Cancer Biomarker Discovery Research Articles

Large-Scale Plasma Proteome Epitome Profiling is an Efficient Tool for the Discovery of Cancer Biomarkers

Current proteomic technologies focus on the quantification of protein levels, while little effort is dedicated to the development of system approaches to simultaneously monitor proteome variability and abundance. Protein variants may display different immunogenic epitopes detectable by monoclonal antibodies. Epitope variability results from alternative splicing, posttranslational modifications, processing, degradation, and complex formation and possesses dynamically changing availability of interacting surface structures that frequently serve as reachable epitopes and often carry different functions. Thus, it is highly likely that the presence of some of the accessible epitopes correlates with function under physiological and pathological conditions. To enable the exploration of the impact of protein variation on the immunogenic epitome first, here, we present a robust and analytically validated PEP technology for characterizing immunogenic epitopes of the plasma. To this end, we prepared mAb libraries directed against the normalized human plasma proteome as a complex natural immunogen. Antibody producing hybridomas were selected and cloned. Monoclonal antibodies react with single epitopes, thus profiling with the libraries is expected to profile many epitopes which we define by the mimotopes, as we present here. Screening blood plasma samples from control subjects (n = 558) and cancer patients (n = 598) for merely 69 native epitopes displayed by 20 abundant plasma proteins resulted in distinct cancer-specific epitope panels that showed high accuracy (AUC 0.826–0.966) and specificity for lung, breast, and colon cancer. Deeper profiling (≈290 epitopes of approximately 100 proteins) showed unexpected granularity of the epitope-level expression data and detected neutral and lung cancer–associated epitopes of individual proteins. Biomarker epitope panels selected from a pool of 21 epitopes of 12 proteins were validated in independent clinical cohorts. The results demonstrate the value of PEP as a rich and thus far unexplored source of protein biomarkers with diagnostic potential.

SERS Immunosensors for Cancer Markers Detection.

Early diagnosis and monitoring are essential for the effective treatment and survival of patients with different types of malignancy. To this end, the accurate and sensitive determination of substances in human biological fluids related to cancer diagnosis and/or prognosis, i.e., cancer biomarkers, is of ultimate importance. Advancements in the field of immunodetection and nanomaterials have enabled the application of new transduction approaches for the sensitive detection of single or multiple cancer biomarkers in biological fluids. Immunosensors based on surface-enhanced Raman spectroscopy (SERS) are examples where the special properties of nanostructured materials and immunoreagents are combined to develop analytical tools that hold promise for point-of-care applications. In this frame, the subject of this review article is to present the advancements made so far regarding the immunochemical determination of cancer biomarkers by SERS. Thus, after a short introduction about the principles of both immunoassays and SERS, an extended presentation of up-to-date works regarding both single and multi-analyte determination of cancer biomarkers is presented. Finally, future perspectives on the field of SERS immunosensors for cancer markers detection are briefly discussed.

Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis

ObjectiveGene expression profiling techniques measure the transcription of thousands of genes in a parallel manner. With more and more hepatocellular carcinoma (HCC) transcriptomic data becoming available, the high-throughput data provides an unprecedented opportunity to discover HCC diagnostic biomarkers. In this work, we propose a bioinformatics method based on dynamic network entropy analysis, called DNEA, to identify potential pathway biomarkers for HCC occurrence and development by integrating transcriptome and interactome. MethodsWe firstly collect the pathways documented in different knowledge-bases and then impose the genome-wide human transcriptomic data of multistage cancerous tissues during the development and progression of HCC. After linking the gene sets of pathways into individual connected networks, we map the corresponding gene expression information onto these pathways. The dynamic network entropy of individual pathways is calculated to evaluate its activities and dysfunctionalities during the disease occurrence and development. We use the overall significant difference in the entropic dynamics during the time course to prioritize distinctive pathways during disease progression. Then machine learning classification methods are employed to screen out pathway biomarkers with the classification ability to distinguish different-stage samples of HCC progression. ResultsPathway biomarkers discovered based on DNEA demonstrate good classification performance in measuring HCC progression. The classification accuracy is as follows: DNA replication pathway (mean AUC = 0.82, 20 genes) from KEGG, FMLP pathway (mean AUC = 0.84, 14 genes) from BioCarta, and downstream signaling of activated FGFR pathway (mean AUC = 0.80, 15 genes) from Reactome. At the same time, previous studies have shown that these genes and pathways screened are closely related to the occurrence and development of HCC in terms of oncogenesis dysfunctions. ConclusionsOur method for cancer biomarker discovery based on dynamic network entropy analysis is effective and efficient in identifying pathway biomarkers related to the progression of complex diseases.

