Exploration of variations in proteome and metabolome for predictive diagnostics and personalized treatment algorithms: Innovative approach and examples for potential clinical application

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning

Cancer is an extremely complex disease and each type of cancer usually has several different subtypes. Multi-omics data can provide more comprehensive biological information for identifying and discovering cancer subtypes. However, existing unsupervised cancer subtyping methods cannot effectively learn comprehensive shared and specific information of multi-omics data. Therefore, a novel method is proposed based on shared and specific representation learning. For each omics data, two autoencoders are applied to extract shared and specific information, respectively. To reduce redundancy and mutual interference, orthogonality constraint is introduced to separate shared and specific information. In addition, contrastive learning is applied to align the shared information and strengthen their consistency. Finally, the obtained shared and specific information for all samples are used for clustering tasks to achieve cancer subtyping. Experimental results demonstrate that the proposed method can effectively capture shared and specific information of multi-omics data and outperform other state-of-the-art methods on cancer subtyping.

Micafungin effect on Pseudomonas aeruginosa metabolome, virulence and biofilm: potential quorum sensing inhibitor

The prevalence of antibiotic resistance in Pseudomonas aeruginosa places a heavy burden on the health care sectors urging the need to find alternative, non-antibiotic strategies. The interference with the P. aeruginosa quorum sensing (QS) system represents a promising alternative strategy to attenuate the bacterial virulency and its ability to form biofilms. Micafungin has been reported to impede the pseudomonal biofilm formation. However, the influences of micafungin on the biochemical composition and metabolites levels of P. aeruginosa have not been explored. In this study, the effect of micafungin (100 µg/mL) on the virulence factors, QS signal molecules and the metabolome of P. aeruginosa was studied using exofactor assay and mass spectrometry-based metabolomics approaches. Furthermore, confocal laser scanning microscopy (CLSM) using the fluorescent dyes ConA-FITC and SYPRO® Ruby was used to visualize micafungin disturbing effects on the pseudomonal glycocalyx and protein biofilm-constituents, respectively. Our findings showed that micafungin significantly decreased the production of various QS-controlled virulence factors (pyocyanin, pyoverdine, pyochelin and rhamnolipid), along with a dysregulation in the level of various metabolites involved in QS system, lysine degradation, tryptophan biosynthesis, TCA cycle, and biotin metabolism. In addition, the CLSM examination showed an altered matrix distribution. The presented findings highlight the promising role of micafungin as a potential quorum sensing inhibitor (QSI) and anti-biofilm agent to attenuate P. aeruginosa pathogenicity. In addition, they point to the promising role of metabolomics study in investigating the altered biochemical pathways in P. aeruginosa.

Mass spectrometry analysis of phosphotyrosine-containing proteins

Tyrosine phosphorylation is a crucial posttranslational modification that is involved in various aspects of cell biology and often has functions in cancers. It is necessary not only to identify the specific phosphorylation sites but also to quantify their phosphorylation levels under specific pathophysiological conditions. Because of its high sensitivity and accuracy, mass spectrometry (MS) has been widely used to identify endogenous and synthetic phosphotyrosine proteins/peptides across a range of biological systems. However, phosphotyrosine-containing proteins occur in extremely low abundance and they degrade easily, severely challenging the application of MS. This review highlights the advances in both quantitative analysis procedures and enrichment approaches to tyrosine phosphorylation before MS analysis and reviews the differences among phosphorylation, sulfation, and nitration of tyrosine residues in proteins. In-depth insights into tyrosine phosphorylation in a wide variety of biological systems will offer a deep understanding of how signal transduction regulates cellular physiology and the development of tyrosine phosphorylation-related drugs as cancer therapeutics.

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.

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.

Host and microbial-derived metabolites for Clostridioides difficile infection: Contributions, mechanisms and potential applications

Clostridioides difficile infection (CDI), which mostly occurs in hospitalized patients, is the most common and costly health care-associated disease. However, the biology of C. difficile remains incompletely understood. Current therapeutics are still challenged by the frequent recurrence of CDI. Advances in metabolomics facilitate our understanding of the etiology of CDI, which is not merely an alteration in the structure of the gut microbial community but also a dysbiosis metabolic setting promoting the germination, expansion and virulence of C. difficile. Therefore, we summarized the gut microbial and metabolic profiles for CDI under different conditions, such as those of postantibiotic treatment and postfecal microbiota transplantation. The current understanding of the role of host and gut microbial-derived metabolites as well as other nutrients in preventing or alleviating the disease symptoms of CDI will also be provided in this review. We hope that a specific nutrient-centric dietary strategy or the administration of certain nutrients to the colon could serve as an alternate line of investigation for the prophylaxis and mitigation of CDI in the future. Nevertheless, rigorously designed basic studies and randomized controlled trials need to be conducted to assess the functional mechanisms and effects of such therapeutics.

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.

