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PubMed2003

Proteomic profiling of heat shock protein 70 family members as biomarkers for hepatitis C virus-related hepatocellular carcinoma.

To identify proteins linked to the pathogenesis of hepatocellular carcinoma (HCC) associated with hepatitis C virus (HCV), we profiled protein expression levels in samples of HCC. To identify essential proteins, ten samples of HCV-related HCC were analyzed by two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry. These experiments revealed increased levels of nine proteins in cancerous tissues compared to levels in corresponding noncancerous liver tissues. We focused on four members of the heat shock protein 70 family: 78 kDa glucose-regulated protein (GRP78), heat shock cognate 71 kDa protein (HSC70), 75 kDa glucose-regulated protein (GRP75), and heat shock 70 kDa protein 1 (HSP70.1). These results were confirmed by immunoblot analysis. In an additional 11 samples, the same expression patterns of these four proteins were observed. In total, 21 samples showed statistically significant up-regulation of GRP78, GRP75 and HSP70.1 in cancerous tissues. HSC70 showed a tendency toward overexpression. There has been no report describing overexpression of these four proteins simultaneously in HBV-related HCC as well as nonviral HCC. Our results suggest that these four proteins play important roles in the pathogenesis of HCV-related HCC and could be molecular targets for diagnosis and treatment of this disease.

Biomarkers, Tumor↗
PubMed2006

Serial changes in urinary proteome profile of membranous nephropathy: implications for pathophysiology and biomarker discovery.

Membranous nephropathy is one of the most common causes of primary glomerular diseases worldwide. The present study adopted a gel-based proteomics approach to better understand the pathophysiology and define biomarker candidates of human membranous nephropathy using an animal model of passive Heymann nephritis (PHN). Clinical characteristics of Sprague-Dawley rats injected with rabbit anti-Fx1A antiserum mimicked those of human membranous nephropathy. Serial urine samples were collected at Days 0, 10, 20, 30, 40, and 50 after the injection with anti-Fx1A (number of rats = 6; total number of gels = 36). Urinary proteome profiles were examined using 2D-PAGE and SYPRO Ruby staining. Quantitative intensity analysis and ANOVA with Tukey post-hoc multiple comparisons revealed 37 differentially expressed proteins among 6 different time-points. These altered proteins were successfully identified by MALDI-TOF MS and classified into 6 categories: (i) proteins with decreased urinary excretion during PHN; (ii) proteins with increased urinary excretion during PHN; (iii) proteins with increased urinary excretion during PHN, but which finally returned to basal levels; (iv) proteins with increased urinary excretion during PHN, but which finally declined below basal levels; (v) proteins with undetectable levels in the urine during PHN; and (vi) proteins that were detectable in the urine only during PHN. Most of these altered proteins have functional significance in signaling pathways, glomerular trafficking, and controlling the glomerular permeability. The ones in categories (v) and (vi) may serve as biomarkers for detecting or monitoring membranous nephropathy. After normalization of the data with 24-h urine creatinine excretion, changes in 34 of initially 37 differentially expressed proteins remained statistically significant. These data underscore the significant impact of urinary proteomics in unraveling disease pathophysiology and biomarker discovery.

Biomarkers↗
PubMed2004

Gene expression profiling of human colon xenograft tumors following treatment with SU11248, a multitargeted tyrosine kinase inhibitor.

Biomarkers that indicate biological activity and/or efficacy are a potentially useful tool in the development of molecularly targeted therapeutics. It is useful, though challenging, to identify biomarkers during preclinical development in order to impact decision-making during early clinical development. SU11248 is an oral, selective multitargeted tyrosine kinase inhibitor currently in Phase II oncology clinical trials. It exhibits direct antitumor and antiangiogenic activity via inhibition of the receptor tyrosine kinases PDGFR, VEGFR, KIT and FLT3. To identify clinically translatable biomarkers of SU11248 activity, expression profiling was performed on Colo205 human xenograft tumors following treatment with SU11248. Over 100 transcripts changed in abundance in SU11248 as compared to vehicle-treated tumors. Nine candidate transcripts, chosen based on putative function, were also analysed and validated by TaqMan. One such potential biomarker, cadherin-11, was further evaluated at the protein level and was found to have increased expression in xenograft tumors after SU11248 treatment. Interestingly, cadherin-11 expression was also detected via immunohistochemical analysis of archived solid tumors, indicating the technical feasibility of translating this putative biomarker to clinical studies. Importantly, SU11248 treatment also resulted in increased expression of cadherin-11 protein in human tumor biopsies in three out of seven patients examined and confirms the feasibility of using transcriptional profiling of preclinical models to identify clinically translatable biomarkers.

