Liquid chromatography-mass spectrometry based serum peptidomic approach for renal clear cell carcinoma diagnosis

Bioinformatic Analysis of the Wound Peptidome Reveals Potential Biomarkers and Antimicrobial Peptides

Wound infection is a common and serious medical condition with an unmet need for improved diagnostic tools. A peptidomic approach, aided by mass spectrometry and bioinformatics, could provide novel means of identifying new peptide biomarkers for wound healing and infection assessment. Wound fluid is suitable for peptidomic analysis since it is both intimately tied to the wound environment and is readily available. In this study we investigate the peptidomes of wound fluids derived from surgical drainages following mastectomy and from wound dressings following facial skin grafting. By applying sorting algorithms and open source third party software to peptidomic label free tandem mass spectrometry data we provide an unbiased general methodology for analyzing and differentiating between peptidomes. We show that the wound fluid peptidomes of patients are highly individualized. However, differences emerge when grouping the patients depending on wound type. Furthermore, the abundance of peptides originating from documented antimicrobial regions of hemoglobin in infected wounds may contribute to an antimicrobial wound environment, as determined by in silico analysis. We validate our findings by compiling literature on peptide biomarkers and peptides of physiological significance and cross checking the results against our dataset, demonstrating that well-documented peptides of immunological significance are abundant in infected wounds, and originate from certain distinct regions in proteins such as hemoglobin and fibrinogen. Ultimately, we have demonstrated the power using sorting algorithms and open source software to help yield insights and visualize peptidomic data.

Using serum peptidomics to discovery the diagnostic marker for different stage of ulcerative colitis

The use of peptidomics to find diagnostic markers has attracted increasing clinical attention. Ulcerative colitis (UC) is a type of inflammatory bowel disease, and the traditional auxiliary diagnostic technique is colonoscopy. However, this invasive method is not effective in distinguishing between patients with endoscopic remission and healthy people, which carries the risk of delayed diagnosis of UC. In this study, we used peptidomics to find serum diagnostic markers for different stages of UC. A total of 78 serum samples were collected to form a training set (60 samples) and a testing set (18 samples). Among them, patients with active UC, remitting UC and healthy people accounted for one third each. The nano-liquid chromatography coupled with hybrid linear trap quadrupole orbitrap mass spectrometry was used for detection of low molecular weight peptides in serum. According to the protein database search and de novo sequencing algorithm, forty peptides were simultaneously identified in all samples. Six biomarker peptides were screened in the training set through orthogonal partial least-squares-discriminant analysis and receiver operating characteristic curve analysis. These six peptides were derived from proteins involved in coagulation and complement activation. We evaluated the diagnostic ability of the six peptides in the testing set through hierarchical cluster analysis, and showed that perturbation of these peptides could distinguish patients with active UC, patients with remitting UC and healthy people. This study validated the feasibility of serum peptidomics for the discovery of diagnostic markers, and provided a potential method for diagnosing different stages of UC.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

Probing the endogenous peptidomes of cancer for biomarkers: A new endeavor

The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.

Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps

Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.

Liquid biopsy: a step forward towards precision medicine in urologic malignancies

There is a growing trend towards exploring the use of a minimally invasive "liquid biopsy" to identify biomarkers in a number of cancers, including urologic malignancies. Multiple aspects can be assessed in circulating cell-free DNA, including cell-free DNA levels, integrity, methylation and mutations. Other prospective liquid biopsy markers include circulating tumor cells, circulating RNAs (miRNA, lncRNAs and mRNAs), cell-free proteins, peptides and exosomes have also emerged as non-invasive cancer biomarkers. These circulating molecules can be detected in various biological fluids, including blood, urine, saliva and seminal plasma. Liquid biopsies hold great promise for personalized medicine due to their ability to provide multiple non-invasive global snapshots of the primary and metastatic tumors. Molecular profiling of circulating molecules has been a stepping-stone to the successful introduction of several non-invasive multi-marker tests into the clinic. In this review, we provide an overview of the current state of cell-free DNA-based kidney, prostate and bladder cancer biomarker research and discuss the potential utility other circulating molecules. We will also discuss the challenges and limitations facing non-invasive cancer biomarker discovery and the benefits of this growing area of translational research.

The proteomic landscape of renal tumors

INTRODUCTION

Renal cell carcinoma (RCC) is the most fatal of the common urologic cancers, with approximately 35% of patients dying within 5 years following diagnosis. Therefore, there is a need for non-invasive markers that are capable of detecting and determining the severity of small renal masses at an early stage in order to tailor treatment and follow-up. Proteomic studies have proved to be very useful in the study of tumors. Areas covered: In this review, we will detail the current knowledge obtained by the different proteomic approaches, focusing on MS-based strategies, used to investigate RCC biology in order to identify diagnostic, prognostic and predictive biomarkers on tissue, cultured cells and biological fluids. Expert commentary: Currently, no reliable biomarkers or targets for RCC have been translated into the clinical setting. Moreover, despite the efforts of proteomics and other -omics disciplines, only a small number of them have been observed as shared targets between the different analytical platforms and biological specimens. The difficulty to define a specific molecular pattern for RCC and its subtypes highlights a peculiar profile and a heterogeneity that must be taken into account in future studies.

