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PubMed2006

Global molecular and morphological effects of 24-hour chromium(VI) exposure on Shewanella oneidensis MR-1.

The biological impact of 24-h ("chronic") chromium(VI) [Cr(VI) or chromate] exposure on Shewanella oneidensis MR-1 was assessed by analyzing cellular morphology as well as genome-wide differential gene and protein expression profiles. Cells challenged aerobically with an initial chromate concentration of 0.3 mM in complex growth medium were compared to untreated control cells grown in the absence of chromate. At the 24-h time point at which cells were harvested for transcriptome and proteome analyses, no residual Cr(VI) was detected in the culture supernatant, thus suggesting the complete uptake and/or reduction of this metal by cells. In contrast to the untreated control cells, Cr(VI)-exposed cells formed apparently aseptate, nonmotile filaments that tended to aggregate. Transcriptome profiling and mass spectrometry-based proteomic characterization revealed that the principal molecular response to 24-h Cr(VI) exposure was the induction of prophage-related genes and their encoded products as well as a number of functionally undefined hypothetical genes that were located within the integrated phage regions of the MR-1 genome. In addition, genes with annotated functions in DNA metabolism, cell division, biosynthesis and degradation of the murein (peptidoglycan) sacculus, membrane response, and general environmental stress protection were upregulated, while genes encoding chemotaxis, motility, and transport/binding proteins were largely repressed under conditions of 24-h chromate treatment.

Bacterial Proteins↗
PubMed2001

A novel genetic island of meningitic Escherichia coli K1 containing the ibeA invasion gene (GimA): functional annotation and carbon-source-regulated invasion of human brain microvascular endothelial cells.

The IbeA (ibe10) gene is an invasion determinant contributing to E. coli K1 invasion of the blood-brain barrier. This gene has been cloned and characterized from the chromosome of an invasive cerebrospinal fluid isolate of E. coli K1, strain RS218 (018:K1: H7). In the present study, a genetic island of meningitic E. coli containing ibeA (GimA) has been identified. A 20.3-kb genomic DNA island unique to E. coli K1 strains has been cloned and sequenced from an RS218 E. coli K1 genomic DNA library. Fourteen new genes have been identified in addition to the ibeA. The DNA sequence analysis indicated that the ibeA gene cluster was localized to the 98 min region and consisted of four operons, ptnIPKC, cglDTEC, gcxKRCI and ibeRAT. The G+C content (46.2%) of unique regions of the island is substantially different from that (50.8%) of the rest of the E. coli chromosome. By computer-assisted analysis of the sequences with DNA and protein databases (GenBank and PROSITE databases), the functions of the gene products could be anticipated, and were assigned to the functional categories of proteins relating to carbon source metabolism and substrate transportation. Glucose was shown to enhance E. coli penetration of human brain microvascular endothelial cells and exogenous cAMP was able to block the stimulating effect of glucose, suggesting that catabolic regulation may play a role in control of E. coli K1 invasion gene expression. Our data suggest that this genetic island may contribute to E. coli invasion of the blood-brain barrier through a carbon-source-regulated process.

Amino Acid Sequence↗
PubMedDate not supplied

Knowledge-assisted recognition of cluster boundaries in gene expression data.

BACKGROUND AND MOTIVATION: DNA microarray technology has made it possible to determine the expression levels of thousands of genes in parallel under multiple experimental conditions. Genome-wide analyses using DNA microarrays make a great contribution to the exploration of the dynamic state of genetic networks, and further lead to the development of new disease diagnosis technologies. An important step in the analysis of gene expression data is to classify genes with similar expression patterns into the same groups. To this end, hierarchical clustering algorithms have been widely used. Major advantages of hierarchical clustering algorithms are that investigators do not need to specify the number of clusters in advance and results are presented visually in the form of a dendrogram. However, since traditional hierarchical clustering methods simply provide results on the statistical characteristics of expression data, biological interpretations of the resulting clusters are not easy, and it requires laborious tasks to unveil hidden biological processes regulated by members in the clusters. Therefore, it has been a very difficult routine for experts. OBJECTIVE: Here, we propose a novel algorithm in which cluster boundaries are determined by referring to functional annotations stored in genome databases. MATERIALS AND METHODS: The algorithm first performs hierarchical clustering of gene expression profiles. Then, the cluster boundaries are determined by the Variance Inflation Factor among the Gene Function Vectors, which represents distributions of gene functions in each cluster. Our algorithm automatically specifies a cutoff that leads to functionally independent agglomerations of genes on the dendrogram derived from similarities among gene expression patterns. Finally, each cluster is annotated according to dominant gene functions within the respective cluster. RESULTS AND CONCLUSIONS: In this paper, we apply our algorithm to two gene expression datasets related to cell cycle and cold stress response in budding yeast Saccharomyces cerevisiae. As a result, we show that the algorithm enables us to recognize cluster boundaries characterizing fundamental biological processes such as the Early G1, Late G1, S, G2 and M phases in cell cycles, and also provides novel annotation information that has not been obtained by traditional hierarchical clustering methods. In addition, using formal cluster validity indices, high validity of our algorithm is verified by the comparison through other popular clustering algorithms, K-means, self-organizing map and AutoClass.