SIAH1 reverses chemoresistance in epithelial ovarian cancer via ubiquitination of YBX-1

Chemoresistance is a severe outcome among patients with epithelial ovarian cancer (EOC) that leads to a poor prognosis. YBX-1 has been shown to cause treatment failure and cancer progression in EOC. However, strategies that directly target YBX-1 are not yet conceivable. Here, we identified that SIAH1 which was downregulated in chemoresistant EOC samples and cell lines functioned as novel E3 ligases to trigger degradation of YBX-1 at cytoplasm by RING finger domain. Mechanistic studies show that YBX-1 was ubiquitinated by SIAH1 at lys304 that lead to the instability of its target m5C-modified mRNAs, thus sensitized EOC cells to cDDP. Overexpression of SIAH1 enhanced the antitumor efficacy of cisplatin in vitro and in vivo, which were partially impaired by ectopic expression of YBX-1 or depletion of YBX-1 ubiquitination. In summary, our data identify the SIAH1/YBX-1 interaction as a therapeutic target for overcoming EOC chemoresistance.

Mass spectrometry analysis of human tear fluid biomarkers specific for ocular and systemic diseases in the context of 3P medicine

Over the last two decades, a large number of non-communicable/chronic disorders reached an epidemic level on a global scale such as diabetes mellitus type 2, cardio-vascular disease, several types of malignancies, neurological and eye pathologies-all exerted system's enormous socio-economic burden to primary, secondary, and tertiary healthcare. The paradigm change from reactive to predictive, preventive, and personalized medicine (3PM/PPPM) has been declared as an essential transformation of the overall healthcare approach to benefit the patient and society at large. To this end, specific biomarker panels are instrumental for a cost-effective predictive approach of individualized prevention and treatments tailored to the person. The source of biomarkers is crucial for specificity and reliability of diagnostic tests and treatment targets. Furthermore, any diagnostic approach preferentially should be noninvasive to increase availability of the biomaterial, and to decrease risks of potential complications as well as concomitant costs. These requirements are clearly fulfilled by tear fluid, which represents a precious source of biomarker panels. The well-justified principle of a "sick eye in a sick body" makes comprehensive tear fluid biomarker profiling highly relevant not only for diagnostics of eye pathologies but also for prediction, prognosis, and treatment monitoring of systemic diseases. One prominent example is the Sicca syndrome linked to a cascade of severe complications that include dry eye, neurologic, and oncologic diseases. In this review, protein profiles in tear fluid are highlighted and corresponding biomarkers are exemplified for several relevant pathologies, including dry eye disease, diabetic retinopathy, cancers, and neurological disorders. Corresponding analytical approaches such as sample pre-processing, differential proteomics, electrophoretic techniques, high-performance liquid chromatography (HPLC), enzyme-linked immuno-sorbent assay (ELISA), microarrays, and mass spectrometry (MS) methodology are detailed. Consequently, we proposed the overall strategies based on the tear fluid biomarkers application for 3P medicine practice. In the context of 3P medicine, tear fluid analytical pathways are considered to predict disease development, to target preventive measures, and to create treatment algorithms tailored to individual patient profiles.

Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental "causal" role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a "historical" process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process - and not a static-stable condition - treatments should be tailored according to the "timing-frame" of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes - from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.

Variability of multi-omics profiles in a population-based child cohort

BACKGROUND

Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood.

METHODS

We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability.

RESULTS

All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability.

CONCLUSIONS

Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

Based on urine metabolomics to study the mechanism of Qi-deficiency affecting type 2 diabetes rats using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry

Qi-deficiency also called energy deficiency, which approximates to the term of sub-health in contemporary medical theory. Diabetes is similar to the symptoms of "xiaoke" in traditional Chinese medicine (TCM) which is linked with Qi-deficiency. However, the mechanism of Qi-deficiency on type 2 diabetes (T2D) has not been completely elucidated. In this study, a model on Qi-deficiency T2D rat was established by using diet with high fat and high sugar and small-dose STZ induction combined with exhaustive swimming, and the model was evaluated by pathological section, hematological index and serum biochemical parameters. Applying urine metabolomics based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry to explore the underlying molecular mechanism of Qi-deficiency on T2D and 32 urinary metabolites were identified as prospective biomarkers for Qi-deficiency T2D rats. Metabolic pathway analysis indicated that synthesis and degradation of ketone bodies, starch and sucrose metabolism, phenylalanine metabolism, arachidonic acid metabolism, butanoate metabolism and TCA cycle, etc., were closely related to potential mechanisms of Qi-deficiency on T2D. The metabolomics results can provide reliable data support for complex TCM syndrome diagnosis.

Identification of Novel Biomarkers for Evaluating Disease Severity in House-Dust-Mite-Induced Allergic Rhinitis by Serum Metabolomics

The aim of this study was to identify differences in serum metabolomics profiles of house-dust-mite (HDM)-induced allergic rhinitis (AR) patients compared to controls and to explore novel biomarkers reflecting disease severity. Serum samples were collected from 29 healthy controls and HDM-induced 72 AR patients, including 30 mild patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomics detection was performed, and orthogonal partial least square discriminate analysis was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from controls. MSAR patients also could be discriminated from MAR patients based on their metabolic fingerprints. Most observed metabolite changes were related to glycine, serine, and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism, and fatty acid metabolism. Levels of sarcosine, sphingosine-1-phosphate, cytidine, and linoleic acid significantly correlated with the total nasal symptom score and visual analogue scale in AR patients. These results suggest that metabolomics profiling may provide novel insights into the pathophysiological mechanisms of HDM-induced AR and contribute to its evaluation of disease severity.

