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At least 667 records · Page 37Linked to original sources
PubMed2005

Testing differential gene expression in functional groups. Goeman's global test versus an ANCOVA approach.

OBJECTIVES: Single genes are not, in general, the primary focus of gene expression experiments. The researcher might be more interested in relevant pathways, functional sets, or genomic regions consisting of several genes. Efficient statistical tools to handle this task are of interest to research of biology and medicine. METHODS: A simultaneous test on phenotype main effect and gene-phenotype interaction in a two-way layout linear model is introduced as a global test on differential expression for gene groups. Its statistical properties are compared with those of the global test for groups of genes by Goeman et al. in a preliminary simulation study. The procedure presented also allows adjusting for covariates. RESULTS: The proposed ANCOVA global test is equivalent to Goeman's global test in a setting of independent genes. In our simulation setting for correlated genes, both tests lose power, however with a stronger loss for Goeman's test. Especially in cases where the asymptotic distribution cannot be used, the stratified use of the ANCOVA global test shows a better performance than Goeman's test. CONCLUSIONS: Our ANCOVA-based approach is a competitive alternative to Goeman's global test in assessing differential gene expression between groups. It can be extended and generalized in several ways by a modification of the projection matrix.

Algorithms↗
PubMed2006

Comparative proteomic analysis of nucleic acid-binding proteins in ten human tumor cell lines.

Failure in regulation of genes involved in growth and division of cells may result in pathological conditions, particularly cancer. Regulation is exerted at various levels of the transcriptional and post-transcriptional processes involving mainly nucleic acid-binding proteins. Here, we systematically explored the proteome of ten different cell lines in search for proteins potentially serving as molecular markers and/or targets for monitoring prognostic outcome and clinical therapies. High-throughput analysis, two-dimensional electrophoresis coupled to matrix-assisted laser desorption/ionization mass spectrometry, identified 72 nucleotide-binding proteins and their interacting partners, which were differentially expressed in the cell lines investigated. Out of the 72 identified proteins, 33 of them were specifically expressed in a single cell line (for e.g., replication protein A 32 kDa, transcription intermediary factor 1, heterogeneous ribonucleoproteins). Moreover, tumor-related proteins including breast carcinoma amplified sequence 2, zinc finger proteins, chromobox protein homologs were identified in individual cell lines. The present findings demonstrate that rich protein information can be obtained by means of proteomic analysis for better understanding of oncogenesis and pathogenesis in a global way, which in turn represents the basis for the rational designs of diagnostic and therapeutic methods.

Biomarkers, Tumor↗
PubMed2006

Characterizing the reproducibility of a protein profiling method for the analysis of mouse bronchoalveolar lavage fluid.

The detection of biomarkers in biological fluids has been advanced by the introduction of mass spectrometry screening methods such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS), which enables the detection of the presence and the molecular mass of proteins in unfractionated mixtures. The generation of reproducible mass spectra over the course of an experiment is vital in obtaining data in which differences in protein profiles between diseased and healthy states can be assessed correctly. We have developed a protocol to automate the collection of protein profiling data from a large number of samples using MALDI-TOFMS, and we used these samples to characterize the technical reproducibility of the method. This protocol has been used for the analysis of proteins found in bronchoalveolar lavage fluid samples from mice with the ultimate goal of enabling the discovery of differential expression patterns predictive of the development of chronic obstructive pulmonary disease. Samples were purified using magnetic bead-based technology and analyzed on an AnchorChip target plate. Our results demonstrate that the number of peaks detected reproducibly decreases significantly as sample size increases, which motivates the need for technical replicates to be explicitly included in the analysis of MALDI-TOF-based protein profiling studies.

Acrolein↗
PubMed2006

Patterns of p73 N-terminal isoform expression and p53 status have prognostic value in gynecological cancers.

