Open Access

Proteomic Analysis of the Non-genetic Response to Cisplatin in Lung Cancer Cells


1Laboratório de Genômica Estrutural e Funcional, Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil

2Departamento de Biologia Molecular e Biotecnologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil

Cancer Diagnosis & Prognosis Jul-Aug; 1(3): 235-243 DOI: 10.21873/cdp.10032
Received 26 March 2021 | Revised 21 March 2023 | Accepted 27 April 2021
Corresponding author
Karina Mariante Monteiro, Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Caixa Postal 15005, 91501-970 Porto Alegre, RS, Brazil.


Background: Drug resistance is the main cause of therapy failure in advanced lung cancer. Although non-genetic mechanisms play important roles in tumor chemoresistance, drug-induced epigenetic reprogramming is still poorly understood. Materials and Methods: The A549 cell line was used to generate cells with non-genetic resistance to cisplatin (CDDP), namely A549/CDDP cells. Bioorthogonal non-canonical amino acid tagging (BONCAT) and mass spectrometry were used to identify proteins modulated by CDDP in A549 and A549/CDDP cells. Results: Proteins related to proteostasis, telomere maintenance, cell adhesion, cytoskeletal remodeling, and cell redox homeostasis were found enriched in both cell lines upon CDDP exposure. On the other hand, proteins involved in drug response, metabolic pathways and mRNA processing and splicing were up-regulated by CDDP only in A549/CDDP cells. Conclusion: Our study revealed proteome dynamics involved in the non-genetic response to CDDP, pointing out potential targets to monitor and overcome epigenetic resistance in lung cancer.
Keywords: BONCAT, cisplatin, drug resistance, lung cancer, proteomics

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death in the world (1). Cisplatin (CDDP)-based chemotherapy is the standard first-line treatment for inoperable, advanced NSCLC (2). However, drug resistance remains a major challenge for successful treatment. Cell models have been widely used to study cancer drug resistance (3,4). However, the cell models with high levels of resistance used in most studies have shown limited translation to clinical application (5). In fact, cell lines considered clinically relevant exhibit resistance levels similar to those found in cells isolated from patients after chemotherapy (from 2- to 5-fold increase in resistance) (3). These clinically relevant models often show unstable resistance, which suggests that resistance acquisition is mainly due to gene expression reprogramming instead of genetic alterations.

Accumulating evidence has shown that non-genetic mechanisms play an important role in the chemoresistance of a variety of tumors (6,7). During non-genetic evolution, gene expression programs that improve cancer cell adaptability and survival are selected and/or induced by drug treatment (7-9). However, drug-induced epigenetic reprogramming is still poorly characterized, mainly because it is a highly dynamic process whose analysis is technically challenging.

Bioorthogonal non-canonical amino acid tagging (BONCAT) is a powerful tool to monitor protein dynamics in response to a wide variety of stimuli. In the BONCAT method, an artificial amino acid (e.g. L-azidohomoalanine, a surrogate for L-methionine) carrying an azide or alkyne group is incorporated into newly synthesized proteins, thus allowing for selective detection or purification of tagged proteins by click chemistry (10,11).

Herein, we performed a proteomic analysis of non-genetic resistance to CDDP in lung cancer cells. We used BONCAT and liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a time-course analysis to selectively label, purify and identify proteins synthesized by CDDP-sensitive and -resistant A549 cells in response to drug exposure.

Materials and Methods

Cell culture and chemoresistance induction. An in vitro cellular model for studying non-genetic resistance to CDDP was developed from A549 cells. A549 cells were maintained in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS), 100 units/ml penicillin and 100 μg/ml streptomycin in a humidified atmosphere of 5% CO2 at 37˚C. The CDDP-resistant subline, A549/CDDP, was obtained by a stepwise drug selection protocol, in which A549 parental cells (5×105) were continuously exposed to increasing concentrations of CDDP (0.1, 0.2, 0.3, 0.4 and 0.5 μM in 0.9% NaCl solution) for 72 h each. A549/CDDP cells were independently generated three times to produce the biological replicates used in the experiments. CDDP-resistant cells were maintained in culture medium containing 0.5 μM of CDDP to maintain the resistant phenotype. CDDP cytotoxicity was determined by sulforhodamine B (SRB) assay (12).

