Energy Metabolism Heterogeneity-Based Molecular Biomarkers for Ovarian Cancer

Energy metabolism heterogeneity is a hallmark in ovarian cancer; namely, the Warburg and reverse Warburg effects coexist in ovarian cancer. Exploration of energy metabolism heterogeneity benefits the discovery of the effective biomarkers for ovarian cancers. The integrative analysis of transcriptomics (20,115 genes in 419 ovarian cancer samples), proteomics (205 differentially expressed proteins), and mitochondrial proteomics (1198 mitochondrial differentially expressed proteins) revealed (i) the upregulations of rate-limiting enzymes PKM2 in glycolysis, IDH2 in Krebs cycle, and UQCRH in oxidative phosphorylation (OXPHOS) pathways, (ii) the upregulation of PDHB that converts pyruvate from glycolysis into acetyl-CoA in Krebs cycle, and (iii) that miRNA (hsa-miR-186-5p) and RNA-binding protein (EIF4AIII) had target sites in those key proteins in energy metabolism pathways. Furthermore, lncRNA SNHG3 interacted with miRNA (hsa-miR-186-5p) and RNA-binding protein (EIF4AIII). Those results were confirmed in the ovarian cancer cell model and tissues. It clearly concluded that lncRNA SNHG3 regulates energy metabolism through miRNA (hsa-miR-186-5p) and RNA-binding protein (EIF4AIII) to regulate the key proteins in the energy metabolism pathways. SNHG3 inhibitor might interfere with the energy metabolism to treat ovarian cancers. These findings provide more accurate understanding of molecular mechanisms of ovarian cancers and discovery of effective energy-metabolism-heterogeneity therapeutic drug for ovarian cancers

Mitochondrial Proteomic and Molecular Network Alterations in Human Ovarian Cancers

Mitochondrion is a multi-functional organelle, which plays important role in human ovarian cancers. Mitochondrial quantitative proteomics was used to detect, identify, and quantify proteins from mitochondrial samples prepared from ovarian cancer and normal control ovary tissues. A total of 5115 mitochondrial proteins and 1198 mitochondrial differentially expressed proteins (mtDEPs) were identified in human ovarian cancer compared to control tissues. Pathway network analysis revealed multiple pathway network changes to involve those mitochondrial proteins and mtDEPs. These findings provide the scientific data about the role of mitochondria plays in ovarian cancer, and offer the source for discovery of mitochondrial biomarker for ovarian cancers

Application of Two-Dimensional Gel Electrophoresis in Combination with Mass Spectrometry in the Study of Hormone Proteoforms

Hormone is a category of important endocrine regulatory proteins in human endocrine systems. Clarification of hormone proteoforms directly leads to understanding of its biological roles. Two-dimensional gel electrophoresis (2DGE) in combination with mass spectrometry (MS) plays important roles in identification of hormone proteoforms such as human growth hormone (hGH) proteoforms and human prolactin (hPRL) proteoforms. This book chapter will review the hormone proteoforms focusing on hGH and hPRL, the methodology of hormone proteoform study, and future perspective of human hormone proteoform study to find biomarkers for in-depth understanding of molecular mechanisms, and individualized and precise diagnosis, therapy, and prognostic assessment of hormone-related diseases

The Use of Gel Electrophoresis and Mass Spectrometry to Identify Nitroproteins in Nervous System Tumors

Protein tyrosine nitration is an important molecular event in nervous system tumor such as glioma and pituitary adenomas. It is the essential step to identify the protein targets and exact modified sites of tyrosine nitration for addressing the biological roles of protein tyrosine nitration in nervous system tumors and discovering effective biomarkers to understand in-depth molecular mechanisms and determine new diagnosis strategy and novel therapeutic targets. One/two-dimensional gel electrophoresis (1DGE, 2DGE), or nitrotyrosine affinity column (NTAC), coupled with tandem mass spectrometry (MS/MS) have been successfully applied in the analysis of nitroproteins in nervous system tumors. This article address the basic concept of protein tyrosine nitration, nitroproteomics methodology based on gel electrophoresis/immunoaffinity enrichment and tandem mass spectrometry, and the current status of nitroprotein study in nervous system tumors. The established nitroproteomics approach is easily translated to study other diseases

