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Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis

Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis

Published on: November 18, 2019

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

Na Li^1,2,3, Xianquan Zhan^1,2,3,4

¹1Key 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.

The EPMA Journal

|July 2, 2019

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.

Insights

Molecular network alterations drive ovarian cancer (OC) pathogenesis. This study identified key pathway networks and biomarkers for OC diagnosis, prognosis, and treatment, aiding personalized medicine approaches.

Area of Science:

Oncology
Molecular Biology
Bioinformatics

Background:

Ovarian cancer (OC) pathogenesis is characterized by molecular network changes.
Network-based biomarkers are crucial for effective OC treatment strategies.

Purpose of the Study:

To identify key pathway-network alterations in OC.
To discover network-based biomarkers for clarifying molecular mechanisms and guiding OC treatment.

Main Methods:

Utilized Ingenuity Pathway Analysis (IPA) on 1198 human tissue mitochondrial differentially expressed proteins (mtDEPs).
Validated key mtDEPs in OC cell models using qRT-PCR and Western blot.
Integrated mtDEPs with Cancer Genome Atlas (TCGA) data for hub molecule identification and survival analysis.

Main Results:

Identified 25 significant networks and 192 canonical pathways, with 52 altered in OC pathogenesis.
Validated specific mtDEPs (e.g., TPM1, CALR, GSTP1) at mRNA and protein levels.
Discovered 102 hub molecules, with 62 significantly related to OC survival risk, leading to a seven-hub molecule signature model for risk assessment.

Conclusions:

Provided comprehensive signaling pathway network profiling for OC.
Offered data for discovering pathway network-based biomarkers for OC diagnosis, prognosis, and treatment.
Clarified molecular mechanisms and identified therapeutic targets, benefiting personalized medicine for OC.

Read full analysis

Keywords:

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

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