Proteomics biomarkers for solid tumors: Current status and future prospects

Interaction between modified magnetic nanoparticles and human albumin: Kinetics and isotherm studies and application in protein depletion

Magnetic nanoparticles modified with tetraethyl orthosilicate (Fe3O4@TEOS) and bovine serum albumin (Fe3O4@TEOS@BSA) were evaluated as sorbent in albumin depletion from human serum samples by magnetic dispersive solid phase extraction. Characterization studies were carried out by X-ray diffraction, thermogravimetry, Fourier transform infrared spectroscopy, zeta potential, and scanning electron microscopy. Both nanoparticles also showed high thermal stability and pH-dependent surface charges. The human serum albumin adsorption protocol was optimized using a central composite rotatable design. Nanoparticle mass, pH, and albumin concentration were the most influential variables. Avrami's fractional order and Freundlich isotherm models best fitted the data for human albumin adsorption kinetic and isotherm studies for Fe3O4@TEOS and Fe3O4@TEOS@BSA, and the maximum adsorption capacities were 11.93 and 14.89 mg g-1, respectively. The protein desorption was influenced by the pH of samples and eluent volume. Electrophoresis in a polyacrylamide gel containing sodium dodecyl sulfate showed different patterns of serum protein bands when consecutive depletions were performed. The Fe3O4@TEOS showed greater affinity for HSA and efficiency in depletion. The process was versatile, and the depleted albumin proportion could be controlled by the nanoparticle masses. The proposed method is a powerful sample preparation technique for rapid, reliable, and specific depletion of albumin.

An original study assessing biomarker success rate in breast cancer recurrence biomarker research

BACKGROUND

Breast cancer is the second most common cause of cancer mortality worldwide. Biomarker discovery has led to advances in understanding molecular phenotyping and thus has a great potential for precision management of this diverse disease. Despite increased interest in the biomarker field, only a small number of breast cancer biomarkers are known to be clinically useful. Therefore, it is very important to characterise the success rate of biomarkers in this field and study potential reasons for the deficit. We therefore aim to achieve quantitative characterisation of the biomarker translation gap by tracking the progress of prognostic biomarkers associated with breast cancer recurrence.

METHODS

An electronic systematic search was conducted in Medline and Embase databases using keywords and mesh headings associated with breast cancer recurrence biomarkers (1940-2023). Abstracts were screened, and primary clinical studies involving breast cancer recurrence biomarkers were selected. Upon identification of relevant literature, we extracted the biomarker name, date of publication and journal name. All analyses were performed using IBM SPSS Statistics and GraphPad prism (La Jolla, California, USA).

RESULTS

A total of 19,195 articles were identified, from which 4597 articles reported breast cancer biomarkers associated with recurrence. Upon data extraction, 2437 individual biomarkers were identified. Out of these, 23 are currently recommended for clinical use, which corresponds to only 0.94% of all discovered biomarkers.

CONCLUSIONS

This study characterised for the first time the translational gap in the field of recurrence-related breast cancer biomarkers, indicating that only 0.94% of identified biomarkers were recommended for clinical use. This denotes an evident barrier in the biomarker research field and emphasises the need for a clearer route from biomarker discovery through to implementation.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Superbinder based phosphoproteomic landscape revealed PRKCD_pY313 mediates the activation of Src and p38 MAPK to promote TNBC progression

Phosphorylation proteomics is the basis for the study of abnormally activated kinase signaling pathways in breast cancer, which facilitates the discovery of new oncogenic agents and drives the discovery of potential targets for early diagnosis and therapy of breast cancer. In this study, we have explored the aberrantly active kinases in breast cancer development and to elucidate the role of PRKCD_pY313 in triple negative breast cancer (TNBC) progression. We collected 47 pairs of breast cancer and paired far-cancer normal tissues and analyzed phosphorylated tyrosine (pY) peptides by Superbinder resin and further enriched the phosphorylated serine/threonine (pS/pT) peptides using TiO2 columns. We mapped the kinases activity of different subtypes of breast cancer and identified PRKCD_pY313 was upregulated in TNBC cell lines. Gain-of-function assay revealed that PRKCD_pY313 facilitated the proliferation, enhanced invasion, accelerated metastasis, increased the mitochondrial membrane potential and reduced ROS level of TNBC cell lines, while Y313F mutation and low PRKCD_pY313 reversed these effects. Furthermore, PRKCD_pY313 significantly upregulated Src_pY419 and p38_pT180/pY182, while low PRKCD_pY313 and PRKCD_Y313F had opposite effects. Dasatinib significantly inhibited the growth of PRKCD_pY313 overexpression cells, and this effect could be enhanced by Adezmapimod. In nude mice xenograft model, PRKCD_pY313 significantly promoted tumor progression, accompanied by increased levels of Ki-67, Bcl-xl and Vimentin, and decreased levels of Bad, cleaved caspase 3 and ZO1, which was opposite to the trend of Y313F group. Collectively, the heterogeneity of phosphorylation exists in different molecular subtypes of breast cancer. PRKCD_pY313 activates Src and accelerates TNBC progression, which could be inhibited by Dasatinib.

Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning

OBJECTIVE

Rheumatoid arthritis and Ankylosing spondylitis are two common autoimmune inflammatory rheumatic diseases that negatively affect activities of daily living and can lead to structural and functional disability, reduced quality of life. Here, this study utilized Fourier transform infrared (FTIR) spectroscopy on dried serum samples and achieved early diagnosis of rheumatoid arthritis and ankylosing spondylitis based on deep learning models.

METHOD

A total of 243 dried serum samples were collected in this study, including 81 samples each from ankylosing spondylitis, rheumatoid arthritis, and healthy controls. Three multi-scale convolutional modules with different specifications were designed based on the multi-scale convolutional neural network (MSCNN) to effectively fuse the local features to enhance the generalization ability of the model. The FTIR was then combined with the MSCNN model to achieve a non-invasive, fast, and accurate diagnosis of ankylosing spondylitis, rheumatoid arthritis, and healthy controls.