Mass spectrometry‐based extracellular vesicle micromolecule detection in cancer biomarker discovery: An overview of metabolomics and lipidomics

AbstractExtracellular vesicles (EVs) become one of the most important sources of cancer biomarkers during the past decade due to their wide distribution in body fluids and physiological stability. Previous studies mainly focused on nucleic acids and proteins, while lipids and metabolites were largely neglected. Noticeably, many of those micromolecules exhibited a high abundance in EVs. Revealing the metabolomics and lipidomics of EVs would provide more comprehensive information for biomarker discovery. With the rapid development of mass spectrometry (MS) facilities, MS‐based micromolecule detection has become an emerging technique for EVs studies. Increasing evidence demonstrated the presence of EV‐associated metabolites and lipids in different types of samples (e.g., cell, urine, serum, stool), which exhibited promising performance in cancer diagnosis, prognosis, and prediction of treatment responses. This review aims to summarize advances in micromolecule profiling of EVs for cancer biomarker discovery, with an emphasis on MS‐based metabolomic and lipidomic analytical techniques. Challenges in this field, including the minimum sample quantity, normalization methods, and compound identifications, are also discussed along with possible solutions.

32P Improvement of diagnostic accuracy of breast cancer using multi-protein signature markers through machine learning

We have developed a 3-protein signature blood marker (Mastocheck®) for early diagnosis of breast cancer. The purpose of this study is to improve the performance of the previously developed blood markers. Blood from 196 breast cancer patients and 196 healthy control groups were prospectively collected.Through the development of a biomarker detectable library, PepQuant, peptides that are optimal for MS/MS detection were selected. After chemically synthesizing these selected proteins, these were quantified by multiple reaction monitoring (MRM) methods. Seven final proteins were derived by applying the PepQuant library for breast cancer biomarker discovery and verification. Machine learning algorithms was trained as protein candidates identified between breast cancer patients and healthy controls. As in previous studies, performance evaluation was conducted based on sensitivity, specificity, accuracy, false positive rate, false negative rate, positive predictive value, and negative predictive value for breast cancer diagnosis. The sensitivity, specificity, and accuracy of Mastocheck®, were 69.4%, 83.7%, and 76.5%, respectively, which is relatively similar to that of previous studies’ results. The false positive rate(FPR) and the false negative rate(FNR) were 16.3% and 30.7%, and the positive predictive value(PPV) and negative predictive value(NPV) were 81.0% and 73.2%, respectively. During the study when 7-protein signature was combined with an artificial intelligence(AI) technique for the analysis, the sensitivity, specificity, and accuracy of diagnosis were 88.3%, 83.2%, and 85.7%, respectively, showing superior performance compared to Mastocheck®. The FPR and FNR were 16.8% and 11.7%, indicating that the FNR was improved by 20% compared to Mastecheck®. In addition, it was recognized that the PPV and NPV were also improved to 84.0% and 87.6%. Through the collection of new prospective samples, the study confirmed that the diagnostic performance of Mastocheck® was repeatedly maintained. In addition, breast cancer diagnosis using 7-protein signatures with AI model showed that breast cancer diagnosis can be remarkably improved.

A Fluid Multivalent Magnetic Interface for High-Performance Isolation and Proteomic Profiling of Tumor-Derived Extracellular Vesicles.

Isolation and analysis of tumor-derived extracellular vesicles (T-EVs) are important for clinical cancer management. Here, we develop a fluid multivalent magnetic interface (FluidmagFace) in a microfluidic chip for high-performance isolation, release, and protein profiling of T-EVs. The FluidmagFace increases affinity by 105-fold with fluidity-enhanced multivalent binding to improve isolation efficiency by 13.9% compared with a non-fluid interface. Its anti-adsorption property and microfluidic hydrodynamic shear minimize contamination, increasing detection sensitivity by two orders of magnitude. Moreover, its reversibility and expandability allow high-throughput recovery of T-EVs for mass spectrometric protein analysis. With the chip, T-EVs are detected in all tested cancer samples with identification of differentially expressed proteins compared with healthy controls. The FluidmagFace opens a new avenue to isolation and release of targets for cancer diagnosis and biomarker discovery.