The MAPK Pathway-Based Drug Therapeutic Targets in Pituitary Adenomas

Mitogen-activated protein kinases (MAPKs) include ERK, p38, and JNK MAPK subfamilies, which are crucial regulators of cellular physiology, cell pathology, and many diseases including cancers. For the MAPK signaling system in pituitary adenomas (PAs), the activation of ERK signaling is generally thought to promote cell proliferation and growth; whereas the activations of p38 and JNK signaling are generally thought to promote cell apoptosis. The role of MAPK in treatment of PAs is demonstrated through the effects of currently used medications such as somatostatin analogs such as SOM230 and OCT, dopamine agonists such as cabergoline and bromocriptine, and retinoic acid which inhibit the MAPK pathway. Further, there are potential novel therapies based on putative molecular targets of the MAPK pathway, including 18beta-glycyrrhetinic acid (GA), dopamine-somatostatin chimeric compound (BIM-23A760), ursolic acid (UA), fulvestrant, Raf kinase inhibitory protein (RKIP), epidermal growth factor pathway substrate number 8 (Eps8), transmembrane protein with EGF-like and two follistatin-like domains (TMEFF2), cold inducible RNA-binding protein (CIRP), miR-16, and mammaliansterile-20-like kinase (MST4). The combined use of ERK inhibitor (e.g., SOM230, OCT, or dopamine) plus p38 activator (e.g., cabergoline, bromocriptine, and fulvestrant) and/or JNK activator (e.g., UA), or the development of single drug (e.g., BIM-23A760) to target both ERK and p38 or JNK pathways, might produce better anti-tumor effects on PAs. This article reviews the advances in understanding the role of MAPK signaling in pituitary tumorigenesis, and the MAPK pathway-based potential therapeutic drugs for PAs

The Anti-Cancer Effects of Anti-Parasite Drug Ivermectin in Ovarian Cancer

Ivermectin is an old, common, and classic anti-parasite drug, which has been found to have a broad-spectrum anti-cancer effect on multiple human cancers. This chapter will focus on the anti-cancer effects of ivermectin on ovarian cancer. First, ivermectin was found to suppress cell proliferation and growth, block cell cycle progression, and promote cell apoptosis in ovarian cancer. Second, drug pathway network, qRT-PCR, and immunoaffinity blot analyses found that ivermectin acts through molecular networks to target the key molecules in energy metabolism pathways, including PFKP in glycolysis, IDH2 and IDH3B in Kreb’s cycle, ND2, ND5, CYTB, and UQCRH in oxidative phosphorylation, and MCT1 and MCT4 in lactate shuttle, to inhibit ovarian cancer growth. Third, the integrative analysis of TCGA transcriptomics and mitochondrial proteomics in ovarian cancer revealed that 16 survival-related lncRNAs were mediated by ivermectin, SILAC quantitative proteomics analysis revealed that ivermectin extensively inhibited the expressions of RNA-binding protein EIF4A3 and 116 EIF4A3-interacted genes including those key molecules in energy metabolism pathways, and also those lncRNAs regulated EIF4A3-mRNA axes. Thus, ivermectin mediated lncRNA-EIF4A3-mRNA axes in ovarian cancer to exert its anticancer capability. Further, lasso regression identified the prognostic model of ivermectin-related three-lncRNA signature (ZNRF3-AS1, SOS1-IT1, and LINC00565), which is significantly associated with overall survival and clinicopathologic characteristics in ovarian cancer patients. These ivermectin-related molecular pattern alterations benefit for prognostic assessment and personalized drug therapy toward 3P medicine practice in ovarian cancer

Ovarian cancer subtypes based on the regulatory genes of RNA modifications: Novel prediction model of prognosis