RESULTS

Spectral analysis shows that the curves and waveforms of the three spectral graphs are similar. The main differences are distributed in the spectral regions of 3300-3250 cm-1, 3000-2800 cm-1, 1750-1500 cm-1, and 1500-1300 cm-1, which represent: Amides, fatty acids, cholesterol, proteins with a carboxyl group, amide II, free amino acids, and polysaccharides. Four classification models, namely artificial neural network (ANN), convolutional neural network (CNN), improved AlexNet model, and multi-scale convolutional neural network (MSCNN) were established. Through comparison, it was found that the diagnostic AUC value of the MSCNN model was 0.99, and the accuracy rate was as high as 0.93, which was much higher than the other three models.

CONCLUSION

The study demonstrated the superiority of MSCNN in distinguishing ankylosing spondylitis from rheumatoid arthritis and healthy controls. FTIR may become a rapid, sensitive, and non-invasive means of diagnosing rheumatism.

Clinicopathological correlation of insulin-like growth factor binding protein 3 and their death receptor in patients with gastric cancer

Background and purpose

The insulin-like growth factor binding protein 3 (IGFBP-3) and its novel death receptor (IGFBP-3R) have been exhibited to have tumor suppressor effects. Despite their prognostic value in some cancers, they have not been elucidated in gastric cancer.

Experimental approach

We collected 68 samples from patients with gastric cancer. IGFBP-3 and IGFBP-3R expression levels were evaluated with quantitative real-time polymerase chain reaction (RT-PCR) and western blotting in patients. The relationship between prognostic factors and IGFBP-3/IGFBP-3R expression was also evaluated.

Findings/Results

Our results showed that IGFBP-3 and IGFBP-3R expression was reduced significantly in tumor tissues. We found that there was an association between the reduction of IGFBP-3 with lymph node metastasis and tumor-node-metastasis (TNM) staging. Besides, IGFBP-3R expression was associated with tumor size, lymph node metastasis, differentiation, and TNM classification. Interestingly, we presented that the downregulation of IGFBP-3R was stage-dependent. In survival analysis, our findings showed that low levels of IGFBP-3R mRNA expression exhibited a close correlation with survival rate.

Conclusion and implications

The findings of this study showed that the expression levels of IGFBP-3 and IGFBP-3R are valuable prognostic factors. Despite the potential of IGFBP-3, IGFBP-3R plays a significant role as a prognostic factor in gastric cancer. However, these findings need to be developed and confirmed by further studies.

Serum IGFBP-1 as a promising diagnostic and prognostic biomarker for colorectal cancer

Our previous study showed that levels of circulating insulin-like growth factor binding protein-1 (IGFBP-1) has potential diagnostic value for early-stage upper gastrointestinal cancers. This study aimed to assess whether serum IGFBP-1 is a potential diagnostic and prognostic biomarker for CRC patients. IGFBP-1 mRNA expression profile data of peripheral blood in colorectal cancer (CRC) patients were downloaded and analyzed from Gene Expression Omnibus database. We detected serum IGFBP-1 in 138 CRC patients and 190 normal controls using enzyme-linked immunosorbent assay. Blood IGFBP-1 mRNA levels were higher in CRC patients than those in normal controls (P = 0.027). In addition, serum IGFBP-1 protein levels in the CRC group were significantly higher than those in normal control group (P < 0.0001). Serum IGFBP-1 demonstrated better diagnostic accuracy for all CRC and early-stage CRC, respectively, when compared with carcinoembryonic antigen (CEA), carbohydrate antigen19-9 (CA 19-9) or the combination of CEA and CA19-9. Furthermore, Cox multivariate analysis revealed that serum IGFBP-1 was an independent prognostic factor for OS (HR = 2.043, P = 0.045). Our study demonstrated that serum IGFBP-1 might be a potential biomarker for the diagnosis and prognosis of CRC. In addition, the nomogram might be helpful to predict the prognosis of CRC.

Transformation of proteins into reproductive DNA templates for sensitive quantification of PSA

A switch DNA template was designed to transform proteins into linear DNA strands of 97 nt. The linear DNA was subjected to quantitative polymerase chain reaction (qPCR) to determine the initial copy number, which correlated positively with the protein concentration. A restriction endonuclease was used to remove background amplification. Using prostate-specific antigen (PSA) as a protein model, the established method quantified PSA in the range of 10-18-10-13 mol/mL (detection limit = 0.034 pg/mL) with an R2 of 0.974. Good repeatability and specificity of the method were demonstrated. The established method was successfully applied for the quantification of serum PSA levels in women. Significant differences in PSA levels were observed between healthy participants and polycystic ovary syndrome patients.

Single-cell RNA sequencing reveals the lineage of malignant epithelial cells and upregulation of TAGLN2 promotes peritoneal metastasis in gastric cancer

BACKGROUND

Peritoneal metastasis (PM) is an important factor contributing to poor prognosis in patients with gastric cancer (GC). Transcriptomic sequencing has been used to explore the molecular changes in metastatic cancers, but comparing the bulk RNA-sequencing data between primary tumors and metastases in PM studies is unreasonable due to the small proportion of tumor cells in PM tissues.

METHODS

We performed single-cell RNA-sequencing analysis on four gastric adenocarcinoma specimens, including one primary tumor sample (PT), one adjacent nontumoral sample (PN), one peritoneal metastatic sample (MT) and one normal peritoneum sample (MN), from the same patient. Pseudotime trajectory analysis was used to display the process by which nonmalignant epithelial cells transform into tumor cells and then metastasize to the peritoneum. Finally, in vitro and in vivo assays were used to validate one of the selected genes that promote peritoneal metastasis.

RESULTS

Single-cell RNA sequencing showed that a development curve was found from normal mucosa to tumor tissues and then into metastatic sites on peritoneum. TAGLN2 was found to trigger this metastasis process. The migration and invasion capability of GC cells were changed by downregulating and upregulating TAGLN2 expression. Mechanistically, TAGLN2 might modulate tumor metastasis via alterations in cell morphology and several signaling pathways, thus promoting epithelial-mesenchymal transition (EMT).

