Targeted proteomics for biomarker discovery and validation of hepatocellular carcinoma in hepatitis C infected patients

Low-Abundance Protein Enrichment for Medical Applications: The Involvement of Combinatorial Peptide Library Technique

The discovery of low- and very low-abundance proteins in medical applications is considered a key success factor in various important domains. To reach this category of proteins, it is essential to adopt procedures consisting of the selective enrichment of species that are present at extremely low concentrations. In the past few years pathways towards this objective have been proposed. In this review, a general landscape of the enrichment technology situation is made first with the presentation and the use of combinatorial peptide libraries. Then, a description of this peculiar technology for the identification of early-stage biomarkers for well-known pathologies with concrete examples is given. In another field of medical applications, the determination of host cell protein traces potentially present in recombinant therapeutic proteins, such as antibodies, is discussed along with their potentially deleterious effects on the health of patients on the one hand, and on the stability of these biodrugs on the other hand. Various additional applications of medical interest are disclosed for biological fluids investigations where the target proteins are present at very low concentrations (e.g., protein allergens).

Long non-coding RNAs reveal new regulatory mechanisms controlling gene expression

A plethora of non-coding RNAs have been found in eukaryotes, notably with the advent of modern sequencing technologies to analyze the transcriptome. Apart from the well-known housekeeping RNA genes (such as the ribosomal RNA or the transfer RNA), many thousands of transcripts detected are not evidently linked to a protein-coding gene. These, so called non-coding RNAs, may code for crucial regulators of gene expression, the small si/miRNAs, for small peptides (translated under specific conditions) or may act as long RNA molecules (antisense, intronic or intergenic long non-coding RNAs or lncRNAs). The lncRNAs interact with members of multiple machineries involved in gene regulation. In this review, we discussed about how plant lncRNAs permitted to discover new regulatory mechanisms acting in epigenetic control, chromatin 3D structure and alternative splicing. These novel regulations diversified the expression patterns and protein variants of target protein-coding genes and are an important element of the response of plants to environmental stresses and their adaptation to changing conditions.

Advances in Aptamer-Based Biomarker Discovery

The discovery and identification of biomarkers promote the rational and fast development of medical diagnosis and therapeutics. Clinically, the application of ideal biomarkers still is limited due to the suboptimal technology in biomarker discovery. Aptamers are single-stranded deoxyribonucleic acid or ribonucleic acid molecules and can selectively bind to varied targets with high affinity and specificity. Compared with antibody, aptamers have desirable advantages, such as flexible design, easy synthesis and convenient modification with different functional groups. Currently, different aptamer-based technologies have been developed to facilitate biomarker discovery, especially CELL-SELEX and SOMAScan technology. CELL-SELEX technology is mainly used to identify cell membrane surface biomarkers of various cells. SOMAScan technology is an unbiased biomarker detection method that can analyze numerous and even thousands of proteins in complex biological samples at the same time. It has now become a large-scale multi-protein biomarker discovery platform. In this review, we introduce the aptamer-based biomarker discovery technologies, and summarize and highlight the discovered emerging biomarkers recently in several diseases.

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

The role of HSP90 molecular chaperones in hepatocellular carcinoma

Misfolded proteins have enhanced formation of toxic oligomers and nonfunctional protein copies lead to recruiting wild-type protein types. Heat shock protein 90 (HSP90) is a molecular chaperone generated by cells that are involved in many cellular functions through regulation of folding and/or localization of large multi-protein complexes as well as client proteins. HSP90 can regulate a number of different cellular processes including cell proliferation, motility, angiogenesis, signal transduction, and adaptation to stress. HSP90 makes the mutated oncoproteins able to avoid misfolding and degradation and permits the malignant transformation. As a result, HSP90 is an important factor in several signaling pathways associated with tumorigenicity, therapy resistance, and inhibiting apoptosis. Clinically, the upregulation of HSP90 expression in hepatocellular carcinoma (HCC) is linked with advanced stages and inappropriate survival in cases suffering from this kind of cancer. The present review comprehensively assesses HSP90 functions and its possible usefulness as a potential diagnostic biomarker and therapeutic option for HCC.

Coding or Noncoding, the Converging Concepts of RNAs

Technological advances over the past decade have unraveled the remarkable complexity of RNA. The identification of small peptides encoded by long non-coding RNAs (lncRNAs) as well as regulatory functions mediated by non-coding regions of mRNAs have further complicated our understanding of the multifaceted functions of RNA. In this review, we summarize current evidence pointing to dual roles of RNA molecules defined by their coding and non-coding potentials. We also discuss how the emerging roles of RNA transform our understanding of gene expression and evolution.