BackgroundOvarian cancer (OC) is a female reproductive system tumor. RNA modifications play key roles in gene expression regulation. The growing evidence demonstrates that RNA methylation is critical for various biological functions, and that its dysregulation is related to the progression of cancer in human.MethodOC samples were classified into different subtypes (Clusters 1 and 2) based on various RNA-modification regulatory genes (RRGs) in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) by nonnegative matrix factorization method (NMF). Based on differently expressed RRGs (DERRGs) between clusters, a pathologically specific RNA-modification regulatory gene signature was constructed with Lasso regression. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic ability of the identified model. The correlations of clinicopathological features, immune subtypes, immune scores, immune cells, and tumor mutation burden (TMB) were also estimated between different NMF clusters and riskscore groups.ResultsIn this study, 59 RRGs in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) were obtained from TCGA database. These RRGs were interactional, and sample clusters based on these regulators were significantly correlated with survival rate, clinical characteristics (involving survival status and pathologic stage), drug sensibility, and immune microenvironment. Furthermore, Lasso regression based on these 21 DERRGs between clusters 1 and 2 constructed a four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1). Based on this signature, 307 OC patients were classified into high- and low-risk groups based on median value of riskscores from lasso regression. This identified signature was significantly associated with overall survival, radiation therapy, age, clinical stage, cancer status, and immune cells (involving CD4+ memory resting T cells, plasma cells, and Macrophages M1) of ovarian cancer patients. Further, GSEA revealed that multiple biological behaviors were significantly enriched in different groups.ConclusionsOC patients were classified into two subtypes per these RRGs. This study identified four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1) in OC, which was an independent prognostic model for patient stratification, prognostic evaluation, and prediction of response to immunotherapy in ovarian cancer by classifying OC patients into high- and low-risk groups

Prolactin Proteoform Pattern Changed in Human Pituitary Adenoma Relative to Control Pituitary Tissues

PRL gene-encoded prolactin is synthesized in the ribosome in the pituitary and then secretes into blood circulation to reach its target organ and exerts its biological roles, for example, involving in production, growth, development, immunoregulation, and metabolism. Multiple post-translational modifications and other unknown factors might be involved in this process to cause different prolactin proteoforms with differential isoelectric point (pI) and relative mass (Mr ). Pituitary adenomas are the common disease occurring in pituitary organ to affect the endocrine system. Two-dimensional gel electrophoresis (2DGE) was used to separate prolactin proteoforms according to their pI and Mr , followed by identification with Western blot and mass spectrometry (MS) analyses. Six prolactin proteoforms were identified in control pituitary tissues, and this prolactin proteoform pattern was significantly changed in different hormone subtypes of nonfunctional pituitary adenomas (NF−, LH+, FSH+, and LH+/FSH+) and prolactinomas (PRL+). Further, bioinformatics analysis revealed that different prolactin proteoforms might bind to different short- or long-PRL receptor-mediated signaling pathways. These findings clearly demonstrated that prolactin proteoform pattern existed in human pituitary and changed in different subtypes of pituitary adenomas. It is the scientific data to in-depth study prolactin functions, and to discover the prolactin proteoform biomarkers for PRL-related adenomas

Abnormal Ubiquitination of Ubiquitin-Proteasome System in Lung Squamous Cell Carcinomas

Ubiquitination is an important post-translational modification. Abnormal ubiquitination is extensively associated with cancers. Lung squamous cell carcinoma (LUSC) is the most common pathological type of lung cancer, with unclear molecular mechanism and the poor overall prognosis of LUSC patient. To uncover the existence and potential roles of ubiquitination in LUSC, label-free quantitative ubiquitomics was performed in human LUSC vs. control tissues. In total, 627 ubiquitinated proteins (UPs) with 1209 ubiquitination sites were identified, including 1133 (93.7%) sites with quantitative information and 76 (6.3%) sites with qualitative information. KEGG pathway enrichment analysis found that UPs were significantly enriched in ubiquitin-mediated proteolysis pathway (hsa04120) and proteasome complex (hsa03050). Further analysis of 400 differentially ubiquitinated proteins (DUPs) revealed that 11 subunits of the proteasome complex were differentially ubiquitinated. These findings clearly demonstrated that ubiquitination was widely present in the ubiquitin-proteasome pathway in LUSCs. At the same time, abnormal ubiquitination might affect the function of the proteasome to promote tumorigenesis and development. This book chapter discussed the status of protein ubiquitination in the ubiquitin-proteasome system (UPS) in human LUSC tissues, which offered the scientific data to elucidate the specific molecular mechanisms of abnormal ubiquitination during canceration and the development of anti-tumor drugs targeting UPS