Proteomic profiling of human plasma for cancer biomarker discovery

Characterization of the plasma proteome from healthy adult dogs

Introduction

Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine.

Methods

In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose.

Results and discussion

Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

Multimodal Analytical Tools to Enhance Mechanistic Understanding of Aortic Valve Calcification

This review focuses on technologies at the core of calcific aortic valve disease (CAVD) and drug target research advancement, including transcriptomics, proteomics, and molecular imaging. We examine how bulk RNA sequencing and single-cell RNA sequencing have engendered organismal genomes and transcriptomes, promoting the analysis of tissue gene expression profiles and cell subpopulations, respectively. We bring into focus how the field is also largely influenced by increasingly accessible proteome profiling techniques. In unison, global transcriptional and protein expression analyses allow for increased understanding of cellular behavior and pathogenic pathways under pathologic stimuli including stress, inflammation, low-density lipoprotein accumulation, increased calcium and phosphate levels, and vascular injury. We also look at how direct investigation of protein signatures paves the way for identification of targetable pathways for pharmacologic intervention. Here, we note that imaging techniques, once a clinical diagnostic tool for late-stage CAVD, have since been refined to address a clinical need to identify microcalcifications using positron emission tomography/computed tomography and even detect in vivo cellular events indicative of early stage CAVD and map the expression of identified proteins in animal models. Together, these techniques generate a holistic approach to CAVD investigation, with the potential to identify additional novel regulatory pathways.

Proteomic analysis of serum extracellular vesicles from biliary tract infection patients to identify novel biomarkers

Biliary tract infection (BTI), a commonly occurring abdominal disease, despite being extensively studied for its initiation and underlying mechanisms, continues to pose a challenge in the quest for identifying specific diagnostic biomarkers. Extracellular vesicles (EVs), which emanate from diverse cell types, serve as minute biological entities that mirror unique physiological or pathological conditions. Despite their potential, there has been a relatively restricted exploration of EV-oriented methodologies for diagnosing BTI. To uncover potent protein biomarkers for BTI patients, we applied a label-free quantitative proteomic method known for its unbiased and high-throughput nature. Furthermore, 192 differentially expressed proteins surfaced within EVs isolated from individuals afflicted with BTI. Subsequent GO and KEGG analyses pinpointed Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) and Crumbs homolog 3 (CRB3) as noteworthy biomarkers. Validation via data analysis of plasma-derived EV samples confirmed their specificity to BTI. Our study leveraged an unbiased proteomic tool to unveil CEACAM1 and CRB3 as promising protein biomarkers in serum EVs, presenting potential avenues for the advancement of diagnostic systems for BTI detection.

Research progress on the multi-omics and survival status of circulating tumor cells

In the dynamic process of metastasis, circulating tumor cells (CTCs) emanate from the primary solid tumor and subsequently acquire the capacity to disengage from the basement membrane, facilitating their infiltration into the vascular system via the interstitial tissue. Given the pivotal role of CTCs in the intricate hematogenous metastasis, they have emerged as an essential resource for a deeper comprehension of cancer metastasis while also serving as a cornerstone for the development of new indicators for early cancer screening and new therapeutic targets. In the epoch of precision medicine, as CTC enrichment and separation technologies continually advance and reach full fruition, the domain of CTC research has transcended the mere straightforward detection and quantification. The rapid advancement of CTC analysis platforms has presented a compelling opportunity for in-depth exploration of CTCs within the bloodstream. Here, we provide an overview of the current status and research significance of multi-omics studies on CTCs, including genomics, transcriptomics, proteomics, and metabolomics. These studies have contributed to uncovering the unique heterogeneity of CTCs and identifying potential metastatic targets as well as specific recognition sites. We also review the impact of various states of CTCs in the bloodstream on their metastatic potential, such as clustered CTCs, interactions with other blood components, and the phenotypic states of CTCs after undergoing epithelial-mesenchymal transition (EMT). Within this context, we also discuss the therapeutic implications and potential of CTCs.

Biological insights from plasma proteomics of non-small cell lung cancer patients treated with immunotherapy

Introduction

Immune checkpoint inhibitors have made a paradigm shift in the treatment of non-small cell lung cancer (NSCLC). However, clinical response varies widely and robust predictive biomarkers for patient stratification are lacking. Here, we characterize early on-treatment proteomic changes in blood plasma to gain a better understanding of treatment response and resistance.

Methods

Pre-treatment (T0) and on-treatment (T1) plasma samples were collected from 225 NSCLC patients receiving PD-1/PD-L1 inhibitor-based regimens. Plasma was profiled using aptamer-based technology to quantify approximately 7000 plasma proteins per sample. Proteins displaying significant fold changes (T1:T0) were analyzed further to identify associations with clinical outcomes using clinical benefit and overall survival as endpoints. Bioinformatic analyses of upregulated proteins were performed to determine potential cell origins and enriched biological processes.

Results

The levels of 142 proteins were significantly increased in the plasma of NSCLC patients following ICI-based treatments. Soluble PD-1 exhibited the highest increase, with a positive correlation to tumor PD-L1 status, and, in the ICI monotherapy dataset, an association with improved overall survival. Bioinformatic analysis of the ICI monotherapy dataset revealed a set of 30 upregulated proteins that formed a single, highly interconnected network, including CD8A connected to ten other proteins, suggestive of T cell activation during ICI treatment. Notably, the T cell-related network was detected regardless of clinical benefit. Lastly, circulating proteins of alveolar origin were identified as potential biomarkers of limited clinical benefit, possibly due to a link with cellular stress and lung damage.

Conclusions

Our study provides insights into the biological processes activated during ICI-based therapy, highlighting the potential of plasma proteomics to identify mechanisms of therapy resistance and biomarkers for outcome.

Machine learning identifies SLC6A14 as a novel biomarker promoting the proliferation and metastasis of pancreatic cancer via Wnt/β-catenin signaling

Pancreatic cancer (PC) has the poorest prognosis compared to other common cancers because of its aggressive nature, late detection, and resistance to systemic treatment. In this study, we aimed to identify novel biomarkers for PC patients and further explored their function in PC progression. We analyzed GSE62452 and GSE28735 datasets, identifying 35 differentially expressed genes (DEGs) between PC specimens and non-tumors. Based on 35 DEGs, we performed machine learning and identified eight diagnostic genes involved in PC progression. Then, we further screened three critical genes (CTSE, LAMC2 and SLC6A14) using three GEO datasets. A new diagnostic model was developed based on them and showed a strong predictive ability in screen PC specimens from non-tumor specimens in GEO, TCGA datasets and our cohorts. Then, clinical assays based on TCGA datasets indicated that the expression of LAMC2 and SLC6A14 was associated with advanced clinical stage and poor prognosis. The expressions of LAMC2 and SLC6A14, as well as the abundances of a variety of immune cells, exhibited a significant positive association with one another. Functionally, we confirmed that SLC6A14 was highly expressed in PC and its knockdown suppressed the proliferation, migration, invasion and EMT signal via regulating Wnt/β-catenin signaling pathway. Overall, our findings developed a novel diagnostic model for PC patients. SLC6A14 may promote PC progression via modulating Wnt/β-catenin signaling. This work offered a novel and encouraging new perspective that holds potential for further illuminating the clinicopathological relevance of PC as well as its molecular etiology.