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

Integrated multi-omics analysis of ovarian cancer using variational autoencoders

Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

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.

Mass spectrometry-based proteomics analyses of post-translational modifications and proteoforms in human pituitary adenomas

Pituitary adenoma (PA) is a common intracranial neoplasm, which affects the hypothalamus-pituitary-target organ axis systems, and is hazardous to human health. Post-translational modifications (PTMs), including phosphorylation, ubiquitination, nitration, and sumoylation, are vitally important in the PA pathogenesis. The large-scale analysis of PTMs could provide a global view of molecular mechanisms for PA. Proteoforms, which are used to define various protein structural and functional forms originated from the same gene, are the future direction of proteomics research. The global studies of different proteoforms and PTMs of hypophyseal hormones such as growth hormone (GH) and prolactin (PRL) and the proportion change of different GH proteoforms or PRL proteoforms in human pituitary tissue could provide new insights into the clinical value of pituitary hormones in PAs. Multiple quantitative proteomics methods, including mass spectrometry (MS)-based label-free and stable isotope-labeled strategies in combination with different PTM-peptide enrichment methods such as TiO₂ enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides increase the feasibility for researchers to study PA proteomes. This article reviews the research status of PTMs and proteoforms in PAs, including the enrichment method, technical limitation, quantitative proteomics strategies, and the future perspectives, to achieve the goals of in-depth understanding its molecular pathogenesis, and discovering effective biomarkers and clinical therapeutic targets for predictive, preventive, and personalized treatment of PA patients.

Metabolomics study of cerebrospinal fluid from diabetic rats with cognitive impairment simultaneously treated with Panax quinquefolius and Acorus gramineus

A metabolomics approach was used to explore the effects of Panax quinquefolius (PQ) and Acorus gramineus (AG) on learning and memory in rats with diabetic-induced cognitive impairment. Thirty Wistar rats were divided into three groups, namely, the normal group, model group, and PQ-AG group (PQ-AG group, 1.80 g/kg/d). Diabetes was induced by intraperitoneal injection of streptozotocin (65 mg/kg). Cerebrospinal fluid (CSF) was collected via cisterna magna puncture, and the Morris water maze method was used to evaluate learning and memory in rats after 11 weeks of PQ-AG treatment. Metabolic profiling of CSF samples was performed by using UPLC-Q-TOF-MS. Compared with the normal group, the escape latency of the Morris water maze was significantly prolonged in model group rats after 12 weeks (p < 0.01). Compared with the model group, however, the escape latency was significantly shortened in PQ-AG group rats (p < 0.05). In multivariate statistical analysis, we identified 33 potential biomarkers, and six biomarkers were altered by PQ-AG. These biomarkers were involved in the metabolism of pyrimidine; nicotinate, and nicotinamide; glycine, serine, and threonine; and ascorbate and aldarate. Taken collectively, our results indicate that PQ-AG can attenuate diabetic-induced cognitive impairment by affecting a variety of metabolic pathways. Our results provide an experimental basis for studying the mechanism of action of PQ-AG.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

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.

Hypoglycemic Effect of Prolamin from Cooked Foxtail Millet (Setaria italic) on Streptozotocin-Induced Diabetic Mice

Millet proteins have been demonstrated to possess glucose-lowering and lipid metabolic disorder modulation functions against diabetes; however, the molecular mechanisms underlying their anti-diabetic effects remain unclear. The present study aimed to investigate the hypoglycemic effect of prolamin from cooked foxtail millet (PCFM) on type 2 diabetic mice, and explore the gut microbiota and serum metabolic profile changes that are associated with diabetes attenuation by PCFM. Our diabetes model was established using a high-fat diet combined with streptozotocin before PCFM or saline was daily administrated by gavage for 5 weeks. The results showed that PCFM ameliorated glucose metabolism disorders associated with type 2 diabetes. Furthermore, the effects of PCFM administration on gut microbiota and serum metabolome were investigated. 16S rRNA gene sequencing analysis indicated that PCFM alleviated diabetes-related gut microbiota dysbiosis in mice. Additionally, the serum metabolomics analysis revealed that the metabolite levels disturbed by diabetes were partly altered by PCFM. Notably, the decreased D-Glucose level caused by PCFM suggested that its anti-diabetic potential can be associated with the activation of glycolysis and the inhibition of gluconeogenesis, starch and sucrose metabolism and galactose metabolism. In addition, the increased serotonin level caused by PCFM may stimulate insulin secretion by pancreatic β-cells, which contributed to its hypoglycemic effect. Taken together, our research demonstrated that the modulation of gut microbiota composition and the serum metabolomics profile was associated with the anti-diabetic effect of PCFM.

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.

