbe, G.; Uzan, J.; Lavoué, V.; Mimoun, C.; Huchon, C.; Koskas, M.; Costaz, H.; Margueritte, F.; Dabi, Y.; Touboul, C.; Bendifallah, S.; Ouldamer, L.; Delanoy, N.; Nguyen-Xuan, H.T.; Bats, A.S.; Azaïs, H. A suggested modification to FIGO stage IV epithelial ovarian cancer. Cancers,  2023, 15(3), 706.
 [http://dx.doi.org/10.3390/cancers15030706] [PMID: 36765667]

[31]
Zhu, J.W.; Wong, F.; Szymiczek, A.; Ene, G.E.V.; Zhang, S.; May, T.; Narod, S.A.; Kotsopoulos, J.; Akbari, M.R. Evaluating the utility of ctDNA in detecting residual cancer and predicting recurrence in patients with serous ovarian cancer. Int. J. Mol. Sci.,  2023, 24(18), 14388.
 [http://dx.doi.org/10.3390/ijms241814388] [PMID: 37762691]

[32]
Lu, H.; Arshad, M.; Thornton, A.; Avesani, G.; Cunnea, P.; Curry, E.; Kanavati, F.; Liang, J.; Nixon, K.; Williams, S.T.; Hassan, M.A.; Bowtell, D.D.L.; Gabra, H.; Fotopoulou, C.; Rockall, A.; Aboagye, E.O. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat. Commun.,  2019, 10(1), 764.
 [http://dx.doi.org/10.1038/s41467-019-08718-9] [PMID: 30770825]

[33]
Weigelt, B.; Vargas, H.A.; Selenica, P.; Geyer, F.C.; Mazaheri, Y.; Blecua, P.; Conlon, N.; Hoang, L.N.; Jungbluth, A.A.; Snyder, A.; Ng, C.K.Y.; Papanastasiou, A.D.; Sosa, R.E.; Soslow, R.A.; Chi, D.S.; Gardner, G.J.; Shen, R.; Reis-Filho, J.S.; Sala, E. Radiogenomics analysis of intratumor heterogeneity in a patient with high-grade serous ovarian cancer. JCO Precis. Oncol.,  2019, 3(3), 1-9.
 [http://dx.doi.org/10.1200/PO.18.00410] [PMID: 32914032]

[34]
Crispin-Ortuzar, M.; Woitek, R.; Reinius, M.A.V.; Moore, E.; Beer, L.; Bura, V.; Rundo, L.; McCague, C.; Ursprung, S.; Escudero Sanchez, L.; Martin-Gonzalez, P.; Mouliere, F.; Chandrananda, D.; Morris, J.; Goranova, T.; Piskorz, A.M.; Singh, N.; Sahdev, A.; Pintican, R.; Zerunian, M.; Rosenfeld, N.; Addley, H.; Jimenez-Linan, M.; Markowetz, F.; Sala, E.; Brenton, J.D. Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer. Nat. Commun.,  2023, 14(1), 6756.
 [http://dx.doi.org/10.1038/s41467-023-41820-7] [PMID: 37875466]

[35]
Sharbatoghli, M.; Vafaei, S.; Aboulkheyr Es, H.; Asadi-Lari, M.; Totonchi, M.; Madjd, Z. Prediction of the treatment response in ovarian cancer: A ctDNA approach. J. Ovarian Res.,  2020, 13(1), 124.
 [http://dx.doi.org/10.1186/s13048-020-00729-1] [PMID: 33076944]

[36]
Dai, D.; Li, Q.; Zhou, P.; Huang, J.; Zhuang, H.; Wu, H.; Chen, B. Analysis of omics data reveals nucleotide excision repair-related genes signature in highly-grade serous ovarian cancer to predict prognosis. Front. Cell Dev. Biol.,  2022, 10, 874588.
 [http://dx.doi.org/10.3389/fcell.2022.874588] [PMID: 35769257]

[37]
Zhang, M.; Cheng, S.; Jin, Y.; Zhao, Y.; Wang, Y. Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer. Biochim. Biophys. Acta Rev. Cancer,  2021, 1875(2), 188503.
 [http://dx.doi.org/10.1016/j.bbcan.2021.188503] [PMID: 33421585]