Integrative Multianalytical Model Based on Novel Plasma Protein Biomarkers for Distinguishing Lung Adenocarcinoma and Benign Pulmonary Nodules

Given the pressing clinical problem of making a decision in diagnosis for subjects with pulmonary nodules, we aimed to discover novel plasma protein biomarkers for lung adenocarcinoma (LUAD) and benign pulmonary nodules (BPNs) and then develop an integrative multianalytical model to guide the clinical management of LUAD and BPN patients. Through label-free quantitative plasma proteomic analysis (data are available via ProteomeXchange with identifier PXD046731), 12 differentially expressed proteins (DEPs) in LUAD and BPN were screened. The diagnostic abilities of DEPs were validated in two independent validation cohorts. The results showed that the levels of three candidate proteins (PRDX2, PON1, and APOC3) were lower in the plasma of LUAD than in BPN. The three candidate proteins were combined with three promising computed tomography indicators (spiculation, vascular notch sign, and lobulation) and three traditional markers (CEA, CA125, and CYFRA21-1) to construct an integrative multianalytical model, which was effective in distinguishing LUAD from BPN, with an AUC of 0.904, a sensitivity of 81.44%, and a specificity of 90.14%. Moreover, the model possessed impressive diagnostic performance between early LUADs and BPNs, with the AUC, sensitivity, specificity, and accuracy of 0.868, 65.63%, 90.14%, and 82.52%, respectively. This model may be a useful auxiliary diagnostic tool for LUAD and BPN by achieving a better balance of sensitivity and specificity.

Engineering Biomimetic Microenvironment for Organoid

Organoid is an emerging frontier technology in the field of life science, in which pluripotent stem cells or tissue-derived differentiated/progenitor cells form 3D structures according to their multi-directional differentiation potential and self-assembly ability. Nowadays, although various types of organoids are widely investigated, their construction is still complicated in operation, uncertain in yield, and poor in reproducibility for the structure and function of native organs. Constructing a biomimetic microenvironment for stem cell proliferation and differentiation in vitro is recognized as a key to driving this field. This review reviews the recent development of engineered biomimetic microenvironments for organoids. First, the composition of the matrix for organoid culture is summarized. Then, strategies for engineering the microenvironment from biophysical, biochemical, and cellular perspectives are discussed in detail. Subsequently, the newly developed monitoring technologies are also reviewed. Finally, a brief conclusion and outlook are presented for the inspiration of future research.

Glial cell proteome using targeted quantitative methods for potential multi-diagnostic biomarkers

Glioblastoma is one of the most malignant primary brain cancer. Despite surgical resection with modern technology followed by chemo-radiation therapy with temozolomide, resistance to the treatment and recurrence is common due to its aggressive and infiltrating nature of the tumor with high proliferation index. The median survival time of the patients with glioblastomas is less than 15 months. Till now there has been no report of molecular target specific for glioblastomas. Early diagnosis and development of molecular target specific for glioblastomas are essential for longer survival of the patients with glioblastomas. Development of biomarkers specific for glioblastomas is most important for early diagnosis, estimation of the prognosis, and molecular target therapy of glioblastomas. To that end, in this study, we have conducted a comprehensive proteome study using primary cells and tissues from patients with glioblastoma. In the discovery stage, we have identified 7429 glioblastoma-specific proteins, where 476 proteins were quantitated using Tandem Mass Tag (TMT) method; 228 and 248 proteins showed up and down-regulated pattern, respectively. In the validation stage (20 selected target proteins), we developed quantitative targeted method (MRM: Multiple reaction monitoring) using stable isotope standards (SIS) peptide. In this study, five proteins (CCT3, PCMT1, TKT, TOMM34, UBA1) showed the significantly different protein levels (t-test: p value ≤ 0.05, AUC ≥ 0.7) between control and cancer groups and the result of multiplex assay using logistic regression showed the 5-marker panel showed better sensitivity (0.80 and 0.90), specificity (0.92 and 1.00), error rate (10 and 2%), and AUC value (0.94 and 0.98) than the best single marker (TOMM34) in primary cells and tissues, respectively. Although we acknowledge that the model requires further validation in a large sample size, the 5 protein marker panel can be used as baseline data for the discovery of novel biomarkers of the glioblastoma.

Identification of novel candidate biomarkers for acute myocardial infarction by the Olink proteomics platform

BACKGROUND

Both pathological and normal processes depend on proteins. In this study, plasma protein profiles were analyzed by a novel proximity extension assay (PEA) to identify potential pathogenic mechanisms and diagnostic biomarkers in patients diagnosed with acute myocardial infarction (AMI).

METHODS

In this study, we identified a total of 92 plasma proteins using the Olink Target 96 Cardiovascular III panel in a cohort consisting of 30 healthy controls (HC), 28 patients with unstable angina (UA) and 30 patients with AMI. Subsequently, we conducted a differential expression analysis to identify protein molecules that were specifically expressed in patients with AMI. To gain insights into the potential functional mechanisms of these differentially expressed molecules, we performed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Following that, the utilization of least absolute shrinkage and selection operator (LASSO) regression facilitated the identification of potential protein biomarkers, enabling the differentiation between AMI and UA. A diagnostic model was subsequently developed through logistic regression, and the effectiveness of these markers was assessed using receiver operating characteristic (ROC) analysis. Ultimately, the diagnostic capabilities of these potential biomarkers were validated in an independent validation cohort consisting of 30 UA cases and 30 AMI cases.

RESULTS

In this study, a comprehensive analysis of plasma proteins identified a total of 92 proteins. Further analysis using analysis of variance revealed that 25 proteins exhibited specific expression in the AMI group compared to the HC and UA groups. Additionally, KEGG enrichment analysis indicated that these differentially expressed proteins were primarily associated with the activation of cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, and GnRH signaling pathway. AGRP, TGM2, IL6, GH1, and CA5A were identified through LASSO regression as prospective protein biomarkers for distinguishing between UA and AMI. The diagnostic model comprising these five proteins exhibited exceptional performance in both the discovery and validation datasets, surpassing AUC values of 0.9.

CONCLUSION

The findings of our study provide additional insights into the involvement of the inflammatory response and AKT cascade response in the development of AMI. Moreover, we have identified potential protein markers that could be utilized for the accurate diagnosis of AMI. These results offer a fresh perspective for clinical decision-making in the context of AMI.

Circulating Plasma miRNA Homologs in Mice and Humans Reflect Familial Cerebral Cavernous Malformation Disease

Patients with familial cerebral cavernous malformation (CCM) inherit germline loss of function mutations and are susceptible to progressive development of brain lesions and neurological sequelae during their lifetime. To date, no homologous circulating molecules have been identified that can reflect the presence of germ line pathogenetic CCM mutations, either in animal models or patients. We hypothesize that homologous differentially expressed (DE) plasma miRNAs can reflect the CCM germline mutation in preclinical murine models and patients. Herein, homologous DE plasma miRNAs with mechanistic putative gene targets within the transcriptome of preclinical and human CCM lesions were identified. Several of these gene targets were additionally found to be associated with CCM-enriched pathways identified using the Kyoto Encyclopedia of Genes and Genomes. DE miRNAs were also identified in familial-CCM patients who developed new brain lesions within the year following blood sample collection. The miRNome results were then validated in an independent cohort of human subjects with real-time-qPCR quantification, a technique facilitating plasma assays. Finally, a Bayesian-informed machine learning approach showed that a combination of plasma levels of miRNAs and circulating proteins improves the association with familial-CCM disease in human subjects to 95% accuracy. These findings act as an important proof of concept for the future development of translatable circulating biomarkers to be tested in preclinical studies and human trials aimed at monitoring and restoring gene function in CCM and other diseases.

