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PubMedDate not supplied

Comparative proteomics analysis of human pituitary adenomas: current status and future perspectives.

This article will review the published research on the elucidation of the mechanisms of pituitary adenoma formation. Mass spectrometry (MS) plays a key role in those studies. Comparative proteomics has been used with the long-term goal to locate, detect, and characterize the differentially expressed proteins (DEPs) in human pituitary adenomas; to identify tumor-related and -specific biomarkers; and to clarify the basic molecular mechanisms of pituitary adenoma formation. The methodology used for comparative proteomics, the current status of human pituitary proteomics studies, and future perspectives are reviewed. The methodologies that are used in comparative proteomics studies of human pituitary adenomas are readily exportable to other different areas of cancer research.

Adenoma↗
PubMed2004

Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents.

In this paper we try to identify potential biomarkers for early stroke diagnosis using surface-enhanced laser desorption/ionization mass spectrometry coupled with analysis tools from machine learning and data mining. Data consist of 42 specimen samples, i.e., mass spectra divided in two big categories, stroke and control specimens. Among the stroke specimens two further categories exist that correspond to ischemic and hemorrhagic stroke; in this paper we limit our data analysis to discriminating between control and stroke specimens. We performed two suites of experiments. In the first one we simply applied a number of different machine learning algorithms; in the second one we have chosen the best performing algorithm as it was determined from the first phase and coupled it with a number of different feature selection methods. The reason for this was 2-fold, first to establish whether feature selection can indeed improve performance, which in our case it did not seem to confirm, but more importantly to acquire a small list of potentially interesting biomarkers. Of the different methods explored the most promising one was support vector machines which gave us high levels of sensitivity and specificity. Finally, by analyzing the models constructed by support vector machines we produced a small set of 13 features that could be used as potential biomarkers, and which exhibited good performance both in terms of sensitivity, specificity and model stability.

Adult↗
PubMed2004

Gene expression imaging with radiolabeled peptides.

An approach to image radiolabeled peptide localization at tumor sites by inducing tumor cells to synthesize membrane expressed human somatostatin receptor subtype 2 (hSSTr2) with a high affinity for radiolabeled somatostatin analogues is described. The use of gene transfer technology to induce expression of high affinity membrane hSSTr2 can enhance the specificity and degree of radiolabeled peptide localization in tumors. Employing this strategy, induction of high levels of hSSTr2 expression with selective tumor uptake of radiolabeled peptides was achieved in both subcutaneous non-small cell lung cancer and intraperitoneal ovarian cancer mouse human tumor xenograft models. The features of this genetic transduction imaging approach are: (1) constitutive expression of a tumor-associated receptor is not required; (2) tumor cells are altered to express a new target receptor or increased quantities of a constitutive receptor at levels which may significantly increase tumor targeting of radiolabeled peptides compared to uptake in normal tissues; (3) gene transfer can be accomplished by local or regional injection of adenoviral vectors; (4) it is feasible to target adenovirus vectors to tumor cells by modifying adenoviral tropism (binding) or by the use of tumor-specific promoters such that the hSSTr2 will be specifically expressed in the desired tumor; and (5) this technique can be used to image expression of a second therapeutic gene.

Animals↗
PubMed2006

Modification of gene expression of the small airway epithelium in response to cigarette smoking.

The earliest morphologic evidence of changes in the airways associated with chronic cigarette smoking is in the small airways. To help understand how smoking modifies small airway structure and function, we developed a strategy using fiberoptic bronchoscopy and brushing to sample the human small airway (10th-12th order) bronchial epithelium to assess gene expression (Affymetrix HG-U133A and HG-133 Plus 2.0 array) in phenotypically normal smokers (n = 16, 25 +/- 7 pack-years) compared to matched nonsmokers (n = 17). Compared to samples from large (second to third order) bronchi, the small airway samples had a higher proportion of ciliated cells, but less basal, undifferentiated, and secretory cells, and contained Clara cells. Even though the smokers were phenotypically normal, microarray analysis of gene expression of the small airway epithelium of the smokers compared to the nonsmokers demonstrated up- and downregulation of genes in multiple categories relevant to the pathogenesis of chronic obstructive lung disease (COPD), including genes coding for cytokines/innate immunity, apoptosis, mucin, response to oxidants and xenobiotics, and general cellular processes. In the context that COPD starts in the small airways, these gene expression changes in the small airway epithelium in phenotypically normal smokers are candidates for the development of therapeutic strategies to prevent the onset of COPD.