Integration of quantitative phosphoproteomics and transcriptomics revealed phosphorylation-mediated molecular events as useful tools for a potential patient stratification and personalized treatment of human nonfunctional pituitary adenomas

Background

Invasiveness is a very challenging clinical problem in nonfunctional pituitary adenomas (NFPAs), and currently, there are no effective invasiveness-related molecular biomarkers. The post-neurosurgery treatment is much different as for invasive and noninvasive NFPAs. The aim of this study was to integrate phosphoproteomics and transcriptomics data to reveal phosphorylation-mediated molecular events for invasive characteristics of NFPAs to achieve a potential tool for patient stratification, and prognostic/predictive assessment to discriminate invasive from noninvasive NFPAs for personalized attitude.

Methods

The 6-plex tandem mass tag (TMT) labeling reagents coupled with TiO₂ enrichment of phosphopeptides and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used to identify and quantify each phosphoprotein and phosphosite in NFPAs and controls. Differentially expressed genes (DEGs) between invasive NFPA and control tissues were obtained from the Gene Expression Omnibus (GEO) database. The overlapping analysis was performed between phosphoprotiens and invasive DEGs. Gene Ontology (GO) enrichment, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) analyses were used to analyze these overlapped molecules.

Results

In total, 1035 phosphoproteins with 2982 phosphorylation sites were identified in NFPAs vs. controls, and 2751 DEGs were identified in invasive NFPAs vs. controls. Overlapping analysis of these phosphoproteins and DEGs exposed 130 overlapped molecules (phosphoproteins; invasive DEGs). GO enrichment and KEGG pathway analyses of 130 overlapped molecules revealed multiple biological processes and signaling pathway network alterations, including cell-cell adhesion, platelet activation, GTPase signaling pathway, protein kinase signaling, calcium signaling pathway, estrogen signaling pathway, glucagon signaling pathway, cGMP-PKG signaling pathway, GnRH signaling pathway, inflammatory mediator regulation of TRP channels, vascular smooth muscle contraction, and Fc gamma R-mediated phagocytosis, which were obviously associated with tumor invasive characteristics. For 130 overlapped molecules, PPI network-based molecular complex detection (MCODE) identified 10 hub molecules, namely SLC2A4, TSC2, AKT1, SCG3, ALB, APOL1, ACACA, SPARCL1, CHGB, and IGFBP5. These hub molecules are involved in multiple signaling pathways and represent potential predictive/prognostic markers in NFPA patients as well as they represent potential therapeutic targets.

Conclusions

This study provided the first large-scale phosphoprotein profiling and phosphorylation-related signaling pathway network alterations in human NFPA tissues. Further, overlapping analysis of phosphoproteins and invasive DEGs revealed the phosphorylation-mediated signaling pathway network changes in invasive NFPAs. These findings are the precious resource for in-depth insight into the molecular mechanisms of NFPAs, as well as for the discovery of effective phosphoprotein biomarkers and therapeutic targets for invasive NFPAs.

Identification of pathology-specific regulators of m6A RNA modification to optimize lung cancer management in the context of predictive, preventive, and personalized medicine

Relevance

Lung cancer is the most common malignant tumor with high morbidity (11.6% of the total diagnosed cancer cases) and mortality (18.4% of the total cancer deaths), and its 5-year survival rate is very low (20%). Clarification of any molecular events and the discovery of effective biomarkers will offer increasing promise for lung canner management. N⁶-methyladenosine (m⁶A) modification is one of the important RNA modifications that are closely associated with lung cancer, and are tightly regulated by m⁶A regulators. Elucidation of pathology-specific m⁶A regulators will directly contribute to lung cancer medical services in the context of predictive, preventive, and personalized medicine (PPPM).

Purpose

To investigate pathology-specific regulators of m⁶A RNA modifications in lung cancer and further inspect the m⁶A regulator gene signature as useful tools for PPPM in lung cancers.

Methods

The gene expression data of 19 m⁶A regulators (m⁶A-methyltransferases-ZC3H13, KIAA1429, RBM15/15B, WTAP, and METTL3/14; demethylases-FTO and ALKBH5; and m⁶A-binding proteins-HNRNPC, YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and HNRNPA2B1) and clinical data of 1013 lung cancer patients [511 lung adenocarcinoma (LUAD) and 502 lung squamous carcinoma (LUSC)] and 109 controls (Con) were obtained from the TCGA database. Quantitative real-time PCR (qRT-PCR) was used to verify m⁶A regulators in lung cancer cell lines. Protein-protein interaction (PPI), gene co-expression, survival analysis, and heatmap were used to analyze these m⁶A regulators in this set of lung cancer clinical data. Lasso regression was used to optimize the pathology-specific m⁶A regulator gene signature. Gene set enrichment analysis (GSEA) was used to reveal the functional characteristics of m⁶A regulators.