A Comparative Proteomic Analysis to Explore the Influencing Factors on Endometritis Using LC-MS/MS

The inflammatory system activated by uterine infection is associated with decreased fertility. Diseases can be detected in advance by identifying biomarkers of several uterine diseases. Escherichia coli is one of the most frequent bacteria that is involved in pathogenic processes in dairy goats. The purpose of this study was to investigate the effect of endotoxin on protein expression in goat endometrial epithelial cells. In this study, the LC-MS/MS approach was employed to investigate the proteome profile of goat endometrial epithelial cells. A total of 1180 proteins were identified in the goat Endometrial Epithelial Cells and LPS-treated goat Endometrial Epithelial Cell groups, of which, 313 differentially expressed proteins were accurately screened. The proteomic results were independently verified by WB, TEM and IF techniques, and the same conclusion was obtained. To conclude, this model is suitable for the further study of infertility caused by endometrial damage caused by endotoxin. These findings may provide useful information for the prevention and treatment of endometritis.

Proteomic Characterization of SAS Cell-Derived Extracellular Vesicles in Relation to Both BPA and Neutron Irradiation Doses

Boron neutron capture therapy (BNCT) is a selective radiotherapy based on nuclear reaction that occurs when ¹⁰B atoms accumulated in cancer cells are irradiated by thermal neutrons, triggering a nuclear fission response leading to cell death. Despite its growing importance in cancer treatment, molecular characterization of its effects is still lacking. In this context, proteomics investigation can be useful to study BNCT effect and identify potential biomarkers. Hence, we performed proteomic analysis with nanoLC-MS/MS (liquid chromatography coupled to tandem mass spectrometry) on extracellular vesicles (EVs) isolated from SAS cultures treated or not with ¹⁰B-boronophenylalanine (BPA) and different doses of neutron irradiation, to study the cellular response related to both boron administration and neutrons action. Despite the interference of fetal bovine serum in the medium, we were able to stratify BPA- and BPA+ conditions and to identify EVs-derived proteins characterizing pathways potentially related to a BNCT effect such as apoptosis, DNA repair and inflammatory response. In particular, KLF11, SERPINA1 and SERPINF2 were up-regulated in BPA+, while POLE and SERPINC1 were up-regulated in BPA-. These results provide the first proteomic investigation of EVs treated with BNCT in different conditions and highlight the potentiality of proteomics for improving biomarkers identification and mechanisms understanding of BNCT.

Circulating tumour cells for early detection of clinically relevant cancer

Given that cancer mortality is usually a result of late diagnosis, efforts in the field of early detection are paramount to reducing cancer-related deaths and improving patient outcomes. Increasing evidence indicates that metastasis is an early event in patients with aggressive cancers, often occurring even before primary lesions are clinically detectable. Metastases are usually formed from cancer cells that spread to distant non-malignant tissues via the blood circulation, termed circulating tumour cells (CTCs). CTCs have been detected in patients with early stage cancers and, owing to their association with metastasis, might indicate the presence of aggressive disease, thus providing a possible means to expedite diagnosis and treatment initiation for such patients while avoiding overdiagnosis and overtreatment of those with slow-growing, indolent tumours. The utility of CTCs as an early diagnostic tool has been investigated, although further improvements in the efficiency of CTC detection are required. In this Perspective, we discuss the clinical significance of early haematogenous dissemination of cancer cells, the potential of CTCs to facilitate early detection of clinically relevant cancers, and the technological advances that might improve CTC capture and, thus, diagnostic performance in this setting.

Proteomic analysis of uterine lavage fluid of dairy cows at different time after delivery by mass spectrometry

Endometritis is a common disease in the reproductive system, which is the infection and inflammation of the endometrium. In severe cases, it can affect the myometrium and adversely affect the subsequent fertility of dairy cows. We used a mass spectrometry-based technique to compare proteomics of uterine lavage fluid between healthy cows and cows with cytological endometritis classified according to 100-day postpartum pregnancy results and diagnosis result. The uterine lavage fluid of dairy cows collected at 15 and 30 days after delivery was analyzed. 15 days postpartum, we identified a total of 1129 proteins in the control and cytological endometritis (CEM) groups. Among them, 160 proteins were accurately screened out. 30 days postpartum, we identified a total of 846 proteins in the control and cytological endometritis (CEM) groups. Among them, 186 proteins were accurately cytological endometritis (CEM). Endometritis is a costly reproductive disease in lactating cows, which needs to be diagnosed in time. Using proteomics method based on gel mass spectrometry, we compared the proteome of uterine lavage fluid of dairy cows with and without cytological endometritis to characterize the changes of proteomic characteristics associated with postpartum uterine disease. To provide reference for clinical application and basic research.

RNF113A targeted by miR-197 promotes proliferation and inhibits autophagy via CXCR4/CXCL12/AKT/ERK/Beclin1 axis in cervical cancer

Ring Finger Protein 113 (RNF113A), an ubiquitin E3 ligase, is genetically associated with many biological processes, including proliferation, differentiation, cell death, and neurogenesis. Recently, RNF113A has been found to be an abnormal expression in many diseases, such as X-linked trichothiodystrophy syndrome and esophageal cancer. Here, we explore the potential mechanism of RNF113A in the progression of cervical cancer (CC). In this study, we evaluated the expression level and biological function of RNF113A in CC both in vitro and in vivo by bioinformatic prediction, DIA proteomic analysis, compensation experiment, Co-IP, dual-luciferase reporter assay and nude mouse xenograft to identify the RNF113A-associated autophagy pathways involved with tumorigenesis. Consistent with the prediction from biological information analysis, we found that RNF113A was highly expressed in human CC tissues and cells. In addition, this study illustrated that the high expression of RNF113A dramatically promoted proliferation and suppressed autophagy both in vitro and in vivo. In contrast, low expression of RNF113A enhanced autophagy activities and inhibited tumor growth in CC. We also found that miRNA-197, the level of which (negative correlation with RNF113A) declined in human CC, directly restrained the expression of RNF113A. Mechanistically, proteomic and mechanistic assays uncovered that RNF113A confirmed as the direct downstream target of miR-197, promoted proliferation and restrained autophagy in CC not through direct ubiquitination degradation of autophagy marker Beclin1 but via CXCR4/CXCL12/AKT/ERK/Beclin1 signal transduction axis. In summary, we found a new miR-197/RNF113 A/CXCR4/CXCL12/AKT/ERK/Beclin1 regulation pathway that plays an important part in the survival and progression of CC.

Challenges and Opportunities in Clinical Diagnostic Routine of Envenomation Using Blood Plasma Proteomics

Specific and sensitive tools for the diagnosis and monitoring of accidents by venomous animals are urgently needed. Several diagnostic and monitoring assays have been developed; however, they have not yet reached the clinic. This has resulted in late diagnoses, which represents one of the main causes of progression from mild to severe disease. Human blood is a protein-rich biological fluid that is routinely collected in hospital settings for diagnostic purposes, which can translate research progress from the laboratory to the clinic. Although it is a limited view, blood plasma proteins provide information about the clinical picture of envenomation. Proteome disturbances in response to envenomation by venomous animals have been identified, allowing mass spectrometry (MS)-based plasma proteomics to emerge as a tool in a range of clinical diagnostics and disease management that can be applied to cases of venomous animal envenomation. Here, we provide a review of the state of the art on routine laboratory diagnoses of envenomation by snakes, scorpions, bees, and spiders, as well as a review of the diagnostic methods and the challenges encountered. We present the state of the art on clinical proteomics as the standardization of procedures to be performed within and between research laboratories, favoring a more excellent peptide coverage of candidate proteins for biomarkers. Therefore, the selection of a sample type and method of preparation should be very specific and based on the discovery of biomarkers in specific approaches. However, the sample collection protocol (e.g., collection tube type) and the processing procedure of the sample (e.g., clotting temperature, time allowed for clotting, and anticoagulant used) are equally important to eliminate any bias.