Biomarkers↗
PubMed2025

CD40 transcriptomic expression patterns across malignancies: implications for clinical trials of CD40 agonists.

BACKGROUND: CD40 is a T-cell co-stimulatory receptor targeted by next-generation immunotherapies. We conducted a pan-cancer transcriptome analysis of CD40, its ligand, and related immune markers to evaluate co-expression patterns and clinical outcomes. METHODS: We analyzed transcriptome data for CD40, its ligand, and other common checkpoints and co-stimulators (PD-1, PD-L1, PD-L2, CTLA-4, LAG-3, ICOS, CD27, CD28, OX40, and GITR). RNA expression was classified as high (75-100th percentile), moderate (25-74th), or low (0-24th) against a reference population of 735 previously tested solid tumors. RESULTS: Of 514 patients, 114 (22%) showed high, 247 (48%) moderate, and 153 (30%) low CD40 RNA expression. High CD40 expression was most frequent in liver and bile duct (42%), pancreatic (42%), and ovarian (40%) cancers. Both high CD40 and low-moderate CD40 ligand expression-potentially conducive to CD40 agonist therapy-was most frequent in ovarian (33%) and pancreatic (24%) cancer. In both UCSD (N = 514) and TCGA (N = 10,953) cohorts, high CD40 expression significantly correlated with high CD28 and GITR. High CD40 RNA levels were not prognostic for overall survival (OS) from metastatic disease (P = 0.2) (n = 272 immune checkpoint inhibitor (ICI)-naïve patients). High CD40 expression correlated with longer OS from immunotherapy initiation (n = 217 ICI-treated patients; P = 0.04, univariable analysis), but not multivariable analysis, suggesting it may not be an independent predictive biomarker. CONCLUSION: High CD40 expression correlated with liver and bile duct, pancreatic, and ovarian cancers, as well as with CD28 and GITR transcripts. Immune marker co-expression in individual patients merits further exploration for the development of CD40-based and other immunotherapy interventions.

Humans↗
PubMed2006

Classification of neuroblastoma patients by published gene-expression markers reveals a low sensitivity for unfavorable courses of MYCN non-amplified disease.

Currently, Pubmed lists 385 marker genes for neuroblastoma outcome. Using a customized neuroblastoma-microarray, we evaluated the prognostic impact of the gene-expression pattern of 349 of these candidates (90.6%) in 127 neuroblastoma patients with divergent outcome. By significance analysis of microarrays (SAM) and both uncorrected and Bonferroni-corrected ANOVA, 166/349 (47.5%), 218/349 (62.5%) and 128/349 (36.4%) candidates showed significant differential expression between patients with contrasting outcome. By Prediction Analysis for Microarrays (PAM), a 38-gene-classifier was derived from all markers, which classified patients outcome with an overall accuracy of 78.5%. However, patients with unfavorable outcome of MYCN non-amplified disease were largely misclassified (accuracy: 35%), suggesting that these courses are not identified by current marker genes.

Analysis of Variance↗
PubMed2005

Proteomic profiling of human urine using multi-dimensional protein identification technology.

Human urine samples are ideal for proteomic profiling and have tremendous potential as sources of biomarkers. Multi-dimensional protein identification technology (MudPIT) is an effective approach to analyzing human urine or other fluids dominated by diverse metabolites. MudPIT analysis was used to identify 87 proteins in just 15 ml of human urine. A high throughput, reproducible, and sensitive technology, MudPIT may soon be used for more proteomic analyses of metabolites.

Humans↗
PubMed2026

Comprehensive bioinformatics analysis identifies candidate ciliogenesis-related genes preferentially associated with N0-stage lung squamous cell carcinoma.