CONCLUSIONS

In summary, we identified and validated TAGLN2 as a novel gene involved in GC peritoneal metastasis. This study provided valuable insight into the mechanisms of GC metastasis and developed a potential therapeutic target to prevent GC cell dissemination.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.

Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians

Hepatocellular carcinoma (HCC) is the most predominant primary liver cancer, causing many illnesses and deaths worldwide. The insidious clinical presentation, difficulty in early diagnosis, and the highly malignant nature make the prognosis of HCC extremely poor. The complex and heterogeneous pathogenesis of HCC poses significant challenges to developing therapies. Urine-based biomarkers for HCC, including diagnostic, prognostic, and monitoring markers, may be valuable supplements to current tools such as serum α-fetoprotein (AFP) and seem promising for progress in precision medicine. Herein, we reviewed the major urinary biomarkers for HCC and assessed their potential for clinical application. Molecular types, testing platforms, and methods for building multimolecule models in the included studies have shown great diversity, thus providing abundant novel tools for future clinical transformation and applications.

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

Role of circulating exosomal biomarkers and their diagnostic accuracy in pancreatic cancer

Background and Aim

New biomarkers have the potential to facilitate early diagnosis of pancreatic cancer (PC). Circulating exosomes are cell-derived protein complexes containing RNA that can be used as indicators of cancer development. The aim of this review is to evaluate the current literature involving PC patient groups for highly accurate exosomal biomarkers.

Methods

The literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eight-hundred and seventy-five studies were identified across various databases (Ovid MEDLINE, Embase, and Cochrane) published between 2009 and 2020. Nine studies fulfilled the inclusion criteria: human PC patients, diagnosis as outcome of interest, serum biomarker of exosomal content, reporting of diagnostic values, and disease progress. Area under the curve (AUC) of the exosomal biomarker was compared against that of CA19-9.

Results

Nine papers were reviewed for relevant outcomes based on the inclusion criteria. These studies involved 565 participants (331 PC, 234 controls; male/female ratio 1.21; mean age 64.1). Tumor staging was reported in all studies, with 45.6% of PC patients diagnosed with early-stage PC (T1-2). The mRNA panel (ARG1, CD63, CK18, Erbb3, GAPDH, H3F3A, KRAS, ODC1) and GPC 1 reported the highest performing sensitivity and specificity at 100% each. The microRNA panel (miR-10b, miR-21, miR-30c, miR-181a, and miR-let7a), mRNA panel (ARG1, CD63, CK18, Erbb3, GAPDH, H3F3A, KRAS, ODC1), and GPC 1 showed a perfect AUC of 1.0. Five studies compared the AUC of the exosomal biomarker against CA19-9, each being superior to that of CA19-9.

Conclusion

The potential of exosomal biomarkers remains promising in PC diagnosis. Standardization of future studies will allow for larger comparative analyses and overcoming contrasting findings.

LRG1 is an adipokine that promotes insulin sensitivity and suppresses inflammation

While dysregulation of adipocyte endocrine function plays a central role in obesity and its complications, the vast majority of adipokines remain uncharacterized. We employed bio-orthogonal non-canonical amino acid tagging (BONCAT) and mass spectrometry to comprehensively characterize the secretome of murine visceral and subcutaneous white and interscapular brown adip ocytes. Over 600 proteins were identified, the majority of which showed cell type-specific enrichment. We here describe a metabolic role for leucine-rich α-2 glycoprotein 1 (LRG1) as an obesity-regulated adipokine secreted by mature adipocytes. LRG1 overexpression significantly improved glucose homeostasis in diet-induced and genetically obese mice. This was associated with markedly reduced white adipose tissue macrophage accumulation and systemic inflammation. Mechanistically, we found LRG1 binds cytochrome c in circulation to dampen its pro-inflammatory effect. These data support a new role for LRG1 as an insulin sensitizer with therapeutic potential given its immunomodulatory function at the nexus of obesity, inflammation, and associated pathology.

Progress of proteomics-driven precision medicine: From a glycosylation view

Currently, cancer is one of the leading causes of death worldwide, partially owing to the lack of early diagnosis methods and effective therapies. With the rapid development of various omics, the precision medicine strategy becomes a promising way to increase the survival rates by considering individual differences. Glycosylation is one of the most essential protein post-translational modifications and plays important roles in a variety of biological processes. Therefore, it is highly possible to acquire understanding of the molecular mechanisms as well as discover novel potential markers for diagnosis and prognosis based on glycoproteomics research. This review summarizes the recent glycoproteomics studies about N-glycosylation of several cancer types, mainly in the past 5 years. We also highlight corresponding mass spectrometry-based analytical methods to give a brief overview on the main techniques applied in glycoproteomics.

Human peripheral blood mononuclear cells: A review of recent proteomic applications

Human peripheral blood mononuclear cells (PBMCs) represent a sentinel blood sample which reacts to different pathophysiological stimuli in the form of immunological responses/immunophenotypic changes. The study of molecular content of PBMCs can provide better understanding of immune processes giving the possibility of monitoring the health conditions of the host organism. Proteomic analysis of PBMCs can achieve mentioned goal as important immune-related biomarkers are easily accessible for analysis. PBMCs have been gaining attention in different research areas including preclinical or clinical investigations. In this review, recent applications of proteomic analysis of PBMCs are described and discussed. Approaches are divided based on different proteomic workflows such as in-gel, in-solution and on-filter modes. The effect of various diseases such as autoimmune, cancer, neurodegenerative, viral, metabolic, and various immune stimulations such as radiation, vaccine, corticosteroids over PBMCs proteome, are described with emphasis on promising protein biomarker candidates. This article is protected by copyright. All rights reserved.