Animals↗
PubMed2002

Cysteine-rich secretory protein-3: a potential biomarker for prostate cancer.

Electronic profiling of publicly available expressed sequence tag databases identified a gene, cysteine-rich secretoryprotein-3 (CRISP-3), that is up-regulated in prostate cancer, and of which the expression is relatively prostate-specific. The objective of this study was to examine the potential of CRISP-3 as a biomarker for prostate cancer. In transient transfection studies, CRISP-3 was found to be a secretory protein. Using a multiple tissue dot blot experiment, CRISP-3 transcript was identified in a limited number of human tissues including the prostate. In situ hybridization experiments indicated that CRISP-3 mRNA is epithelial-specific and is up-regulated in prostate adenocarcinoma compared with benign prostate tissue. CRISP-3 mRNA overexpression in cancer was confirmed using quantitative real-time reverse-transcription-PCR using benign prostatic epithelia and adenocarcinoma (in 5 of 5 cases) isolated by laser capture microdissection, as well as bulk tissues (in 20 of 23 cases) from surgically resected human prostates. These findings suggest that CRISP-3 is a potential biomarker for prostate cancer.

Adenocarcinoma↗
PubMed2003

High-throughput proteomics and protein biomarker discovery in an experimental model of inflammatory hyperalgesia: effects of nimesulide.

OBJECTIVE: The goal of this study was to derive a systematic approach for the identification of protein species profiles that selectively identify subgroups of similar but distinct pain models and correlate with the therapeutic efficacy (or lack thereof) of drug intervention. METHODS: Using high-throughput surface-enhanced laser desorption ionization (SELDI) mass spectrometry proteomic technology based on ProteinChip arrays, we conducted a comparative analysis of the profile of protein expression in cerebral spinal fluid (CSF) from rats exposed to either injection of complete Freund's adjuvant (CFA) [a model of inflammatory pain] or chronic constriction injury (CCI) [a model of neuropathic pain]. The CFA model was then further studied for the effects of treatment with the NSAID, nimesulide (a preferential cyclo-oxygenase [COX]-2 inhibitor). RESULTS: Among other observations, we found that the content of two metal (copper) binding protein species (2.9 and 3.2kDa) and three anionic protein species (4.0, 6.9 and 8.2kDa) were increased in the CSF of rats with inflammatory pain in a time-dependent fashion, at 7 and 14 days after CFA injection. These changes were highly selective for the CFA model, as no detectable increase in these protein biomarkers was found in the CCI neuropathic pain model. Further, we found that most of the changes in the biomarker protein species induced by the inflammatory pain were prevented by treatment with nimesulide and correlated with the antihyperalgesic effect of this drug. CONCLUSION: This study demonstrates that CSF biomarker profiles, as detected by SELDI technology, can consistently and reproducibly differentiate inflammatory from neuropathic pain, and reflect the analgesic action produced by the preferential COX-2 inhibitor, nimesulide. The characterisation and identification of these biomarkers will provide invaluable insight into the pathophysiology of pain mechanisms, in addition to further understanding of the value of nimesulide in the treatment of inflammatory pain.

Analysis of Variance↗
PubMed2006

Genomics and proteomics of bone cancer.