Challenges and opportunities for single cell computational proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it's necessary to realize that current algorithms being employed on single-cell data were designed for bulk data, and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data, and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

DIGE Analysis of Clinical Specimens

Two-dimensional difference gel electrophoresis (2D-DIGE) is an elegant gel electrophoretic analytical tool for comparative protein assessment. It is based on two-dimensional gel electrophoresis (2D-GE) separation of fluorescently labeled protein extracts. The tagging procedures are designed to not interfere with the chemical properties of proteins with respect to their pI and electrophoretic mobility, once a proper labeling protocol is followed. The use of an internal pooled standard makes 2D-DIGE a highly accurate quantitative method enabling multiple protein samples to be separated on the same two-dimensional gel. Technical limitations of this technique (i.e., underrating of low abundant, high molecular mass and integral membrane proteins) are counterbalanced by the incomparable separation power which allows proteoforms and unknown PTM (posttranslational modification) identification. Moreover, the image matching and cross-gel statistical analysis generates robust quantitative results making data validation by independent technologies successful.

DIGE Analysis of Immunodepleted Plasma

This chapter focuses on upstream immunodepletion of high-abundance proteins from plasma samples and subsequent analysis by fluorescence two-dimensional difference gel electrophoresis (2D-DIGE). The abundances of proteins in biofluid proteomes, such as serum, plasma, saliva, and bronchoalveolar lavage fluid (BALF), can exceed ten orders of magnitude. This substantial dynamic range is problematic for the detection of medium and low-abundance proteins by 2D-DIGE analysis. To increase the detection, quantification, and identification of medium-low-abundance proteins, the targeted depletion of known abundant proteins with antibody columns has been successfully employed. From the literature, it is clear that the performance of abundant protein depletion with immunodepletion columns has been successful in broadening the coverage of the biofluid proteome and facilitating the identification of disease-specific biomarkers. The task for a successful biomarker strategy involves the combination of a reproducible and robust fractionation method, coupled with a highly accurate quantitative method, a task that is exemplified by combining both immunodepletion and 2D-DIGE together to discover significant proteins associated with the disease phenotype.

Protein Analysis of A. halleri and N. caerulescens Hyperaccumulators When Exposed to Nano and Ionic Forms of Cd and Zn

Hyperaccumulator plant species growing on metal-rich soils can accumulate high quantity of metals and metalloids in aerial tissues, and several proteomic studies on the molecular mechanisms at the basis of metals resistance and hyperaccumulation have been published. Hyperaccumulator are also at the basis of the phytoremediation strategy to remove metals more efficiently from polluted soils or water. Arabidopsis halleri and Noccea caerulescens are both hyperaccumulators of metals and nano-metals. In this study, the change in some proteins in A. halleri and N. caerulescens was assessed after the growth in soil with cadmium and zinc, provided as sulphate salts (CdSO₄ and ZnSO₄) or sulfide quantum dots (CdS QDs and ZnS QDs). The protein extracts obtained from plants after 30 days of growth were analyzed by 2D-gel electrophoresis (2D SDS-PAGE) and identified by MALDI-TOF/TOF mass spectrometry. A bioinformatics analysis was carried out on quantitative protein differences between control and treated plants. In total, 43 proteins resulted in being significatively modulated in A. halleri, while 61 resulted in being modulated in N. caerulescens. Although these two plants are hyperaccumulator of both metals and nano-metals, at protein levels the mechanisms involved do not proceed in the same way, but at the end bring a similar physiological result.

Current advances in antibody-based serum biomarker studies: From protein microarray to phage display

PURPOSE

This review aims to summarize the technological advances in the field of antibody-based biomarker studies by proteome microarray and phage display. In addition, the possible development directions of this field are also discussed.

EXPERIMENTAL DESIGN

We have focused on the antibody profiling by proteome microarray and phage display, including the technological advances, the tools/resources constructed, and the characteristics of both platforms.

RESULTS

With the help of tools/resources and technological advances in proteome microarray and phage display, the efficiency of profiling antibody-based biomarkers in serum samples has been greatly improved.

CONCLUSIONS

In the past few years, proteome microarray and phage display, especially the latter one, have already demonstrated their capacity and efficiency for biomarker identification. In the near future, we believe that more antibody-based biomarkers could be identified, and some of them could eventually be developed into real clinical applications.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Systems Biology Approach to Investigate Biomarkers, Boron-10 Carriers, and Mechanisms Useful for Improving Boron Neutron Capture Therapy

Systems biology approach, carried out with high-throughput omics technologies, has become a fundamental aspect of the study of complex diseases like cancer. It can molecularly characterize subjects, physiopathological conditions, and interactions, allowing a precise description, to reach personalized medicine. In particular, proteomics, typically performed with liquid chromatography coupled to mass spectrometry, is a powerful tool for systems biology, giving the possibility to perform diagnosis, patient stratification, and prediction of therapy effects. Boron Neutron Capture Therapy (BNCT) is a selective antitumoral radiotherapy based on a nuclear reaction that occurs when ¹⁰B atoms are irradiated by low-energy thermal neutrons, leading to cell death, thanks to the production of high-energy α particles. Since BNCT is recently becoming an important therapy for the treatment of different types of solid tumors such as gliomas, head and neck cancers, and others, it can take advantage of molecular investigation to improve the understanding of effects and mechanisms and so help its clinical applications. In this context, proteomics can provide a better understanding of mechanisms related to BNCT effect, identify potential biomarkers, and individuate differential responses by specific patients, stratifying responders and nonresponders. Another key aspect of BNCT is the study of new potential Boron-10 carriers to improve the selectivity of Boron delivery to tumors and proteomics can be important in this application, studying the effectiveness of new boron delivery agents, including protein-based carriers, also using computational studies that can investigate new molecules, such as boronated monoclonal antibodies, for improving BNCT.

Differential plasma proteomes of the patients with Opisthorchiasis viverrini and cholangiocarcinoma identify a polymeric immunoglobulin receptor as a potential biomarker

In Southeast Asian countries, nitrosamine compounds and the liver fluke Opisthorchis viverrini have long been identified as carcinogens for cholangiocarcinoma (CHCA). In order to effectively treat O. viverrini infections and prevent the development of CHCA, methods for disease detection are needed. This study aims to identify biomarkers for O. viverrini infection and CHCA. In the discovery phase, technical triplicates of five pooled plasma pools (10 plasma each) of healthy control subjects (noOVCCA), O. viverrini subjects (OV), and cholangiocarcinoma subjects (CCA), underwent solution-based digestion, with the label-free method, using a Thermo Scientific™ Q Exactive™ HF hybrid quadrupole-Orbitrap mass spectrometer and UltiMate 300 LC systems. The noOVCCA, OV, and CCA groups demonstrated different profiles and were clustered, as illustrated by PCA and heat map analysis. The STRING and reactome analysis showed that both OV and CCA groups up-regulated proteins targeting immune system-related proteins. Differential proteomic profiles, S100A9, and polymeric immunoglobulin receptor (PIGR) were specifically expressed in the CCA group. During the validation phase, another 50 plasma samples were validated via the PIGR sandwich ELISA. Using PIGR >1.559 ng/ml as a cut-off point, 78.00% sensitivity, 71.00% specificity, and AUC = 0.8216, were obtained. It is sufficient to differentially diagnose cholangiocarcinoma patients from healthy patients and those with Opisthorchiasis viverrini. Hence, in this study, PIGR was identified and validated as a potential biomarker for CHCA. Plasma PIGR is suggested for screening CHCA, especially in an endemic region of O. viverrini infection.