The era of Cas12 and Cas13 CRISPR-based disease diagnosis

Clustered regularly interspaced short palindromic repeats (CRISPR) and associated protein (Cas) systems, since their discovery, have found growing applications in cell imaging, transcription modulation, therapeutics and diagnostics. Discovery of Cas12 and Cas13 have brought a new dimension to the field of disease diagnosis. These endonucleases have been extensively used for diagnosis of viral diseases in humans and animals and to a lesser extent in plants. The exigency of SARS-CoV-2 pandemic has highlighted the potential of CRISPR-Cas systems and sparked the development of innovative point-of-care diagnostic technologies. Rapid adaptation of CRISPR-chemistry combined with sensitive read-outs for emerging pathogens make them ideal candidates for detection and management of diseases in future. CRISPR-based approaches have been recruited for the challenging task of cancer detection and prognosis. It stands to reason that the field of CRISPR-Cas-based diagnosis is likely to expand with Cas12 and Cas13 playing a pivotal role. Here we focus exclusively on Cas12- and Cas13-based molecular diagnosis in humans, animals and plants including the detection of SARS-coronavirus. The CRISPR-based diagnosis of plant and animal diseases have not found adequate mention in previous reviews. We discuss various advancements, the potential shortfalls and challenges in the widespread adaptation of this technology for disease diagnosis.

Longitudinal Bottom-Up Proteomics of Serum, Serum Extracellular Vesicles, and Cerebrospinal Fluid Reveals Candidate Biomarkers for Early Detection of Glioblastoma in a Murine Model

Glioblastoma Multiforme (GBM) is a brain tumor with a poor prognosis and low survival rates. GBM is diagnosed at an advanced stage, so little information is available on the early stage of the disease and few improvements have been made for earlier diagnosis. Longitudinal murine models are a promising platform for biomarker discovery as they allow access to the early stages of the disease. Nevertheless, their use in proteomics has been limited owing to the low sample amount that can be collected at each longitudinal time point. Here we used optimized microproteomics workflows to investigate longitudinal changes in the protein profile of serum, serum small extracellular vesicles (sEVs), and cerebrospinal fluid (CSF) in a GBM murine model. Baseline, pre-symptomatic, and symptomatic tumor stages were determined using non-invasive motor tests. Forty-four proteins displayed significant differences in signal intensities during GBM progression. Dysregulated proteins are involved in cell motility, cell growth, and angiogenesis. Most of the dysregulated proteins already exhibited a difference from baseline at the pre-symptomatic stage of the disease, suggesting that early effects of GBM might be detectable before symptom onset.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Pathophysiological Implications of Urinary Peptides in Hepatocellular Carcinoma

Simple Summary

In this study, the application of capillary electrophoresis mass spectrometry enabled identification of 31 urinary peptides significantly associated with hepatocellular carcinoma diagnosis and prognosis. Further assessment of these peptides lead to prediction of cellular proteases involved in their development namely Meprin A subunit α and Kallikrein-6. Subsequent identification of the proteases was verified by immunohistochemistry in normal liver, cirrhosis and hepatocellular carcinoma. Histopathological assessment of the proteases revealed numerical gradient staining signifying their involvement in liver fibrosis and hepatocellular carcinoma formation. The discovered urinary peptides offered a potential noninvasive tool for diagnosis and prognosis of hepatocellular carcinoma.

Abstract

Hepatocellular carcinoma (HCC) is known to be associated with protein alterations and extracellular fibrous deposition. We investigated the urinary proteomic profiles of HCC patients in this prospective cross sectional multicentre study. 195 patients were recruited from the UK (Coventry) and Germany (Hannover) between 1 January 2013 and 30 June 2019. Out of these, 57 were HCC patients with a background of liver cirrhosis (LC) and 138 were non-HCC controls; 72 patients with LC, 57 with non-cirrhotic liver disease and 9 with normal liver function. Analysis of the urine samples was performed by capillary electrophoresis (CE) coupled to mass spectrometry (MS). Peptide sequences were obtained and 31 specific peptide markers for HCC were identified and further integrated into a multivariate classification model. The peptide model demonstrated 79.5% sensitivity and 85.1% specificity (95% CI: 0.81–0.93, p < 0.0001) for HCC and 4.1-fold increased risk of death (95% CI: 1.7–9.8, p = 0.0005). Proteases potentially involved in HCC progression were mapped to the N- and C-terminal sequence motifs of the CE-MS peptide markers. In silico protease prediction revealed that kallikrein-6 (KLK6) elicits increased activity, whilst Meprin A subunit α (MEP1A) has reduced activity in HCC compared to the controls. Tissue expression of KLK6 and MEP1A was subsequently verified by immunohistochemistry.

Diabetes Mellitus and Its Implications in Aortic Stenosis Patients

Aortic stenosis (AS) and diabetes mellitus (DM) are both progressive diseases that if left untreated, result in significant morbidity and mortality. Several studies revealed that the prevalence of DM is substantially higher in patients with AS and, thus, the progression from mild to severe AS is greater in those patients with DM. DM and common comorbidities associated with both diseases, DM and AS, increase patient management complexity and make aortic valve replacement the only effective treatment. For that reason, a better understanding of the pathogenesis underlying both these diseases and the relationships between them is necessary to design more appropriate preventive and therapeutic approaches. In this review, we provided an overview of the main aspects of the relationship between AS and DM, including common comorbidities and risk factors. We also discuss the established treatments/therapies in patients with AS and DM.

Serum N-glycan profiles differ for various breast cancer subtypes

Breast cancer is the most prevalent cancer in women. Early detection of this disease improves survival and therefore population screenings, based on mammography, are performed. However, the sensitivity of this screening modality is not optimal and new screening methods, such as blood tests, are being explored. Most of the analyses that aim for early detection focus on proteins in the bloodstream. In this study, the biomarker potential of total serum N-glycosylation analysis was explored with regard to detection of breast cancer. In an age-matched case-control setup serum protein N-glycan profiles from 145 breast cancer patients were compared to those from 171 healthy individuals. N-glycans were enzymatically released, chemically derivatized to preserve linkage-specificity of sialic acids and characterized by high resolution mass spectrometry. Logistic regression analysis was used to evaluate associations of specific N-glycan structures as well as N-glycosylation traits with breast cancer. In a case-control comparison three associations were found, namely a lower level of a two triantennary glycans and a higher level of one tetraantennary glycan in cancer patients. Of note, various other N-glycomic signatures that had previously been reported were not replicated in the current cohort. It was further evaluated whether the lack of replication of breast cancer N-glycomic signatures could be partly explained by the heterogenous character of the disease since the studies performed so far were based on cohorts that included diverging subtypes in different numbers. It was found that serum N-glycan profiles differed for the various cancer subtypes that were analyzed in this study.

Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.

Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression

Cancer is one of the largest contributors to the burden of chronic disease in the world and is the second leading cause of death globally. It is associated with episodes of low-oxygen stress (hypoxia or ischemia/reperfusion) that promotes cancer progression and therapeutic resistance. Efforts have been made in the past using traditional proteomic approaches to decipher oxygen deprivation stress-related mechanisms of the disease initiation and progression and to identify key proteins as a therapeutic target for the treatment and prevention. Despite the potential benefits of proteomic in translational research for the discovery of new drugs, the therapeutic outcome with this approach has not met expectations in clinical trials. This is mainly due to the disease complexity which possess a multifaceted molecular pathology. Therefore, novel strategies to identify and characterize clinically important sets of modulators and molecular events for multi-target drug discovery are needed. Here, we review important past and current studies on proteomics in cancer with an emphasis on recent pioneered labeling approaches in mass spectrometry (MS)-based systematic quantitative analysis to improve clinical success. We also discuss the results of the selected innovative publications that integrate advanced proteomic technologies (e.g. MALDI-MSI, pSILAC/SILAC/iTRAQ/TMT-LC-MS/MS, MRM-MS) for comprehensive analysis of proteome dynamics in different biosystems, including cell type, cell species, and subcellular proteome (i.e. secretome and chromatome). Finally, we discuss the future direction and challenges in the application of these technological advancements in mass spectrometry within the context of cancer and hypoxia.

Quantitative proteomic profiling of Cervicovaginal fluid from pregnant women with term and preterm birth

Background

Preterm birth (PTB) is one of major causes of perinatal mortality and neonatal morbidity, but knowledge of its complex etiology is still limited. Here we present cervicovaginal fluid (CVF) protein profiles of pregnant women who subsequently delivered at spontaneous preterm or term, aiming to identify differentially expressed CVF proteins in PTB and term birth.

Methods

The CVF proteome of women who sequentially delivered at preterm and term was analyzed using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with two-dimensional nanoflow liquid chromatography-tandem mass spectrometry (2D-nLC-MS/MS). We compared the CVF proteome of PTB (n = 5) and control subjects (term birth, n = 7) using pooled control CVF (term birth, n = 20) as spike-in standard.

Results

We identified 1294 CVF proteins, of which 605 were newly identified proteins. Of 990 proteins quantified in both PTB and term birth, 52 proteins were significantly up/down-regulated in PTB compared to term birth. The differentially expressed proteins were functionally associated to immune response, endopeptidase inhibitors and structural constituent of cytoskeleton. Finally, we confirm the down-regulation of SERPINB7 (a serine-type protease inhibitor) in PTB compared to control by Western blot.

Conclusions

Taken together, our study provide quantitative CVF proteome profiles of pregnant women who ultimately delivered at preterm and term. These promising results could help to improve the understanding of PTB etiology and to discover biomarkers for asymptomatic PTB.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12953-021-00171-1.

Comparative evaluation of two methods for LC-MS/MS proteomic analysis of formalin fixed and paraffin embedded tissues

The proteomics of formalin-fixed, paraffin-embedded (FFPE) samples has advanced significantly during the last two decades, but there are many protocols and few studies comparing them directly. There is no consensus on the most effective protocol for shotgun proteomic analysis. We compared the in-solution digestion with RapiGest and Filter Aided Sample Preparation (FASP) of FFPE prostate tissues stored 7 years and mirroring fresh frozen samples, using two label-free data-independent LC-MS/MS acquisitions. RapiGest identified more proteins than FASP, with almost identical numbers of proteins from fresh and FFPE tissues and 69% overlap, good preservation of high-MW proteins, no bias regarding isoelectric point, and greater technical reproducibility. On the other hand, FASP yielded 20% fewer protein identifications in FFPE than in fresh tissue, with 64-69% overlap, depletion of proteins >70 kDa, lower efficiency in acidic and neutral range, and lower technical reproducibility. Both protocols showed highly similar subcellular compartments distribution, highly similar percentages of extracted unique peptides from FFPE and fresh tissues and high positive correlation between the absolute quantitation values of fresh and FFPE proteins. In conclusion, RapiGest extraction of FFPE tissues delivers a proteome that closely resembles the fresh frozen proteome and should be preferred over FASP in biomarker and quantification studies. SIGNIFICANCE: Here we analyzed the performance of two sample preparation methods for shotgun proteomic analysis of FFPE tissues to give a comprehensive overview of the obtained proteomes and the resemblance to its matching fresh frozen counterparts. These findings give us better understanding towards competent proteomics analysis of FFPE tissues. It is hoped that it will encourage further assessments of available protocols before establishing the most effective protocol for shotgun proteomic FFPE tissue analysis.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.

Prognostic implication of glycolysis related gene signature in non-small cell lung cancer

Abnormal glycolysis is one of the hallmarks of cancer and plays an important role in its development. This study was devoted to identify glycolysis related genes as prognostic biomarkers for non-small cell lung cancer (NSCLC). The mRNA expression profile and clinical follow-up data were obtained using The Cancer Genome Atlas (TCGA) database. The validation set was obtained by bootstrap method of random repeated sampling. A total of 200 glycolysis-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and 46 genes were significantly associated with overall survival (OS). Five genes (PKP2, LDHA, HMMR, COL5A1 and B3GNT3) were eventually identified to calculate risk score of NSCLC patients.

