Format

Send to

Choose Destination
EPMA J. 2019 Jun 8;10(2):153-172. doi: 10.1007/s13167-019-00170-5. eCollection 2019 Jun.

Signaling pathway network alterations in human ovarian cancers identified with quantitative mitochondrial proteomics.

Li N1,2,3, Zhan X1,2,3,4.

Author information

1
1Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008 Hunan People's Republic of China.
2
2Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008 Hunan People's Republic of China.
3
3State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008 Hunan People's Republic of China.
4
4National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 88 Xiangya Road, Changsha, 410008 Hunan People's Republic of China.

Abstract

Relevance:

Molecular network changes are the hallmark of the pathogenesis of ovarian cancers (OCs). Network-based biomarkers benefit for the effective treatment of OC.

Purpose:

This study sought to identify key pathway-network alterations and network-based biomarkers for clarification of molecular mechanisms and treatment of OCs.

Methods:

Ingenuity 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.

Results:

Pathway 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.

Conclusion:

These 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.

KEYWORDS:

Biomarkers; Hub molecules; Mitochondrial proteomics; Ovarian cancer; Pathway networks; Predictive preventive personalized medicine

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center