Quantitative analysis of the mitochondrial proteome in human ovarian carcinomas

Proteomics for Biomarker Discovery in Gynecological Cancers: A Systematic Review

The present study aims to summarize the current biomarker landscape in gynecological cancers (GCs) and incorporate bioinformatics analysis to highlight specific biological processes. The literature was retrieved from PubMed, Web of Science, Embase, Scopus, Ovid Medline, and Cochrane Library. The final search was conducted on December 7, 2022. Prospective registration was completed with the PROSPERO with registration number CRD42023477145. This systematic review covered proteomic research on biomarkers for cervical, endometrial, and ovarian cancers. The PANTHER classification system was used to classify the shortlisted candidate biomarkers (CBs), and the STRING database was utilized to visualize protein-protein interaction networks. A total of 23 articles were included in this systematic review. Consistently regulated CBs in the GCs include collagen alpha-2(I) chain, collagen alpha-1(III) chain, collagen alpha-2(V) chain, calreticulin, protein disulfide-isomerase A3, heat shock protein family A (Hsp70) member 5, prolyl 4-hydroxylase, beta polypeptide, fibrinogen alpha chain, fibrinogen gamma chain, apolipoprotein B-100, apolipoprotein C-IV, and apolipoprotein M. In conclusion, collagens, fibrinogens, chaperones, and apolipoproteins were revealed to be replicated in GCs and to be regulated consistently. These CBs contribute to GC etiology and physiology by participating in collagen fibril organization, blood coagulation, protein folding in endoplasmic reticulum, and lipid transporter activity.

Pharmacoproteomics reveals energy metabolism pathways as therapeutic targets of ivermectin in ovarian cancer toward 3P medical approaches

Objective

Ovarian cancer is the malignant tumor with the highest mortality rate in the female reproductive system, enormous socio-economic burden, and limited effective drug therapy. There is an urgent need to find novel effective drugs for ovarian cancer therapy. Our previous in vitro studies demonstrate that ivermectin effectively inhibits ovarian cancer cells and affects energy metabolism pathways. This study aims to clarify in vivo mechanisms and therapeutic targets of ivermectin in the treatment of ovarian cancer to establish predictive biomarkers, guide personalized treatments, and improve preventive strategies in the framework of 3P medicine.

Methods

A TOV-21G tumor-bearing mouse model was constructed based on histopathological data and biochemical parameters. TMT-based proteomic analysis was performed on tumor tissues from the different treatment groups. All significantly differentially abundant proteins were characterized by hierarchical clustering, Gene Ontology (GO) enrichment analyses, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In addition, the data were integrated and analyzed with the proteomic data of clinical ovarian cancer tissues from our previous study and the proteomic data of ivermectin intervention in ovarian cancer cells to identify key regulators of ivermectin.

Results

Ivermectin (10 mg/kg) had a significant anti-ovarian cancer effect in mice, with a tumor inhibitory rate of 61.5%. Molecular changes in tumor tissue of ivermectin-treated mice were established, and protein-protein interaction (PPI) analysis showed that the main differential pathway networks included the TCA cycle, propanoate metabolism, 2-0xocarboxyacid metabolism, and other pathways. Integrating our previous clinical ovarian cancer tissue and cell experimental data, this study found that ivermectin significantly interfered with the energy metabolic pathways of ovarian cancer, including glycolysis, TCA cycle, oxidative phosphorylation, and other related pathways.

Conclusions

This study evaluated the anti-ovarian cancer effect in vitro and in vivo, and its specific regulatory effect on energy metabolism. The expressions of drug target molecules in the energy metabolism pathway of ovarian cancer will be used to guide the diagnosis and prevention of ovarian cancer. The significant efficacy of ivermectin will be applied to the treatment of ovarian cancer and personalized medication. This has guiding significance for the clinical diagnosis, treatment, personalized medication, and prognosis evaluation of ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00385-1.

The Impact of Mitochondria in Ovarian Cancer Cell Metabolism, Proliferation, and Metastasis

Mitochondrial dysfunctions are significantly implicated in cancer initiation, progression, and metastasis, which have been shown for several cancers including ovarian cancer.An increase in mitochondrial dysfunction is also associated with drug resistance along with cancer progression, which in part is related to its specific microenvironment that is characterized by ascites, low glucose levels, and hypoxia that causes ovarian cancer cells to switch to mitochondrial respiration to enable their survival. Peritoneal ascitic fluid accumulation is a specific feature of ovarian cancer, and it is a major cause of its metastatic spread that also presents challenges for effective treatment. Among the treatment difficulties for ovarian cancer is the mutation rate and frequency of mtDNA in ovarian cancer tissue that can affect the efficiency of chemotherapeutic drugs. The varied and multiple mutations of different types enable metabolic reprogramming, cancer cell proliferation, and drug resistance.New specific information on mechanisms underlying several of the mitochondrial dysfunctions has led to proposing various mitochondrial determinants as targets for ovarian cancer therapy, which include targeting specific mitochondrial proteins and phosphoproteins as well as reactive oxygen species (ROS) that accumulate abnormally in cancer cells. Because of the genetically and histologically heterogeneous nature of the disease, combination therapy approaches will be necessary to combat the disease and achieve progress in effective treatment of ovarian cancer. This chapter will address (1) mitochondrial vulnerabilities underlying dysfunction and disease; (2) mitochondrial dysfunction in ovarian cancer; (3) present treatment difficulties for ovarian cancer and new potential treatment strategies to target ovarian cancer mitochondrial metabolism; and (4) biobehavioral factors influencing ovarian cancer development.