Results

Those 19 m⁶A regulator profiling was significantly differentially expressed in lung cancer tissues relative to control tissues, which was also verified in lung cancer cell lines. Those m⁶A regulators interacted mutually, and those regulator-based sample clusters were correlated with clinical traits, including survival status, gender, tobacco smoking history, primary disease, and pathologic stage. Further, lasso regression based on the 19 m⁶A regulators optimized and identified a three-m⁶A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as independent prognostic factor, which classified 1013 lung cancer patients into high-risk and low-risk groups according to median value (0.84) of the lasso regression risk scores. This three-m⁶A-regulator signature profiling was significantly related to lung cancer overall survival, cancer status, and the above-described clinical traits. Further, GSEA revealed that KIAA1429, METTL3, and IGF2BP1 were significantly related to multiple biological behaviors, including proliferation, apoptosis, metastasis, energy metabolism, drug resistance, and recurrence, and that KIAA1429 and IGF2BP1 had potential target genes, including E2F3, WTAP, CCND1, CDK4, EGR2, YBX1, and TLX, which were associated with cancers.

Conclusion

This study provided the first view of the pathology-specific regulators of m⁶A RNA modification in lung cancers and identified the three-m⁶A-regulator signature (KIAA1429, METTL3, and IGF2BP1) as an independent prognostic model to classify lung cancers into high- and low-risk groups for patient stratification, prognostic assessment, and personalized treatment toward PPPM in lung cancers.

Label-free quantitative identification of abnormally ubiquitinated proteins as useful biomarkers for human lung squamous cell carcinomas

BACKGROUND

Ubiquitination is an important molecular event in lung squamous cell carcinoma (LSCC), which currently is mainly studied in nonsmall cell lung carcinoma cell models but lacking of ubiquitination studies on LSCC tissues. Here, we presented the ubiquitinated protein profiles of LSCC tissues to explore ubiquitination-involved molecular network alterations and identify abnormally ubiquitinated proteins as useful biomarkers for predictive, preventive, and personalized medicine (PPPM) in LSCC.

METHODS

Anti-ubiquitin antibody-based enrichment coupled with LC-MS/MS was used to identify differentially ubiquitinated proteins (DUPs) between LSCC and control tissues, followed by integrative omics analyses to identify abnormally ubiquitinated protein biomarkers for LSCC.

RESULTS

Totally, 400 DUPs with 654 ubiquitination sites were identified,, and motifs A-X (1/2/3)-K* were prone to be ubiquitinated in LSCC tissues. Those DUPs were involved in multiple molecular network systems, including the ubiquitin-proteasome system (UPS), cell metabolism, cell adhesion, and signal transduction. Totally, 44 hub molecules were revealed by protein-protein interaction network analysis, followed by survival analysis in TCGA database (494 LSCC patients and 20,530 genes) to obtain 18 prognosis-related mRNAs, of which the highly expressed mRNAs VIM and IGF1R were correlated with poorer prognosis, while the highly expressed mRNA ABCC1 was correlated with better prognosis. VIM-encoded protein vimentin and ABCC1-encoded protein MRP1 were increased in LSCC, which were all associated with poor prognosis. Proteasome-inhibited experiments demonstrated that vimentin and MRP1 were degraded through UPS. Quantitative ubiquitinomics found ubiquitination level was decreased in vimentin and increased in MRP1 in LSCC. These findings showed that the increased vimentin in LSCC might be derived from its decreased ubiquitination level and that the increased MRP1 in LSCC might be derived from its protein synthesis > degradation. GSEA and co-expression gene analyses revealed that VIM and MRP1 were involved in multiple crucial biological processes and pathways. Further, TRIM2 and NEDD4L were predicted as E3 ligases to regulate ubiquitination of vimentin and MRP1, respectively.

CONCLUSION

These findings revealed ubiquitinomic variations and molecular network alterations in LSCC, which is in combination with multiomics analysis to identify ubiquitination-related biomarkers for in-depth insight into the molecular mechanism and therapeutic targets and for prediction, diagnosis, and prognostic assessment of LSCC.

Multiomics-Based Signaling Pathway Network Alterations in Human Non-functional Pituitary Adenomas