Metabolic labeling and click enrichment of newly synthesized proteins. For BONCAT assay, A549 and A549/CDDP cells were seeded at a density of 3×105 cells/well into 6-well plates and cultured for 18 h. Cells were conditioned in methionine-free RPMI medium (Thermo Fisher Scientific, Waltham, MA, USA) for 1 h at 37˚C to deplete methionine reserves, then the proteins were metabolically labeled with azidohomoalanine (AHA, 1 mM) for 2, 4 or 8 h in the absence or in the presence of CDDP. Each cell line was exposed to CDDP concentrations corresponding to their respective IC50 values. Cells were lysed and newly synthesized proteins were enriched using the Click-iT Protein Enrichment Kit (Thermo Fisher Scientific), as described previously (11). Briefly, AHA-containing proteins were captured onto an alkyne-agarose resin and non-specifically bound proteins were removed by washing with increasingly stringent buffers containing SDS, urea, isopropanol and acetonitrile. The resin-bound proteins were digested with trypsin and the generated peptides were desalted using Oasis HLB cartridges (Waters Corporation, Milford, MA, USA) following manufacturer’s instructions.

Mass spectrometry analysis. Peptides were analyzed by LC-MS/MS using a nanoACQUITY Ultra-Performance Liquid Chromatography (UPLC) system coupled to a Xevo G2-XS Q-Tof mass spectrometer (Waters Corporation) with a low-flow probe at the source. Peptides were separated by analytical chromatography (Acquity UPLC BEH C18, 1.7 μm, 2.1×50 mm, Waters Corporation) at a flow rate of 8 μl/min, using a 7-85% water/acetonitrile 0.1% formic acid linear gradient over 90 min. The MS survey scan was set to 0.5 s and recorded from 50 to 2,000 m/z. MS/MS scans were acquired from 50 to 2000 m/z, and scan time was set to 1 s. Data were collected in data-independent MSE mode. The mass spectrometry data were deposited to the ProteomeXchange Consortium via the PRIDE (13) partner repository with the dataset identifier PXD021779.

Data analysis and functional annotation. LC-MSE data were processed and searched using ProteinLynx Global Server (PLGS 3.0.3, Waters Corporation). The searches were conducted against Homo sapiens protein sequences retrieved from UniProtKB/Swiss-Prot database, with trypsin as enzyme, maximum of one missed cleavage, fixed carbamidomethyl modification for cysteine residues, and oxidation of methionine as variable modification. Peptides and protein tolerances were set as automatic, allowing minimum fragment ion per protein as 5, minimum fragment ion per peptide as 2, minimum peptide matches per proteins as 1 and false discovery rate (FDR) as 4%. Only proteins identified in two out of three biological replicates were considered for qualitative and quantitative analysis in order to improve confidence and reproducibility. Data sets were normalized using the “auto-normalization” function of PLGS and label-free quantitative analysis was performed from peak intensity measurements (Hi3 method) (14) using PLGS ExpressionE algorithm. Proteins with regulation-probability (P) values below 0.05 or higher than 0.95 were taken as differentially regulated between samples. Functional annotation and enrichment analysis were performed using PANTHER (Protein Analysis Through Evolutionary Relationships) database (15) matched with the Homo sapiens genome. The Fisher’s exact test was used with FDR correction. The plots of most representative and significant biological processes were constructed using ggplot2 R package.