GLDC promotes colorectal cancer metastasis through epithelial-mesenchymal transition mediated by Hippo signaling pathway

Cancer metastasis remains a major cause of death in cancer patients, and epithelial-mesenchymal transition (EMT) plays a decisive role in cancer metastasis. Recently, abnormal expression of Glycine Decarboxylase (GLDC) has been demonstrated in tumor progression, and GLDC is up-regulated in cancers, such as lung, prostate, bladder, and cervical cancers. However, the exact role of GLDC in colorectal cancer (CRC) progression remains to be elucidated. The aim of our study was to explore the role of GLDC in CRC metastasis. The GSE75117 database was used to investigate GLDC expression in tumor center and invasive front tissues and we found that GLDC expression levels were higher in the invasive front tissue. GLDC expression levels were negatively correlated with the prognosis of CRC patients. In vitro studies have showed that GLDC can promote invasion and migration of CRC cells by inhibiting the Hippo signaling pathway and regulating the EMT process. Blocking the Hippo signaling pathway with Verteporfin reduced the effect of GLDC on CRC metastasis. In vivo metastasis assays further confirmed that tail vein injection of GLDC+/+ cells induced more lung metastasis, compared to normal CRC cells. The results of this study suggest that GLDC promotes EMT through the Hippo signaling pathway, providing a new therapeutic target for CRC metastasis.

Mitochondrial plasticity supports proliferative outgrowth and invasion of ovarian cancer spheroids during adhesion

Background

Ovarian cancer cells aggregate during or after exfoliation from the primary tumor to form threedimensional spheroids. Spheroid formation provides a survival advantage during peritoneal dissemination in nutrient and oxygen-depleted conditions which is accompanied by a suppressed metabolic phenotype and fragmented mitochondria. Upon arrival to their metastatic sites, spheroids adhere to peritoneal organs and transition to a more epithelial phenotype to support outgrowth and invasion. In this study, we investigated the plasticity of mitochondrial morphology, dynamics, and function upon adhesion.

Methods

Using our slow-developing (MOSE-L) and fast-developing (MOSE-LTICv) ovarian cancer models, we mimicked adhesion and reoxygenation conditions by plating the spheroids onto tissue culture dishes and changing culture conditions from hypoxia and low glucose to normoxia with high glucose levels after adhesion. We used Western Blot, microscopy and Seahorse analyses to determine the plasticity of mitochondrial morphology and functions upon adhesion, and the impact on proliferation and invasion capacities.

Results

Independent of culture conditions, all spheroids adhered to and began to grow onto the culture plates. While the bulk of the spheroid was unresponsive, the mitochondrial morphology in the outgrowing cells was indistinguishable from cells growing in monolayers, indicating that mitochondrial fragmentation in spheroids was indeed reversible. This was accompanied by an increase in regulators of mitobiogenesis, PGC1a, mitochondrial mass, and respiration. Reoxygenation increased migration and invasion in both cell types but only the MOSE-L responded with increased proliferation to reoxygenation. The highly aggressive phenotype of the MOSE-LTICv was characterized by a relative independence of oxygen and the preservation of higher levels of proliferation, migration and invasion even in limiting culture conditions but a higher reliance on mitophagy. Further, the outgrowth in these aggressive cells relies mostly on proliferation while the MOSE-L cells both utilize proliferation and migration to achieve outgrowth. Suppression of proliferation with cycloheximide impeded aggregation, reduced outgrowth and invasion via repression of MMP2 expression and the flattening of the spheroids.

Discussion

Our studies indicate that the fragmentation of the mitochondria is reversible upon adhesion. The identification of regulatory signaling molecules and pathways of these key phenotypic alterations that occur during primary adhesion and invasion is critical for the identification of druggable targets for therapeutic intervention to prevent aggressive metastatic disease.

Evaluation of mitochondrial biogenesis and ROS generation in high-grade serous ovarian cancer

Introduction

Ovarian cancer is one of the leading causes of death for women with cancer worldwide. Energy requirements for tumor growth in epithelial high-grade serous ovarian cancer (HGSOC) are fulfilled by a combination of aerobic glycolysis and oxidative phosphorylation (OXPHOS). Although reduced OXPHOS activity has emerged as one of the significant contributors to tumor aggressiveness and chemoresistance, up-regulation of mitochondrial antioxidant capacity is required for matrix detachment and colonization into the peritoneal cavity to form malignant ascites in HGSOC patients. However, limited information is available about the mitochondrial biogenesis regulating OXPHOS capacity and generation of mitochondrial reactive oxygen species (mtROS) in HGSOC.