The goal of this study was to determine whether patterns of expression profiles of p73 isoforms and of p53 mutational status are useful combinatorial biomarkers for predicting outcome in a gynecological cancer cohort. This is the first such study using matched tumor/normal tissue pairs from each patient. The median follow-up was over two years. The expression of all 5 N-terminal isoforms (TAp73, DeltaNp73, DeltaN'p73, Ex2p73 and Ex2/3p73) was measured by real-time RT-PCR and p53 status was analyzed by immunohistochemistry. TAp73, DeltaNp73 and DeltaN'p73 were significantly upregulated in tumors. Surprisingly, their range of overexpression was age-dependent, with the highest differences delta (tumor-normal) in the youngest age group. Correction of this age effect was important in further survival correlations. We used all 6 variables (five p73 isoform levels plus p53 status) as input into a principal component analysis with Varimax rotation (VrPCA) to filter out noise from non-disease related individual variability of p73 levels. Rationally selected and individually weighted principal components from each patient were then used to train a support vector machine (SVM) algorithm to predict clinical outcome. This SVM algorithm was able to predict correct outcome in 30 of the 35 patients. We use here a mathematical tool for pattern recognition that has been commonly used in e.g. microarray data mining and apply it for the first time in a prognostic model. We find that PCA/SVM is able to test a clinical hypothesis with robust statistics and show that p73 expression profiles and p53 status are useful prognostic biomarkers that differentiate patients with good vs. poor prognosis with gynecological cancers.

Age Factors↗
PubMed2005

A top-down proteomics approach for differentiating thermal resistant strains of Enterobacter sakazakii.

Thermal tolerance has been identified as an important factor relevant to the pathogenicity of Enterobacter sakazakii in human neonates. To identify a biomarker specific for this phenotypic trait, intact protein expression profiles of 12 strains of E. sakazakii were obtained using liquid chromatography mass spectrometry. Proteins were extracted from the bacterial cells, separated by reversed-phase liquid chromatography and mass analyzed. At the end of the chromatography run, the uncharged masses of the multiply charged proteins were determined via automated software routines. The resulting data provided an accurate mass expression profile of the proteins found in the individual strains. From the individual expression profiles, it was possible to identify unique proteins corresponding to strains with thermal resistance. One protein found only in the thermal tolerant strains was sequenced and identified as homologous to a hypothetical protein found in the thermal tolerant bacteria, Methylobacillus flagellatus KT. The protein sequence of this protein was then used to reverse-engineer PCR primers for the gene sequence associated with the protein. In all cases, only thermal tolerant strains of E. sakazakii produced amplified PCR products, demonstrating the specificity of this biomarker.

Amino Acid Sequence↗
PubMed2007

Proteomics profiling of urine with surface enhanced laser desorption/ionization time of flight mass spectrometry.

BACKGROUND: Urine consists of a complex mixture of peptides and proteins and therefore is an interesting source of biomarkers. Because of its high throughput capacity SELDI-TOF-MS is a proteomics technology frequently used in biomarker studies. We compared the performance of seven SELDI protein chip types for profiling of urine using standard chip protocols. RESULTS: Performance was assessed by determining the number of detectable peaks and spot to spot variation for the seven array types and two different matrices: SPA and CHCA. A urine sample taken from one healthy volunteer was applied in eight-fold for each chip type/matrix combination. Data were analyzed for total number of detected peaks (S/N > 5). Spot to spot variation was determined by calculating the average CV of peak intensities. In addition, an inventory was made of detectable peaks with each chip and matrix type. Also the redundancy in peaks detected with the different chip/matrix combinations was determined. A total of 425 peaks (136 non-redundant peaks) could be detected when combining the data from the seven chip types and the two matrices. Most peaks were detected with the CM10 chip with CHCA (57 peaks). The Q10 with CHCA (51 peaks), SEND (48 peaks) and CM10 with SPA (48 peaks) also performed well. The CM10 chip with CHCA also has the best reproducibility with an average CV for peak intensity of 13%. CONCLUSION: The combination of SEND, CM10 with CHCA, CM10 with SPA, IMAC-Cu with SPA and H50 with CHCA provides the optimal information from the urine sample with good reproducibility. With this combination a total of 217 peaks (71 non-redundant peaks) can be detected with CV's ranging from 13 to 26%, depending on the chip and matrix type. Overall, CM10 with CHCA is the best performing chip type.