Inducible Loss of the Aryl Hydrocarbon Receptor Activates Perigonadal White Fat Respiration and Brown Fat Thermogenesis via Fibroblast Growth Factor 21

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor highly expressed in hepatocytes. Researchers have employed global and liver-specific conditional Ahr knockout mouse models to characterize the physiological roles of the AHR; however, the gestational timing of AHR loss in these models can complicate efforts to distinguish the direct and indirect effects of post-gestational AHR deficiency. Utilizing a novel tamoxifen-inducible AHR knockout mouse model, we analyzed the effects of hepatocyte-targeted AHR loss in adult mice. The data demonstrate that AHR deficiency significantly reduces weight gain and adiposity, and increases multilocular lipid droplet formation within perigonadal white adipose tissue (gWAT). Protein and mRNA expression of fibroblast growth factor 21 (FGF21), an important hepatokine that activates thermogenesis in brown adipose tissue (BAT) and gWAT, significantly increases upon AHR loss and correlates with a significant increase of BAT and gWAT respiratory capacity. Confirming the role of FGF21 in mediating these effects, this phenotype is reversed in mice concomitantly lacking AHR and FGF21 expression. Chromatin immunoprecipitation analyses suggest that the AHR may constitutively suppress Fgf21 transcription through binding to a newly identified xenobiotic response element within the Fgf21 promoter. The data demonstrate an important AHR-FGF21 regulatory axis that influences adipose biology and may represent a “druggable” therapeutic target for obesity and its related metabolic disorders.

HST-MRM-MS: A Novel High-Sample-Throughput Multiple Reaction Monitoring Mass Spectrometric Method for Multiplex Absolute Quantitation of Hepatocellular Carcinoma Serum Biomarker

Absolute quantification of clinical biomarkers by mass spectrometry (MS) has been challenged due to low sample-throughput of current multiple reaction monitoring (MRM) methods. For this problem to be overcome, in this work, a novel high-sample-throughput multiple reaction monitoring mass spectrometric (HST-MRM-MS) quantification approach is developed to achieve simultaneous quantification of 24 samples. Briefly, triplex dimethyl reagents (L, M, and H) and eight-plex iTRAQ reagents were used to label the N- and C-termini of the Lys C-digested peptides, respectively. The triplex dimethyl labeling produces three coelute peaks in MRM traces, and the iTRAQ labeling produces eight peaks in MS2, resulting in 24 (3×8) channels in a single experiment. HST-MRM-MS has shown good accuracy ( R² > 0.98 for absolute quantification), reproducibility (RSD < 15%), and linearity (2-3 orders of magnitude). Moreover, the novel method has been successfully applied in quantifying serum biomarkers in hepatocellular carcinoma (HCC)-related serum samples. In conclusion, HST-MRM-MS is an accurate, high-sample-throughput, and broadly applicable MS-based absolute quantification method.

Cyclophilin A was revealed as a candidate marker for human oral submucous fibrosis by proteomic analysis

Oral submucous fibrosis (OSF) is a chronic insidious disease which predisposes to oral cancer. Understanding the molecular markers for OSF is critical for diagnosis and treatment of oral cancer. In this study, the proteins expression profile of OSF tissues was compared to normal mucous tissues by 2 dimensional electrophoresis (2-DE). The 2-DE images were analyzed through cut, spot detection and match analysis using mass spectrometry (MS). Differentially expressed genes were identified as candidates. RT-PCR, Western Blot and immunohistochemistry were performed to validate the difference in expression of the candidates between OSF and normal mucous tissues. The shRNA targeted to the candidates were then transfected by Lipofectamine2000 to the 3T3 cells to study gene function. Cell proliferation and apoptosis were measured by MTT, clonogenic formation, PI and TUNEL staining. From the proteomic analysis, 94 of the 182 selected spots with differential expression were identified by MS analysis and Cyclophilin A (CYPA) was determined to be the OSF-associated protein candidate. The significant differences in expression between OSF and normal tissues were verified and confirmed by RT-PCR, Western blot and Immunohistochemical analysis. Inhibition of CYPA expression by RNA interference suggested its potential activities involved in cell proliferation and apoptosis process. In conclusion, these results indicated a novel molecular mechanism of OSF pathogenesis and demonstrated CYPA as a potential biomarker and gene intervention targets of OSF. These data may help the development for therapeutics of oral cancer.

