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Quantitative proteomic analysis of ovarian cancer cells identified mitochondrial proteins associated with Paclitaxel resistance.

ABSTRACT: Paclitaxel has been widely used as an anti-mitotic agent in chemotherapy for a variety of cancers and adds substantial efficacy as the first-line chemotherapeutic regimen for ovarian cancers. However, the frequent occurrence of paclitaxel resistance limits its function in long-term management. Despite abundant clinical and cellular demonstration of paclitaxel resistant tumors, the molecular mechanisms leading to paclitaxel resistance are poorly understood. Using genomic approaches, we have previously identified an association between a BTB/POZ gene, Nac1, and paclitaxel resistance in ovarian cancer. The experiments presented here have applied multiple quantitative proteomic methods to identify protein changes associated with paclitaxel resistance and Nac1 function. The SKOV-3 ovarian serous carcinoma cell line, which has inducible expression of dominant negative Nac1, was used to determine the paclitaxel treatment associated changes in the presence and absence of functional Nac1. Quantitative proteomic analyses were performed using iTRAQ labeling and mass spectrometry. Two label-free quantitative proteomic methods: LC-MS and spectral count were used to increase confidence of proteomic quantification. A total of 1371 proteins were quantified by at least one of the quantitative proteomic methods. Candidate proteins related to paclitaxel and NAC1 function were identified in this study. Go analysis of the protein changes identified upon paclitaxel resistance revealed that cell component enrichment related to mitochondria. Moreover, tubulin and mitochondrial proteins were the major cellular components with changes associated with paclitaxel treatment. This suggests that mitochondria may play a role in paclitaxel resistance.

SUBMITTER: Tian Y

PROVIDER: S-EPMC2989613 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Quantitative proteomic analysis of ovarian cancer cells identified mitochondrial proteins associated with Paclitaxel resistance.

Tian Yuan Y Tan Aik-Choon AC Sun Xiaer X Olson Matthew T MT Xie Zhi Z Jinawath Natini N Chan Daniel W DW Shih Ie-Ming IeM Zhang Zhen Z Zhang Hui H

Proteomics. Clinical applications 20091101 11

Paclitaxel has been widely used as an anti-mitotic agent in chemotherapy for a variety of cancers and adds substantial efficacy as the first-line chemotherapeutic regimen for ovarian cancers. However, the frequent occurrence of paclitaxel resistance limits its function in long-term management. Despite abundant clinical and cellular demonstration of paclitaxel resistant tumors, the molecular mechanisms leading to paclitaxel resistance are poorly understood. Using genomic approaches, we have previ ...[more]

PMID: 21113235

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Project description:Chemotherapy is an important adjuvant therapy for epithelial ovarian cancer (EOC). The main cause of chemotherapy failure in EOC is paclitaxel resistance. The present study aimed to identify novel biomarkers to predict chemosensitivity to paclitaxel and improve our understanding of the molecular mechanisms underlying paclitaxel resistance in EOC. In the present study, the heterogeneity of EOC was evaluated by adenosine triphosphate-tumor chemosensitivity assay (ATP-TCA) in vitro. Fresh samples were collected from 54 EOC cases during cytoreductive surgery. Tumor cells were isolated, cultured, and tested for sensitivity to paclitaxel. Proteins that were differentially expressed between paclitaxel-resistant tissues and paclitaxel-sensitive tissues were identified via isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis. Two upregulated proteins, plexin domain containing 2 (Plxdc2) and cytokeratin 7 (CK7), were selected to verify the iTRAQ method using western blot analysis in EOC tissues with different chemosensitivities (sensitive, weakly sensitive and resistant). There was notable heterogeneity of chemosensitivity in the EOC specimens. Highly to mildly-differentiated or early-stage (I/II) EOC specimens had decreased sensitivity to paclitaxel compared with specimens with low differentiation (P<0.05) or an advanced stage (III; P<0.05), respectively. A total of 496 significantly differentially expressed proteins, including 263 that were downregulated (P<0.05) and 233 that were upregulated (P<0.05) in paclitaxel-resistant tissues compared with paclitaxel-sensitive tissues, were identified using iTRAQ in combination with LC-MS/MS. The expression levels of two proteins associated with paclitaxel resistance, Plxdc2 and CK7, were further validated by western blotting, which revealed that they were upregulated in the paclitaxel-resistant tissues. The present study determined candidate proteins associated with paclitaxel resistance in EOC. Plxdc2 and CK7 may be potential makers for distinguishing patients with paclitaxel-resistant EOC from those with paclitaxel-sensitive EOC.

Project description:RelevanceMolecular network changes are the hallmark of the pathogenesis of ovarian cancers (OCs). Network-based biomarkers benefit for the effective treatment of OC.PurposeThis study sought to identify key pathway-network alterations and network-based biomarkers for clarification of molecular mechanisms and treatment of OCs.MethodsIngenuity Pathway Analysis (IPA) platform was used to mine signaling pathway networks with 1198 human tissue mitochondrial differentially expressed proteins (mtDEPs) and compared those pathway network changes between OCs and controls. The mtDEPs in important cancer-related pathway systems were further validated with qRT-PCR and Western blot in OC cell models. Moreover, integrative analysis of mtDEPs and Cancer Genome Atlas (TCGA) data from 419 patients was used to identify hub molecules with molecular complex detection method. Hub molecule-based survival analysis and multiple multivariate regression analysis were used to identify survival-related hub molecules and hub molecule signature model.ResultsPathway network analysis revealed 25 statistically significant networks, 192 canonical pathways, and 5 significant molecular/cellular function models. A total of 52 canonical pathways were activated or inhibited in cancer pathogenesis, including antigen presentation, mitochondrial dysfunction, GP6 signaling, EIF2 signaling, and glutathione-mediated detoxification. Of them, mtDEPs (TPM1, CALR, GSTP1, LYN, AKAP12, and CPT2) in those canonical pathway and molecular/cellular models were validated in OC cell models at the mRNA and protein levels. Moreover, 102 hub molecules were identified, and they were regulated by post-translational modifications and functioned in multiple biological processes. Of them, 62 hub molecules were individually significantly related to OC survival risk. Furthermore, multivariate regression analysis of 102 hub molecules identified significant seven hub molecule signature models (HIST1H2BK, ALB, RRAS2, HIBCH, EIF3E, RPS20, and RPL23A) to assess OC survival risks.ConclusionThese findings provided the overall signaling pathway network profiling of human OCs; offered scientific data to discover pathway network-based cancer biomarkers for diagnosis, prognosis, and treatment of OCs; and clarify accurate molecular mechanisms and therapeutic targets. These findings benefit for the discovery of effective and reliable biomarkers based on pathway networks for OC predictive and personalized medicine.

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Quantitative proteomic analysis of ovarian cancer cells identified mitochondrial proteins associated with Paclitaxel resistance.

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Quantitative proteomic analysis of ovarian cancer cells identified mitochondrial proteins associated with Paclitaxel resistance.

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