Methods

To evaluate the modulation of OXPHOS in HGSOC tumor samples and ovarian cancer cell lines, we performed proteomic analyses of proteins involved in mitochondrial energy metabolism and biogenesis and formation of mtROS by immunoblotting and flow cytometry, respectively.

Results and discussion

We determined that the increased steady-state expression levels of mitochondrial- and nuclear-encoded OXPHOS subunits were associated with increased mitochondrial biogenesis in HGSOC tumors and ovarian cancer cell lines. The more prominent increase in MT-COII expression was in agreement with significant increase in mitochondrial translation factors, TUFM and DARS2. On the other hand, the ovarian cancer cell lines with reduced OXPHOS subunit expression and mitochondrial translation generated the highest levels of mtROS and significantly reduced SOD2 expression. Evaluation of mitochondrial biogenesis suggested that therapies directed against mitochondrial targets, such as those involved in transcription and translation machineries, should be considered in addition to the conventional chemotherapies in HGSOC treatment.

The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas

A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.

Metabolomic profiling of cerebrospinal fluid reveals an early diagnostic model for central nervous system involvement in acute lymphoblastic leukaemia

The pathogenesis of central nervous system involvement (CNSI) in patients with acute lymphoblastic leukaemia (ALL) remains unclear and a robust biomarker of early diagnosis is missing. An untargeted cerebrospinal fluid (CSF) metabolomics analysis was performed to identify independent risk biomarkers that could diagnose CNSI at the early stage. Thirty-three significantly altered metabolites between ALL patients with and without CNSI were identified, and a CNSI evaluation score (CES) was constructed to predict the risk of CNSI based on three independent risk factors (8-hydroxyguanosine, l-phenylalanine and hypoxanthine). This predictive model could diagnose CNSI with positive prediction values of 95.9% and 85.6% in the training and validation sets respectively. Moreover, CES score increased with the elevated level of central nervous system (CNSI) involvement. In addition, we validated this model by tracking the changes in CES at different stages of CNSI, including before CNSI and during CNSI, and in remission after CNSI. The CES showed good ability to predict the progress of CNSI. Finally, we constructed a nomogram to predict the risk of CNSI in clinical practice, which performed well compared with observed probability. This unique CSF metabolomics study may help us understand the pathogenesis of CNSI, diagnose CNSI at the early stage, and sequentially achieve personalized precision treatment.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

Mitochondrial Dysfunction Pathway Alterations Offer Potential Biomarkers and Therapeutic Targets for Ovarian Cancer

The mitochondrion is a very versatile organelle that participates in some important cancer-associated biological processes, including energy metabolism, oxidative stress, mitochondrial DNA (mtDNA) mutation, cell apoptosis, mitochondria-nuclear communication, dynamics, autophagy, calcium overload, immunity, and drug resistance in ovarian cancer. Multiomics studies have found that mitochondrial dysfunction, oxidative stress, and apoptosis signaling pathways act in human ovarian cancer, which demonstrates that mitochondria play critical roles in ovarian cancer. Many molecular targeted drugs have been developed against mitochondrial dysfunction pathways in ovarian cancer, including olive leaf extract, nilotinib, salinomycin, Sambucus nigra agglutinin, tigecycline, and eupatilin. This review article focuses on the underlying biological roles of mitochondrial dysfunction in ovarian cancer progression based on omics data, potential molecular relationship between mitochondrial dysfunction and oxidative stress, and future perspectives of promising biomarkers and therapeutic targets based on the mitochondrial dysfunction pathway for ovarian cancer.

HSP60 Regulates Lipid Metabolism in Human Ovarian Cancer

Accumulating evidence demonstrates that cancer is an oxidative stress-related disease, and oxidative stress is closely linked with heat shock proteins (HSPs). Lipid oxidative stress is derived from lipid metabolism dysregulation that is closely associated with the development and progression of malignancies. This study sought to investigate regulatory roles of HSPs in fatty acid metabolism abnormality in ovarian cancer. Pathway network analysis of 5115 mitochondrial expressed proteins in ovarian cancer revealed various lipid metabolism pathway alterations, including fatty acid degradation, fatty acid metabolism, butanoate metabolism, and propanoate metabolism. HSP60 regulated the expressions of lipid metabolism proteins in these lipid metabolism pathways, including ADH5, ECHS1, EHHADH, HIBCH, SREBP1, ACC1, and ALDH2. Further, interfering HSP60 expression inhibited migration, proliferation, and cell cycle and induced apoptosis of ovarian cancer cells in vitro. In addition, mitochondrial phosphoproteomics and immunoprecipitation-western blot experiments identified and confirmed that phosphorylation occurred at residue Ser70 in protein HSP60, which might regulate protein folding of ALDH2 and ACADS in ovarian cancers. These findings clearly demonstrated that lipid metabolism abnormality occurred in oxidative stress-related ovarian cancer and that HSP60 and its phosphorylation might regulate this lipid metabolism abnormality in ovarian cancer. It opens a novel vision in the lipid metabolism reprogramming in human ovarian cancer.