A Fluid Multivalent Magnetic Interface for High‐Performance Isolation and Proteomic Profiling of Tumor‐Derived Extracellular Vesicles

AbstractIsolation and analysis of tumor‐derived extracellular vesicles (T‐EVs) are important for clinical cancer management. Here, we develop a fluid multivalent magnetic interface (FluidmagFace) in a microfluidic chip for high‐performance isolation, release, and protein profiling of T‐EVs. The FluidmagFace increases affinity by 105‐fold with fluidity‐enhanced multivalent binding to improve isolation efficiency by 13.9 % compared with a non‐fluid interface. Its anti‐adsorption property and microfluidic hydrodynamic shear minimize contamination, increasing detection sensitivity by two orders of magnitude. Moreover, its reversibility and expandability allow high‐throughput recovery of T‐EVs for mass spectrometric protein analysis. With the chip, T‐EVs were detected in all tested cancer samples with identification of differentially expressed proteins compared with healthy controls. The FluidmagFace opens a new avenue to isolation and release of targets for cancer diagnosis and biomarker discovery.

Cellular Functions of Deubiquitinating Enzymes in Ovarian Adenocarcinoma.

In ovarian cancer patients, the 5-year survival rate is 90% for stages I and II, but only 30% for stages III and IV. Unfortunately, as 75% of the patients are diagnosed at stages III and IV, many experience a recurrence. To ameliorate this, it is necessary to develop new biomarkers for early diagnosis and treatment. The ubiquitin-proteasome system is a post-translational modification that plays an important role in regulating protein stability through ubiquitination. In particular, deubiquitinating enzymes (DUBs) regulate protein stability through deubiquitinating substrate proteins. In this review, DUBs and substrates regulated by these enzymes are summarized based on their functions in ovarian cancer cells. This would be useful for the discovery of biomarkers for ovarian cancer and developing new therapeutic candidates.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review.

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Multimodal data fusion for cancer biomarker discovery with deep learning.

Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.

Abstract 4632: Spatial whole transcriptome analysis of differential expression for biomarker discovery in colorectal cancer

Abstract Background: Colorectal cancer (CRC) is one of the leading causes of cancer death, which is frequently caused by genetic or epigenetic changes. The study of tumor microenvironment (TME) profiling may provide a better knowledge of spatial gene expression patterns of known CRC markers, visualize the cell composition and localization, and serve to aid the understanding of the mechanisms of tumor formation. BioChain Institute, Inc. is currently investigating the TME profiling of CRC through tissue-wide, whole transcriptome analysis for the ability to discover potential therapeutic targets. Methods: BioChain’s formalin-fixed paraffin-embedded (FFPE) tissue samples from 3 primary colon adenocarcinoma tumors (PT), and their matched adjacent normal tissues (PN), were used to investigate the TME. The images of Hematoxylin and Eosin (H&E)-stained tissue sections were annotated by our pathologist. The spatial whole transcriptome was analyzed using 10x Genomics Visium Spatial Gene Expression for FFPE. This assay provided about 5,000 spatially barcoded spots with mRNA-binding oligonucleotides to capture the gene expression. The integration of the imaging and sequencing data enabled a spatially resolved transcriptome of CRC heterogeneity. Results: The annotations of both PT and PN tissue samples were found to be correlated with the spatial gene expression clustering. With the overlaying of total gene counts for each spot on the H&E-stained tissue image, about 16,000-17,000 genes were detected in each sample. The median of ~5,500 and ~1,600 genes per spot were expressed in PT and PN samples respectively. The study of gene expression levels revealed the overexpression of several well-known CRC markers (APOE, SCD, IGFBP3, TIMP1, SPARC) and the down-regulation of numerous markers (DES, CA1, KLF4) in PT samples significantly. Conclusion: Our spatial analysis results provide insight into tumor heterogeneity, spatial organization of cells within the TME and biomarker identification. BioChain is in a unique position to facilitate researchers with a plethora of oncologic and other diseased tissue types, as well as the spatial whole transcriptome analysis. Our study enables the understanding of the TME, discovery of potential diagnostic and prognostic targets, especially for the development of personalized therapy. Citation Format: Elim Cheung, Vidyodhaya Sundaram, Baowen Zhang, Lutong Zhang, Tong Lu. Spatial whole transcriptome analysis of differential expression for biomarker discovery in colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4632.