A549/CDDP cells displayed clinically relevant levels of drug resistance, with an IC50 value 3.5-fold higher than that of the parental A549 cells (Figure 1A). A549/CDDP cells presented unstable resistance, gradually resuming the resistance level of the parental cells after ~30-45 days of cultivation in drug-free medium (data not shown). A549/CDDP cells, therefore, represent a cellular model for studying non-genetic resistance to CDDP.

Protein dynamics induced by CDDP in A549 and A549/CDDP cells were evaluated by BONCAT and LC-MS/MS. Considering all time-points of the experiment (2, 4 and 8 h), a total of 173 and 151 unique proteins were identified from A549 cells cultured in absence and presence of CDDP, respectively. A total of 148 and 153 unique newly synthesized proteins were identified in A549/CDDP cells during the time-course experiment in absence and presence of CDDP, respectively. The complete lists of proteins identified in each experiment and sample are available at

Initially, we compared the proteins synthesized by A549 and A549/CDDP cells in drug-free medium to identify differences in their steady-state proteome. The complete list of proteins differentially expressed between A549 and A549/CDDP cells during culture in drug-free medium is available at A549 and A549/CDDP cells presented different expression profiles, with the enrichment of Gene Ontology (GO) terms related to protein folding, stabilization and turnover, telomere organization and maintenance, cellular adhesion, actin cytoskeleton, and cellular response to drug in A549/CDDP cells (Figure 1B). The complete list of GO annotations is available at Our results suggest that these biological processes were selected/induced by drug treatment during the acquisition of non-genetic resistance by A549/CDDP cells.

Next, proteins synthesized by A549 and A549/CDDP cells in the presence and absence of CDDP were compared, time-point by time-point, to identify proteome changes induced by drug exposure. The complete lists of proteins induced by CDDP in each time-point of the experiment are available at Considering all time-points of the experiment, proteins induced by CDDP in A549 and A549/CDDP cells are presented in Figure 2A and Table I. GO terms enriched by CDDP in each cell are shown in Figure 2B. GO terms related to telomere maintenance, protein folding and stabilization, cell adhesion, cytoskeleton, and cell redox homeostasis were found enriched upon CDDP exposure in both A549 and A549/CDDP cells, while proteins involved response to drug, metabolic pathways and regulation of mRNA processing/splicing were induced by CDDP only in A549/CDDP cells.


Herein, we used BONCAT and LC-MS/MS to monitor gene expression reprogramming induced by CDDP in lung cancer cells with distinct phenotypes of drug sensitivity. Our results revealed that proteins involved in proteostasis, telomere maintenance, cell adhesion, cytoskeleton remodeling and cell redox homeostasis were up-regulated by CDDP in both A549 and A549/CDDP cells. These results highlight the importance of these molecular pathways in the non-genetic adaptive response to therapy. Interestingly, the profile of biological processes enriched in A549 cells after CDDP treatment is very similar to those identified in the steady-state proteome of A549/CDDP cells, which reinforces the participation of these molecular mechanisms in promoting cell survival during drug exposure and suggests their positive selection in CDDP-resistant cells. It is also important to note that the biological processes identified in our cellular model of non-genetic resistance resemble those described in the genetic resistance to CDDP (16,17), which suggests that comparable pro-survival pathways could be activated by genetic and epigenetic mechanisms. In this sense, it is not surprising that most proteins up-regulated by CDDP are related to stress response pathways, as this signaling is of crucial importance in limiting cellular damage and enhancing cell survival.

Chaperones, foldases and proteases involved in the protein quality control network were found up-regulated by CDDP treatment in A549 and A549/CDDP cells. During stress response, cells use quality-control strategies to maintain protein homeostasis (proteostasis) (18). Thus, cells with up-regulated quality control machinery may be more efficient to cope with protein misfolding stress caused by CDDP exposure. In fact, unfolded protein response (UPR) activation is commonly observed in cancer and correlates with drug resistance (19).