Near-Infrared-Responded High Sensitivity Nanoprobe for Steady and Visualized Detection of Albumin in Hepatic Organoids and Mouse Liver

Exploring the advanced techniques for protein detection facilitates cell fate investigation. However, it remains challenging to quantify and visualize the protein with one single probe. Here, a luminescent approach to detect hepatic cell fate marker albumin in vitro and living cell labeling with upconversion nanoparticles (UCNPs), which are conjugated with antibody (Ab) and rose bengal hexanoic acid (RBHA) is reported. To guarantee the detection quality and accuracy, an "OFF-ON" strategy is adopted: in the presence of albumin, the luminescence of nanoparticles remains suppressed owing to energy transfer to the quencher. Upon albumin binding to the antibody, the luminescence is recovered under near-infrared light. In various bio-samples, the UCNPs-Ab-RBHA (UCAR) nanoprobe can sense albumin with a broad detection range (5-315 ng mL^-1 ). When applied to liver ductal organoid culture medium, the UCAR can monitor hepatocyte differentiation in real time by sensing the secreted albumin. Further, UCAR enables live imaging of cellular albumin in cells, organoids, and tissues. In a CCl₄ -induced liver injury model, UCAR detects reduced albumin in liver tissue and serum. Thus, a biocompatible nanoprobe for both quantification and imaging of protein in complex biological environment with superior stability and high sensitivity is provided.

Screening anlotinib responders via blood-based proteomics in non-small cell lung cancer

Anlotinib has been demonstrated to be effective in advanced non-small cell lung cancer (NSCLC) patients. The response stratification of anlotinib remains unclear. In this study, plasma samples from 28 anlotinib-treated NSCLC patients (discovery cohort: 14 responders and 14 non-responders) were subjected to proteomic analysis, and plasma samples from 35 anlotinib-treated NSCLC patients (validation cohort) were subjected to validation analysis. Liquid chromatography-tandem mass spectrometry analysis was performed on samples with different time points, namely baseline (BL), best response (BR), and progression disease (PD). Bioinformatics analysis was performed to screen for the underlying differential proteins. Enzyme-linked immunosorbent assay was performed to detect plasma ARHGDIB, FN1, CDH1, and KNG1 levels respectively. The Kaplan-Meier survival analysis was used for biomarker-based responsive stratification. Our results indicated that differential proteins between responders and non-responders showed that proteomic technology potentially contributes to biomarker screening in plasma samples at BL. Furthermore, our results suggested that the detection of plasma ARHGDIB, FN1, CDH1, and KNG1 levels have potential predictive value for anlotinib response both in the discovery cohort and validation cohort. Collectively, this study offers novel insights into the value of plasma biomarker screening via proteomic examination and suggests that plasma ARHGDIB, FN1, CDH1, and KNG1 levels could be used as biomarkers for anlotinib stratification in NSCLC patients.

A review of mass spectrometry-based analyses to understand COVID-19 convalescent plasma mechanisms of action

The spread of coronavirus disease 2019 (COVID‐19) viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has become a worldwide pandemic claiming several thousands of lives worldwide. During this pandemic, several studies reported the use of COVID‐19 convalescent plasma (CCP) from recovered patients to treat severely or critically ill patients. Although this historical and empirical treatment holds immense potential as a first line of response against eventual future unforeseen viral epidemics, there are several concerns regarding the efficacy and safety of this approach. This critical review aims to pinpoint the possible role of mass spectrometry‐based analysis in the identification of unique molecular component proteins, peptides, and metabolites of CCP that explains the therapeutic mechanism of action against COVID‐19. Additionally, the text critically reviews the potential application of mass spectrometry approaches in the search for novel plasma biomarkers that may enable a rapid and accurate assessment of the safety and efficacy of CCP. Considering the relative low‐cost value involved in the CCP therapy, this proposed line of research represents a tangible scientific challenge that will be translated into clinical practice and help save several thousand lives around the world, specifically in low‐ and middle‐income countries.

Differential RNA aptamer affinity profiling on plasma as a potential diagnostic tool for bladder cancer

The molecular composition of blood is a signature of human health, reflected in the thousands of blood biomarkers known for human diseases. However, establishing robust disease markers is challenging due to the diversity of individual samples. New sequencing methods have simplified biomarker discovery for circulating DNA and RNA while protein profiling is still laborious and costly. To harness the power of high-throughput sequencing to profile the protein content of a biological sample, we developed a method termed APTASHAPE that uses oligonucleotide aptamers to recognize proteins in complex biofluids. We selected a large pool of 2′Fluoro protected RNA sequences to recognize proteins in human plasma and identified a set of 33 cancer-specific aptamers. Differential enrichment of these aptamers after selection against 1 μl of plasma from individual patients allowed us to differentiate between healthy controls and bladder cancer-diagnosed patients (91% accuracy) and between early non-invasive tumors and late stage tumors (83% accuracy). Affinity purification and mass spectrometry of proteins bound to the predictive aptamers showed the main target proteins to be C4b-binding protein, Complement C3, Fibrinogen, Complement factor H and IgG. The APTASHAPE method thus provides a general, automated and highly sensitive platform for discovering potential new disease biomarkers.

A Novel Secreted Protein-Related Gene Signature Predicts Overall Survival and Is Associated With Tumor Immunity in Patients With Lung Adenocarcinoma

Secreted proteins are important proteins in the human proteome, accounting for approximately one-tenth of the proteome. However, the prognostic value of secreted protein-related genes has not been comprehensively explored in lung adenocarcinoma (LUAD). In this study, we screened 379 differentially expressed secretory protein genes (DESPRGs) by analyzing the expression profile in patients with LUAD from The Cancer Genome Atlas database. Following univariate Cox regression and least absolute shrinkage and selection operator method regression analysis, 9 prognostic SPRGs were selected to develop secreted protein-related risk score (SPRrisk), including CLEC3B, C1QTNF6, TCN1, F2, FETUB, IGFBP1, ANGPTL4, IFNE, and CCL20. The prediction accuracy of the prognostic models was determined by Kaplan–Meier survival curve analysis and receiver operating characteristic curve analysis. Moreover, a nomogram with improved accuracy for predicting overall survival was established based on independent prognostic factors (SPRrisk and clinical stage). The DESPRGs were validated by quantitative real-time PCR and enzyme-linked immunosorbent assay by using our clinical samples and datasets. Our results demonstrated that SPRrisk can accurately predict the prognosis of patients with LUAD. Patients with a higher risk had lower immune, stromal, and ESTIMATE scores and higher tumor purity. A higher SPRrisk was also negatively associated with the abundance of CD8⁺ T cells and M1 macrophages. In addition, several genes of the human leukocyte antigen family and immune checkpoints were expressed in low levels in the high-SPRrisk group. Our results provided some insights into assessing individual prognosis and choosing personalized treatment modalities.