The univariate and multivariate Cox regression analysis indicated that the risk score was an independent prognostic factor (training set: HR=2.126, 95% CI [1.605, 2.815], p<0.001; validation set: HR=2.298, 95%CI [1.450, 3.640], p<0.001). Patients assigned to the high-risk group were associated with poor OS compared with patients in the low-risk group (training set: P=7.946e-06; validation set: P=6.368e-07). Receiver operating characteristic (ROC) curve and stratification analysis also demonstrated the potential prognostic performance. In conclusion, we constructed a novel glycolysis related risk signature which might contribute to predicting the prognosis of NSCLC.

Inflammatory Proteins HMGA2 and PRTN3 as Drivers of Vulvar Squamous Cell Carcinoma Progression

Current knowledge on the biology of squamous cell vulvar carcinoma (VSCC) is limited. We aimed to identify protein markers of VSCC tumors that would permit to stratify patients by progression risk. Early-stage tumors from patients who progressed (progVSCC) and from those who were disease-free (d-fVSCC) during follow-up, along with normal vulvar tissues were examined by mass spectrometry-based proteomics. Differentially expressed proteins (DEPs) were then verified in solid tissues and blood samples of patients with VSCC tumors and vulvar premalignant lesions. In progVSCC vs. d-fVSCC tumors, the immune response was the most over-represented Gene Ontology category for the identified DEPs. Pathway profiling suggested bacterial infections to be linked to aggressive VSCC phenotypes. High Mobility Group AT-Hook 2 (HMGA2) and Proteinase 3 (PRTN3) were revealed as proteins predicting VSCC progression. HMGA2 and PRTN3 abundances are associated with an aggressive phenotype, and hold promise as markers for VSCC patient stratification. It appears that vulvovaginal microflora disturbances trigger an inflammatory response contributing to cancer progression, suggesting that bacterial rather than viral infection status should be considered in the development of targeted therapies in VSCC.

Autoantibodies against tumor-associated antigens in sputum as biomarkers for lung cancer

Tumor antigens (TAs) can initiate host immune responses and produce TA-associated autoantibody (TAAbs), potential cancer biomarkers. Sputum is directly generated from the upper and lower airways, and thus can be used as a surrogate sample for the diagnosis of lung cancer based on molecular analysis. To develop sputum TAAb biomarkers for the early detection of lung cancer, the leading cause of cancer death, we probed a protein microarray containing more than 9,000 antigens with sputum supernatants of a discovery set of 30 lung cancer patients and 30 cancer-free smokers. Twenty-eight TAs with higher reactivity in sputum of lung cancer cases vs. controls were identified. The diagnostic significance of TAAbs against the TAs was determined by enzyme-linked immunosorbent assays (ELISAs) in sputum of the discovery set and additional 166 lung cancer patients and 213 cancer-free smokers (validation set). Three sputum TAAbs against DDX6, ENO1, and 14-3-3ζ were developed as a biomarker panel with 81% sensitivity and 83% specificity for diagnosis of lung cancer, regardless of stages, locations, and histological types of lung tumors. This study provides the first evidence that sputum TAAbs could be used as biomarkers for the early detection of lung cancer.

Differentially modulated proteins associated with Leishmaniasis-a systematic review of in-vivo and in-vitro studies

High-throughput proteomic technologies are widely used for understanding the disease mechanism, drug-resistant mechanism, and to identify drug targets and markers for diagnostics. Studies with proteomics applications, relating to Leishmaniasis, are being constantly reported in the literature. However, from such studies, a readily accessible knowledge of differentially modulated proteins associated with Leishmaniasis is lacking. Hence, we performed a systematic review concerning differentially modulated proteins (DMP) in Leishmania as well as host infected with Leishmania from the published articles between the years 2000 and 2019. This review is classified into five different sections, namely, DMP in the host after Leishmania infection, DMP between different strains of Leishmania, DMP in drug-resistant Leishmania, DMP in Leishmania under stress, and DMP in different life stages of Leishmania. A lot of consensuses could be observed among the DMP in drug-resistant and stressed Leishmania. In addition to the review, a database was constructed with the data collected in this study (protein accession ID, protein name, gene name, host organism, experimental conditions, fold change, and regulatory data). A total of 2635 records are available in the database. We believe this review and the database will help the researcher in understanding the disease better and provide information for the targeted proteomics study related to Leishmaniasis. Database availability: http://ldepdb.biomedinformri.com/ .

Ovarian Cancer: Tumor-Specific Urinary Micro-Peptides Profiling as Potential Biomarkers for Early Diagnosis

Ovarian cancer is the second major lethal gynecologic malignancy in developing countries. This study aimed to characterize urinary micro-peptides as potential diagnostic biomarkers for ovarian cancer. In a prospective, longitudinal and case-controlled study and following informed consent, urine and plasma samples were collected from 112 women with histologically-proven ovarian cancer and 200 apparently healthy age-matched volunteers. Urinary micro-peptides were detected and sequenced using SDS-PAGE and Edman degradation technique. Serum CA125 was detected in less than a quarter (23.2%, 26/112) of patients. One or more urinary micro-peptides were detected in about two thirds of the patients (62.5%, 70/112). A total of 40 patients had three bands (57.1%, 40/70), while two bands (15 and 35 kDa) were detected in 28.6% (20/70) of the patients. Isolated 45 kDa band was seen in 14.3% (10/70). No urinary micro-peptide was detected in the volunteers. The 15 and 35 kDa bands disappeared after 6 months of regular chemotherapy, while the 45 kDa band persisted in 2.9% (2/70) of the patients after treatment. The micro-peptides were identified as: Catalase (45 kDa), α-1 Acid Glycoprotein (35 kDa) and Peroxiredoxin-2 (15 kDa). Urinary catalase, α-1 Acid Glycoprotein and Peroxiredoxin-2 can be useful biomarkers for early detection and treatment response of ovarian cancer.