Proteins reported to be involved in telomere maintenance, such as TRiC/CCT complex (20) and hnRNPs (21), had their expression increased in A549 and A549/CDDP cells after CDDP exposure. Human telomeric DNA (tandem repeats of 5’-TTAGGG-3’ sequences) is a potential target for CDDP-induced cross-links (22). Cell treatment with CDDP results in markedly shortened telomeres, which can induce apoptosis (23,24). Thus, enhanced mechanisms of telomere maintenance can be involved in resistance to CDDP-induced apoptosis.

Proteins related to cell adhesion and cytoskeletal rearrangements were found up-regulated by CDDP exposure in both A549 and A549/CDDP cells. Cell adhesion to extracellular matrix (ECM) elicits activation of different pro-survival signaling pathways which contribute to tumor development and chemoresistance, in a mechanism referred as cell adhesion-mediated drug resistance (CAM-DR) (25,26). In addition, cell adhesion triggers cytoskeleton reorganization, regulating cellular stiffness (26). Cisplatin has been reported to induce considerable remodeling of actin cytoskeleton, increasing stress fibers and cell stiffness (27). CDDP-resistant cell lines showed a significantly higher cell stiffness when compared to their drug-sensitive counterparts (28). Therefore, proteins involved in cell adhesion and cytoskeletal rearrangement could be relevant targets to counteract cellular resistance to CDDP (26,29,30).

The expression of detoxifying enzymes was induced by CDDP in A549 and A549/CDDP cells. CDDP generates a robust oxidative stress and, therefore, cells need to develop antioxidant mechanisms to deal with drug toxicity (31). Cells with an increased detoxification capacity have been reported to be more chemoresistant (32). In accordance, our results indicate that detoxifying enzymes may play a relevant role in non-genetic response to CDDP.

On the other hand, some biological processes, such as drug response, metabolic pathways, and mRNA processing and splicing, were up-regulated by CDDP only in A549/CDDP cells, which suggest their relevance to the development and/or maintenance of a drug resistance phenotype.

Proteins identified in A549/CDDP cells associated to drug response include ALDH3A1, HMOX1 and NQO1 proteins. Aldehyde dehydrogenases are markers of cancer stem cells and associated with cancer chemoresistance (33). HMOX1 and NQO1 have cytoprotective roles and enhance resistance to anticancer therapies (34,35).

mRNA processing and splicing were also differentially represented in A549 and A549/CDDP cells upon CDDP exposure. Post-transcriptional mechanisms increase proteome diversity, enhancing tumor cell adaptation to chemotherapy (36). Therefore, it is likely that these mechanisms play major roles in the development of non-genetic resistance to CDDP.

Regarding mechanisms of transcriptional regulation, we identified the BTF3 transcription factor up-regulated in A549/CDDP cells after CDDP exposure. BFT3 expression has been associated with cancer stem cells (37), which are known to be involved in cancer growth, metastasis and chemoresistance.

Possible differences in the metabolism of A549 and A549/CDDP cells upon CDDP exposure were also detected by our proteomic approach, with the up-regulation of proteins involved in glycolysis and pentose phosphate pathways in A549/CDDP cells. Metabolic reprogramming is often associated with drug resistance, as the altered metabolism can confer adaptive, proliferative, and survival advantages in adverse conditions (38). Our results pointed that metabolic reprogramming could also be a non-genetic resistance mechanism to CDDP.

The results presented herein shed light on the mechanisms of gene expression regulation involved in the non-genetic resistance to CDDP. Knowing these mechanisms is a fundamental step for the development of novel strategies to monitor and counteract non-genetic resistance in cancer.

Conflicts of Interest

The Authors declare that no conflicts of interest exist with regard to the present study.

Authors’ Contributions

Conceptualization: C.S.D. and K.M.M.; Methodology: C.S.D. and K.M.M; Investigation: C.S.D., C.L.M. and N.A.C.; Formal analysis: C.S.D. and K.M.M.; Visualization: C.S.D.; Project administration: K.M.M.; Funding acquisition: K.M.M.; Resources: H.B.F., A.Z. and K.M.M.; Writing – original draft: C.S.D.; Writing – review and editing: H.B.F., A.Z. and K.M.M.