Although the control of bone metastasis has been the focus of intensive investigation, relatively little is known about the molecular mechanisms that regulate or predict the process, even though widespread skeletal dissemination is an important step in the progression of many tumors. As a result, understanding the complex interactions contributing to the metastatic behavior of tumor cells is essential for the development of effective therapies. Using a state-of-the-art combination of gene expression profiling and functional annotation of human tumor cells, and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry of patient serum, we have shown that changes in tumor biochemistry correlate with disease progression and help to define the aggressive tumor phenotype. Based on these approaches, it is apparent that the metastatic phenotype of tumor cells is extremely complex. The identification of the phenotype of tumor cells has benefited greatly from the application of gene expression profiling (microarray analysis). This technology has been used by many investigators to identify changes in gene expression and cytokine and growth factor elaboration (such as interleukin 8). The tumor phenotype(s) presumably also include changes in the cell surface carbohydrate profile (via altered glycosyltransferase expression) and heparan sulfate expression (via increased heparanase activity), to name but a few. These specific alterations in gene expression, identified by functional annotation of accumulated microarray data, have been validated using a variety of approaches. Collectively, the data described here suggest that each of these activities is associated with distinct aspects of the aggressive tumor cell phenotype. Collectively, the data suggest that multiple factors constitute the complex phenotype of metastatic tumor cells. In particular, the differences observed in gene expression profiles and serum protein biomarkers play a critical role in defining the mechanisms responsible for bone-specific colonization and growth of tumors in bone. Future studies will identify the mechanisms that participate in the formation of secondary tumor growths of cancers in bone.

Biomarkers, Tumor↗
PubMed2005

Identification of CD70 as a diagnostic biomarker for clear cell renal cell carcinoma by gene expression profiling, real-time RT-PCR and immunohistochemistry.

The underlying molecular mechanisms of renal cell carcinoma (RCC) are poorly understood and more reliable markers for early diagnosis are needed. Hence, alternative strategies for biomarker discovery with appropriate validation technologies have to be performed. To elucidate genesis and progression of RCC we used high parallel chip based gene expression profiling comparing normal and tumour tissues. We compared corresponding control and tumour tissue samples from 10 patients with clear cell RCC. We isolated RNA from histologically well characterised tissue sections and performed reverse transcription, labelling and linear RNA amplification. Samples were hybridised on microarrays containing 642 human cDNAs. Of the 352 differentially expressed genes found, CD70 and FRA2 were selected for further evaluation by real-time RT-PCR. The analysis all showed a high potential to discriminate between normal and tumour tissue. Moreover, increased CD70 mRNA expression in tumour cells could be correlated to its expression at the protein level. Immunohistochemistry (IHC) showed very strong expression of CD70 in all tumour samples but no expression in adjacent normal kidney tissue. With our combined approach we were able to identify CD70 as a new marker for RCC, which may be useful in the future for improved immunohistochemical diagnosis.

Antigens, CD↗
PubMed2006

Gene expression signatures and biomarkers of noninvasive and invasive breast cancer cells: comprehensive profiles by representational difference analysis, microarrays and proteomics.

We have characterized comprehensive transcript and proteomic profiles of cell lines corresponding to normal breast (MCF10A), noninvasive breast cancer (MCF7) and invasive breast cancer (MDA-MB-231). The transcript profiles were first analysed by a modified protocol for representational difference analysis (RDA) of cDNAs between MCF7 and MDA-MB-231 cells. The majority of genes identified by RDA showed nearly complete concordance with microarray results, and also led to the identification of some differentially expressed genes such as lysyl oxidase, copper transporter ATP7A, EphB6, RUNX2 and a variant of RUNX2. The altered transcripts identified by microarray analysis were involved in cell-cell or cell-matrix interaction, Rho signaling, calcium homeostasis and copper-binding/sensitive activities. A set of nine genes that included GPCR11, cadherin 11, annexin A1, vimentin, lactate dehydrogenase B (upregulated in MDA-MB-231) and GREB1, S100A8, amyloid beta precursor protein, claudin 3 and cadherin 1 (downregulated in MDA-MB-231) were sufficient to distinguish MDA-MB-231 from MCF7 cells. The downregulation of a set of transcripts for proteins involved in cell-cell interaction indicated these transcripts as potential markers for invasiveness that can be detected by methylation-specific PCR. The proteomic profiles indicated altered abundance of fewer proteins as compared to transcript profiles. Antisense knockdown of selected transcripts led to inhibition of cell proliferation that was accompanied by altered proteomic profiles. The proteomic profiles of antisense transfectants suggest the involvement of peptidyl-prolyl isomerase, Raf kinase inhibitor and 80 kDa protein kinase C substrate in mediating the inhibition of cell proliferation.