Proteomics and peptidomics: moving toward precision medicine in urological malignancies

Urological malignancies are a major cause of morbidity and mortality worldwide. Advances in early detection, diagnosis, prognosis and prediction of treatment response can significantly improve patient care. Proteomic and peptidomic profiling studies are at the center of kidney, prostate and bladder cancer biomarker discovery and have shown great promise for improved clinical assessment. Mass spectrometry (MS) is the most widely employed method for proteomic and peptidomic analyses. A number of MS platforms have been developed to facilitate accurate identification of clinically relevant markers in various complex biological samples including tissue, urine and blood. Furthermore, protein profiling studies have been instrumental in the successful introduction of several diagnostic multimarker tests into the clinic. In this review, we will provide a brief overview of high-throughput technologies for protein and peptide based biomarker discovery. We will also examine the current state of kidney, prostate and bladder cancer biomarker research as well as review the journey toward successful clinical implementation.

Peptidome profiling of umbilical cord plasma associated with gestational diabetes-induced fetal macrosomia

Fetal macrosomia, defined as a birth weight ≥4000g, may affect 15-45% of newborns of women with gestational diabetes mellitus (GDM). The associations between endogenous peptides and gestational diabetes-induced macrosomia have not been investigated extensively by peptidome analysis. Here, we analyzed the umbilical cord plasma by combining ultrafiltration using molecular weight cut-off filters and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate potential associations of GDM with macrosomia. As macrosomic babies have increased susceptibility to obesity, diabetes and cardiovascular diseases in later life, we also aimed to identify specific biomarkers to detect these future diseases. Thirty pairs of GDM mothers and controls were randomly divided into three subgroups. We identified 235 peptides of around 1000-3000Da, originating from 115 proteins. Analyzing the cleavage sites revealed that these peptides were cleaved in regulation, which may reflect the protease activity and distribution in umbilical cord plasma. Four identified peptides, of 2471.7, 1077.2, 1446.5 and 2372.7Da, were significantly differentially expressed in the GDM macrosomia groups compared with controls, whose precursors may play a critical role in developing GDM macrosomia. We provide for the first time a validated GDM macrosomia peptidome profile and identify potential biomarkers linking the effects of macrosomia to later-life diseases.

BIOLOGICAL SIGNIFICANCE

Fetal macrosomia is the predominant adverse outcome of gestational diabetes mellitus (GDM), which is a frequent medical condition during pregnancy. Till now, the detailed molecular mechanisms underlying gestational diabetes-induced macrosomia are still not elucidated. With high detection sensitivity and high throughput of peptidome technology, it is now possible to systemically identify peptides possibly involved in the umbilical cord plasma of GDM induced macrosomia cases. With LC-MS/MS based quantification, totally, we identified 235 peptides originated from 115 precursor proteins. And four peptides of 2471.7, 1077.2, 1446.5 and 2372.7Da differentially expressed between GDM cases and compared controls. A precursor protein of 1077.2Da was fibrinogen alpha chain (FGA), which was also identified in the Ai et al. [29] study with a downregulated manner in the serum samples of GDM cases. And further analysis the cleavage pattern of the identified peptides revealed that the enzymes in tissues cleaved the protein according to their rules. Thus, this quantitative peptidome approach can identify related peptides that may play a role in the gestational diabetes-induced macrosomia, and give candidate biomarkers contributing to the development of later-life diseases in macrosomic babies.

Serum peptidome profiling analysis for the identification of potential biomarkers in cervical intraepithelial neoplasia patients

Cervical intraepithelial neoplasia (CIN) is a precancerous disease of cervical squamous cell carcinoma. We Used Mass Spectrometry based peptidome profile study to predict the transformation of CIN1, which is the primary stage of this lesion. . Serum samples of 34 Cervical squamous cell carcinoma patients, 31 healthy controls, and 29 CIN1 samples were analyzed. Peptides were purified by WCX magnetic beads (Bioyong), and analyzed by MALDI TOF (Bruker). Raw data were analyzed by BioExplorer software (Bioyong). The results showed 14 mass peaks with significant differences. The diagnosis model is established by analyzing peptide profiles of 15 SCC patients and 20 healthy women serum, with a sensitivity of 100% and specificity of 100.00%. In validation set, the SCC diagnosis model also had good performance with a sensitivity of 80%, a specificity of 100%. In addition, this model could predict 29 CIN1 patients with accuracy of 55.17%. These results would provide a new method to predict the trend of CIN1 and take effective measures for high risk group timely.