This work was funded by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), grant number 16/2551-0000 286-0 (K.M.M.). C.S.D. and C.L.M. were supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) scholarships. N.A.C. was supported by a Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) scholarship. We thank the Uniprote-MS (CBiot/UFRGS) for technical support with LC-MS/MS.


1 Bray F Ferlay J Soerjomataram I Siegel RL Torre LA & Jemal A Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68(6) 394 - 424 2018. PMID: 30207593. DOI: 10.3322/caac.21492
2 Duma N Santana-Davila R & Molina JR Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment. Mayo Clin Proc. 94(8) 1623 - 1640 2019. PMID: 31378236. DOI: 10.1016/j.mayocp.2019.01.013
3 McDermott M Eustace AJ Busschots S Breen L Crown J Clynes M O’Donovan N & Stordal B In vitro development of chemotherapy and targeted therapy drug-resistant cancer cell lines: a practical guide with case studies. Front Oncol. 4 40 2014. PMID: 24639951. DOI: 10.3389/fonc.2014.00040
4 Amaral MVS DE Sousa Portilho AJ DA Silva EL DE Oliveira Sales L DA Silva Maués JH DE Moraes MEA & Moreira-Nunes CA Establishment of drug-resistant cell lines as a model in experimental oncology: a review. Anticancer Res. 39(12) 6443 - 6455 2019. PMID: 31810908. DOI: 10.21873/anticanres.13858
5 Gillet JP Calcagno AM Varma S Marino M Green LJ Vora MI Patel C Orina JN Eliseeva TA Singal V Padmanabhan R Davidson B Ganapathi R Sood AK Rueda BR Ambudkar SV & Gottesman MM Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc Natl Acad Sci USA. 108(46) 18708 - 18713 2011. PMID: 22068913. DOI: 10.1073/pnas.1111840108
6 Terlizzi M Colarusso C Pinto A & Sorrentino R Drug resistance in non-small cell lung Cancer (NSCLC): Impact of genetic and non-genetic alterations on therapeutic regimen and responsiveness. Pharmacol Ther. 202 140 - 148 2019. PMID: 31226345. DOI: 10.1016/j.pharmthera.2019.06.005
7 Marine JC Dawson SJ & Dawson MA Non-genetic mechanisms of therapeutic resistance in cancer. Nat Rev Cancer. 20(12) 743 - 756 2020. PMID: 33033407. DOI: 10.1038/s41568-020-00302-4
8 Bell CC & Gilan O Principles and mechanisms of non-genetic resistance in cancer. Br J Cancer. 122(4) 465 - 472 2020. PMID: 31831859. DOI: 10.1038/s41416-019-0648-6
9 Shaffer SM Dunagin MC Torborg SR Torre EA Emert B Krepler C Beqiri M Sproesser K Brafford PA Xiao M Eggan E Anastopoulos IN Vargas-Garcia CA Singh A Nathanson KL Herlyn M & Raj A Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature. 546(7658) 431 - 435 2017. PMID: 28607484. DOI: 10.1038/nature22794
10 Dieterich DC Link AJ Graumann J Tirrell DA & Schuman EM Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc Natl Acad Sci USA. 103(25) 9482 - 9487 2006. PMID: 16769897. DOI: 10.1073/pnas.0601637103
11 Eichelbaum K Winter M Berriel Diaz M Herzig S & Krijgsveld J Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat Biotechnol. 30(10) 984 - 990 2012. PMID: 23000932. DOI: 10.1038/nbt.2356