Patient Management in Aortic Stenosis: Towards Precision Medicine Through Protein Analysis, Imaging and Diagnostic Tests

Aortic stenosis is the most frequent valvular disease in developed countries. It progresses from mild fibrocalcific leaflet changes to a more severe leaflet calcification at the end stages of the disease. Unfortunately, symptoms of aortic stenosis are unspecific and only appear when it is too late, complicating patients' management. The global impact of aortic stenosis is increasing due to the growing elderly population. The disease supposes a great challenge because of the multiple comorbidities of these patients. Nowadays, the only effective treatment is valve replacement, which has a high cost in both social and economic terms. For that reason, it is crucial to find potential diagnostic, prognostic and therapeutic indicators that could help us to detect this disease in its earliest stages. In this article, we comprehensively review several key observations and translational studies related to protein markers that are promising for being implemented in the clinical field as well as a discussion about the role of precision medicine in aortic stenosis.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

A portable pencil-like immunosensor for point-of-care testing of inflammatory biomarkers

Portable devices for immunoassays are in high demand for point-of-care testing (POCT) of biomarkers. Here, we report a robust portable pencil-like immunosensor (PPS) platform for the determination of three inflammatory biomarkers including interleukin-6 (IL-6), procalcitonin (PCT), and C-reactive protein (CRP) in human serum samples. The PPS platform is composed of a unique pencil-like optical-fiber-based sensor, a reagent strip consisting of a series of pencil-cap-like wells, and a battery-powered photon counting detector for recording chemiluminescence. The PPS probe moves from well to well with a plug-into/out approach and goes through the immunoassay steps. Each fiber probe in the PPS platform can be sequentially used in up to 10 assays by simply propelling the intact probe out of the pencil body. The PPS platform is well-integrated into a portable suitcase-like device (32 cm × 23 cm × 11 cm) and is only 3 kg in weight. The sensor has good repeatability and can maintain 90% response after 14 days of storage at room temperature, showing its ability for assays in the field. The good linear relationship and efficient dynamic range with a limit-of-detection (LOD) of 1.05 pg/mL for IL-6, 10.64 pg/mL for PCT, and 29.40 ng/mL for CRP are obtained. The assay results are compared with clinical methods, and the findings confirm the high accuracy and precision of the proposed method. The proposed PPS platform is versatile and operable with minimal instruments and technical skills and simplifies the process of immune analysis, thus has great prospects for POCT of biomarkers. Graphical abstract.

Serum IGFBP-1 as a potential biomarker for diagnosis of early-stage upper gastrointestinal tumour

BACKGROUND

Early detection would improve upper gastrointestinal cancer prognosis. We aimed to identify serum protein biomarker for the detection of early-stage upper gastrointestinal cancer.

METHODS

We performed a three-tiered study including 2028 participants from three medical centres. First, we applied two different antibody arrays to screen candidate serum proteins that increased in 20 patients with oesophageal squamous cell carcinoma (ESCC) compared with 20 normal controls. We then evaluated the selected protein by enzyme-linked immunosorbent assay in 1064 participants including 731 upper gastrointestinal cancer patients (287 ESCCs, 237 oesophagogastric junction adenocarcinomas (EJAs), and 207 stomach cancers) and 333 normal controls. The diagnostic value of the selected protein was finally validated in two independent cohorts of ESCC patients and controls (n=472 and 452, respectively). The receiver operating characteristic was used to calculate diagnostic accuracy.

FINDINGS

Serum insulin-like growth factor binding protein-1 (IGFBP-1) identified in both antibody arrays showed significantly elevated levels in upper gastrointestinal cancers, compared with normal controls. Serum IGFBP-1 provided high diagnostic accuracy of early-stage ESCC, EJA, stomach and cancer (areas under the curve: 0·898, 0·936 and 0·864, respectively). This protein maintained diagnostic performance for early-stage ESCC in independent cohorts 1 and 2 (0·849 and 0·911, respectively). Additionally, serum levels of IGFBP-1 dropped significantly after surgical resection of primary tumours, compared with the corresponding pre-operative ESCC samples (p < 0·05).

INTERPRETATION

Serum IGFBP-1 represents a promising diagnostic biomarker to detect early-stage upper gastrointestinal cancer.

Molecular Imaging of Endometriosis Tissues using Desorption Electrospray Ionization Mass Spectrometry

Endometriosis is a pathologic condition affecting approximately 10% of women in their reproductive years. Characterized by abnormal growth of uterine endometrial tissue in other body areas, endometriosis can cause severe abdominal pain and/or infertility. Despite devastating consequences to patients' quality of life, the causes of endometriosis are not fully understood and validated diagnostic markers for endometriosis have not been identified. Molecular analyses of ectopic and eutopic endometrial tissues could lead to enhanced understanding of the disease. Here, we apply desorption electrospray ionization (DESI) mass spectrometry (MS) imaging to chemically and spatially characterize the molecular profiles of 231 eutopic and ectopic endometrial tissues from 89 endometriosis patients. DESI-MS imaging allowed clear visualization of endometrial glandular and stromal regions within tissue samples. Statistical models built from DESI-MS imaging data allowed classification of endometriosis lesions with overall accuracies of 89.4%, 98.4%, and 98.8% on training, validation, and test sample sets, respectively. Further, molecular markers that are significantly altered in ectopic endometrial tissues when compared to eutopic tissues were identified, including fatty acids and glycerophosphoserines. Our study showcases the value of MS imaging to investigate the molecular composition of endometriosis lesions and pinpoints metabolic markers that may provide new knowledge on disease pathogenesis.

MicroRNA-21 as a diagnostic marker for hepatocellular carcinoma: A systematic review and meta-analysis

Background

MicroRNA-21 (miR-21) is one of the oncogenic miRNAs which may be a potential diagnostic biomarker for hepatocellular carcinoma (HCC).

Methods

We systematically searched Medline, Embase, the Cochrane Library, ISI Web of Knowledge, Scopus from inception to August 15, 2018, and reference lists of identified primary studies. Two independent investigators extracted patient and study characteristics. The sensitivity and specificity of microRNA-21 for HCC detection and were analyzed with a random effect model. The area under summary receiver operating characteristic curve (AUC) was used to estimate overall test performance.