The mitochondrial landscape of ovarian cancer: emerging insights

Ovarian cancer (OC) is known to be the most lethal cancer in women worldwide, and its etiology is poorly understood. Recent studies show that mitochondrial DNA (mtDNA) content as well as mtDNA and nuclear genes encoding mitochondrial proteins influence OC risk. This review presents an overview of role of mitochondrial genetics in influencing OC development and discusses the contribution of mitochondrial proteome in OC development, progression and therapy. A role of mitochondrial genetics in racial disparity is also highlighted. In-depth understanding of role of mitochondria in OC will help develop strategies toward prevention and treatment and improving overall survival in women with OC.

Human growth hormone proteoform pattern changes in pituitary adenomas: Potential biomarkers for 3P medical approaches

Relevance

Human growth hormone (hGH) is synthesized, stored, and secreted by somatotroph cells in the pituitary gland, and promotes human growth and metabolism. Compared to a normal pituitary, a GH-secreting pituitary adenoma can secrete excessive GH to cause pathological changes in body tissues. GH proteoform changes would be associated with GH-related disease pathogenesis.

Purpose

This study aimed to elucidate changes in GH proteoforms between GH-secreting pituitary adenomas and control pituitaries for the predictive diagnostics, targeted prevention, and personalization of medical services.

Methods

The isoelectric point (pI) and relative molecular mass (Mr) are two basic features of a proteoform that can be used to effectively array and detect proteoforms with two-dimensional gel electrophoresis (2DGE) and 2DGE-based western blot. GH proteoforms were characterized with liquid chromatography (LC) and mass spectrometry (MS). Phosphoproteomics, ubiquitinomics, acetylomics, and bioinformatics were used to analyze post-translational modifications (PTMs) of GH proteoforms in GH-secreting pituitary adenoma tissues and control pituitaries.

Results

Sixty-six 2D gel spots were found to contain hGH, including 46 spots (46 GH proteoforms) in GH-secreting pituitary adenomas and 35 spots (35 GH proteoforms) in control pituitaries. Further, 35 GH proteoforms in control pituitary tissues were matched with 35 of 46 GH proteoforms in GH-secreting pituitary adenoma tissues; and 11 GH proteoforms were presented in only GH-secreting pituitary adenoma tissues but not in control pituitary tissues. The matched 35 GH proteoforms showed quantitative changes in GH-secreting pituitary adenomas compared to the controls. The quantitative levels of those 46 GH proteoforms in GH-secreting pituitary adenomas were significantly different from those 35 GH proteoforms in control pituitaries. Meanwhile, different types of PTMs were identified among those GH proteoforms. Phosphoproteomics identified phosphorylation at residues Ser77, Ser132, Ser134, Thr174, and Ser176 in hGH. Ubiquitinomics identified ubiquitination at residue Lys96 in hGH. Acetylomics identified acetylation at reside Lys171 in hGH. Deamination was identified at residue Asn178 in hGH.

Conclusion

These findings provide the first hGH proteoform pattern changes in GH-secreting pituitary adenoma tissues compared to control pituitary tissues, and the status of partial PTMs in hGH proteoforms. Those data provide in-depth insights into biological roles of hGH in GH-related diseases, and identify hGH proteoform pattern biomarkers for treatment of a GH-secreting pituitary adenoma in the context of 3P medicine -predictive diagnostics, targeted prevention, and personalization of medical services.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-021-00232-7.

SILAC quantitative proteomics analysis of ivermectin-related proteomic profiling and molecular network alterations in human ovarian cancer cells

The antiparasitic agent ivermectin offers more promises to treat a diverse range of diseases. However, a comprehensive proteomic analysis of ivermectin-treated ovarian cancer (OC) cells has not been performed. This study sought to identify ivermectin-related proteomic profiling and molecular network alterations in human OC cells. Stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative proteomics was used to study the human OC TOV-21G cells. After TOV-21G cells underwent 10 passages in SILAC-labeled growth media, TOV-21G cells were treated with 10 ml of 20 μmol/L ivermectin in cell growing medium for 24 h. The SILAC-labeled proteins were digested with trypsin; tryptic peptides were identified with mass spectrometry (MS). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to mine signaling pathway alterations with ivermectin-related proteins in TOV-21G cells. Gene ontology (GO) analysis was used to explore biological functions of ivermectin-related proteins, including biological processes (BPs), cellular components (CCs), and molecular functions (MFs). The protein-protein interaction network was analyzed with molecular complex detection (MCODE) to identify hub modules. In total, 4,447 proteins were identified in ivermectin-treated TOV-21G cells. KEGG analysis revealed 89 statistically significant signaling pathways. Interestingly, the clustering analysis of these pathways showed that ivermectin was involved in various cancer pathogenesis processes, including modulation of replication, RNA metabolism, and translational machinery. GO analysis revealed 69 statistically significant CCs, 87 MFs, and 62 BPs. Furthermore, MCODE analysis identified five hub modules, including 147 hub molecules. Those hub modules involved ribosomal proteins, RNA-binding proteins, cell-cycle progression-related proteins, proteasome subunits, and minichromosome maintenance proteins. These findings demonstrate that SILAC quantitative proteomics is an effective method to analyze ivermectin-treated cells, provide the first ivermectin-related proteomic profiling and molecular network alterations in human OC cells, and provide deeper insights into molecular mechanisms and functions of ivermectin to inhibit OC cells.