Journal Article↗
PubMed2003

A preliminary analysis of non-small cell lung cancer biomarkers in serum.

OBJECTIVE: To identify potential serum biomarkers that could be used to discriminate lung cancers from normal. METHODS: Proteomic spectra of twenty-eight serum samples from patients with non-small cell lung cancer and twelve from normal individuals were generated by SELDI (Surfaced Enhanced Laser Desorption/Ionization) Mass Spectrometry. Anion-exchange columns were used to fractionate the sera into 6 designated pH groups. Two different types of protein chip arrays, IMAC-Cu and WCX2, were employed. Samples were examined in PBSII Protein Chip Reader (Ciphergen Biosystem Inc) and the discriminatory profiling between cancer and normal samples was analyzed with Biomarker Pattern software. RESULTS: Five distinct potential lung cancer biomarkers with higher sensitivity and specificity were found, with four common biomarkers in both IMAC-Cu and WCX2 chip; the remaining biomarker occurred only in WCX2 chip. Two biomarkers were up-regulated while three biomarkers were down-regulated in the serum samples from patients with non-small cell lung cancer. The sensitivities provided by the individual biomarkers were 75%-96.43% and specificities were 75%-100%. CONCLUSIONS: The preliminary results suggest that serum is a capable resource for detecting specific non-small cell lung cancer biomarkers. SELDI mass spectrometry is a useful tool for the detection and identification of new potential biomarker of non-small cell lung cancer in serum.

Adult↗
PubMed2006

Temporal changes in gene expression in rainbow trout exposed to ethynyl estradiol.

We examined changes in the genomic response during continuous exposure to the xenoestrogen ethynyl estradiol. Isogenic rainbow trout Oncorhynchus mykiss were exposed to nominal concentrations of 100 ng/L ethynyl estradiol (EE2) for a period of 3 weeks. At fixed time points within the exposure, fish were euthanized, livers harvested and RNA extracted. Fluorescently labeled cDNA were generated and hybridized against a commercially available Salmonid array (GRASP project, University of Victoria, Canada) spotted with 16,000 cDNAs. The slides were scanned to measure abundance of a given transcript in each sample relative to controls. Data were analyzed via Genespring (Silicon Genetics) to identify a list of up and down regulated genes, and to determine gene clustering patterns that can be used as "expression signatures". Gene ontology was determined using the annotation available from the GRASP website. Our analysis indicates each exposure time period generated specific gene expression profiles. Changes in gene expression were best understood by grouping genes by their gene expression profiles rather than examining fold change at a particular time point. Many of the genes commonly used as biomarkers of exposure to xenoestrogens were not induced initially and did not have gene expression profiles typical of the majority of genes with altered expression.

Animals↗
PubMed2026

Effects of permissive hypercapnia on intraoperative cerebral oxygenation and early postoperative cognitive function in older patients with fragile brain function during the non-acute phase undergoing laparoscopic colorectal surgery: A randomized controlled trial.