Algorithms↗
PubMed2025

Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere.

Increased availability of whole-genome sequencing (WGS) has facilitated the study of rare variants (RVs) in complex diseases. Multiple RV association tests are available to study the relationship between genotype and phenotype, but most do not fully leverage the availability of variant-level functional annotations. We propose genome-wide rare variant enrichment evaluation (gruyere), an empirical Bayesian framework that complements existing methods by learning global, trait-specific weights for functional annotations to improve variant prioritization. We apply gruyere to WGS data from the Alzheimer's Disease Sequencing Project to identify Alzheimer disease (AD)-associated genes and annotations. Growing evidence suggests that the disruption of microglial regulation is a key contributor to AD risk, yet existing methods have not examined rare non-coding effects that incorporate such cell-type-specific information. To address this gap, we (1) define per-gene non-coding RV test sets using predicted enhancer and promoter regions in microglia and other brain cell types (oligodendrocytes, astrocytes, and neurons) and (2) include cell-type-specific variant effect predictions (VEPs) as functional annotations. gruyere identifies 13 significant genetic associations not detected by other RV methods, four of which remain significant in omnibus tests. We find that deep-learning-based VEPs for splicing, transcription factor binding, and chromatin state are highly predictive of functional non-coding RVs. Our study establishes a robust framework incorporating functional annotations, coding RVs, and cell-type-associated non-coding RVs to perform genome-wide association tests, uncovering AD-relevant genes and annotations.

Alzheimer Disease↗
PubMed2005

Cross genome phylogenetic analysis of human and Drosophila G protein-coupled receptors: application to functional annotation of orphan receptors.

BACKGROUND: The cell-membrane G-protein coupled receptors (GPCRs) are one of the largest known superfamilies and are the main focus of intense pharmaceutical research due to their key role in cell physiology and disease. A large number of putative GPCRs are 'orphans' with no identified natural ligands. The first step in understanding the function of orphan GPCRs is to identify their ligands. Phylogenetic clustering methods were used to elucidate the chemical nature of receptor ligands, which led to the identification of natural ligands for many orphan receptors. We have clustered human and Drosophila receptors with known ligands and orphans through cross genome phylogenetic analysis and hypothesized higher relationship of co-clustered members that would ease ligand identification, as related receptors share ligands with similar structure or class. RESULTS: Cross-genome phylogenetic analyses were performed to identify eight major groups of GPCRs dividing them into 32 clusters of 371 human and 113 Drosophila proteins (excluding olfactory, taste and gustatory receptors) and reveal unexpected levels of evolutionary conservation across human and Drosophila GPCRs. We also observe that members of human chemokine receptors, involved in immune response, and most of nucleotide-lipid receptors (except opsins) do not have counterparts in Drosophila. Similarly, a group of Drosophila GPCRs (methuselah receptors), associated in aging, is not present in humans. CONCLUSION: Our analysis suggests ligand class association to 52 unknown Drosophila receptors and 95 unknown human GPCRs. A higher level of phylogenetic organization was revealed in which clusters with common domain architecture or cellular localization or ligand structure or chemistry or a shared function are evident across human and Drosophila genomes. Such analyses will prove valuable for identifying the natural ligands of Drosophila and human orphan receptors that can lead to a better understanding of physiological and pathological roles of these receptors.