Abstract 6781: Spatial omics using spatially-resolved laser-activated cell sorting for cancer biomarker discovery

Abstract Cancer is spatially heterogeneous in terms of genetic molecules. Revealing genomic, epigenomic, transcriptomic, and epitranscriptomic features that are unique to the malignant cell populations within cancer lead to cancer biomarker discoveries that can be translated into diagnostic or therapeutic tools. Here, we introduce spatially-resolved laser-activated cell sorting (SLACS) technology coupled to next generation sequencing and mass spectrometry that analyzes genetic molecules from regions of interest within spatial context. Specifically, we demonstrate application of SLACS in various cancers such as breast cancer, glioblastoma, glioma, meningioma, multiple myeloma, and leukemia. Examples of discovering cancer biomarker and their mechanism of action within spatial context are also provided. Specifically, we describe Select-seq which isolates the regions of interest as small as single cells from the immunofluorescence stained tissue and obtains the full-length transcriptome data. We demonstrated Select-seq on tumor tissue section from triple negative breast cancer patient who have received neoadjuvant chemotherapy. Select-seq produced full-length spatial transcriptome data, with which we analyzed the transcriptomic and epitranscriptomic features including alternative splicing variant, complementarity-determining region analysis of B cells, and even adenosine-to-inosine base editing. We were also able to analyze the immune- and stem-cell-like microniches, in which the complex mechanisms behind ferroptosis inhibition might suggest therapeutic options for the TNBC patients. Bridging spatial technologies to omics technologies, the discovery of specific markers within spatial context will provide insights into the next generation diagnostics and cancer therapeutics such as cancer vaccines. Citation Format: Amos C. Lee, Sumin Lee, Sunghoon Kwon. Spatial omics using spatially-resolved laser-activated cell sorting for cancer biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6781.

Abstract 6564: Biomarker discovery in triple negative breast cancer using RNA-sequencing analysis

Abstract Breast cancer is the most common form of cancer, and afflicted over 2.2 million people in 2020. The sheer volume of patients who suffer from breast cancer warrants continued research and discovery efforts to improve treatment options. Triple negative breast cancer (TNBC) affects 10-15% of breast cancer patients. Unlike other forms of breast cancer, TNBC does not have estrogen or progesterone receptors and makes little to none of the HER2 protein. Due to the lack of these biomarkers that are typical treatment targets for other kinds of breast cancers, the hormone therapies and drugs that target other breast cancers are often ineffective against TNBC. This leaves chemotherapy and radiation therapy as the main treatment options. Although chemotherapy and radiation have treatment benefits, the recurrence rate after treatment is around 40%. Furthermore, these treatment options are very detrimental to the body, resulting in a weaker patient and a necessary recovery time between treatments. In this study, we analyzed publicly available RNA-sequencing (RNA-seq) data to identify the upregulated and downregulated transcriptional mechanism(s) that play a role in TNBC compared to healthy breast tissue. The analysis of the RNA-seq data was completed with the Automated Reproducible Modular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR), which trims, maps, and quantifies the mRNA sequencing reads to the human transcriptome for each read. The ARMOR program identified more than 12,000 differentially expressed genes between TNBC and healthy breast tissue samples. We then applied an artificial intelligence-based classification method, a random forest algorithm, to identify new biomarkers that best differentiate TNBC cells from healthy cells. These specific transcriptional biomarkers for TNBC could potentially be used as diagnostic biomarkers or as therapeutic targets. We anticipate that this data may expand the treatment options for TNBC. Citation Format: Jenna B. Poulsen, Mauri E. Dobbs, Naomi Rapier-Sharman, Brett E. Pickett. Biomarker discovery in triple negative breast cancer using RNA-sequencing analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6564.

Abstract 5315: Identification of urinary proteome signatures associated with triple-negative breast cancer