Prognostic role of an inflammation scoring system in radical resection of oral squamous cell carcinoma

Background

Inflammatory markers can influence the postoperative prognosis and outcome of malignant tumors. However, the role of inflammatory factors in oral squamous cell carcinoma (OSCC) are still debatable. The primary objective of this investigation was to detect the preoperative blood fibrinogen and neutrophil–lymphocyte ratio (NLR) in OSCC patients and to determine the predictive validity of F-NLR (combined fibrinogen and NLR score).

Methods

A total of 365 patients with oral cancer after surgery were separated into three classes: F-NLR of 2, with hyperfibrinogenemia (> 250 mg/dL) and high NLR (> 3.2); F-NLR of 1, with only one higher index; and F-NLR of 0, with no higher indices. Univariate and multivariate analyses were used to identify risk factors for the demographic and clinical characteristics of patients in the three F-NLR groups. Kaplan–Meier survival analysis was used to assess the prognosis.

Results

Preoperative F-NLR showed a relatively better predictive role in oral cancer prognosis than fibrinogen and NLR alone. Multivariate analysis revealed that F-NLR has the potential to be an independent predictor for OSCC cancer-specific survival (P < 0.001). Patients with high scores had a relatively poorer prognosis than those with low scores (P < 0.001).

Conclusions

Our findings indicate that blood F-NLR may serve as an independent prognostic factor in OSCC patients.

Insights on Proteomics-Driven Body Fluid-Based Biomarkers of Cervical Cancer

Cervical cancer is one of the top malignancies in women around the globe, which still holds its place despite being preventable at early stages. Gynecological conditions, even maladies like cervical cancer, still experience scrutiny from society owing to prevalent taboo and invasive screening methods, especially in developing economies. Additionally, current diagnoses lack specificity and sensitivity, which prolong diagnosis until it is too late. Advances in omics-based technologies aid in discovering differential multi-omics profiles between healthy individuals and cancer patients, which could be utilized for the discovery of body fluid-based biomarkers. Body fluids are a promising potential alternative for early disease detection and counteracting the problems of invasiveness while also serving as a pool of potential biomarkers. In this review, we will provide details of the body fluids-based biomarkers that have been reported in cervical cancer. Here, we have presented our perspective on proteomics for global biomarker discovery by addressing several pertinent problems, including the challenges that are confronted in cervical cancer. Further, we also used bioinformatic methods to undertake a meta-analysis of significantly up-regulated biomolecular profiles in CVF from cervical cancer patients. Our analysis deciphered alterations in the biological pathways in CVF such as immune response, glycolytic processes, regulation of cell death, regulation of structural size, protein polymerization disease, and other pathways that can cumulatively contribute to cervical cancer malignancy. We believe, more extensive research on such biomarkers, will speed up the road to early identification and prevention of cervical cancer in the near future.

Galectin-1-Dependent Mitochondria Apoptosis Plays an Essential Role in the Potential Protein Targets of DBDCT-Induced Hepatotoxicity as Revealed by Quantitative Proteomic Analyses

Di-n-butyl-di-(4-chlorobenzohydroxamato) tin(IV) (DBDCT), a new patent agent, exhibited strong antitumor activity. In some cases, its activity was close to or even higher than cisplatin, a first-line clinical metallic agent. Similar to platinum compounds, it also showed toxicity. However, the effective targets and mechanisms for specific toxicity and biological activity are still unclear. In this study, proteomic analysis revealed that 146 proteins (98 upregulated and 48 downregulated) were differentially identified by label-free LC-MS/MS after DBDCT treatment. Meanwhile, network analysis of these differential proteins suggested that protein Galectin-1 (Gal-1) could regulate the apoptosis process (15 related proteins), which played an essential role in the potential targets of DBDCT-induced hepatotoxicity. Furthermore, it was demonstrated that DBDCT might promote ROS production, activate NF-κB p65, inhibit Ras and p-ERK1/2 expressions, increase the level of Gal-1, subsequently upregulate the expressions of Bax, p53, Fas, and FasL, and downregulate the expression of Bcl-2. As a result of these modulations, caspase cascades were finally activated, which executed apoptosis in HL7702 liver cells. Correspondingly, NAC (inhibitor of ROS), PDTC (inhibitor of NF-κB), EGF (ERK1/2 activator), and OTX008 (inhibitor of Gal-1) were found to reverse and abolish the DBDCT-associated cytotoxicity partially. In conclusion, Gal-1 might be the potential target for toxicity and biological activity. Moreover, the present study will lay the groundwork for future research about di-n-butyl-di-(4-chlorobenzohydroxamato) tin structure optimization and developing it into a new potential anticancer agent.

Liquid biopsy: the value of different bodily fluids

Liquid biopsies have gained an increasing interest in the last years among medical and scientific communities. Indeed, the value of liquid effusions, while less invasive and more accurate techniques, has been markedly highlighted. Peripheral blood comprises the most often analyzed sample, but recent evidences have pointed out the huge importance of other bodily fluids, including pleural and peritoneal fluids, urine, saliva and cerebrospinal fluid in the detection and monitoring of different tumor types. In face to these advances, this review aims to provide an overview of the value of tumor-associated mutations, detectable in different effusions, and how they can be used in clinical practice, namely in prognosis assessment and early disease and minimal disease recurrence detection, and in predicting the treatment response or acquired-resistance development.

Recent Multiomics Approaches in Endometrial Cancer

Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

The proteomic landscape of ovarian cancer cells in response to melatonin

Ovarian cancer (OC) is the most lethal gynecological malignancy with a highly negative prognosis. Melatonin is an indoleamine secreted by the pineal gland during darkness and has shown antitumor activity in both in vitro and in vivo experiments. Herein, we investigated the influence of melatonin on the proteome of human ovarian carcinoma cells (SKOV-3 cell line) using the Ultimate 3000 LC Liquid NanoChromatography equipment coupled to a Q-Exactive mass spectrometry. After 48 h of treatment, melatonin induced a significant cytotoxicity especially with the highest melatonin concentration. The proteomic profile revealed 639 proteins in the control group, and 98, 110, and 128 proteins were altered by melatonin at the doses of 0.8, 1.6, and 2.4 mM, respectively. Proteins associated with the immune system and tricarboxylic acid cycle were increased in the three melatonin-exposed groups of cells. Specifically, the dose of 2.4 mM led to a reduction in molecules associated with protein synthesis, especially those of the ribosomal protein family. We also identified 28 potential genes shared between normal ovarian tissue and OC in all experimental groups, and melatonin was predicted to alter genes encoding ribosomal proteins. Notably, the set of proteins changed by melatonin was linked to a better prognosis for OC patients. We conclude that melatonin significantly alters the proteome of SKOV-3 cells by changing proteins involved with the immune response and mitochondrial metabolism. The concentration of 2.4 mM of melatonin promoted the largest number of protein changes. The evidence suggests that melatonin may be an effective therapeutic strategy against OC.