Non-functional pituitary adenoma (NFPA) seriously affects hypothanamus-pituitary-target organ axis system, with a series of molecule alterations in the multiple levels of genome, transcriptome, proteome, and post-translational modifications, and those molecules mutually interact in a molecular-network system. Meta analysis coupled with IPA pathway-network program was used to comprehensively analyze nine sets of documented NFPA omics data, including NFPA quantitative transcriptomics data [280 differentially expressed genes (DEGs)], NFPA quantitative proteomics data [50 differentially expressed proteins (DEPs)], NFPA mapping protein data (218 proteins), NFPA mapping protein nitration data (9 nitroproteins and 3 non-nitrated proteins), invasive NFPA quantitative transriptomics data (346 DEGs), invasive NFPA quantitative proteomics data (57 DEPs), control mapping protein data (1469 proteins), control mapping protein nitration data (8 nitroproteins), and control mapping phosphorylation data (28 phosphoproteins). A total of 62 molecular-networks with 861 hub-molecules and 519 canonical-pathways including 54 cancer-related canonical pathways were revealed. A total of 42 hub-molecule panels and 9 canonical-pathway panels were identified to significantly associate with tumorigenesis. Four important molecular-network systems, including PI3K/AKT, mTOR, Wnt, and ERK/MAPK pathway-systems, were confirmed in NFPAs by PTMScan experiments with altered expression-patterns and phosphorylations. Nineteen high-frequency hub-molecules were also validated in NFPAs with PTMScan experiment with at least 2.5-fold changes in expression or phosphorylation, including ERK, ERK1/2, Jnk, MAPK, Mek, p38 MAPK, AKT, PI3K complex, p85, PKC, FAK, Rac, Shc, HSP90, NFκB Complex, histone H3, AP1, calmodulin, and PLC. Furthermore, mTOR and Wnt pathway-systems were confirmed in NFPAs by immunoaffinity Western blot analysis, with significantly decreased expression of PRAS40 and increased phosphorylation levels of p-PRAS40 (Thr246) in mTOR pathway in NFPAs compared to controls, and with the decreased protein expressions of GSK-3β and GSK-3β, significantly increased phosphorylation levels of p-GSK3α (Ser21) and p-GSK3β (Ser9), and increased expression level of β-catenin in Wnt pathway in NFPAs compared to controls. Those findings provided a comphrensive and large-scale pathway network data for NFPAs, and offer the scientific evidence for insights into the accurate molecular mechanisms of NFPA and discovery of the effective biomarkers for diagnosis, prognosis, and determination of therapeutic targets.

Quantitative Analysis of Proteome in Non-functional Pituitary Adenomas: Clinical Relevance and Potential Benefits for the Patients

Background: Non-functional pituitary adenoma (NFPA) is a common tumor that occurs in the pituitary gland, and generally without any symptoms at its early stage and without clinical elevation of hormones, which is commonly diagnosed when it grows up to compress its surrounding tissues and organs. Currently, the pathogenesis of NFPA has not been clarified yet. It is necessary to investigate molecular alterations in NFPA, and identify reliable biomarkers and drug therapeutic targets for effective treatments. Methods: Tandem mass tags (TMT)-based quantitative proteomics was used to identify and quantify proteins in NFPAs. GO and KEGG enrichment analyses were used to analyze the identified proteins. Differentially expressed genes (DEGs) between NFPA and control tissues were obtained from GEO datasets. These two sets of protein and gene data were analyzed to obtain overlapped molecules (genes; proteins), followed by further GO and KEGG pathway analyses of these overlapped molecules, and molecular network analysis to obtain the hub molecules with Cytoscape. Two hub molecules (SRC and AKT1) were verified with Western blotting. Results: Totally 6076 proteins in NFPA tissues were identified, and 3598 DEGs between NFPA and control tissues were identified from GEO database. Overlapping analysis of 6076 proteins and 3598 DEGs obtained 1088 overlapped molecules (DEGs; proteins). KEGG pathway analysis of 6076 proteins obtained 114 statistically significant pathways, including endocytosis, and spliceosome signaling pathways. KEGG pathway analysis of 1088 overlapped molecules obtained 52 statistically significant pathways, including focal adhesion, cGMP-PKG pathway, and platelet activation signaling pathways. These pathways play important roles in cell energy supply, adhesion, and maintenance of the tumor microenvironment. According to the association degree in Cytoscape, ten hub molecules (DEGs; proteins) were identified, including GAPDH, ALB, ACACA, SRC, ENO2, CALM1, POTEE, HSPA8, DECR1, and AKT1. Western-blotting analysis confirmed the upregulated expressions of SRC and PTMScan experiment confirmed the increased levels of pAKT1, in NFPAs compared to controls. Conclusions: This study established the large-scale quantitative protein profiling of NFPA tissue proteome. It offers a basis for subsequent in-depth proteomics analysis of NFPAs, and insight into the molecular mechanism of NFPAs. It also provided the basic data to discover reliable biomarkers and therapeutic targets for NFPA patients.

Chronic inflammation: key player and biomarker-set to predict and prevent cancer development and progression based on individualized patient profiles

A strong relationship exists between tumor and inflammation, which is the hot point in cancer research. Inflammation can promote the occurrence and development of cancer by promoting blood vessel growth, cancer cell proliferation, and tumor invasiveness, negatively regulating immune response, and changing the efficacy of certain anti-tumor drugs. It has been demonstrated that there are a large number of inflammatory factors and inflammatory cells in the tumor microenvironment, and tumor-promoting immunity and anti-tumor immunity exist simultaneously in the tumor microenvironment. The typical relationship between chronic inflammation and tumor has been presented by the relationships between Helicobacter pylori, chronic gastritis, and gastric cancer; between smoking, development of chronic pneumonia, and lung cancer; and between hepatitis virus (mainly hepatitis virus B and C), development of chronic hepatitis, and liver cancer. The prevention of chronic inflammation is a factor that can prevent cancer, so it effectively inhibits or blocks the occurrence, development, and progression of the chronic inflammation process playing important roles in the prevention of cancer. Monitoring of the causes and inflammatory factors in chronic inflammation processes is a useful way to predict cancer and assess the efficiency of cancer prevention. Chronic inflammation-based biomarkers are useful tools to predict and prevent cancer.

