Comprehensive analysis of the role of ubiquitin-specific peptidases in colorectal cancer:A systematic review

BACKGROUND Colorectal cancer(CRC)is the third most frequent and the second most fatal cancer.The search for more effective drugs to treat this disease is ongoing.A better understanding of the mechanisms of CRC development and progression may reveal new therapeutic strategies.Ubiquitin-specific peptidases(USPs),the largest group of the deubiquitinase protein family,have long been implicated in various cancers.There have been numerous studies on the role of USPs in CRC;however,a comprehensive view of this role is lacking.AIM To provide a systematic review of the studies investigating the roles and functions of USPs in CRC.METHODS We systematically queried the MEDLINE(via PubMed),Scopus,and Web of Science databases.RESULTS Our study highlights the pivotal role of various USPs in several processes implicated in CRC:Regulation of the cell cycle,apoptosis,cancer stemness,epithelial–mesenchymal transition,metastasis,DNA repair,and drug resistance.The findings of this study suggest that USPs have great potential as drug targets and noninvasive biomarkers in CRC.The dysregulation of USPs in CRC contributes to drug resistance through multiple mechanisms.CONCLUSION Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CRC.

Proteomic insights on the metabolism in inflammatory bowel disease

Inflammatory bowel diseases(IBD)are chronic and relapsing inflammatory conditions of the gut that include Crohn's disease and ulcerative colitis.The pathogenesis of IBD is not completely unraveled,IBD are multi-factorial diseases with reported alterations in the gut microbiota,activation of different immune cell types,changes in the vascular endothelium,and alterations in the tight junctions’structure of the colonic epithelial cells.Proteomics represents a useful tool to enhance our biological understanding and to discover biomarkers in blood and intestinal specimens.It is expected to provide reproducible and quantitative data that can support clinical assessments and help clinicians in the diagnosis and treatment of IBD.Sometimes a differential diagnosis of Crohn's disease and ulcerative colitis and the prediction of treatment response can be deducted by finding meaningful biomarkers.Although some non-invasive biomarkers have been described,none can be considered as the“gold standard”for IBD diagnosis,disease activity and therapy outcome.For these reason new studies have proposed an“IBD signature”,which consists in a panel of biomarkers used to assess IBD.The above described approach characterizes“omics”and in this review we will focus on proteomics.

Mass spectrometry-based proteomics for biomarker discovery in the Drosophila model of Parkinson's disease

Parkinson's disease(PD)is the second most prevalent neurodegenerative disorder worldwide.Despite extensive research,the etiology of both familial and sporadic PD remains unclear.While most PD cases are sporadic,a significant minority are linked to genetic mutations,notably in the synuclein-alpha(SNCA)and leucine-rich repeat kinase 2(LRRK2)genes.Animal models,such as Drosophila melanogaster(D.melanogaster),enable detailed study of these genetic mutations and their neurotoxic effects.Recent advancements in mass spectrometry-based proteomics have enhanced our understanding of PD by facilitating comprehensive analysis of protein expression and interactions in mutant and wild-type organisms,potentially revealing novel therapeutic targets.This review highlights the pivotal role of mass spectrometry-based proteomics in advancing PD research,emphasizing the contributions of D.melanogaster models in identifying potential biomarkers.

Liquid biopsy in ovarian cancer:Catching the silent killer before it strikes

Epithelial ovarian cancer(EOC)is the most lethal gynaecological malignancy in the western world.The majority of women presenting with the disease are asymptomatic and it has been dubbed the“silent killer”.To date there is no effective minimally invasive method of stratifying those with the disease or screening for the disease in the general population.Recent molecular and pathological discoveries,along with the advancement of scientific technology,means there is a real possibility of having disease-specific liquid biopsies available within the clinical environment in the near future.In this review we discuss these discoveries,particularly in relation to the most common and aggressive form of EOC,and their role in making this possibility a reality.