BACKGROUND AND PURPOSE: Older adults with non-acute fragile brain function (NFBF) may be particularly susceptible to perioperative disturbances in cerebral oxygenation and postoperative neurocognitive decline. Permissive hypercapnia (PHC) may enhance cerebral oxygenation, but its effects in this population remain unclear. We examined whether PHC-based ventilation improves intraoperative regional cerebral oxygen saturation (rSO2) and early postoperative cognitive outcomes in older patients with NFBF undergoing elective laparoscopic colorectal surgery. METHODS: In this single-center, single-blind randomized trial, 76 patients were assigned in a 1:1 ratio to PHC-based or conventional ventilation. The primary outcome was the absolute change in rSO2 from baseline (T0) to the end of surgery (T4). Analyses followed the intention-to-treat principle, with prespecified per-protocol sensitivity analysis. Secondary outcomes included intraoperative rSO2 trajectories, cerebral oxygen extraction-related indices, early postoperative cognitive screening, serum neuron-specific enolase and interleukin-6, and safety outcomes. RESULTS: PHC significantly increased rSO2 relative to conventional ventilation (left: adjusted mean difference [aMD] 10.64, 95% CI 8.96-12.33; right: aMD 10.16, 95% CI 8.22-12.11; both P&#xa0;<&#xa0;0.001), with consistent sensitivity results. Repeated-measures analyses showed persistently higher intraoperative rSO2 in the PHC group. Cerebral oxygen extraction-related indices were generally lower with PHC. However, early postoperative cognitive outcomes and serum biomarkers did not differ between groups. Emergence time was modestly longer with PHC, whereas adverse events were comparable. CONCLUSIONS: PHC-based ventilation favorably modified intraoperative cerebral oxygenation and oxygen-extraction profiles but did not translate into detectable early postoperative cognitive or biomarker benefits in older adults with NFBF.

Humans↗
PubMed2006

Time-dependent plasma protein changes in streptozotocin-induced diabetic rats before and after fungal polysaccharide treatments.

Previous studies about protein modulation with chemically induced models of diabetes in animals have yielded conflicting results, in that many investigators have reported different regulation patterns for the same proteins. Therefore, it is reasonable to determine biomarkers for prognosis and diagnosis of diabetes with time profiling for the candidate proteins. In this regard, we examined the influence of hypoglycemic fungal polysaccharides (EPS) on the time-dependent plasma protein alterations in streptozotocin-induced diabetic rats. The 2-DE analysis of rat plasma demonstrated that about 50 proteins from about 900 visualized spots were found to be differentially regulated, of which 20 spots were identified as principal diabetes-associated proteins. The results of time profiling revealed that most of the identified proteins showed significant alterations in a time-dependent manner during 14 days, with notable trends. Nine out of the twenty proteins displayed very similar time profiles between normal healthy and EPS-treated diabetic rats. Interestingly, the altered profiles of several proteins by diabetes induction almost returned to control levels after EPS treatments. In particular, we found a clear distinction in differential expression of oxidative stress proteins (ceruloplasmin and transferrin) and lipid metabolism related proteins (Apo A-I, Apo A-IV, and Apo E) in the STZ-induced diabetic rats. The data presented here have identified and characterized the time-dependent changes in plasma proteins associated with EPS treatment in STZ-induced diabetic rats, thereby leading to the discovery of early-response and late-response biomarkers in diabetic and EPS-treated states.

Animals↗
PubMed2024

Identification and analysis of key genes related to efferocytosis in colorectal cancer.

UNLABELLED: The impact of efferocytosis-related genes (ERGs) on the diagnosis of colorectal cancer (CRC) remains unclear. In this study, efferocytosis-associated biomarkers for the diagnosis of CRC were identified by integrating data from transcriptome sequencing and public databases. Finally, the expression of biomarkers was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Our study may provide a reference for CRC diagnosis. BACKGROUND: It has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC). METHODS: In our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS: There were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187. CONCLUSION: Taken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.

Humans↗
PubMed2025

Identification of potential biomarkers and mechanisms for keloid disorder based on comprehensive bioinformatics analysis and machine learning algorithms.