Abstract Triple-negative breast cancer (TNBC), an aggressive breast cancer subtype, accounts for ~15% of all breast cancers and is associated with a poor prognosis and high rates of recurrence and distant metastases. Patients diagnosed with TNBC have limited treatment options and are often restricted to chemotherapy-based regimens, in stark contrast to patients with other breast cancer subtypes where the development of targeted therapeutics have significantly improved patient outcomes. TNBC is characterized by the loss of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2. This and the genetic and molecular tumor heterogeneity of TNBC has not only limited advances in the development of targeted therapy but also in the identification of biomarkers that can predict disease status and monitor treatment response. Advances in next-generation sequencing technologies have led to the identification of promising clinically actionable mutations however these tests often require invasive procedures to obtain tumor tissue and lack the ability to monitor treatment efficacy or detect recurrence, highlighting the need for non-invasive markers. A number of serum protein biomarkers have been evaluated in TNBC however none have yet to be successfully deployed in the clinic as indicators of disease. We have previously established that the urinary proteome can serve as a source of highly specific and sensitive biomarkers that can detect disease status and stage of multiple cancer types. Within the context of our global proteomics approach to cancer biomarker discovery, we have utilized the SomaScan discovery platform to identify 281 proteins that are differentially expressed in the urine of patients with TNBC compared to the urine of age- and sex-matched controls. The gene expression profiles of these proteins in TNBC tumor tissue were compared to those of normal breast tissue utilizing the R2 Genomics Platform. Proteins whose gene expression profile was consistent with the proteomic data were validated for the ability to distinguish between TNBC and non-TNBC diagnoses using mono-specific ELISAs of patient urine samples. These results demonstrate that a global proteomics approach coupled with clinical gene expression data can be effectively utilized to identify potential biomarkers for TNBC. These findings identify clinically relevant proteins that should be further explored as potential biomarkers in the development of non-invasive diagnostics for TNBC. (This work was supported by the Breast Cancer Research Foundation and the Nile Albright Research Foundation). Citation Format: Michael N. Lombardo, Roopali Roy, Susan Pories, Meg Lotz, Rama Aldakhlallah, Simon T. Dillon, Towia A. Libermann, Marsha A. Moses. Identification of urinary proteome signatures associated with triple-negative breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5315.

Identifying Diagnostic Biomarkers of Breast Cancer Based on Gene Expression Data and Ensemble Feature Selection

Background: In recent years, the identification of biomarkers or signatures based on gene expression profiling data has attracted much attention in bioinformatics. The successful discovery of breast cancer (BRCA) biomarkers will be beneficial in reducing the risk of BRCA among patients for early detection. Methods: This paper proposes an Ensemble Feature Selection method to screen biomarkers (abbreviat-ed as EFSmarker) for BRCA from publically available gene expression data. Firstly, we employ twelve filter feature selection methods, namely median, variance, Chi-square, Relief, Pearson and Spearman correlation, mutual information, minimal-redundancy-maximal-relevance criterion, ridge regression, decision tree and random forest with Gini index and accuracy index, to calculate the importance (weights or coefficients) of all features on the training dataset. Secondly, we apply the logistic regres-sion classifier on the test dataset to calculate the classification AUC value of each feature subset indi-vidually selected by twelve methods. Thirdly, we provide an ensemble feature selection method by ag-gregating feature importance with classification AUC value. In particular, we establish a feature im-portance score (FIS) to evaluate the importance of each feature underlying all feature selection methods. Finally, the features with higher FIS are taken as identified biomarkers. Results: With the direction of the FIS index induced by the EFSmarker method, 12 genes (COL10A1, COL11A1, MMP11, LOC728264, FIGF, GJB2, INHBA, CD300LG, IGFBP6, PAMR1, CXCL2 and FXYD1) are regarded as diagnostic biomarkers for BRCA. Especially, COL10A1, ranked first with a FIS value of 0.663, is identified as the most credible biomarker. The findings justified via gene and protein expression validation, functional enrichment analysis, literature checking and independent dataset validation verify the effectiveness and efficiency of these selected biomarkers. Conclusion: Our proposed biomarker discovery strategy not only utilizes the feature contribution but also considers the prediction accuracy simultaneously, which may also serve as a model for identifying unknown biomarkers for other diseases from high-throughput gene expression data. The source code and data are available at https://github.com/zpliulab/EFSmarker.

Molecularly Imprinted Polymers for the Determination of Cancer Biomarkers

Biomarkers can provide critical information about cancer and many other diseases; therefore, developing analytical systems for recognising biomarkers is an essential direction in bioanalytical chemistry. Recently molecularly imprinted polymers (MIPs) have been applied in analytical systems to determine biomarkers. This article aims to an overview of MIPs used for the detection of cancer biomarkers, namely: prostate cancer (PSA), breast cancer (CA15-3, HER-2), epithelial ovarian cancer (CA-125), hepatocellular carcinoma (AFP), and small molecule cancer biomarkers (5-HIAA and neopterin). These cancer biomarkers may be found in tumours, blood, urine, faeces, or other body fluids or tissues. The determination of low concentrations of biomarkers in these complex matrices is technically challenging. The overviewed studies used MIP-based biosensors to assess natural or artificial samples such as blood, serum, plasma, or urine. Molecular imprinting technology and MIP-based sensor creation principles are outlined. Analytical signal determination methods and the nature and chemical structure of the imprinted polymers are discussed. Based on the reviewed biosensors, the results are compared, and the most suitable materials for each biomarker are discussed.