Multi-factor combined biomarker screening strategy to rapidly diagnose Alzheimer's disease and evaluate drug effect based on a rat model

Alzheimer's disease(AD)represents the main form of dementia;however,valid diagnosis and treatment measures are lacking.The discovery of valuable biomarkers through omics technologies can help solve this problem.For this reason,metabolomic analysis using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS)was carried out on plasma,hippocampus,and cortex samples of an AD rat model.Based on the metabolomic data,we report a multi-factor combined biomarker screening strategy to rapidly and accurately identify potential biomarkers.Compared with the usual procedure,our strategy can identify fewer biomarkers with higher diagnostic specificity and sensitivity.In addition to diagnosis,the potential biomarkers identified using our strategy were also beneficial for drug evaluation.Multi-factor combined biomarker screening strategy was used to identify differential metabolites from a rat model of amyloid beta peptide 1e40(Aβ_(1-40))plus ibotenic acid-induced AD(compared with the controls)for the first time;lysophosphatidylcholine(LysoPC)and intermediates of sphingolipid metabolism were screened as potential biomarkers.Subsequently,the effects of donepezil and pine nut were successfully reflected by regulating the levels of the abovementioned biomarkers and metabolic profile distribution in partial least squaresdiscriminant analysis(PLS-DA).This novel biomarker screening strategy can be used to analyze other metabolomic data to simultaneously enable disease diagnosis and drug evaluation.

A different vision of translational research in biomarker discovery: a pilot study on circulatory mitochondrial proteins as Parkinson’s disease potential biomarkers

Background The identification of circulating biomarkers that closely correlate with Parkinson’s Disease(PD)has failed several times in the past.Nevertheless,in this pilot study,a translational approach was conducted,allowing the evaluation of the plasma levels of two mitochondrial-related proteins,whose combination leads to a robust model with potential diagnostic value to discriminate the PD patients from matched controls.Methods The proposed translational approach was initiated by the analysis of secretomes from cells cultured under control or well-defined oxidative stress conditions,followed by the identification of proteins related to PD pathologic mechanisms that were altered between the two states.This pipeline was further translated into the analysis of undepleted plasma samples from 28 control and 31 PD patients.Results From the secretome analysis,several mitochondria-related proteins were found to be differentially released between control and stress conditions and to be able to distinguish the two secretomes.Similarly,two mitochondrial-related proteins were found to be significantly changed in a PD cohort compared to matched controls.Moreover,a linear discriminant model with potential diagnostic value to discriminate PD patients was obtained using the combination of these two proteins.Both proteins are associated with apoptotic mitochondrial changes,which may correspond to potential indicators of cell death.Moreover,one of these proteins,the VPS35 protein,was reported in plasma for the first time,and its quantification was only possible due to its previous identification in the secretome analysis.Conclusions In this work,an adaptation of a translational pipeline for biomarker selection was presented and transposed to neurological diseases,in the present case Parkinson’s Disease.The novelty and success of this pilot study may arise from the combination of:i)a translational research pipeline,where plasma samples are interrogated using knowledge previously obtained from the evaluation of cells’secretome under oxidative stress;ii)the combined used of statistical analysis and an informed selection of candidates based on their link with relevant disease mechanisms,and iii)the use of SWATH-MS,an untargeted MS method that allows a complete record of the analyzed samples and a targeted data extraction of the quantitative values of proteins previously identified.

Proteomics techniques in protein biomarker discovery

Protein biomarkers represent specific biological activities and processes, so they have had a critical role in cancer diagnosis and medical care for more than 50 years. With the recent improvement in proteomics technologies, thousands of protein biomarker candidates have been developed for diverse disease states. Studies have used different types of samples for proteomics diagnosis. Samples were pretreated with appropriate techniques to increase the selectivity and sensitivity of the downstream analysis and purified to remove the contaminants. The purified samples were analyzed by several principal proteomics techniques to identify the specific protein. In this study, recent improvements in protein biomarker discovery, verification, and validation are investigated. Furthermore, the advantages, and disadvantages of conventional techniques, are discussed. Studies have used mass spectroscopy (MS) as a critical technique in the identification and quantification of candidate biomarkers. Nevertheless, after protein biomarker discovery, verification and validation have been required to reduce the false-positive rate where there have been higher number of samples. Multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), and selected reaction monitoring (SRM), in combination with stable isotope-labeled internal standards, have been examined as options for biomarker verification, and enzyme-linked immunosorbent assay (ELISA) for validation.

Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery

The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large data sets of proteins or peptides involved in various biologic and disease progression processes generating testable hypothesis for complex biologic questions. This review provides an introduction to relevant topics in proteomics and peptidomics including biologic material selection, sample preparation, separation techniques, peptide fragmentation, post-translational modifications, quantification, bioinformatics, and biomarker discovery and validation. In addition, current literature, remaining challenges, and emerging technologies for proteomics and peptidomics are presented.