BACKGROUND: Keloid disorder (KD) encompasses a spectrum of fibroproliferative dermal conditions, the pathogenesis remains complex and incompletely understood. This study sought to identify biomarkers and potential therapeutic targets for KD through an integrative bioinformatics approach and machine learning analysis of RNA sequencing data. METHODS: RNA sequencing was performed on skin tissue samples from 13 patients with KD and 14 healthy controls. Using weighted gene co-expression network analysis and differential expression analysis revealed differentially expressed key module genes, and the CytoHubba plugin identified candidate genes. Subsequently analyzed using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) methods to pinpoint feature genes associated with KD. Following this, biomarkers were determined through expression level validation, enrichment analysis, and immune infiltration analysis. RESULTS: A total of 420 differentially expressed key module genes were identified, and the top 10 genes with DMNC values were selected as candidate genes. Five feature genes were selected through LASSO and SVM-RFE, with NID2, MFAP2, COL8A1, and P4HA3 showing significant expression differences between KD and control samples, along with consistent expression patterns across datasets, identified as potential biomarkers. These four biomarkers were proved to possess high diagnostic potential, and they were found to exhibit significant positive correlations with one another. Functional enrichment analysis indicated that the primary KEGG pathways associated with these biomarkers included "steroid hormone biosynthesis" and "cytokine-cytokine receptor interaction." Moreover, immune infiltration analysis revealed that the four biomarkers were negatively correlated with type 17 T helper cells and positively correlated with 15 immune cell types, including activated B cells and central memory CD4 T cells. CONCLUSION: In conclusion, NID2, MFAP2, COL8A1, and P4HA3 were identified as key biomarkers for KD, offering new avenues for more targeted and effective diagnostic and therapeutic strategies for managing this condition.

Humans↗
PubMed2006

ZD6474 inhibits tumor growth and intraperitoneal dissemination in a highly metastatic orthotopic gastric cancer model.

Angiogenesis inhibitors have been used to treat some cancers, but the therapeutic potential of these agents for gastric cancer has remained unclear. To investigate their therapeutic potential, we examined the effect of ZD6474, an agent that selectively targets vascular endothelial growth factor receptor-2 (VEGFR-2; KDR) tyrosine kinase and epidermal growth factor receptor (EGFR) tyrosine kinase, in a highly metastatic orthotopic model using an undifferentiated gastric cancer cell line, 58As1. ZD6474 (100 mg/kg/day, p.o., 2 weeks) significantly inhibited tumor growth (p < 0.05 vs. control) and reduced tumor dissemination into the peritoneal cavity (p < 0.05 vs. control). In addition, to identify putative tumor biomarkers that would reflect the effects of ZD6474 treatment in clinical settings, we examined the gene expression profiles of implanted gastric tumors treated with ZD6474 in vivo. Twenty-eight candidate genes were identified, including IGFBP-3, ADM, ANGPTL4, PLOD2, DSIPI, NDRG1, ENO2, HIG2 and BNIP3L, which are known to be hypoxia-inducible genes. These genes and gene products may be useful biomarkers for monitoring the effects of ZD6474 treatment. ZD6474 also improved the survival of mice with implanted another undifferentiated gastric cancer cell line, 44As3. In conclusion, our results suggest that ZD6474 may have clinical activity against gastric cancer, particularly undifferentiated gastric cancer with peritoneal dissemination. We also identified putative biomarkers for monitoring the pharmacodynamic effects of ZD6474 by gene expression profiling.

Biomarkers, Tumor↗
PubMed2025

Epstein-Barr Virus-Associated Gastric Cancer: A Histopathologic Study With Comprehensive Molecular Profiling.

A subset of gastric cancers (GCs) is linked to Epstein-Barr virus (EBV) infection. This study aims to characterize the histopathological and molecular features of EBV-associated GCs (EBVaGCs), focusing on predictive biomarkers and genomic and transcriptomic analysis. A total of 35 primary EBVaGCs were considered. The presence of EBV was confirmed with in situ hybridization. Immunohistochemical analyses for HER2, PD-L1, claudin 18.2, and mismatch repair proteins were performed. Genomic and transcriptomic profiles were assessed using AmoyDx Master Panel, which can identify single-nucleotide variants, InDels, and copy number variations on 571 hot genes, as well as microsatellite status, tumor molecular burden, and homologous recombination deficiency at the DNA level; however, at the RNA level, it identifies rearrangements/fusions in 45 genes and also quantifies the expression of 2396 cancer-related transcripts. The following histotypes were identified: carcinoma with lymphoid stroma (CLS; 69%), tubular (20%), and mixed (11%). Most cases were associated with atrophic gastritis (71%), and only 11% with dysplasia. The vast majority (94%) of EBVaGCs expressed EBV-encoded RNA in all tumor cells. Mismatch repair deficiency and HER2 overexpression were each observed in 6% of cases, whereas all tumors had a PD-L1-combined positive score &#x2265;10. Sixty-six percent of cases showed moderate/strong claudin 18.2 expression in &#x2265;75% of cancer cells. The most frequently altered genes were PIK3CA (41%) and ARID1A (17%). Transcriptomic analysis revealed substantial differential gene expression between EBVaGCs and EBV-negative controls, with upregulation of genes involved in antigen presentation, natural killer cell-mediated cytotoxicity, and cytokine-cytokine receptor interaction in EBVaGCs. Within EBVaGC, CLS showed higher expression of immune-related transcripts and higher PD-L1 expression than other histotypes. This study establishes EBVaGC as a distinct molecular class, with a distinctive profile of genomic alterations and expression of predictive biomarkers, and also with a unique immune microenvironment with enhanced cytotoxic activity. The findings highlight EBV's role in early tumor development and EBVaG-CLS as a distinct subgroup within EBVaGC, characterized by unique morphologic features and a pronounced immune activation profile.