Proteomics in dogs: a systematic review

Proteomic analysis is having a rapid development as a method for the detection of biomarkers of diseases in dogs. Dogs in addition to their importance as companion animals, serve as important animal models for research. This study aims to systematically review evidence regarding the studies performed in proteomics in dogs, and specifically those made in serum, saliva, urine and/or plasma. Information searched in October 2020, January 2021 and August 2021, for English language publications of the last decade (2010-2020) were obtained from electronic databases. Screening, data extraction and risk of bias assessment were undertaken by two investigators. The risk of bias was evaluated using the Review Manager (RevMan 5) tool. Meta-analysis and case report studies were not included in this review. Through the screening process a total of 557 publications were identified after the removal of duplicates. Out of these, 65 were fully evaluated and 44 of these were included in the review. Most studies evaluated the proteome of disease and compared it with a healthy population, and most of the articles included were made on serum, followed by saliva. The overall risk of bias for all studies was high, due to an absence in the generation of random sequence. Overall proteomic analysis has allowed the discovery of new physiopathological pathways of diseases and potential biomarkers in the dog, which are addressed in this review.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Therapeutic Targeting of Cancer: Epigenetic Homeostasis

A large number of studies have revealed that epigenetics plays an important role in cancer development. However, the currently-developed epigenetic drugs cannot achieve a stable curative effect. Thus, it may be necessary to redefine the role of epigenetics in cancer development. It has been shown that embryonic development and tumor development share significant similarities in terms of biological behavior and molecular expression patterns, and epigenetics may be the link between them. Cell differentiation is likely a manifestation of epigenetic homeostasis at the cellular level. In this article, we introduced the importance of epigenetic homeostasis in cancer development and analyzed the shortcomings of current epigenetic treatment regimens. Understanding the dynamic process of epigenetic homeostasis in organ development can help us characterize cancer according to its differentiation stages, explore new targets for cancer treatment, and improve the clinical prognosis of patients with cancer.

Comparative Proteomic Analysis to Investigate the Pathogenesis of Oral Adenoid Cystic Carcinoma

Adenoid cystic carcinoma (ACC) belongs to salivary gland malignancies commonly occurring in an oral cavity with a poor long-term prognosis. The potential biomarkers and cellular functions acting on local recurrences and distant metastases remain to be illustrated. Proteomics is the core content of precision medicine research, which provides accurate information for early detection of cancer, benign and malignant diagnosis, classification and personalized medication, efficacy monitoring, and prognosis judgment. To obtain a comprehensive regulation network and supply clues for the treatment of oral ACC (OACC), we utilized mass spectrometry-based quantitative proteomics to analyze the protein expression profile in paired tumor and adjacent normal tissues. We identified a total of 40,547 specific peptides and 4454 differentially expressed proteins (DEPs), in which HAPLN1 was the most upregulated protein and BPIFB1 was the most downregulated. Then, we annotated the functions and characteristics of DEPs in detail from the aspects of gene ontology, subcellular structural localization, KEGG, and protein domain to thoroughly understand the identified and quantified proteins. Glycosphingolipid biosynthesis and glycosaminoglycan degradation pathways showed the biggest difference according to KEGG analysis. Moreover, we confirmed 20 proteins from the ECM-receptor signaling pathway by a parallel reaction monitoring quantitative detection and 19 proteins were quantified. This study provides useful insights to analyze DEPs in OACC and guide in-depth thinking of the pathogenesis from a proteomics view for anticancer mechanisms and potential biomarkers.

Understanding Cervical Cancer through Proteomics

Cancer is one of the leading public health issues worldwide, and the number of cancer patients increases every day. Particularly, cervical cancer (CC) is still the second leading cause of cancer death in women from developing countries. Thus, it is essential to deepen our knowledge about the molecular pathogenesis of CC and propose new therapeutic targets and new methods to diagnose this disease in its early stages. Differential expression analysis using high-throughput techniques applied to biological samples allows determining the physiological state of normal cells and the changes produced by cancer development. The cluster of differential molecular profiles in the genome, the transcriptome, or the proteome is analyzed in the disease, and it is called the molecular signature of cancer. Proteomic analysis of biological samples of patients with different grades of cervical intraepithelial neoplasia (CIN) and CC has served to elucidate the pathways involved in the development and progression of cancer and identify cervical proteins associated with CC. However, several cervical carcinogenesis mechanisms are still unclear. Detecting pathologies in their earliest stages can significantly improve a patient's survival rate, prognosis, and recurrence. The present review is an update on the proteomic study of CC.

Trajectories of frailty in aging: Prospective cohort study

Background

Emerging evidence suggests that there is significant variability in the progression of frailty in aging. We aimed to identify latent subpopulations of frailty trajectories, and examine their clinical and biological correlates.

Methods

We characterized frailty using a 41-item cumulative deficit score at baseline and annual visits up to 12 years in 681 older adults (55% women, mean age 74·6 years). Clinical risk profile and walking while talking performance as a clinical marker of trajectories were examined. Mortality risk associated with trajectories was evaluated using Cox regression adjusted for established survival predictors, and reported as hazard ratios (HR). Proteome-wide analysis was done.

Findings

Latent class modeling identified 4 distinct frailty trajectories: relatively stable (34·4%) as well as mild (36·1%), moderate (24·1%) and severely frail (5·4%). Four distinct classes of frailty trajectories were also shown in an independent sample of 515 older adults (60% women, 68% White, 26% Black). The stable group took a median of 31 months to accumulate one additional deficit compared to 20 months in the severely frail group. The worst trajectories were associated with modifiable risk factors such as low education, living alone, obesity, and physical inactivity as well as slower walking while talking speed. In the pooled sample, mild (HR 2·33, 95% CI 1·30–4·18), moderate (HR 2·49, 95% CI 1·33–4·66), and severely frail trajectories (HR 5·28, 95% CI 2·68–10·41) had higher mortality compared to the stable group. Proteomic analysis showed 11 proteins in lipid metabolism and growth factor pathways associated with frailty trajectories.

Conclusion

Frailty shows both stable and accelerated patterns in aging, which can be distinguished clinically and biologically.

A Quantitative Serum Proteomic Analysis Helps to Explore the Comprehensive Mechanism and Identify Serum Biomarkers of Shengmai Injection's Effect on Isoproterenol-Induced Myocardial Ischemia in Rats

Shengmai injection (SMI), a traditional Chinese medicine formula with the nature of multicomponent and multi-target, has been widely used in clinic for treating cardiovascular diseases in China; however, its comprehensive mechanism of action remains unclear. In this study, a TMT-based quantitative serum proteomics was performed to explore SMI’s global mechanism and help identify serum biomarkers of its effect on isoproterenol (ISO)-induced myocardial ischemia rats. The results of TMT-based proteomic analysis identified 227, 100, and 228 differentially expressed proteins (DEPs) for the model compared to the control group, SMI pretreatment + model compared to the model group, and SMI pretreatment + model compared to the control group, respectively. Based on bioinformatics analyses of gene ontology (GO), KEGG pathways, and the protein-protein interaction (PPI) networks for the DEPs, it is concluded that the comprehensive mechanism of SMI’s effect on ISO-induced myocardial ischemia injury includes regulation of energy metabolism, reducing endothelial cell permeability, regulation of vessel and cardiac contractility, anti-inflammation, and prevention of cell apoptosis. Furthermore, 10 common DEPs were found, and six of them were regulated in model vs. control group, while back-regulated in SMI pretreatment + model vs. model group. Among them, three functional proteins of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Fas apoptotic inhibitory molecule 3 (FAIM3), and uncharacterized protein (M0R5J4), which were verified by the PRM analysis, might be the potential serum biomarkers on SMI’s effects. Overall, this serum proteomics of SMI not only provides insights into the comprehensive mechanism underlying SMI’s effects on ischemic heart disease but also helps identify serum biomarkers for directing SMI’s cardioprotective effects.