The Mitochondrial Proteome of Tumor Cells: A SnapShot on Methodological Approaches and New Biomarkers

Simple Summary

Mitochondria are central hubs of cellular signaling, energy metabolism, and redox balance. The plasticity of these cellular organelles is an essential requisite for the cells to cope with different stimuli and stress conditions. Cancer cells are characterized by changes in energy metabolism, mitochondrial signaling, and dynamics. These changes are driven by alterations in the mitochondrial proteome. For this reason, in the last years a focus of basic and cancer research has been the implementation and optimization of technologies to investigate changes in the mitochondrial proteome during cancer initiation and progression. This review presents an overview of the most used technologies to investigate the mitochondrial proteome and recent evidence on changes in the expression levels and delocalization of certain proteins in and out the mitochondria for shaping the functional properties of tumor cells.

Abstract

Mitochondria are highly dynamic and regulated organelles implicated in a variety of important functions in the cell, including energy production, fatty acid metabolism, iron homeostasis, programmed cell death, and cell signaling. Changes in mitochondrial metabolism, signaling and dynamics are hallmarks of cancer. Understanding whether these modifications are associated with alterations of the mitochondrial proteome is particularly relevant from a translational point of view because it may contribute to better understanding the molecular bases of cancer development and progression and may provide new potential prognostic and diagnostic biomarkers as well as novel molecular targets for anti-cancer treatment. Making an inventory of the mitochondrial proteins has been particularly challenging given that there is no unique consensus targeting sequence that directs protein import into mitochondria, some proteins are present at very low levels, while other proteins are expressed only in some cell types, in a particular developmental stage or under specific stress conditions. This review aims at providing the state-of-the-art on methodologies used to characterize the mitochondrial proteome in tumors and highlighting the biological relevance of changes in expression and delocalization of proteins in and out the mitochondria in cancer biology.

Quantitative proteomics revealed energy metabolism pathway alterations in human epithelial ovarian carcinoma and their regulation by the antiparasite drug ivermectin: data interpretation in the context of 3P medicine

Objective

Energy metabolism abnormality is the hallmark in epithelial ovarian carcinoma (EOC). This study aimed to investigate energy metabolism pathway alterations and their regulation by the antiparasite drug ivermectin in EOC for the discovery of energy metabolism pathway-based molecular biomarker pattern and therapeutic targets in the context of predictive, preventive, and personalized medicine (PPPM) in EOC.

Methods

iTRAQ-based quantitative proteomics was used to identify mitochondrial differentially expressed proteins (mtDEPs) between human EOC and control mitochondrial samples isolated from 8 EOC and 11 control ovary tissues from gynecologic surgery of Chinese patients, respectively. Stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative proteomics was used to analyze the protein expressions of energy metabolic pathways in EOC cells treated with and without ivermectin. Cell proliferation, cell cycle, apoptosis, and important molecules in energy metabolism pathway were examined before and after ivermectin treatment of different EOC cells.

Results

In total, 1198 mtDEPs were identified, and various mtDEPs were related to energy metabolism changes in EOC, with an interesting result that EOC tissues had enhanced abilities in oxidative phosphorylation (OXPHOS), Kreb's cycle, and aerobic glycolysis, for ATP generation, with experiment-confirmed upregulations of UQCRH in OXPHOS; IDH2, CS, and OGDHL in Kreb's cycle; and PKM2 in glycolysis pathways. Importantly, PDHB that links glycolysis with Kreb's cycle was upregulated in EOC. SILAC-based quantitative proteomics found that the protein expression levels of energy metabolic pathways were regulated by ivermectin in EOC cells. Furthermore, ivermectin demonstrated its strong abilities to inhibit proliferation and cell cycle and promote apoptosis in EOC cells, through molecular networks to target PFKP in glycolysis; IDH2 and IDH3B in Kreb's cycle; ND2, ND5, CYTB, and UQCRH in OXPHOS; and MCT1 and MCT4 in lactate shuttle to inhibit EOC growth.

Conclusions

Our findings revealed that the Warburg and reverse Warburg effects coexisted in human ovarian cancer tissues, provided the first multiomics-based molecular alteration spectrum of ovarian cancer energy metabolism pathways (aerobic glycolysis, Kreb's cycle, oxidative phosphorylation, and lactate shuttle), and demonstrated that the antiparasite drug ivermectin effectively regulated these changed molecules in energy metabolism pathways and had strong capability to inhibit cell proliferation and cell cycle progression and promote cell apoptosis in ovarian cancer cells. The observed molecular changes in energy metabolism pathways bring benefits for an in-depth understanding of the molecular mechanisms of energy metabolism heterogeneity and the discovery of effective biomarkers for individualized patient stratification and predictive/prognostic assessment and therapeutic targets/drugs for personalized therapy of ovarian cancer patients.