Biomarker Discovery in Parkinson's Disease:Present Challenges and Future Opportunities

Parkinson＇s disease （PD） is the second most common neurodegenerative disorder affecting more than 1% of the older population. Histopathologically, PD is characterized by a severe loss of dopaminergic neurons in the substantia nigra and cytoplasmic inclusions composed of insoluble protein aggregates （Lewy bodies）, which lead to a pro- gressive movement disorder including the classic triad of tremor, bradykinesia, and rigidity.

VSOLassoBag:a variable-selection oriented LASSO bagging algorithm for biomarker discovery in omic-based translational research

Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research.With its advantages in both feature shrinkage and biological interpretability,Least Absolute Shrinkage and Selection Operator(LASSO)algorithm is one of the most popular methods for the scenarios of clinical biomarker development.However,in practice,applying LASSO on omics-based data with high dimensions and low-sample size may usually result in an excess number of predictive variables,leading to the overfitting of the model.Here,we present VSOLassoBag,a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient and stable variables with high confidence from omics-based data.Using a bagging strategy in combination with a parametric method or inflection point search method,VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates.The application of VSOLassoBag on both simulation datasets and real-world datasets shows that the algorithm can effectively identify markers for either case-control binary classification or prognosis prediction.In addition,by comparing with multiple existing algorithms,VSOLassoBag shows a comparable performance under different scenarios while resulting in fewer features than others.In summary,VSOLassoBag,which is available at https://seqworld.com/VSOLassoBag/under the GPL v3 license,provides an alternative strategy for selecting reliable biomarkers from high-dimensional omics data.For user’s convenience,we implement VSOLassoBag as an R package that provides multithreading computing configurations.

Single-cell Analysis Technologies for Immuno-oncology Research:from Mechanistic Delineation to Biomarker Discovery

The successes with immune checkpoint blockade(ICB)and chimeric antigen receptor(CAR)-T-cell therapy in treating multiple cancer types have established immunotherapy as a powerful curative option for patients with advanced cancers.Unfortunately,many patients do not derive benefit or long-term responses,highlighting a pressing need to perform complete investigation of the underlying mechanisms and the immunotherapy-induced tumor regression or rejection.In recent years,a large number of single-cell technologies have leveraged advances in characterizing immune system,profiling tumor microenvironment,and identifying cellular heterogeneity,which establish the foundations for lifting the veil on the comprehensive crosstalk between cancer and immune system during immunotherapies.In this review,we introduce the applications of the most widely used single-cell technologies in furthering our understanding of immunotherapies in terms of underlying mechanisms and their association with therapeutic outcomes.We also discuss how single-cell analyses help to deliver new insights into biomarker discovery to predict patient response rate,monitor acquired resistance,and support prophylactic strategy development for toxicity management.Finally,we provide an overview of applying cutting-edge single-cell spatial-omics to point out the heterogeneity of tumor–immune interactions at higher level that can ultimately guide to the rational design of next-generation immunotherapies.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts,as these stress states can lead to suboptimal growth and lower yields of target products.To enable the design of genetic circuits for reporting or mitigating the stress states,it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria.Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria,motivating computational methods that can identify robust biomarkers for experimental characterisation and verification.Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis,this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments.We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model,gene regulatory network and co-expression network.We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification(mean f1-score achieved at 0.99),as well as in a range of independent datasets(mean f1-score achieved at 0.98).We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B.subtilis gene regulatory network(GRN)and the number of associated modules in a B.subtilis co-expression network(CEN).GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes.We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.

Polar Gini Curve:A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data

In this work,we describe the development of Polar Gini Curve,a method for characterizing cluster markers by analyzing single-cell RNA sequencing(scRNA-seq)data.Polar Gini Curve combines the gene expression and the 2D coordinates(“spatial”)information to detect patterns of uniformity in any clustered cells from scRNA-seq data.We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster,which can be generated during routine scRNA-seq data analysis.To quantify the extent to which a gene is uniformly distributed in a cell cluster space,we combine two polar Gini curves(PGCs)—one drawn upon the cell-points expressing the gene(the“foreground curve”)and the other drawn upon all cell-points in the cluster(the“background curve”).We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster—thus having spatially divergent gene expression patterns within the cluster.Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster—thus having uniform gene expression patterns within the cluster.Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data.We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies.Using this framework to analyze a real-world neonatal mouse heart cell dataset,the detected biomarkers may characterize novel subtypes of cardiac muscle cells.The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/or https://figshare.com/projects/Polar_Gini_Curve/76749.