Amino Acid Sequence↗
PubMed2003

Characterization of the human Ly-6 antigens, the newly annotated member Ly-6K included, as molecular markers for head-and-neck squamous cell carcinoma.

The E48 antigen is a successfully explored molecular marker for the diagnosis and therapy of HNSCC. The applicability of E48 as an HNSCC-associated antigen, however, is restricted due to its heterogeneous expression in 30% of tumors; and identification of additional target antigens is therefore desired. E48 belongs to the Ly-6 antigen family, comprising a group of highly homologous, low m.w., GPI-anchored surface proteins, of which some show tissue-restricted expression patterns. To identify novel human HNSCC-associated Ly-6 members with squamous cell-associated expression patterns, we performed comprehensive gene-screening consisting of BLAST searches within GenBank databases, followed by expression analysis. Using this approach, the Ly-6K gene could be annotated as a novel member of the human Ly-6 family. Expression of the human Ly-6 genes E48, Ly-6K, PSCA, GML, RIG-E, G6C and Ly-6H was prescreened by qualitative RT-PCR and subsequently analyzed by quantitative RT-PCR in normal keratinocytes, HNSCC cell lines, normal mucosa, HNSCC tumors as well as normal peripheral blood and bone marrow cells. PSCA was highly expressed in normal mucosa, but 100-fold decreased expression was seen in HNSCC. For Ly-6H, GML and G6C, no or very low expression was observed in keratinocytes and HNSCC. Expression of RIG-E was high in normal and malignant squamous cells and in peripheral blood and bone marrow cells, thus limiting its applicability as an HNSCC-associated marker. In contrast, besides the E48 gene, the Ly-6K gene also appeared to be selectively expressed in HNSCC and normal squamous cells. Moreover, expression of Ly-6K was shown in HNSCC cell lines, in which no E48 expression could be detected. These data justify further evaluation of Ly-6K as potential target antigen for the diagnosis and therapy of HNSCC.

Amino Acid Sequence↗
PubMed2006

[Gene expression profiles on three kinds of genotype hepatitis C virus core protein in Huh-7 cell line with microarray analysis].

OBJECTIVE: To analyze three kinds of genotype hepatitis C virus (HCV) core protein expressed in human hepatoma (Huh-7) cell line and to recognize HCV core proteins biological function and its pathogenic mechanism. METHODS: The Huh-7 cell expressed three kinds of core proteins were established respectively. Affymetrix human gene chip was used for identifying the gene expression dependently on Affymetrix's protocol. All genes changed by 3 or 1.5 folds between the transfected cells and a control cells were further analyzed, and annotated by using NetAffx analysis through Affymetrix website and were categorized based on their biological processes. RESULTS: The HCV-1b core protein caused 16 genes up/down-regulated expression, of which the immune response genes of PF4V1 and SPP1 were up-regulated 3.4 or 4.4 folds respectively. The HCV-2a core protein had caused the immune response gene CXCL5 and apoptosis gene BTF a down-regulated expression of 3.4 and 3.1 folds respectively, but caused the apoptosis genes of HRK and LZTS1 an up-regulated expression of 3.2 and 3.4 folds respectively. As compared with HCV 1b or 2a core protein, HCV-4b core protein caused 111 genes expression changing and it had more obvious effects on gene expression. If we applied 1.5 fold change for a comparison gene expression, a few of the same gene expression profiles might be caused by these two core proteins. CONCLUSION: The three kinds of HCV core protein should have its own expression character and be mainly shown in immune responses, signal transduction, apoptosis, etc. It should be helpful for our recognizing the HCV core protein biological function and its pathogenic mechanism.

Carcinoma, Hepatocellular↗
PubMed2005

Linking pathway gene expressions to the growth inhibition response from the National Cancer Institute's anticancer screen and drug mechanism of action.

Novel strategies are proposed to quantitatively analyze and relate biological pathways to drug responses using gene expression and small-molecule growth inhibition data (GI(50)) derived from the National Cancer Institute's 60 cancer cells (NCI(60)). We have annotated groups of drug GI(50) responses with pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta, and functional categories defined by Gene Ontology (GO), through correlations between pathway gene expression patterns and drug GI(50) profiles. Drug-gene-pathway relationships may then be utilized to find drug targets or target-specific drugs. Significantly correlated pathways and the gene products involved represent interesting targets for further exploration, whereas drugs that are significantly correlated with only certain pathways are more likely to be target specific. Separate pathway clustering finds that pathways engaged in the same biological process tend to have similar drug correlation patterns. The biological and statistical significances of our method are established by comparison to known small-molecule inhibitor-gene target relationships reported in the literature and by standard randomization procedures. The results of our pathway, gene expression and drug-induced growth inhibition associations, can serve as a basis for proposing testable hypotheses about potential anticancer drugs, their targets, and mechanisms of action.