Results

A total of 515 HCC patients, and 338 healthy or chronic hepatitis controls from six published studies were enrolled in this meta-analysis. All articles were published in English with moderate-to-high quality. The overall pooled sensitivity and specificity were 85.2% (73.3% to 88.4%) and 79.2% (68.4% to 87.0%), respectively. The AUC area was 0.89 (95% CI: 0.85-0.91). The studies had moderate heterogeneity (I2=70.11%). None of the subgroups investigated-ethnicity, controls, sample source-could account for the heterogeneity.

Conclusion

MiR-21 is a helpful biomarker for early diagnosis of HCC. Nevertheless, the results of the test must be interpreted carefully in the context of medical history, erological tests and imaging examinations for HCC surveillance.

Transgelin-2: Biochemical and Clinical Implications in Cancer and Asthma

Transgelin-2 has been regarded as an actin-binding protein that induces actin gelation and regulates actin cytoskeleton. However, transgelin-2 has recently been shown to relax the myosin cytoskeleton of the airway smooth muscle cells by acting as a receptor for extracellular metallothionein-2. From a clinical perspective, these results support transgelin-2 as a promising therapeutic target for diseases such as cancer and asthma. The inhibition of transgelin-2 prevents actin gelation and thereby cancer cell proliferation, invasion, and metastasis. Conversely, the activation of transgelin-2 with specific agonists relaxes airway smooth muscles and reduces pulmonary resistance in asthma. Here, we review new studies on the biochemical properties of transgelin-2 and discuss their clinical implications for the treatment of immune, oncogenic, and respiratory disorders.

Cancer Treatment in the Genomic Era

The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.

Deciphering nasopharyngeal carcinoma pathogenesis via proteomics

Introduction: Nasopharyngeal carcinoma (NPC) is a distinct head and neck squamous cell carcinoma in its etiological association of Epstein-Barr virus (EBV) infection, hidden anatomical location, remarkable racial and geographical distribution, and high incidence of locoregional recurrence or metastasis. Thanks to the advancements in proteomics in recent decades, more understanding of the disease etiology, carcinogenesis, and progression has been gained, potentially deciphering the molecular characteristics of the malignancy. Areas covered: In this review, we provide an overview of the proteomic aberrations that are likely involved or drive NPC development and progression, focusing on the contributions of major EBV-encoded factors, intercommunication with environment, protein features of high metastasis and therapy resistance, and protein-protein interactions that allow NPC cells to evade immune recognition and elimination. Finally, multistep carcinogenesis and subtypes of NPC from a proteomic perspective are inquired. Expert commentary: Proteomic studies have covered various aspects involved in NPC pathogenesis, yet much remains to be uncovered. Coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation are critical in ensuring the emergence of good science out of NPC proteomics. NPC proteogenomics and proteoform analysis are two promising fields to promote the application of the proteomic findings from bench to bedside.

A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome

The collection of proteins secreted from a cell-the secretome-is of particular interest in cancer pathophysiology due to its diagnostic potential and role in tumorigenesis. However, cancer secretome studies are often limited to one tissue or cancer type or focus on biomarker prediction without exploring the associated functions. We therefore conducted a pan-cancer analysis of secretome gene expression changes to identify candidate diagnostic biomarkers and to investigate the underlying biological function of these changes. Using transcriptomic data spanning 32 cancer types and 30 healthy tissues, we quantified the relative diagnostic potential of secretome proteins for each cancer. Furthermore, we offer a potential mechanism by which cancer cells relieve secretory pathway stress by decreasing the expression of tissue-specific genes, thereby facilitating the secretion of proteins promoting invasion and proliferation. These results provide a more systematic understanding of the cancer secretome, facilitating its use in diagnostics and its targeting for therapeutic development.

Urinary proteomic biomarkers in oncology: ready for implementation?

Introduction: Biomarkers are expected to improve the management of cancer patients by enabling early detection and prediction of therapeutic response. Proteins reflect a molecular phenotype, have high potential as biomarkers, and also are key targets for intervention. Given the ease of collection and proximity to certain tumors, the urinary proteome is a rich source of biomarkers and several proteins have been already implemented. Areas covered: We examined the literature on urine proteins and proteome analysis in oncology from reports published during the last 5 years to generate an overview on the status of urine protein and peptide biomarkers, with emphasis on their actual clinical value. Expert commentary: A few studies report on biomarkers that are ready to be implemented in patient management, among others in bladder cancer and cholangiocarcinoma. These reports are based on multi-marker approaches. A high number of biomarkers, though, has been described in studies with low statistical power. In fact, several of them have been consistently reported across different studies. The latter should be the focus of attention and be tested in properly designed confirmatory and ultimately, prospective investigations. It is expected that multi-marker classifiers for a specific context-of-use, will be the preferred path toward clinical implementation.

Promise and Implementation of Proteomic Prostate Cancer Biomarkers

Prostate cancer is one of the most commonly diagnosed malignancy and the fifth leading cause of cancer mortality in men. Despite the broad use of prostate-specific antigen test that resulted in an increase in number of diagnosed cases, disease management needs to be improved. Proteomic biomarkers alone and or in combination with clinical and pathological risk calculators are expected to improve on decreasing the unnecessary biopsies, stratify low risk patients, and predict response to treatment. To this end, significant efforts have been undertaken to identify novel biomarkers that can accurately discriminate between indolent and aggressive cancer forms and indicate those men at high risk for developing prostate cancer that require immediate treatment. In the era of “big data” and “personalized medicine” proteomics-based biomarkers hold great promise to provide clinically applicable tools, as proteins regulate all biological functions, and integrate genomic information with the environmental impact. In this review article, we aim to provide a critical assessment of the current proteomics-based biomarkers for prostate cancer and their actual clinical applicability. For that purpose, a systematic review of the literature published within the last 10 years was performed using the Web of Science Database. We specifically discuss the potential and prospects of use for diagnostic, prognostic and predictive proteomics-based biomarkers, including both body fluid- and tissue-based markers.