Biomarkers, Tumor↗
PubMed2026

Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.

MOTIVATION: Large scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding. RESULTS: To facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine-a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex 'cancer profiles' built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency. AVAILABILITY: The Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601.

Humans↗
PubMed2005

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling.

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.

Allergens↗
PubMed2007

Ovarian cancer biomarkers: a focus on genomic and proteomic findings.

Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers; several studies, analyzing clinical data and pathological features on ovarian cancers, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomarker discovery, but their validity should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management.Therefore, considerable interest lies in identifying molecular prognostic biomarkers and protein indicators to guide treatment decisions and clinical follow up; the current state of knowledge about the potential clinical value of gene expression profiling in ovarian cancer is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian tumors, identifying different subtypes of ovarian cancer and identifying cancer likely to be responsive to therapy.In this elaborate we discuss the use of novel molecules, discovered by proteomics and genomics approaches, as potential protein biomarkers in the management of ovarian cancer, to improve the anticancer therapy for malignant ovarian tumors and to monitor the clinical follow up.

Ovarian cancer↗
PubMed2004

Computational protein biomarker prediction: a case study for prostate cancer.

BACKGROUND: Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. RESULTS: Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. CONCLUSIONS: Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.

Biomarkers, Tumor↗
PubMed2026

MULTIPREVENT: Integrated screening for smoking-related multimorbidity using low-dose chest computed tomography.

OBJECTIVES: Tobacco consumption, combined with individual genetic predispositions, contributes to an age-dependent risk not only for lung cancer but also for other non-communicable diseases (NCDs) such as cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), osteoporosis, and diabetes. The MULTIPREVENT project aims to validate whether low-dose computed tomography (LDCT) of the chest, combined with simple biomarkers, functional tests, and genomic profiling, can serve as an effective tool for comprehensive health assessment and risk prediction of multimorbidity in adults. STUDY DESIGN: The study is based on a prospective epidemiological design involving 3000 participants from the MOLTEST-BIS lung cancer screening cohort (2016-2018). These participants, aged 50-79 years (during MOLTEST-BIS) and with a smoking history of at least 30 pack-years, will undergo two follow-up assessments in 2025-2027 and 2030-2032. METHODS: Each follow-up includes LDCT, spirometry, standardized blood pressure measurement, anthropometric evaluation, biomarker assessment (lipid profile, lipoprotein(a), glycated haemoglobin), and health-related questionnaires. Genetic profiling will be performed using the Illumina Infinium Global Screening Arrays approach to identify inherited predispositions to major NCDs. All data, clinical, imaging (including radiomics), molecular, and genetic, will be integrated through machine learning algorithms to develop AI-based risk prediction models. RESULTS: The MULTIPREVENT study is expected to generate a wide range of scientific, clinical, and infrastructural results that will serve as a foundation for future public health initiatives in integrated prevention. CONCLUSIONS: By linking imaging and biochemical markers, genetic susceptibility, and clinical parameters within a longitudinal design, MULTIPREVENT will establish data-driven, AI-supported prevention strategies aimed at reducing morbidity and mortality among adults exposed to tobacco. The project will also serve as a model for population-based multimorbidity prevention programs.

Humans↗
PubMed2009

An outlook on ovarian cancer and borderline ovarian tumors: focus on genomic and proteomic findings.

Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers. Ovarian tumors can be classified on the basis of the cells of origin in epithelial, stromal and germ cell tumors. Epithelial ovarian tumors display great histological heterogeneity and can be further subdivided into benign, intermediate or borderline, and invasive tumors. Several studies on ovarian tumors, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomolecular study and genomic discovery; unfortunately, validity of these should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management. Therefore, considerable interest lies in identifying molecular and protein biomarkers and indicators to guide treatment decisions and clinical follow up. In this review, the current state of knowledge about the genoproteomic and potential clinical value of gene expression profiling in ovarian cancer and ovarian borderline tumors is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian cancers and borderline tumors, identifying different genotypes of ovarian tissue and identifying proteins linked to cancer or tumor development. By these targets, authors focus on the use of novel molecules, developed on the proteomics and genomics researches, as potential protein biomarkers in the management of ovarian cancer or borderline tumor, overlooking on current state of the art and on future perspectives of researches.