12 Vichai V & Kirtikara K Sulforhodamine B colorimetric assay for cytotoxicity screening. Nat Protoc. 1(3) 1112 - 1116 2006. PMID: 17406391. DOI: 10.1038/nprot.2006.179
13 Perez-Riverol Y Csordas A Bai J Bernal-Llinares M Hewapathirana S Kundu DJ Inuganti A Griss J Mayer G Eisenacher M Pérez E Uszkoreit J Pfeuffer J Sachsenberg T Yilmaz S Tiwary S Cox J Audain E Walzer M Jarnuczak AF Ternent T Brazma A & Vizcaíno JA The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47(D1) D442 - D450 2019. PMID: 30395289. DOI: 10.1093/nar/gky1106
14 Silva JC Denny R Dorschel CA Gorenstein M Kass IJ Li GZ McKenna T Nold MJ Richardson K Young P & Geromanos S Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem. 77(7) 2187 - 2200 2005. PMID: 15801753. DOI: 10.1021/ac048455k
15 Mi H Muruganujan A Ebert D Huang X & Thomas PD PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47(D1) D419 - D426 2019. PMID: 30407594. DOI: 10.1093/nar/gky1038
16 Shen DW Pouliot LM Hall MD & Gottesman MM Cisplatin resistance: a cellular self-defense mechanism resulting from multiple epigenetic and genetic changes. Pharmacol Rev. 64(3) 706 - 721 2012. PMID: 22659329. DOI: 10.1124/pr.111.005637
17 Mansoori B Mohammadi A Davudian S Shirjang S & Baradaran B The different mechanisms of cancer drug resistance: a brief review. Adv Pharm Bull. 7(3) 339 - 348 2017. PMID: 29071215. DOI: 10.15171/apb.2017.041
18 Chen B Retzlaff M Roos T & Frydman J Cellular strategies of protein quality control. Cold Spring Harb Perspect Biol. 3(8) a004374 2011. PMID: 21746797. DOI: 10.1101/cshperspect.a004374
19 Avril T Vauléon E & Chevet E Endoplasmic reticulum stress signaling and chemotherapy resistance in solid cancers. Oncogenesis. 6(8) e373 - e373 2020. DOI: 10.1038/oncsis.2017.72
20 Freund A Zhong FL Venteicher AS Meng Z Veenstra TD Frydman J & Artandi SE Proteostatic control of telomerase function through TRiC-mediated folding of TCAB1. Cell. 159(6) 1389 - 1403 2014. PMID: 25467444. DOI: 10.1016/j.cell.2014.10.059
21 Shishkin SS Kovalev LI Pashintseva NV Kovaleva MA & Lisitskaya K Heterogeneous nuclear ribonucleoproteins involved in the functioning of telomeres in malignant cells. Int J Mol Sci. 20(3) 745 2019. PMID: 30744200. DOI: 10.3390/ijms20030745
22 Ishibashi T & Lippard SJ Telomere loss in cells treated with cisplatin. Proc Natl Acad Sci U S A. 95(8) 4219 - 4223 1998. PMID: 9539717. DOI: 10.1073/pnas.95.8.4219
23 Zhang RG Zhang RP Wang XW & Xie H Effects of cisplatin on telomerase activity and telomere length in BEL-7404 human hepatoma cells. Cell Res. 12(1) 55 - 62 2002. PMID: 11942411. DOI: 10.1038/
24 Uziel O Beery E Dronichev V Samocha K Gryaznov S Weiss L Slavin S Kushnir M Nordenberg Y Rabinowitz C Rinkevich B Zehavi T & Lahav M Telomere shortening sensitizes cancer cells to selected cytotoxic agents: in vitro and in vivo studies and putative mechanisms. PLoS One. 5(2) e9132 2010. PMID: 20161752. DOI: 10.1371/journal.pone.0009132
25 Damiano JS Cress AE Hazlehurst LA Shtil AA & Dalton WS Cell adhesion mediated drug resistance (CAM-DR): role of integrins and resistance to apoptosis in human myeloma cell lines. Blood. 93(5) 1658 - 1667 1999. PMID: 10029595.