Antineoplastic Agents↗
PubMedDate not supplied

Proteome annotations and identifications of the human pulmonary fibroblast.

We hereby report on a three year project initiative undertaken by our research team encompassing large-scale protein expression profiling and annotations of human primary lung fibroblast cells. An overview is given of proteomic studies of the fibroblast target cell involved in several diseases such as asthma, idiopatic pulmonary disease, and COPD. It has been the objective within our research team to map and identify the protein expressions occurring in both activated-, as well as resting cell states. The JGGL database www.2DDB.org has been built around these data, allowing advanced hypothesis building using the interactive query bioinformatic tools developed. Gene ontology has been applied to these annotations, classifying and correlating protein expressions to function. The localization as well as the biological processes involved for the annotations are being presented including an annotation-, and sequence-identification strategy, resulting in close to 2000 protein identities. Both gel based, high resolution 2D-gels, and liquid-phase separation (three-dimensional HPLC), as well as the combination of gel- and LC-based approaches (1D-gels and nano-capillary LC, reversed-phase) were utilized. Protein sequencing and structure identities were acquired by a combination of MALDI-, and electrospray-mass spectrometry techniques. Phenotypical and morphological characterizations were also made for this human disease target cell in both stimulated- and resting-cell states. The use of functional assays that demonstrate the key regulating role of growth factors and cytokine stimuli such as PDGF, TGF-beta, and EGF and the effect of ECM molecules such as Biglycan, are also presented and discussed.

Amino Acid Sequence↗
PubMed2005

Genome-wide screening for gene function using RNAi in mammalian cells.

Mammalian genome sequencing has identified numerous genes requiring functional annotation. The discovery that dsRNA can direct gene-specific silencing in both model organisms and mammalian cells through RNA interference (RNAi) has provided a platform for dissecting the function of independent genes. The generation of large-scale RNAi libraries targeting all predicted genes within mouse, rat and human cells, combined with the large number of cell-based assays, provides a unique opportunity to perform high-throughput genetics in these complex cell systems. Many different formats exist for the generation of genome-wide RNAi libraries for use in mammalian cells. Furthermore, the use of these libraries in either genetic screens or genetic selections allows for the identification of known and novel genes involved in complex cellular phenotypes and biological processes, some of which underpin human disease. In this review, we examine genome-wide RNAi libraries used in model organisms and mammalian cells and provide examples of how these information rich reagents can be used for determining gene function, discovering novel therapeutic targets and dissecting signalling pathways, cellular processes and complex phenotypes.

Animals↗
PubMed2025

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.

Reasoning about hypotheses and updating knowledge through empirical observations are central to scientific discovery. In this work, we applied logic-based machine learning methods to drive biological discovery by guiding experimentation. Genome-scale metabolic network models (GEMs) - comprehensive representations of metabolic genes and reactions - are widely used to evaluate genetic engineering of biological systems. However, GEMs often fail to accurately predict the behaviour of genetically engineered cells, primarily due to incomplete annotations of gene interactions. The task of learning the intricate genetic interactions within GEMs presents computational and empirical challenges. To efficiently predict using GEM, we describe a novel approach called Boolean Matrix Logic Programming (BMLP) by leveraging Boolean matrices to evaluate large logic programs. We developed a new system, [Formula: see text], which guides cost-effective experimentation and uses interpretable logic programs to encode a state-of-the-art GEM of a model bacterial organism. Notably, [Formula: see text] successfully learned the interaction between a gene pair with fewer training examples than random experimentation, overcoming the increase in experimental design space. [Formula: see text] enables rapid optimisation of metabolic models to reliably engineer biological systems for producing useful compounds. It offers a realistic approach to creating a self-driving lab for biological discovery, which would then facilitate microbial engineering for practical applications.