PURPOSE: There is few research on which genes play an important role in tumors without lymph metastasis. This study aimed to identify candidate molecular alterations preferentially associated with N0-stage LUSC. METHODS: we conducted a comprehensive bioinformatics analysis using publicly available The Cancer Genome Atlas (TCGA) data. Differentially expressed genes (DEGs) were identified separately by comparing N0 tumors and N+ tumors with normal lung tissues. Genes dysregulated in both N0 and N+ tumors were excluded to identify candidate N0-associated genes PPI networks were constructed using STRING and Cytoscape, with module analysis performed via MCODE. Hub genes were identified using multiple Cytohubba algorithms. Functional enrichment analyses were conducted using GO, and KEGG pathways using DAVID. Gene interaction networks were further explored using GeneMANIA. Immune cell infiltration was evaluated with TIMER. Associations with pathological stage and patient survival were assessed using GEPIA and other relevant tools. RESULTS: A total of 1103 candidate N0-associated DEGs were identified, including 748 upregulated and 355 downregulated genes. The PPI network contained five major MCODE clusters. One cluster (MCODE 4) included TTC30A, TTC30B, BBS7, and KIF3B genes implicated in ciliogenesis. TTC30B showed significant differential expression across pathological stages in the overall LUSC cohort. Seven consensus hub genes (ERBB2, CHUK, CASP8, NOTCH1, HNF4A, CREBBP, and IRS1) were identified based on their consistent ranking across multiple CytoHubba algorithms. Upregulated candidate N0-associated genes were primarily enriched in immune-related processes, including B-cell-mediated immunity and humoral responses, whereas downregulated genes were enriched in lysosomal and trans-Golgi network-related pathways. Exploratory immune infiltration analyses identified associations between the four ciliogenesis-related genes and several immune cell populations. CONCLUSIONS: This study identified candidate molecular signatures preferentially associated with N0-stage LUSC, including ciliogenesis-related genes and consensus hub genes. These findings provide hypotheses regarding molecular features of N0-stage LUSC and warrant further validation in independent cohorts and experimental studies.

Humans↗
PubMed2026

Expression patterns of potential targets for antibody-directed therapy in metastatic castration-resistant prostate cancer patients.

INTRODUCTION: Survival in metastatic castration-resistant prostate cancer (mCRPC) patients remains limited and treatment is complicated by tumor heterogeneity. As antibody-based therapeutics emerge, identifying actionable antigen targets and patient subgroups most likely to benefit is essential. MATERIALS & METHODS: Gene expression of 62 antibody-targetable proteins was analyzed in 296 mCRPC biopsies. These genes encode proteins targeted by approved or investigational antibody-based cancer therapeutics. Associations between target expression with genomic classifications and transcriptomic subtypes were evaluated. Target expression was also assessed in tumors with low expression of established mCRPC targets. Subgroup-specific targets were validated in an independent cohort and single-cell transcriptomics. RESULTS: Established targets KLK2, FOLH1 (PSMA) and STEAP1 showed the highest median expression across the cohort. Target expression did not correlate with genomic classifications, including homologous recombination deficiency, microsatellite instability, CDK12, TP53, PTEN or AR alterations Target expression did associate with transcriptomic subtypes: CRPC-AR (driven by androgen receptor-signaling) and CRPC-SCL (stem cell-like features, AP-1/YAP/TAZ-driven), displayed the highest expression of multiple targets, including KLK2, FOLH1, and SLC44A4. CRPC-NE (neuroendocrine phenotype) showed heterogeneous expression, with high CD46 expression, whereas CRPC-WNT (Wnt-signaling driven) generally showed low target expression. Notably, CD46 was highly expressed in tumors with low KLK2, FOLH1, and STEAP1 expression, a subgroup associated with poor prognosis. CONCLUSIONS: Although several antibody targets showed broad expression in mCRPC-tumors, expression varied by transcriptomic subtype. Subgroups such as CRPC-WNT expressed fewer targets, suggesting the need for alternative therapeutic strategies. CD46 emerged as a promising target, with wide expression across multiple subtypes, including clinically challenging CRPC-NE and mCRPC tumors lacking expression of established targets.

Humans↗
PubMed2026

Stage-Independent Real-Time Subtype Classification and Comprehensive Biopsy Profiling of Urothelial Carcinomas by the Lund Taxonomy System.