Post‐translational modification and phenotype

Post‐translational modification and phenotype

Nurturing the marriages of urinary liquid biopsies and nano‐diagnostics for precision urinalysis of prostate cancer

AbstractProstate cancer remains the second‐most common cancer diagnosed in men, despite the increasingly widespread use of serum prostate‐specific antigen (PSA) screening. The controversial clinical implications and cost benefits of PSA screening have been highlighted due to its poor specificity, resulting in a high rate of overdiagnosis and underdiagnosis. Thus, the development of novel biomarkers for prostate cancer detection remains an intriguing challenge. Urine is emerging as a source for prostate cancer biomarker discovery. Currently, new urine biomarkers already outperform serum PSA in clinical diagnosis. Meanwhile, the advances in nanotechnology have provided a suite of diagnostic tools to study prostate cancer in more detail, sparking a new era of biomarker discoveries. In this review, we envision that future prostate cancer diagnosis will probably integrate multiplex nano‐diagnostic approaches to detect novel urinary biomarkers. However, challenges remain in differentiating indolent from aggressive cancers to better inform treatment decisions, and clinical translation still needs to be overcome.

Lipid Biomarkers in Liquid Biopsies: Novel Opportunities for Cancer Diagnosis.

Altered cellular metabolism is a well-established hallmark of cancer. Although most studies have focused on the metabolism of glucose and glutamine, the upregulation of lipid metabolism is also frequent in cells undergoing oncogenic transformation. In fact, cancer cells need to meet the enhanced demand of plasma membrane synthesis and energy production to support their proliferation. Moreover, lipids are precursors of signaling molecules, termed lipid mediators, which play a role in shaping the tumor microenvironment. Recent methodological advances in lipid analysis have prompted studies aimed at investigating the whole lipid content of a sample (lipidome) to unravel the complexity of lipid changes in cancer patient biofluids. This review focuses on the application of mass spectrometry-based lipidomics for the discovery of cancer biomarkers. Here, we have summarized the main lipid alteration in cancer patients' biofluids and uncovered their potential use for the early detection of the disease and treatment selection. We also discuss the advantages of using biofluid-derived extracellular vesicles as a platform for lipid biomarker discovery. These vesicles have a molecular signature that is a fingerprint of their originating cells. Hence, the analysis of their molecular cargo has emerged as a promising strategy for the identification of sensitive and specific biomarkers compared to the analysis of the unprocessed biofluid.

Development of a headspace-solid phase microextraction gas chromatography-high resolution mass spectrometry method for analyzing volatile organic compounds in urine: Application in breast cancer biomarker discovery

Background and aimBreast cancer (BC) is the leading cause of cancer-related death in females. The development of non-invasive methods for the early diagnosis of BC still remains challenge. Here, we aimed to discover the urinary volatile organic compounds (VOCs) pattern of BC patients and identify potential VOC biomarkers for BC diagnosis. MethodsUrine samples were analyzed by headspace-solid phase microextraction (HS-SPME) combined with gas chromatography-high resolution mass spectrometry (GC-HRMS). To assure reliable analysis, the factors influencing HS-SPME extraction efficiency were comprehensively investigated and optimized by combing the Plackett–Burman design (PBD) with the central composite design (CCD). The established HS-SPME/GC-HRMS method was validated and applied to analyze urine samples from BC patients (n = 80) and healthy controls (n = 88). ResultsA total number of 134 VOCs belonging to distinct chemical classes were identified by GC-HRMS. BC patients demonstrated unique urinary VOCs pattern. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation between BC patients and healthy controls. Eight potential VOC biomarkers were identified using multivariate and univariate statistical analysis. The predictive ability of candidate VOC biomarkers was further investigated by the random forest (RF) algorithm. The candidate VOC biomarkers yielded 76.3% sensitivity and 85.4% specificity on the training set, and achieved 76.0% sensitivity and 92.3% specificity on the validation set. ConclusionsOverall, this work not only established a standardized HS-SPME/GC-HRMS approach for urinary VOCs analysis, but also highlighted the value of urinary VOCs for BC diagnosis. The knowledge gained from this study paves the way for early diagnosis of BC using urine in a non-invasive manner.