Active learning↗
PubMed2006

High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients.

To identify genetic events underlying the genesis and progression of multiple myeloma (MM), we conducted a high-resolution analysis of recurrent copy number alterations (CNAs) and expression profiles in a collection of MM cell lines and outcome-annotated clinical specimens. Attesting to the molecular heterogeneity of MM, unsupervised classification using nonnegative matrix factorization (NMF) designed for array comparative genomic hybridization (aCGH) analysis uncovered distinct genomic subtypes. Additionally, we defined 87 discrete minimal common regions (MCRs) within recurrent and highly focal CNAs. Further integration with expression data generated a refined list of MM gene candidates residing within these MCRs, thereby providing a genomic framework for dissection of disease pathogenesis, improved clinical management, and initiation of targeted drug discovery for specific MM patients.

Chromosomes, Human↗
PubMed2007

SMAUG is a major regulator of maternal mRNA destabilization in Drosophila and its translation is activated by the PAN GU kinase.

In animals, egg activation triggers a cascade of posttranscriptional events that act on maternally synthesized RNAs. We show that, in Drosophila, the PAN GU (PNG) kinase sits near the top of this cascade, triggering translation of SMAUG (SMG), a multifunctional posttranscriptional regulator conserved from yeast to humans. Although PNG is required for cytoplasmic polyadenylation of smg mRNA, it regulates translation via mechanisms that are independent of its effects on the poly(A) tail. Analyses of mutants suggest that PNG relieves translational repression by PUMILIO (PUM) and one or more additional factors, which act in parallel through the smg mRNA's 3' untranslated region (UTR). Microarray-based gene expression profiling shows that SMG is a major regulator of maternal transcript destabilization. SMG-dependent mRNAs are enriched for gene ontology annotations for function in the cell cycle, suggesting a possible causal relationship between failure to eliminate these transcripts and the cell cycle defects in smg mutants.

3' Untranslated Regions↗
PubMed2003

Radiation biodosimetry: applications for spaceflight.

The multiparametric dosimetry system that we are developing for medical radiological defense applications could be adapted for spaceflight environments. The system complements the internationally accepted personnel dosimeters and cytogenetic analysis of chromosome aberrations, considered the best means of documenting radiation doses for health records. Our system consists of a portable hematology analyzer, molecular biodosimetry using nucleic acid and antigen-based diagnostic equipment, and a dose assessment management software application. A dry-capillary tube reagent-based centrifuge blood cell counter (QBC Autoread Plus, Becton [correction of Beckon] Dickinson Bioscience) measures peripheral blood lymphocytes and monocytes, which could determine radiation dose based on the kinetics of blood cell depletion. Molecular biomarkers for ionizing radiation exposure (gene expression changes, blood proteins) can be measured in real time using such diagnostic detection technologies as miniaturized nucleic acid sequences and antigen-based biosensors, but they require validation of dose-dependent targets and development of optimized protocols and analysis systems. The Biodosimetry Assessment Tool, a software application, calculates radiation dose based on a patient's physical signs and symptoms and blood cell count analysis. It also annotates location of personnel dosimeters, displays a summary of a patient's dosimetric information to healthcare professionals, and archives the data for further use. These radiation assessment diagnostic technologies can have dual-use applications supporting general medical-related care.

Animals↗
PubMed2002

Genome sequence of the endocellular obligate symbiont of tsetse flies, Wigglesworthia glossinidia.

Many insects that rely on a single food source throughout their developmental cycle harbor beneficial microbes that provide nutrients absent from their restricted diet. Tsetse flies, the vectors of African trypanosomes, feed exclusively on blood and rely on one such intracellular microbe for nutritional provisioning and fecundity. As a result of co-evolution with hosts over millions of years, these mutualists have lost the ability to survive outside the sheltered environment of their host insect cells. We present the complete annotated genome of Wigglesworthia glossinidia brevipalpis, which is composed of one chromosome of 697,724 base pairs (bp) and one small plasmid, called pWig1, of 5,200 bp. Genes involved in the biosynthesis of vitamin metabolites, apparently essential for host nutrition and fecundity, have been retained. Unexpectedly, this obligate's genome bears hallmarks of both parasitic and free-living microbes, and the gene encoding the important regulatory protein DnaA is absent.