Bladder cancer is a heterogeneous malignancy with diverse clinical outcomes, and conventional pathological assessment alone is insufficient to capture its underlying biology. Gene expression profiling can stratify tumors into molecular subtypes with prognostic and predictive potential, but the reliability of transcriptomic classification and its clinical utility remains to be established. The translational/observational UROSCANSEQ study (ISRCTN15459149) prospectively evaluates RNA-based Lund Taxonomy (LundTax) molecular subtype classification in a clinical setting. Among 784 consecutive biopsies collected between 2018 and 2022, RNA sequencing was successful for 90% of all biopsies, encompassing 662 bladder cancer patients with a stage distribution of 48% Ta, 27% T1, 24% ≥T2, and 1% CIS. We demonstrate that the LundTax subtype classification algorithm, applied to individual samples, accurately identifies cancer cell phenotypes with characteristic gene and protein expression patterns in a manner robust to RNA quality, data preprocessing strategies, and batch effects, supporting its clinical feasibility across both non-muscle-invasive and muscle-invasive disease. We further extend the LundTax framework by incorporating single-sample molecular risk scores reflecting tumor grade, proliferation, and progression risk, as well as tumor microenvironment signatures. Both risk scores and overall immune and stromal content in biopsies were significantly associated with an increased risk of clinical progression in noninvasive disease. In a separate analysis of the relative cellular composition of the tumor microenvironment, however, only the fraction of natural killer cells remained significant. Together, the expanded LundTax system provides a comprehensive molecular portrait of individual tumor biopsies. By explicitly separating cancer cell-intrinsic phenotypes, prognostic indexes, and microenvironmental signals, the framework minimizes biological confounding and establishes a strong foundation for future studies evaluating clinical outcomes and treatment responses.

Humans↗
PubMed2004

Proteomic approaches in cancer risk and response assessment.

Proteomics is more than just a list-generating exercise where increases or decreases in protein expression are identified. Proteomic technologies will ultimately characterize information-flow through the protein circuitry that interconnects the extracellular microenvironment to the serum or plasma macroenvironment through intracellular signaling systems and their control of gene transcription. The nature of this information can be a cause or a consequence of disease processes and how patients respond to therapy. Analysis of human cancer as a model for how proteomics can have an impact at the bedside can take advantage of several promising new proteomic technologies. These technologies are being developed for early detection and risk assessment, therapeutic targeting and patient-tailored therapy.

Biomarkers, Tumor↗
PubMed2005

Molecular changes in prostatic cancer.

Prostate cancer is one of the most commonly diagnosed and potentially devastating cancers in men, throughout the world. However, the clinical manifestation of this disease varies greatly, from indolent tumours, requiring little or no treatment, to those aggressive cancers which require radical therapies. Prostate cancer, like all other cancers, develops and progresses as a consequence of an accumulation of genetic changes. While several putative genes have been isolated for the development of breast, ovarian and colon cancer, the aetiology and pathogenesis of prostate cancer remains poorly understood. In this review, we discuss important genetic markers in early, metastatic and hormone refractory prostate cancer which may, in the future, be used as markers for diagnosis and prognosis, as well as targets for therapeutic intervention.

Biomarkers, Tumor↗
PubMed2001

Detection of multiple proteins in an antibody-based protein microarray system.

A new antibody-based protein array assay is described. This assay combines the advantages of the specificity of enzyme-linked immunosorbent assays (ELISA), sensitivity of enhanced chemiluminescence (ECL) and high-throughput of microspot. In this system, the capture proteins, either antibodies or antigens are spotted onto membranes in an array format. Biological samples are then incubated with membranes. After antigens or antibodies in the samples bind to their corresponding targets and unbound proteins are washed away, the membranes are exposed to Horseradish Peroxidase (HRP)-conjugated antibody(ies). The signals are finally visualized with ECL system. Experiments demonstrate that multiple cytokines and antibodies can be simultaneously detected using this new approach. The procedure is so simple that no sophisticated equipment is required. The concept should be able to be extended to develop a high-throughput protein array system. Future applications of this new approach include direct protein expression profiling, immunological disease diagnostics and discovery of new biomarkers.

Antibodies↗
PubMed2003

Molecular staging and pharmacogenomics. Clinical implications: from lab to patients and back.

The complexities of human cancer have frustrated attempts to understand its genetic underpinnings and explain the unpredictable behavior of individual tumors. Advances in robotics and computer science, as well as the sequence data from the human genome project are now allowing us to begin to glean useful information from the simultaneous analysis of thousands of data elements from hundreds of tumors. Through the sophisticated analysis of patterns in these complex data sets, we are seeing clues that we may be able to predict the behavior of individual tumors as well as to define novel biomarkers and therapeutic targets. In this review, we will discuss the application of these high-throughput technologies for the study of human lung cancer.

Antineoplastic Agents↗