A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

Metabolic alterations play crucial roles in carcinogenesis, tumor progression, and prognosis in clear cell renal cell carcinoma (ccRCC). A risk score (RS) model for ccRCC consisting of disease-associated metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal and tumor groups from the cancer genome atlas. Out of 70 KEGG metabolic pathways, we found seven and two pathways to be significantly enriched in our normal and tumor groups, respectively. We identified 113 genes enriched in these nine pathways. We further filtered 47 prognostic-related metabolic genes and used Least absolute shrinkage and selection operator (LASSO) analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3, and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic curves, the results were promising. Additionally, multivariate Cox analysis revealed the RS to be an independent prognostic factor. Thereafter, we considered all the independent factors and constructed a nomogram model, which manifested in better prediction capability. We validated our results using a dataset from ArrayExpress and through qRT-PCR. In summary, our study provided a metabolic gene-based RS model that can be used as a prognostic predictor for patients with ccRCC.

Whole-transcriptome Analysis of Fully Viable Energy Efficient Glycolytic-null Cancer Cells Established by Double Genetic Knockout of Lactate Dehydrogenase A/B or Glucose-6-Phosphate Isomerase

BACKGROUND/AIM

Nearly all mammalian tumors of diverse tissues are believed to be dependent on fermentative glycolysis, marked by elevated production of lactic acid and expression of glycolytic enzymes, most notably lactic acid dehydrogenase (LDH). Therefore, there has been significant interest in developing chemotherapy drugs that selectively target various isoforms of the LDH enzyme. However, considerable questions remain as to the consequences of biological ablation of LDH or upstream targeting of the glycolytic pathway.

MATERIALS AND METHODS

In this study, we explore the biochemical and whole transcriptomic effects of CRISPR-Cas9 gene knockout (KO) of lactate dehydrogenases A and B [LDHA/B double KO (DKO)] and glucose-6-phosphate isomerase (GPI KO) in the human colon cancer cell line LS174T, using Affymetrix 2.1 ST arrays.

RESULTS

The metabolic biochemical profiles corroborate that relative to wild type (WT), LDHA/B DKO produced no lactic acid, (GPI KO) produced minimal lactic acid and both KOs displayed higher mitochondrial respiration, and minimal use of glucose with no loss of cell viability. These findings show a high biochemical energy efficiency as measured by ATP in glycolysis-null cells. Next, transcriptomic analysis conducted on 48,226 mRNA transcripts reflect 273 differentially expressed genes (DEGS) in the GPI KO clone set, 193 DEGS in the LDHA/B DKO clone set with 47 DEGs common to both KO clones. Glycolytic-null cells reflect up-regulation in gene transcripts typically associated with nutrient deprivation / fasting and possible use of fats for energy: thioredoxin interacting protein (TXNIP), mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2), PPARγ coactivator 1α (PGC-1α), and acetyl-CoA acyltransferase 2 (ACAA2). Other changes in non-ergometric transcripts in both KOs show losses in "stemness", WNT signaling pathway, chemo/radiation resistance, retinoic acid synthesis, drug detoxification, androgen/estrogen activation, and extracellular matrix reprogramming genes.

CONCLUSION

These findings demonstrate that: 1) The "Warburg effect" is dispensable, 2) loss of the LDHAB gene is not only inconsequential to viability but fosters greater mitochondrial energy, and 3) drugs that target LDHA/B are likely to be ineffective without a plausible combination second drug target.

MASS SPECTROMETRY-BASED MITOCHONDRIAL PROTEOMICS IN HUMAN OVARIAN CANCERS

The prominent characteristics of mitochondria are highly dynamic and regulatory, which have crucial roles in cell metabolism, biosynthetic, senescence, apoptosis, and signaling pathways. Mitochondrial dysfunction might lead to multiple serious diseases, including cancer. Therefore, identification of mitochondrial proteins in cancer could provide a global view of tumorigenesis and progression. Mass spectrometry-based quantitative mitochondrial proteomics fulfils this task by enabling systems-wide, accurate, and quantitative analysis of mitochondrial protein abundance, and mitochondrial protein posttranslational modifications (PTMs). Multiple quantitative proteomics techniques, including isotope-coded affinity tag, stable isotope labeling with amino acids in cell culture, isobaric tags for relative and absolute quantification, tandem mass tags, and label-free quantification, in combination with different PTM-peptide enrichment methods such as TiO₂ enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides, increase flexibility for researchers to study mitochondrial proteomes. This article reviews isolation and purification of mitochondria, quantitative mitochondrial proteomics, quantitative mitochondrial phosphoproteomics, mitochondrial protein-involved signaling pathway networks, mitochondrial phosphoprotein-involved signaling pathway networks, integration of mitochondrial proteomic and phosphoproteomic data with whole tissue proteomic and transcriptomic data and clinical information in ovarian cancers (OC) to in-depth understand its molecular mechanisms, and discover effective mitochondrial biomarkers and therapeutic targets for predictive, preventive, and personalized treatment of OC. This proof-of-principle model about OC mitochondrial proteomics is easily implementable to other cancer types. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Anti-parasite drug ivermectin can suppress ovarian cancer by regulating lncRNA-EIF4A3-mRNA axes

RELEVANCE

Ivermectin, as an old anti-parasite drug, can suppress almost completely the growth of various human cancers, including ovarian cancer (OC). However, its anticancer mechanism remained to be further studied at the molecular levels. Ivermectin-related molecule-panel changes will serve a useful tool for its personalized drug therapy and prognostic assessment in OCs.