[38]
Alegría-Baños, J.A.; Jiménez-López, J.C.; Vergara-Castañeda, A.; de León, D.F.C.; Mohar-Betancourt, A.; Pérez-Montiel, D.; Sánchez-Domínguez, G.; García-Villarejo, M.; Olivares-Pérez, C.; Hernández-Constantino, Á.; González-Santiago, A.; Clara-Altamirano, M.; Arela-Quispe, L.; Prada-Ortega, D. Kinetics of HE4 and CA125 as prognosis biomarkers during neoadjuvant chemotherapy in advanced epithelial ovarian cancer. J. Ovarian Res.,  2021, 14(1), 96.
 [http://dx.doi.org/10.1186/s13048-021-00845-6] [PMID: 34275472]

[39]
Zhang, M.; Wang, Y.; Xu, S.; Huang, S.; Wu, M.; Chen, G.; Wang, Y. Endoplasmic reticulum stress-related ten-biomarker risk classifier for survival evaluation in epithelial ovarian cancer and TRPM2: A potential therapeutic target of ovarian cancer. Int. J. Mol. Sci.,  2023, 24(18), 14010.
 [http://dx.doi.org/10.3390/ijms241814010] [PMID: 37762313]

[40]
Yang, J.; Wang, C.; Zhang, Y.; Cheng, S.; Xu, Y.; Wang, Y. A novel pyroptosis-related signature for predicting prognosis and evaluating tumor immune microenvironment in ovarian cancer. J. Ovarian Res.,  2023, 16(1), 196.
 [http://dx.doi.org/10.1186/s13048-023-01275-2] [PMID: 37730669]

[41]
Wang, X.; Wang, Y.; Sun, F.; Xu, Y.; Zhang, Z.; Yang, C.; Zhang, L.; Lou, G. Novel LncRNA ZFHX4-AS1 as a potential prognostic biomarker that affects the immune microenvironment in ovarian cancer. Front. Oncol.,  2022, 12, 945518.
 [http://dx.doi.org/10.3389/fonc.2022.945518] [PMID: 35903691]

[42]
Shi, X.; Guo, X.; Li, X.; Wang, M.; Qin, R. Loss of Linc01060 induces pancreatic cancer progression through vinculin-mediated focal adhesion turnover. Cancer Lett.,  2018, 433, 76-85.
 [http://dx.doi.org/10.1016/j.canlet.2018.06.015] [PMID: 29913236]

[43]
Li, J.; Liao, T.; Liu, H.; Yuan, H.; Ouyang, T.; Wang, J.; Chai, S.; Li, J.; Chen, J.; Li, X.; Zhao, H.; Xiong, N. Hypoxic glioma stem cell–derived exosomes containing linc01060 promote progression of glioma by regulating the MZF1/C-MYC/HIF1Α axis. Cancer Res.,  2021, 81(1), 114-128.
 [http://dx.doi.org/10.1158/0008-5472.CAN-20-2270] [PMID: 33158815]

[44]
Zhu, L.; Zhang, X.P.; Xu, S.; Hu, M.G.; Zhao, Z.M.; Zhao, G.D.; Xiao, Z.H.; Liu, R. Identification of a CD4+ conventional T cells-related lncRNAs signature associated with hepatocellular carcinoma prognosis, therapy, and tumor microenvironment. Front. Immunol.,  2023, 13, 1111246.
 [http://dx.doi.org/10.3389/fimmu.2022.1111246] [PMID: 36700197]

[45]
Li, L.; Han, J.; Zhang, S.; Dong, C.; Xiao, X. KIF26B-AS1 regulates TLR4 and activates the TLR4 signaling pathway to promote malignant progression of laryngeal cancer. J. Microbiol. Biotechnol.,  2022, 32(10), 1344-1354.
 [http://dx.doi.org/10.4014/jmb.2203.03037] [PMID: 36224753]

[46]
Yang, C.; Xia, B.R.; Zhang, Z.C.; Zhang, Y.J.; Lou, G.; Jin, W.L. Immunotherapy for ovarian cancer: Adjuvant, combination, and neoadjuvant. Front. Immunol.,  2020, 11, 577869.
 [http://dx.doi.org/10.3389/fimmu.2020.577869] [PMID: 33123161]

[47]
Margul, D.; Yu, C.; AlHilli, M.M. Tumor immune microenvironment in gynecologic cancers. Cancers,  2023, 15(15), 3849.
 [http://dx.doi.org/10.3390/cancers15153849] [PMID: 37568665]

[48]
Colombo, I.; Karakasis, K.; Suku, S.; Oza, A.M. Chasing immune checkpoint inhibitors in ovarian cancer: Novel combinations and biomarker discovery. Cancers,  2023, 15(12), 3220.
 [http://dx.doi.org/10.3390/cancers15123220] [PMID: 37370830]