26 Deville SS & Cordes N The Extracellular, Cellular, and Nuclear Stiffness, a Trinity in the Cancer Resistome-A Review. Front Oncol. 9 1376 2019. PMID: 31867279. DOI: 10.3389/fonc.2019.01376
27 Raudenska M Kratochvilova M Vicar T Gumulec J Balvan J Polanska H Pribyl J & Masarik M Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. Sci Rep. 9(1) 1660 2019. PMID: 30733487. DOI: 10.1038/s41598-018-38199-7
28 Sharma S Santiskulvong C Bentolila LA Rao J Dorigo O & Gimzewski JK Correlative nanomechanical profiling with super-resolution F-actin imaging reveals novel insights into mechanisms of cisplatin resistance in ovarian cancer cells. Nanomedicine. 8(5) 757 - 766 2012. PMID: 22024198. DOI: 10.1016/j.nano.2011.09.015
29 Hisano T Ono M Nakayama M Naito S Kuwano M & Wada M Increased expression of T-plastin gene in cisplatin-resistant human cancer cells: identification by mRNA differential display. FEBS Lett. 397(1) 101 - 107 1996. PMID: 8941723. DOI: 10.1016/s0014-5793(96)01150-7
30 Becker M De Bastiani MA Müller CB Markoski MM Castro MA & Klamt F High cofilin-1 levels correlate with cisplatin resistance in lung adenocarcinomas. Tumour Biol. 35(2) 1233 - 1238 2014. PMID: 24018823. DOI: 10.1007/s13277-013-1164-6
31 Dasari S & Tchounwou PB Cisplatin in cancer therapy: molecular mechanisms of action. Eur J Pharmacol. 740 364 - 378 2014. PMID: 25058905. DOI: 10.1016/j.ejphar.2014.07.025
32 Cort A Ozben T Saso L De Luca C & Korkina L Redox control of multidrug resistance and its possible modulation by antioxidants. Oxid Med Cell Longev. 2016 4251912 2016. PMID: 26881027. DOI: 10.1155/2016/4251912
33 Ma I & Allan AL The role of human aldehyde dehydrogenase in normal and cancer stem cells. Stem Cell Rev Rep. 7(2) 292 - 306 2011. PMID: 21103958. DOI: 10.1007/s12015-010-9208-4
34 Aleksunes LM Goedken MJ Rockwell CE Thomale J Manautou JE & Klaassen CD Transcriptional regulation of renal cytoprotective genes by Nrf2 and its potential use as a therapeutic target to mitigate cisplatin-induced nephrotoxicity. J Pharmacol Exp Ther. 335(1) 2 - 12 2010. PMID: 20605904. DOI: 10.1124/jpet.110.170084
35 Podkalicka P Mucha O Józkowicz A Dulak J & Łoboda A Heme oxygenase inhibition in cancers: possible tools and targets. Contemp Oncol (Pozn). 22(1A) 23 - 32 2018. PMID: 29628790. DOI: 10.5114/wo.2018.73879
36 Sciarrillo R Wojtuszkiewicz A Assaraf YG Jansen G Kaspers GJL Giovannetti E & Cloos J The role of alternative splicing in cancer: From oncogenesis to drug resistance. Drug Resist Updat. 53 100728 2020. PMID: 33070093. DOI: 10.1016/j.drup.2020.100728
37 Hu J Sun F Chen W Zhang J Zhang T Qi M Feng T Liu H Li X Xing Y Xiong X Shi B Zhou G & Han B BTF3 sustains cancer stem-like phenotype of prostate cancer via stabilization of BMI1. J Exp Clin Cancer Res. 38(1) 227 2019. PMID: 31138311. DOI: 10.1186/s13046-019-1222-z
38 Ma L & Zong X Metabolic Symbiosis in Chemoresistance: Refocusing the Role of Aerobic Glycolysis. Front Oncol. 10 5 2020. PMID: 32038983. DOI: 10.3389/fonc.2020.00005