PURPOSE

To explore the functional significance of ivermectin-mediated lncRNA-EIF4A3-mRNA axes in OCs and ivermectin-related molecule-panel for its personalized drug therapy monitoring.

METHODS

Based on our previous study, a total of 16 lncRNA expression patterns were analyzed using qRT-PCR before and after ivermectin-treated different OC cell lines (TOV-21G and A2780). Stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative proteomics was used to analyze the protein expressions of EIF4A3 and EIF4A3-binding mRNAs in ovarian cancer cells treated with and without ivermectin. A total of 411 OC patients from the Cancer Genome Atlas (TCGA) database with the selected lncRNA expressions and the corresponding clinical data were included. Lasso regression was constructed to examine the relationship between lncRNA signature and OC survival risk. The overall survival analysis between high-risk and low-risk groups used the Kaplan-Meier method. Heatmap showed the correlation between risk groups and clinical characteristics. The univariate and multivariate models were established with Cox regression.

RESULTS

SILAC-based quantitative proteomics found the protein expression levels of EIF4A3 and 116 EIF4A3-binding mRNAs were inhibited by ivermectin in OC cells. Among the analyzed 16 lncRNAs (HCG15, KIF9-AS1, PDCD4-AS1, ZNF674-AS1, ZNRF3-AS1, SOS1-IT1, LINC00565, SNHG3, PLCH1-AS1, WWTR1-AS1, LINC00517, AL109767.1, STARD13-IT1, LBX2-AS1, LEMD1-AS1, and HOXC-AS3), only 7 lncRNAs (HCG15, KIF9-AS1, PDCD4-AS1, ZNF674-AS1, ZNRF3-AS1, SOS1-IT1, and LINC00565) were obtained for further lasso regression when combined with the results of drug testing and overall survival analysis. Lasso regression identified the prognostic model of ivermectin-related three-lncRNA signature (ZNRF3-AS1, SOS1-IT1, and LINC00565). The high-risk and low-risk groups based on the prognostic model were significantly related to overall survival and clinicopathologic characteristics (survival status, lymphatic invasion, cancer status, and clinical stage) in OC patients and remained independent risk factors according to multivariate COX analysis (p < 0.05).

CONCLUSION

Those findings provided the potential targeted lncRNA-EIF4A3-mRNA pathways of ivermectin in OC, and constructed the effective prognostic model, which benefits discovery of novel mechanism of ivermectin to suppress ovarian cancer cells, and the ivermectin-related molecule-panel changes benefit for its personalized drug therapy and prognostic assessment towards its predictive, preventive, and personalized medicine (PPPM) in OCs.

Differential DNA methylation in high-grade serous ovarian cancer (HGSOC) is associated with tumor behavior

The epigenome offers an additional facet of cancer that can help categorize patients into those at risk of disease, recurrence, or treatment failure. We conducted a retrospective, nested, case-control study of advanced and recurrent high-grade serous ovarian cancer (HGSOC) patients in which we assessed epigenome-wide association using Illumina methylationEPIC arrays to characterize DNA methylation status and RNAseq to evaluate gene expression. Comparing HGSOC tumors with normal fallopian tube tissues we observe global hypomethylation but with skewing towards hypermethylation when interrogating gene promoters. In total, 5,852 gene interrogating probes revealed significantly different methylation. Within HGSOC, 57 probes highlighting 17 genes displayed significant differential DNA methylation between primary and recurrent disease. Between optimal vs suboptimal surgical outcomes 99 probes displayed significantly different methylation but only 29 genes showed an inverse correlation between methylation status and gene expression. Overall, differentially methylated genes point to several pathways including RAS as well as hippo signaling in normal vs primary HGSOC; valine, leucine, and isoleucine degradation and endocytosis in primary vs recurrent HGSOC; and pathways containing immune driver genes in optimal vs suboptimal surgical outcomes. Thus, differential DNA methylation identified numerous genes that could serve as potential biomarkers and/or therapeutic targets in HGSOC.