Protein biomarkers for early detection of diseases: The decisive contribution of combinatorial peptide ligand libraries

The present review deals with biomarker discovery, especially in regard to sample treatment via combinatorial peptide ligand libraries, perhaps the only technique at present allowing deep exploration of biological fluids and tissue extracts in search for low- to very-low-abundance proteins, which could possibly mark the onset of most pathologies. Early-stage biomarkers, in fact, might be the only way to detect the beginning of most diseases thus permitting proper intervention and care. The following cancers are reviewed, with lists of potential biomarkers suggested in various reports: hepatocellular carcinoma, ovarian cancer, breast cancer and pancreatic cancer, together with some other interesting applications. Although panels of proteins have been presented, with robust evidence, as potential early-stage biomarkers in these different pathologies, their approval by FDA as novel biomarkers in routine clinical chemistry settings would require plenty of additional work and efforts from the pharma industry. The science environment in universities could simply not afford such heavy monetary investments.

SIGNIFICANCE

After more than 16years of search for novel biomarkers, to be used in a clinical chemistry set-up, via proteomic analysis (mostly in biological fluids) it was felt a critical review was due. In the present report, though, only papers reporting biomarker discovery via combinatorial peptide ligand libraries are listed and assessed, since this methodology seems to be the most advanced one for digging in depth into low-to very-low-abundance proteins, which might represent important biomarkers for the onset of pathologies. In particular, a large survey has been made for the following diseases, since they appear to have a large incidence on human population and/or represent fatal diseases: ovarian cancer, breast cancer, pancreatic cancer and hepatocellular carcinoma.

Quantitative proteomics in lung cancer

Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.

A Timely Shift from Shotgun to Targeted Proteomics and How It Can Be Groundbreaking for Cancer Research

The fact that cancer is a leading cause of death all around the world has naturally sparked major efforts in the pursuit of novel and more efficient biomarkers that could better serve as diagnostic tools, prognostic predictors, or therapeutical targets in the battle against this type of disease. Mass spectrometry-based proteomics has proven itself as a robust and logical alternative to the immuno-based methods that once dominated the field. Nevertheless, intrinsic limitations of classic proteomic approaches such as the natural gap between shotgun discovery-based methods and clinically applicable results have called for the implementation of more direct, hypothesis-based studies such as those made available through targeted approaches, that might be able to streamline biomarker discovery and validation as a means to increase survivability of affected patients. In fact, the paradigm shifting potential of modern targeted proteomics applied to cancer research can be demonstrated by the large number of advancements and increasing examples of new and more useful biomarkers found during the course of this review in different aspects of cancer research. Out of the many studies dedicated to cancer biomarker discovery, we were able to devise some clear trends, such as the fact that breast cancer is the most common type of tumor studied and that most of the research for any given type of cancer is focused on the discovery diagnostic biomarkers, with the exception of those that rely on samples other than plasma and serum, which are generally aimed toward prognostic markers. Interestingly, the most common type of targeted approach is based on stable isotope dilution-selected reaction monitoring protocols for quantification of the target molecules. Overall, this reinforces that notion that targeted proteomics has already started to fulfill its role as a groundbreaking strategy that may enable researchers to catapult the number of viable, effective, and validated biomarkers in cancer clinical practice.

DNA methyltransferase 1, 3a, and 3b expression in hepatitis C associated human hepatocellular carcinoma and their clinicopathological association

Identification of biomarker will obligate a substantial influence on various cancer management and treatment. We hypothesize that genetic/proteomic and epigenetic studies should be uncovering modifications which may be independently or jointly affect the expression of the genes that are involved in the progression of liver cancer (LC). For this purpose, we examined the effect of expressional changes of DNMTs on HCV infected LC of different genotypes. We found that both mRNA and protein expression levels of DNMT1, 3a, and 3b were upregulated in genotype 1b and 3a HCV infected patients as compared to control. However, DNMT3b mRNA levels did not change in genotypes 2a, 3, and 4, but were upregulated at the protein level by genotype 1b, 2a, and 3a. Furthermore, no significant changes were observed for DNMTs investigated in sample expressing the genotypes 5 and 6. Our findings suggest that HCV at least in part by altering DNMTs expression may play a significant role in HCC progression.

Metastatic colorectal cancer in a cirrhotic liver with synchronous hepatocellular carcinoma

We are reporting a case of a patient with a previous history of colorectal cancer (CRC) and cirrhosis, who developed concurrent liver lesions consistent with hepatocellular carcinoma (HCC); a case which is unique due to the low incidence of multiple cancers, particularly HCC in the setting of previous advanced colorectal carcinoma along, in a cirrhotic liver. We will review the known literature on multiple cancer rates found in patients with known colorectal carcinoma. We will then outline this particular patient's presentation, followed by a discussion as to why the particular concurrent development of HCC in the setting of previous CRC is of note.