Animals↗
PubMed2002

JenPep: a database of quantitative functional peptide data for immunology.

MOTIVATION: The compilation of quantitative binding data underlies attempts to derive tools for the accurate prediction of epitopes in cellular immunology and is part of our concerted goal to develop practical computational vaccinology. RESULTS: JenPep is a family of relational databases supporting the growing community of immunoinformaticians. It contains quantitative data on peptide binding to Major Histocompatibility Complexes (MHCs) and to Transmembrane Peptide Transporter (TAP), as well as an annotated list of T-cell epitopes. AVAILABILITY: The database is available via the Internet. An HTML interface allowing searching of the database can be found at the following address: http://www.jenner.ac.uk/JenPep.

ATP-Binding Cassette Transporters↗
PubMed2005

A mathematical and computational framework for quantitative comparison and integration of large-scale gene expression data.

Analysis of large-scale gene expression studies usually begins with gene clustering. A ubiquitous problem is that different algorithms applied to the same data inevitably give different results, and the differences are often substantial, involving a quarter or more of the genes analyzed. This raises a series of important but nettlesome questions: How are different clustering results related to each other and to the underlying data structure? Is one clustering objectively superior to another? Which differences, if any, are likely candidates to be biologically important? A systematic and quantitative way to address these questions is needed, together with an effective way to integrate and leverage expression results with other kinds of large-scale data and annotations. We developed a mathematical and computational framework to help quantify, compare, visualize and interactively mine clusterings. We show that by coupling confusion matrices with appropriate metrics (linear assignment and normalized mutual information scores), one can quantify and map differences between clusterings. A version of receiver operator characteristic analysis proved effective for quantifying and visualizing cluster quality and overlap. These methods, plus a flexible library of clustering algorithms, can be called from a new expandable set of software tools called CompClust 1.0 (http://woldlab.caltech.edu/compClust/). CompClust also makes it possible to relate expression clustering patterns to DNA sequence motif occurrences, protein-DNA interaction measurements and various kinds of functional annotations. Test analyses used yeast cell cycle data and revealed data structure not obvious under all algorithms. These results were then integrated with transcription motif and global protein-DNA interaction data to identify G1 regulatory modules.

Algorithms↗
PubMed2006

Prognostic significance of fascin expression in advanced colorectal cancer: an immunohistochemical study of colorectal adenomas and adenocarcinomas.

BACKGROUND: Fascin is an actin bundling protein with roles in the formation of cell protrusions and motility of mesenchymal and neuronal cells. Fascin is normally low or absent from epithelia, but is upregulated in several epithelial neoplasms where it may contribute to an invasive phenotype. Here, we report on the prevalence and potential clinical significance of fascin expression in relation to the progression of colorectal adenocarcinoma and to tumor cell proliferation as measured by Ki67 index. METHODS: Conventional tissue sections of 107 colorectal adenomas and 35 adenocarcinomas were analyzed by immunohistochemistry for fascin and Ki67 expression. Fascin expression and Ki67 proliferation index were also investigated by use of a tissue microarray containing cores from a further 158 colorectal adenocarcinomas and 15 adenomas linked to a CCF, IRB-approved database with a mean of 38 months of clinical follow-up. Survival analysis was carried out by the Kaplan-Meier and Cox regression methods. RESULTS: Fascin was not expressed by the normal colonic epithelium. In conventional sections, 16% of adenomas and 26% of adenocarcinomas showed fascin expression in greater than 10% of the tumor cells. In the clinically-annotated tumors, fascin immunoreactivity was more common in tumors located in the proximal colon (p = 0.009), but was not associated with age, gender, or TNM stage. Patients with stage III/IV adenocarcinomas (n = 62) with strong fascin immunoreactivity had a worse prognosis than patients with low or absent fascin, (3-year overall survival of 11% versus 43% for fascin-negative patients; p = 0.023). In adenomas, fascin and Ki67 tended to be inversely correlated at the cellular level; this trend was less apparent in adenocarcinomas. CONCLUSION: Fascin is upregulated in a proportion of adenomas, where its expression is often focal. Strong and diffuse expression was seen in a subset of advanced colorectal adenocarcinomas that correlated with shorter survival in stage III and IV patients. Fascin may have prognostic value as an early biomarker for more aggressive colorectal adenocarcinomas.

Adenocarcinoma↗