Insights into Mycobacterium leprae Proteomics and Biomarkers-An Overview

Although leprosy is curable, the identification of biomarkers for the early diagnosis of leprosy would play a pivotal role in reducing transmission and the overall prevalence of the disease. Leprosy-specific biomarkers for diagnosis, particularly for the paucibacillary disease, are not well defined. Therefore, the identification of new biomarkers for leprosy is one of the prime themes of leprosy research. Studying Mycobacterium leprae, the causative agent of leprosy, at the proteomic level may facilitate the identification, quantification, and characterization of proteins that could be potential diagnostics or targets for drugs and can help in better understanding the pathogenesis. This review aims to shed light on the knowledge gained to understand leprosy or its pathogen employing proteomics and its role in diagnosis.

Serum proteomic analysis of novel predictive serum proteins for neurological prognosis following cardiac arrest

Early prognostication of neurological outcome in comatose patients after cardiac arrest (CA) is vital for clinicians when assessing the survival time of sufferers and formulating appropriate treatment strategies to avoid the withdrawal of life‐sustaining treatment (WLST) from patients. However, there is still a lack of sensitive and specific serum biomarkers for early and accurate identification of these patients. Using an isobaric tag for relative and absolute quantitation (iTRAQ)‐based proteomic approach, we discovered 55 differentially expressed proteins, with 39 up‐regulated secreted serum proteins and 16 down‐regulated secreted serum proteins between three comatose CA survivors with good versus poor neurological recovery. Then, four proteins were selected and were validated via an enzyme‐linked immunosorbent assay (ELISA) approach in a larger‐scale sample containing 32 good neurological outcome patients and 46 poor neurological outcome patients, and it was confirmed that serum angiotensinogen (AGT) and alpha‐1‐antitrypsin (SERPINA1) were associated with neurological function and prognosis in CA survivors. A prognostic risk score was developed and calculated using a linear and logistic regression model based on a combination of AGT, SERPINA1 and neuron‐specific enolase (NSE) with an area under the curve of 0.865 (P < .001), and the prognostic risk score was positively correlated with the CPC value (R = 0.708, P < .001). We propose that the results of the risk score assessment not only reveal changes in biomarkers during neurological recovery but also assist in enhancing current therapeutic strategies for comatose CA survivors.

Clinicopathologic Analysis of Cathepsin B as a Prognostic Marker of Thyroid Cancer

Thyroid cancer incidence has increased worldwide; however, investigations of thyroid cancer-related factors as potential prognosis markers remain insufficient. Secreted proteins from the cancer secretome are regulators of several molecular mechanisms and are, thereby, ideal candidates for potential markers. We aimed to identify a specific factor for thyroid cancer by analyzing the secretome from normal thyroid cells, papillary thyroid cancer (PTC) cells, and anaplastic thyroid cancer cells using mass spectrometry (MS). Cathepsin B (CTSB) showed highest expression in PTC cells compared to other cell lines, and CTSB levels in tumor samples were higher than that seen in normal tissue. Further, among thyroid cancer patients, increased CTSB expression was related to higher risk of lymph node metastasis (LNM) and advanced N stage. Overexpression of CTSB in thyroid cancer cell lines activated cell migration by increasing the expression of vimentin and Snail, while its siRNA-mediated silencing inhibited cell migration by decreasing vimentin and Snail expression. Mechanistically, CTSB-associated enhanced cell migration and upregulation of vimentin and Snail occurred via increased phosphorylation of p38. As our results suggest that elevated CTSB in thyroid cancer induces the expression of metastatic proteins and thereby leads to LNM, CTSB may be a good and clinically relevant prognostic marker.

Proteomic assessment of serum biomarkers of longevity in older men

The biological bases of longevity are not well understood, and there are limited biomarkers for the prediction of long life. We used a high-throughput, discovery-based proteomics approach to identify serum peptides and proteins that were associated with the attainment of longevity in a longitudinal study of community-dwelling men age ≥65 years. Baseline serum in 1196 men were analyzed using liquid chromatography-ion mobility-mass spectrometry, and lifespan was determined during ~12 years of follow-up. Men who achieved longevity (≥90% expected survival) were compared to those who died earlier. Rigorous statistical methods that controlled for false positivity were utilized to identify 25 proteins that were associated with longevity. All these proteins were in lower abundance in long-lived men and included a variety involved in inflammation or complement activation. Lower levels of longevity-associated proteins were also associated with better health status, but as time to death shortened, levels of these proteins increased. Pathway analyses implicated a number of compounds as important upstream regulators of the proteins and implicated shared networks that underlie the observed associations with longevity. Overall, these results suggest that complex pathways, prominently including inflammation, are linked to the likelihood of attaining longevity. This work may serve to identify novel biomarkers for longevity and to understand the biology underlying lifespan.

Proteomics Reveal the Inhibitory Mechanism of Levodopa Against Esophageal Squamous Cell Carcinoma

High recurrence rates and poor survival of patients with esophageal squamous cell carcinoma (ESCC) after treatment make ongoing research on chemoprevention drugs for ESCC particularly important. In this study, we screened a large number of FDA-approved drugs and found levodopa, a drug used to treat Parkinson's disease, had an inhibitory effect on the growth of ESCC cells. To elucidate the molecular mechanisms involved, we applied quantitative proteomics to investigate the anti-tumor activity of levodopa on ESCC. The results suggest that levodopa could down-regulate oxidative phosphorylation, non-alcoholic fatty liver disease, and Parkinson's disease pathways. Major mitochondrial respiratory compounds were involved in the pathways, including succinate dehydrogenase subunit D, NADH-ubiquinone oxidoreductase Fe-S protein 4, and mitochondrial cytochrome c oxidase subunit 3. Down-regulation of these proteins was associated with mitochondrial dysfunction. Western blotting and immunofluorescence results confirmed the proteomics findings. Cell viability assays indicated mitochondrial activity was suppressed after levodopa treatment. Reduced mitochondrial membrane potential was detected using JC-1 staining and TMRE assays. Transmission electron microscopy revealed changes in the morphology of mitochondria. Taken together, these results indicate that levodopa inhibited the growth of ESCC through restraining mitochondria function.

Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)-Based Proteomics of Drug-Metabolizing Enzymes and Transporters

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics is a powerful tool for identifying and quantifying proteins in biological samples, outperforming conventional antibody-based methods in many aspects. LC-MS/MS-based proteomics studies have revealed the protein abundances of many drug-metabolizing enzymes and transporters (DMETs) in tissues relevant to drug metabolism and disposition. Previous studies have consistently demonstrated marked interindividual variability in DMET protein expression, suggesting that varied DMET function is an important contributing factor for interindividual variability in pharmacokinetics (PK) and pharmacodynamics (PD) of medications. Moreover, differential DMET expression profiles were observed across different species and in vitro models. Therefore, caution must be exercised when extrapolating animal and in vitro DMET proteomics findings to humans. In recent years, DMET proteomics has been increasingly utilized for the development of physiologically based pharmacokinetic models, and DMET proteins have also been proposed as biomarkers for prediction of the PK and PD of the corresponding substrate drugs. In sum, despite the existence of many challenges in the analytical technology and data analysis methods of LC-MS/MS-based proteomics, DMET proteomics holds great potential to advance our understanding of PK behavior at the individual level and to optimize treatment regimens via the DMET protein biomarker-guided precision pharmacotherapy.