[49]
Cucolo, L.; Chen, Q.; Qiu, J.; Yu, Y.; Klapholz, M.; Budinich, K.A.; Zhang, Z.; Shao, Y.; Brodsky, I.E.; Jordan, M.S.; Gilliland, D.G.; Zhang, N.R.; Shi, J.; Minn, A.J. The interferon-stimulated gene RIPK1 regulates cancer cell intrinsic and extrinsic resistance to immune checkpoint blockade. Immunity,  2022, 55(4), 671-685.e10.
 [http://dx.doi.org/10.1016/j.immuni.2022.03.007] [PMID: 35417675]

[50]
Song, J.; Yang, R.; Wei, R.; Du, Y.; He, P.; Liu, X. Pan- cancer analysis reveals RIPK2 predicts prognosis and promotes immune therapy resistance via triggering cytotoxic T lymphocytes dysfunction. Mol. Med.,  2022, 28(1), 47.
 [http://dx.doi.org/10.1186/s10020-022-00475-8] [PMID: 35508972]

[51]
Le, D.T.; Durham, J.N.; Smith, K.N.; Wang, H.; Bartlett, B.R.; Aulakh, L.K.; Lu, S.; Kemberling, H.; Wilt, C.; Luber, B.S.; Wong, F.; Azad, N.S.; Rucki, A.A.; Laheru, D.; Donehower, R.; Zaheer, A.; Fisher, G.A.; Crocenzi, T.S.; Lee, J.J.; Greten, T.F.; Duffy, A.G.; Ciombor, K.K.; Eyring, A.D.; Lam, B.H.; Joe, A.; Kang, S.P.; Holdhoff, M.; Danilova, L.; Cope, L.; Meyer, C.; Zhou, S.; Goldberg, R.M.; Armstrong, D.K.; Bever, K.M.; Fader, A.N.; Taube, J.; Housseau, F.; Spetzler, D.; Xiao, N.; Pardoll, D.M.; Papadopoulos, N.; Kinzler, K.W.; Eshleman, J.R.; Vogelstein, B.; Anders, R.A.; Diaz, L.A., Jr Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science,  2017, 357(6349), 409-413.
 [http://dx.doi.org/10.1126/science.aan6733] [PMID: 28596308]

[52]
Wang, H.; Fang, L.; Jiang, J.; Kuang, Y.; Wang, B.; Shang, X.; Han, P.; Li, Y.; Liu, M.; Zhang, Z.; Li, P. The cisplatin-induced lncRNA PANDAR dictates the chemoresistance of ovarian cancer via regulating SFRS2-mediated p53 phosphorylation. Cell Death Dis.,  2018, 9(11), 1103.
 [http://dx.doi.org/10.1038/s41419-018-1148-y] [PMID: 30375398]

[53]
Dai, C.; Xu, P.; Liu, S.; Xu, S.; Xu, J.; Fu, Z.; Cao, J.; Lv, M.; Zhou, J.; Liu, G.; Zhang, H.; Jia, X. Long noncoding RNA ZEB1-AS1 affects paclitaxel and cisplatin resistance by regulating MMP19 in epithelial ovarian cancer cells. Arch. Gynecol. Obstet.,  2021, 303(5), 1271-1281.
 [http://dx.doi.org/10.1007/s00404-020-05858-y] [PMID: 33151424]

[54]
Fathi, M.; Barar, J.; Erfan-Niya, H.; Omidi, Y. Methotrexate-conjugated chitosan-grafted pH- and thermo-responsive magnetic nanoparticles for targeted therapy of ovarian cancer. Int. J. Biol. Macromol.,  2020, 154, 1175-1184.
 [http://dx.doi.org/10.1016/j.ijbiomac.2019.10.272] [PMID: 31730949]

[55]
Han, Z.; Shi, L. Long non-coding RNA LUCAT1 modulates methotrexate resistance in osteosarcoma via miR-200c/ABCB1 axis. Biochem. Biophys. Res. Commun.,  2018, 495(1), 947-953.
 [http://dx.doi.org/10.1016/j.bbrc.2017.11.121] [PMID: 29170124]