Peripheral biomarkers allow differential diagnosis between schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders that pose important challenges for diagnosis by sharing common symptoms, such as delusions and hallucinations. The underlying pathophysiology of both disorders remains largely unknown, and the identification of biomarkers with potential to support diagnosis is highly desirable. In a previous study, we successfully discriminated SCZ and BD patients from healthy control (HC) individuals by employing proton magnetic resonance spectroscopy (¹H-NMR). In this study, ¹H-NMR data treated by chemometrics, principal component analysis (PCA) and supervised partial least-squares discriminant analysis (PLS-DA), provided the identification of metabolites present only in BD (as for instance the 2,3-diphospho-D-glyceric acid, N-acetyl aspartyl-glutamic acid, monoethyl malonate) or only in SCZ (as isovaleryl carnitine, pantothenate, mannitol, glycine, GABA). This may represent a set of potential biomarkers to support the diagnosis of these mental disorders, enabling the discrimination between SCZ and BD, and among these psychiatric patients and HC (as 6-hydroxydopamine was present in BD and SCZ but not in HC). The presence or absence of these metabolites in blood allowed the categorization of 182 independent subjects into one of these three groups. In addition, the presented data suggest disturbances in metabolic pathways in SCZ and BD, which may provide new and important information to support the elucidation and/or new insights into the neurobiology underlying these mental disorders.

TMT-based quantitative proteomics revealed follicle-stimulating hormone (FSH)-related molecular characterizations for potentially prognostic assessment and personalized treatment of FSH-positive non-functional pituitary adenomas

BACKGROUND

Non-functional pituitary adenoma (NFPA) is highly heterogeneous with different hormone expression subtypes. Of them, follicle-stimulating hormone (FSH)-positive expression is an important subtype of NFPAs. It is well-known that FSH exerted its functions through binding its receptor. However, the expression rate of FSH receptor was significantly higher in aggressive pituitary adenomas. This study aimed to investigate the molecular characteristics of FSH-positive NFPAs for effective stratification of patient, target treatment, prognostic assessment, and personalized treatment of FSH-positive NFPAs.

METHODS

Tandem mass tag (TMT)-based quantitative proteomics was used to investigate differentially expressed proteins (DEPs) between FSH-positive and negative NFPAs. Gene ontology and KEGG pathway enrichment analyses were used to analyze the DEPs. Differentially expressed genes (DEGs) between invasive and non-invasive NFPAs from GEO database were analyzed with pathway enrichment analysis. Protein-protein interaction (PPI) networks were constructed based on DEPs in excetral cellular matrix (ECM)-receptor interaction, focal adhesion, and PI3K-Akt pathways. Cytoscape was used to obtain most significant modules. Western blot was used to validate the expressions of upregulated proteins (ITGA1, ITGA6, and ITGB4), the expression and phosphorylated status of Akt in PI3K-Akt pathway, and the expression of FSH receptors in FSH-positive relative to negative NFPAs.

RESULTS

A total of 594 DEPs (374 upregulated and 220 downregulated) were identified between FSH-positive and negative NFPAs. Nineteen KEGG pathway networks were identified to involve DEPs, and reveal molecular differences between FSH-positive and negative NFPAs, including three important pathways that were significantly associated with tumor invasiveness and aggressiveness: ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways. Further, focal adhesion pathway was also confirmed with invasiveness-related NFPA DEG data that were derived from GEO database. Moreover, the significantly upregulated DEPs (ITGA1, ITGA6, and ITGB4) that were associated with tumor invasiveness and aggressiveness were confirmed by immunoaffinity analysis in FSH-positive vs. negative NFPAs. Also, the phosphorylation level but not its expression level of AKT in PI3K-AKT signaling was significantly increased, and the expression level of FSH receptor was significantly increased in FSH-positive relative to negative NFPAs. Also, overlapping analysis of 594 DEPs and 898 DEGs revealed 45 invasiveness-related DEPs, including 11 upregulated DEPs (ITGA6, FARP1, PALLD, PPBP, LIMA1, SCD, UACA, BAG3, CLU, PLEC, and GATM) that were also upregulated genes in invasive NFPAs, and 8 downregulated DEPs (ALCAM, HP, FSTL4, IL13RA2, NPTX2, DPP6, CRABP2, and SLC27A2) that were also downregulated genes in invasive NFPAs.

CONCLUSIONS

FSH-positive expression was an important NFPA subtype. It was the first time for this study to reveal FSH-related proteomic variations and the corresponding molecular network alterations in FSH-positive relative to negative NFPAs. Also, three signaling pathways (ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways) and involved upregulated proteins (ITGA1, ITGA6, ITGB4, pAKT, and FSHR) were significantly associated with tumor invasiveness and aggressiveness, and a set of invasiveness-related DEPs were identified with overlapping analysis of 594 DEPs in FSH-positive vs. negative NFPAs and 898 DEGs in invasive vs. non-invasive NFPAs. These findings offered the scientific evidence to in-depth understand molecular characteristics of FSH-positive NFPAs, and effectively stratify the post-surgery patients for personalized prognostic assessment and targeted treatment of FSH-positive NFPAs.