Ovarian cancer↗
PubMed2005

Identification of biomarkers for tumor endothelial cell proliferation through gene expression profiling.

Extensive efforts are under way to identify antiangiogenic therapies for the treatment of human cancers. Many proposed therapeutics target vascular endothelial growth factor (VEGF) or the kinase insert domain receptor (KDR/VEGF receptor-2/FLK-1), the mitogenic VEGF receptor tyrosine kinase expressed by endothelial cells. Inhibition of KDR catalytic activity blocks tumor neoangiogenesis, reduces vascular permeability, and, in animal models, inhibits tumor growth and metastasis. Using a gene expression profiling strategy in rat tumor models, we identified a set of six genes that are selectively overexpressed in tumor endothelial cells relative to tumor cells and whose pattern of expression correlates with the rate of tumor endothelial cell proliferation. In addition to being potential targets for antiangiogenesis tumor therapy, the expression patterns of these genes or their protein products may aid the development of pharmacodynamic assays for small molecule inhibitors of the KDR kinase in human tumors.

Angiogenesis Inhibitors↗
PubMed2024

Use of extracellular vesicle microRNA profiles in patients with acute myeloid leukemia for the identification of novel biomarkers.

OBJECTIVES: This study aimed to establish clinically significant microRNA (miRNA) sets using extracellular vesicles (EVs) from bone marrow (BM) aspirates of patients with acute myelogenous leukemia (AML), and to identify the genes that interact with these EV-derived miRNAs in AML. MATERIALS AND METHODS: BM aspirates were collected from 32 patients with AML at the time of AML diagnosis. EVs were isolated using size-exclusion chromatography. A total of 965 EV-derived miRNAs were identified in all the samples. RESULTS: We analyzed the expression levels of these EV-derived miRNAs of the favorable (n = 10) and non-favorable (n = 22) risk groups; we identified 32 differentially expressed EV-derived miRNAs in the non-favorable risk group. The correlation of these miRNAs with risk stratification and patient survival was analyzed using the information of patients with AML from The Cancer Genome Atlas (TCGA) database. Of the miRNAs with downregulated expression in the non-favorable risk group, hsa-miR-181b and hsa-miR-143 were correlated with non-favorable risk and short overall survival. Regarding the miRNAs with upregulated expression in the non-favorable risk group, hsa-miR-188 and hsa-miR-501 were correlated with non-favorable risk and could predict poor survival. Through EV-derived miRNAs-mRNA network analysis using TCGA database, we identified 21 mRNAs that could be potential poor prognosis biomarkers. CONCLUSIONS: Overall, our findings revealed that EV-derived miRNAs can serve as biomarkers for risk stratification and prognosis in AML. In addition, these EV-derived miRNA-based bioinformatic analyses could help efficiently identify mRNAs with biomarker potential, similar to the previous cell-based approach.

Humans↗
PubMed2026

Urinary multi-omics reveal non-invasive diagnostic biomarkers in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy. Yet, no rapid, non-invasive biomarkers are available for diagnosis or screening. Urine represents an ideal analyte matrix due to its accessibility, low invasiveness, longitudinal sampling, and the kidney's central role in filtration. Here, we integrated proteomic, lipidomic, and metabolomic analyses of urine from ccRCC patients and controls to identify diagnostic biomarkers. Multi-omics profiling revealed urogenital metabolic dysregulation in ccRCC, including increased lipid metabolism, altered mitochondrial respiration signatures, and elevated urinary lipid content. We identified three urinary protein biomarkers: serum amyloid A1 (SAA1), haptoglobin (HP), and lipocalin 15 (LCN15). Using a parallel reaction monitoring mass spectrometry workflow, we developed a rapid and sensitive assay and combined these markers into a diagnostic UrineScore. The UrineScore achieved 0.96 in an area under the receiver operating characteristic curve analysis in the discovery cohort, and 0.95 in an independent validation cohort. Together, these results support the feasibility of multi-omics-guided urinary biomarker discovery and represent a step toward accessible diagnostic platforms for ccRCC.

Humans↗