Humans↗
PubMed2026

Cell-free DNA methylation biomarkers for the early detection and tumor burden monitoring of gastric cancer.

Development of sensitive biomarkers is required to achieve early detection and tumor burden monitoring in gastric cancer (GC). We performed genome-wide methylation sequencing on 78 tissue and 241 plasma samples from 171 GC patients and 114 healthy controls from two independent clinical centers. Differentially methylated regions (DMRs) were screened using paired GC and normal tissues, and refined through cfDNA profiles with LASSO regression to construct a cfDNA-based biomarker, the GCML-score. The GCML-score, consisting of 13 DMRs, demonstrated excellent diagnostic performance (AUC: 0.95/0.99/0.95 overall and 0.96/0.99/0.82 in early GC for training/internal validation/external validation cohorts). In 12 patients receiving neoadjuvant chemotherapy, dynamic changes in GCML-score were consistent with radiological tumor burden, highlighting its monitoring potential. The GCML-score, derived from genome-wide cfDNA methylation profiling, provides a robust tool for early GC detection and real-time tumor burden monitoring, facilitating improved prognosis and personalized therapeutic strategies.

Journal Article↗
PubMedDate not supplied

Clinical cancer proteomics: promises and pitfalls.

Proteome analysis promises to be valuable for the identification of tissue and serum biomarkers associated with human malignancies. In addition, proteome technologies offer the opportunity to analyze protein expression profiles and to analyze the activity of signaling pathways. Many published proteomic studies of human tumor tissue are associated with weaknesses in tumor representativity, sample contamination by nontumor cells and serum proteins. Studies often include a moderate number of tumors which may not be representative of clinical materials. It is therefore very important that biomarkers identified by proteomics are validated in representative tumor materials by other techniques, such as immunohistochemistry. Proteome technologies can be used to identify disease markers in human serum. Tumor derived proteins are present at nanomolar to picomolar concentrations in cancer patient sera, 10(6)-10(9)-fold lower than albumin, and will give rise to correspondingly smaller spots/peaks in protein separations. This leads to the need to prefractionate serum samples before analysis. Despite various pitfalls, proteomic analysis is a promising approach to the identification of biomarkers, and for generation of protein expression profiles that can be analyzed by artificial learning methods for improved diagnosis of human malignancy. Recent advances in the field of proteomic analysis of human tumors are summarized in the present review.

Biomarkers, Tumor↗
PubMed2005

Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery.

For most cancers, survival rates depend on the early detection of the disease. So far, no biomarkers exist to cope with this difficult task. New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples and/or of the setting up of new clinically relevant test systems. Novel mass spectrometry-(MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-ToF-MS), have shown promising results in the recent literature. Here, proteomic profiles of control and disease states are compared to find biomarkers for diagnosis. This paper aims to address the authors' own work and that of other groups in clinical cancer proteomics based on SELDI-ToF-MS. Shortcomings and hopes for the future are discussed.

Biomarkers, Tumor↗