Innovating the Concept and Practice of Two-Dimensional Gel Electrophoresis in the Analysis of Proteomes at the Proteoform Level

Two-dimensional gel electrophoresis (2DE) is an important and well-established technical platform enabling extensive top-down proteomic analysis. However, the long-held but now largely outdated conventional concepts of 2DE have clearly impacted its application to in-depth investigations of proteomes at the level of protein species/proteoforms. It is time to popularize a new concept of 2DE for proteomics. With the development and enrichment of the proteome concept, any given “protein” is now recognized to consist of a series of proteoforms. Thus, it is the proteoform, rather than the canonical protein, that is the basic unit of a proteome, and each proteoform has a specific isoelectric point (pI) and relative mass (M_r). Accordingly, using 2DE, each proteoform can routinely be resolved and arrayed according to its different pI and M_r. Each detectable spot contains multiple proteoforms derived from the same gene, as well as from different genes. Proteoforms derived from the same gene are distributed into different spots in a 2DE pattern. High-resolution 2DE is thus actually an initial level of separation to address proteome complexity and is effectively a pre-fractionation method prior to analysis using mass spectrometry (MS). Furthermore, stable isotope-labeled 2DE coupled with high-sensitivity liquid chromatography-tandem MS (LC-MS/MS) has tremendous potential for the large-scale detection, identification, and quantification of the proteoforms that constitute proteomes.

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.

Targeted therapies for non-HPV-related head and neck cancer: challenges and opportunities in the context of predictive, preventive, and personalized medicine

Head and neck squamous cell carcinoma (HNSCC) develops in the mucosal lining of the upper aerodigestive tract, principally as a result of exposure to carcinogens present in tobacco products and alcohol, with oncogenic papillomaviruses also being recognized as etiological agents in a limited proportion of cases. As such, there is considerable scope for prevention of disease development and progression. However, despite multimodal approaches to treatment, tumor recurrence and metastatic disease are common problems, and clinical outcome is unsatisfactory. As our understanding of the genetics and biochemical aberrations in HNSCC has improved, so the development and use of molecularly targeted drugs to combat the disease have come to the fore. In this article, we review molecular mechanisms that alter signal transduction downstream of the epidermal growth factor receptor (EGFR) as well as those that perturb orderly cell cycle progression, such as p53 mutation, cyclin overexpression, and loss of cyclin-dependent kinase inhibitor function. We outline some of the tactics that have been employed to combat the altered biochemistry. These include blockade of the EGFR using humanized monoclonal antibodies such as cetuximab and small molecule tyrosine kinase inhibitors (TKIs) such as erlotinib/gefitinib and subsequent generations of TKIs, restoration of p53 function using MIRA compounds, and inhibition of cyclin-dependent kinase and aurora kinase activity using drugs such as palbociclib and alisertib. Knowledge of the underlying molecular mechanisms may be utilizable in order to predict disease behavior and tailor therapeutic interventions in a more personalized approach to improve clinical response. Use of liquid biopsy, omics platforms, and salivary diagnostics hold promise in this regard.

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.

Prolactin Variants in Human Pituitaries and Pituitary Adenomas Identified With Two-Dimensional Gel Electrophoresis and Mass Spectrometry

Human prolactin (hPRL) plays multiple roles in growth, metabolism, development, reproduction, and immunoregulation, which is an important protein synthesized in a pituitary. Two-dimensional gel electrophoresis (2DE) is an effective method in identity of protein variants for in-depth insight into functions of that protein. 2DE, 2DE-based PRL-immunoblot, mass spectrometry, and bioinformatics were used to analyze hPRL variants in human normal (control; n = 8) pituitaries and in five subtypes of pituitary adenomas [NF- (n = 3)-, FSH+ (n = 3)-, LH+ (n = 3)-, FSH+/LH+ (n = 3)-, and PRL+ (n = 3)-adenomas]. Six hPRL variants were identified with different isoelectric point (pI)-relative molecular mass (Mr ) distribution on a 2DE pattern, including variants V1 (pI 6.1; 26.0 kDa), V2 (pI 6.3; 26.4 kDa), V3 (pI 6.3; 27.9 kDa), V4 (pI 6.5; 26.1 kDa), V5 (pI 6.8; 25.9 kDa), and V6 (pI 6.7; 25.9 kDa). Compared to controls, except for variants V2-V6 in PRL-adenomas, V2 in FSH+-adenomas, and V3 in NF--adenomas, the other PRL variants were significantly downregulated in each subtype of pituitary adenomas. Moreover, the pattern of those six PRL variants was significantly different among five subtypes of pituitary adenomas relative to control pituitaries. Different hPRL variants might be involved in different types of PRL receptor-signaling pathways in a given condition. Those findings clearly revealed the existence of six hPRL variants in human pituitaries, and the pattern changes of six hPRL variants among different subtypes of pituitary adenomas, which provide novel clues to further study the functions, and mechanisms of action, of hPRL in human pituitary and in PRL-related diseases, and the potential clinical value in pituitary adenomas.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.