Mitochondrial Dysfunction Pathway Networks and Mitochondrial Dynamics in the Pathogenesis of Pituitary Adenomas

Mitochondrion is a multi-functional organelle, which is associated with various signaling pathway networks, including energy metabolism, oxidative stress, cell apoptosis, cell cycles, autophagy, and immunity process. Mitochondrial proteins have been discovered to modulate these signaling pathway networks, and multiple biological behaviors to adapt to various internal environments or signaling events of human pathogenesis. Accordingly, mitochondrial dysfunction that alters the bioenergetic and biosynthetic state might contribute to multiple diseases, including cell transformation and tumor. Multiomics studies have revealed that mitochondrial dysfunction, oxidative stress, and cell cycle dysregulation signaling pathways operate in human pituitary adenomas, which suggest mitochondria play critical roles in pituitary adenomas. Some drugs targeting mitochondria are found as a therapeutic strategy for pituitary adenomas, including melatonin, melatonin inhibitors, temozolomide, pyrimethamine, 18 beta-glycyrrhetinic acid, gossypol acetate, Yougui pill, T-2 toxin, grifolic acid, cyclosporine A, dopamine agonists, and paeoniflorin. This article reviews the latest experimental evidence and potential biological roles of mitochondrial dysfunction and mitochondrial dynamics in pituitary adenoma progression, potential molecular mechanisms between mitochondria and pituitary adenoma progression, and current status and perspectives of mitochondria-based biomarkers and targeted drugs for effective management of pituitary adenomas.

Identification of clinical trait-related lncRNA and mRNA biomarkers with weighted gene co-expression network analysis as useful tool for personalized medicine in ovarian cancer

RELEVANCE

The pathogenesis and biomarkers of ovarian cancer (OC) remain not well-known in diagnosis, effective therapy, and prognostic assessment in OC personalized medicine. The novel identified lncRNA and mRNA biomarkers from gene co-expression modules associated with clinical traits provide new insight for effective treatment of ovarian cancer.

PURPOSE

Long non-coding RNAs (lncRNAs) are relevant to tumorigenesis via multiple mechanisms. This study aimed to investigate cancer-specific lncRNAs and mRNAs, and their related networks in OCs.

METHODS

This study comprehensively analyzed lncRNAs and mRNAs with associated competing endogenous RNA (ceRNA) network and lncRNA-RNA binding protein-mRNA network in the OC tissues in the Cancer Genome Atlas, including 2562 cancer-specific lncRNAs (n = 352 OC tissues) and 5000 mRNAs (n = 359 OC tissues). The weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression gene modules and their relationship with clinical traits. The statistically significant difference of identified lncRNAs and mRNAs was confirmed with qRT-PCR in OC cells.

RESULTS

An lncRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tissues source site, and vascular invasion, and identified 16 lncRNAs (ACTA2-AS1, CARD8-AS1, HCP5, HHIP-AS1, HOTAIRM1, ITGB2-AS1, LINC00324, LINC00605, LINC01503, LINC01547, MIR31HG, MIR155HG, OTUD6B-AS1, PSMG3-AS1, SH3PXD2A-AS1, and ZBED5-AS1) that were significantly related to overall survival in OC patients. An mRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tumor residual disease, and vascular invasion; and identified 21 hub-mRNA molecules and 11 mRNAs (FBN3, TCF7L1, SBK1, TRO, TUBB2B, PLCG1, KIAA1549, PHC1, DNMT3A, LAMA1, and C10orf82) that were closely linked with OC patients' overall survival. Moreover, the prognostic model of five-gene signature (OTUD6B-AS1, PSMG3-AS1, ZBED5-AS1, SBK1, and PLCG1) was constructed to predict risk score in OC patients. Furthermore, starBase bioinformatics constructed the lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA networks in OCs.

CONCLUSION

These new findings showed that lncRNA-related networks in OCs are a useful resource for identification of biomarkers in OCs.

Quantitative analysis of the human ovarian carcinoma mitochondrial phosphoproteome

To investigate the existence and their potential biological roles of mitochondrial phosphoproteins (mtPPs) in human ovarian carcinoma (OC), mitochondria purified from OC and control tissues were analyzed with TiO₂ enrichment-based iTRAQ quantitative proteomics. Totally 67 mtPPs with 124 phosphorylation sites were identified, which of them included 48 differential mtPPs (mtDPPs). Eighteen mtPPs were reported previously in OCs, and they were consistent in this study compared to previous literature. GO analysis revealed those mtPPs were involved in multiple cellular processes. PPI network indicated that those mtPPs were correlated mutually, and some mtPPs acted as hub molecules, such as EIF2S2, RPLP0, RPLP2, CFL1, MYH10, HSP90, HSPD1, PSMA3, TMX1, VDAC2, VDAC3, TOMM22, and TOMM20. Totally 32 mtPP-pathway systems (p<0.05) were enriched and clustered into 15 groups, including mitophagy, apoptosis, deubiquitination, signaling by VEGF, RHO-GTPase effectors, mitochondrial protein import, translation initiation, RNA transport, cellular responses to stress, and c-MYC transcriptional activation. Totally 29 mtPPs contained a certain protein domains. Upstream regulation analysis showed that TP53, TGFB1, dexamethasone, and thapsigargin might act as inhibitors, and L-dopa and forskolin might act as activators. This study provided novel insights into mitochondrial protein phosphorylations and their potential roles in OC pathogenesis and offered new biomarker resource for OCs.

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

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.