[56]
Yuan, Z.; Zhang, Y.; Cao, D.; Shen, K.; Li, Q.; Zhang, G.; Wu, X.; Cui, M.; Yue, Y.; Cheng, W.; Wang, L.; Qu, P.; Tao, G.; Hou, J.; Sun, L.; Meng, Y.; Li, G.; Li, C.; Shi, H.; Chen, Y. Pegylated liposomal doxorubicin in patients with epithelial ovarian cancer. J. Ovarian Res.,  2021, 14(1), 12.
 [http://dx.doi.org/10.1186/s13048-020-00736-2] [PMID: 33423683]

[57]
Chen, Q.; Yang, H.; Zhu, X.; Xiong, S.; Chi, H.; Xu, W. Integrative analysis of the doxorubicin-associated lncrna–mrna network identifies chemoresistance-associated lnc-TRDMT1-5 as a biomarker of breast cancer progression. Front. Genet.,  2020, 11, 566.
 [http://dx.doi.org/10.3389/fgene.2020.00566] [PMID: 32547604]

[58]
Hong, S.H.; Lee, S.; Kim, H.G.; Lee, H.J.; Jung, K.H.; Lee, S.C.; Lee, N.R.; Yun, J.; Woo, I.S.; Park, K.H.; Kim, K.; Kim, H.Y.; Rha, S.Y.; Byun, J.H. Phase II study of gemcitabine and vinorelbine as second- or third-line therapy in patients with primary refractory or platinum-resistant recurrent ovarian and primary peritoneal cancer by the Korean cancer study group (KCSG)_KCSG GY10-10. Gynecol. Oncol.,  2015, 136(2), 212-217.
 [http://dx.doi.org/10.1016/j.ygyno.2014.11.017] [PMID: 25462205]

[59]
Rothenberg, M.L.; Liu, P.Y.; Wilczynski, S.; Nahhas, W.A.; Winakur, G.L.; Jiang, C.S.; Moinpour, C.M.; Lyons, B.; Weiss, G.R.; Essell, J.H.; Smith, H.O.; Markman, M.; Alberts, D.S. Phase II trial of vinorelbine for relapsed ovarian cancer: A Southwest Oncology Group study. Gynecol. Oncol.,  2004, 95(3), 506-512.
 [http://dx.doi.org/10.1016/j.ygyno.2004.09.004] [PMID: 15581954]

[60]
Ma, J.; Fan, Z.; Tang, Q.; Xia, H.; Zhang, T.; Bi, F. Aspirin attenuates YAP and β-catenin expression by promoting β-TrCP to overcome docetaxel and vinorelbine resistance in triple-negative breast cancer. Cell Death Dis.,  2020, 11(7), 530.
 [http://dx.doi.org/10.1038/s41419-020-2719-2] [PMID: 32661222]

[61]
Tamari, S.; Menju, T.; Toyazaki, T.; Miyamoto, H.; Chiba, N.; Noguchi, M.; Ishikawa, H.; Miyata, R.; Kayawake, H.; Tanaka, S.; Yamada, Y.; Yutaka, Y.; Nakajima, D.; Ohsumi, A.; Hamaji, M.; Date, H. Nrf2/p-Fyn/ABCB1 axis accompanied by p-Fyn nuclear accumulation plays pivotal roles in vinorelbine resistance in non-small cell lung cancer. Oncol. Rep.,  2022, 48(4), 171.
 [http://dx.doi.org/10.3892/or.2022.8386] [PMID: 35959810]

[62]
Busacca, S.; O’Regan, L.; Singh, A.; Sharkey, A.J.; Dawson, A.G.; Dzialo, J.; Parsons, A.; Kumar, N.; Schunselaar, L.M.; Guppy, N.; Nakas, A.; Sheaff, M.; Mansfield, A.S.; Janes, S.M.; Baas, P.; Fry, A.M.; Fennell, D.A. BRCA1/MAD2L1 deficiency disrupts the spindle assembly checkpoint to confer vinorelbine resistance in mesothelioma. Mol. Cancer Ther.,  2021, 20(2), 379-388.
 [http://dx.doi.org/10.1158/1535-7163.MCT-20-0363] [PMID: 33158996]

[63]
Guan, L.Y.; Lu, Y. New developments in molecular targeted therapy of ovarian cancer. Discov. Med.,  2018, 26(144), 219-229.
 [PMID: 30695681]

Rights & Permissions Print Cite

Journal Information

For Authors

For Editors

For Reviewers

Explore Articles

Open Access

Open Access Articles

For Visitors

DOI https://dx.doi.org/10.2174/0109298673281438231217151129	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X

Current Medicinal Chemistry

Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs

Abstract Play Pause

Related Journals

Related Books

Abstract