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At least 127 records · Page 7Linked to original sources

Molecular genetic and morphologic integration in malformations of the nervous system for etiologic classification.

Molecular genetics has brought new insight into the etiology and pathogenesis of nervous system malformations, and provided a means of precise genetic diagnosis including the prenatal detection of many cerebral dysgeneses. Many cerebral malformations previously thought to be a single disorder are now known to be common end results of many independent genetic mutations. Examples are holoprosencephaly and lissencephaly. Gradients of genetic expression along the axes of the neural tube established at the time of gastrulation may explain many varieties and clinical expressions of cerebral malformations, including the involvement of non-neural tissues, such as midfacial hypoplasia from defective neural crest migration. A new classification of CNS malformations is proposed that integrates, but does not discard traditional morphologic criteria, but integrates them with new molecular genetic criteria.

Animals↗

Multiclass molecular cancer classification by kernel subspace methods with effective kernel parameter selection.

Microarray techniques provide new insights into molecular classification of cancer types, which is critical for cancer treatments and diagnosis. Recently, an increasing number of supervised machine learning methods have been applied to cancer classification problems using gene expression data. Support vector machines (SVMs), in particular, have become one of the most effective and leading methods. However, there exist few studies on the application of other kernel methods in the literature. We apply a kernel subspace (KS) method to multiclass cancer classification problems, and assess its validity by comparing it with multiclass SVMs. Our comparative study using seven multiclass cancer datasets demonstrates that the KS method has high performance that is comparable to multiclass SVMs. Furthermore, we propose an effective criterion for kernel parameter selection, which is shown to be useful for the computation of the KS method.

Artificial Intelligence↗

On the nature of global classification.

Molecular sequencing technology has brought biology into the era of global (universal) classification. Methodologically and philosophically, global classification differs significantly from traditional, local classification. The need for uniformity requires that higher level taxa be defined on the molecular level in terms of universally homologous functions. A global classification should reflect both principal dimensions of the evolutionary process: genealogical relationship and quality and extent of divergence within a group. The ultimate purpose of a global classification is not simply information storage and retrieval; such a system should also function as an heuristic representation of the evolutionary paradigm that exerts a directing influence on the course of biology. The global system envisioned allows paraphyletic taxa. To retain maximal phylogenetic information in these cases, minor notational amendments in existing taxonomic conventions should be adopted.

Biological Evolution↗

Progress in epidermolysis bullosa: from eponyms to molecular genetic classification.

Epidermolysis bullosa, a clinically and genetically diverse group of heritable mechanobullous disorders characterized by skin fragility in the cutaneous basement membrane zone, has become a prototype for the recent progress in molecular genetics of genodermatoses. The different forms of epidermolysis bullosa have been linked to mutations in no less than 10 distinct genes encoding the major structural basement membrane zone proteins. This information has formed a basis for refined molecular classification with prognostic implications, improved genetic counseling, and prenatal and preimplantation genetic diagnosis.

Epidermolysis Bullosa↗

[The relationship between the pathological classifications and molecular genetics of hydatidiform moles].

OBJECTIVE: To study the relationship between the pathological classification and molecular genetics of hydatidiform mole. METHOD: 32 cases of hydatidiform mole were analyzed by DNA restriction fragment length polymorphism (RFLP, hybridized with the probe 33.15). RESULTS: DNA from only paternal origin was found in 21, and from both parents in 11. In the formeron the basis of pathological characteristics, the complete hydatidiform mole (CHM) and the partial hydatidiform mole (PHM) were 16 (76%, 16/21) and 5 (24%, 5/21) respectively, and in the later CHM and PHM were 5 (45%, 5/11) and 6 (55%, 6/11), respectively. CONCLUSION: There is not much correlation between pathological classification and molecular genetics of hydatidiform mole.

Female↗

Computational techniques for diversity analysis and compound classification.

Molecular similarity and diversity analysis has played a significant role in computer-aided drug discovery for more than a decade. Compound classification methods have also become increasingly important for the design and organization of compound databases and in silico screening. Here we review these related methodologies and discuss selected applications.

Cluster Analysis↗

[Cluster analysis and identify significance of novel genes related to glioma in molecule classification].

OBJECTIVE: To screen differentially expressed genes in the development of human glioma and establish molecular classification of glioma preliminary based on gene expression using cDNA microarray. METHODS: Brain specimens were obtained from 18 patients with glioma, 10 males and 8 females, aged 14 approximately 62 with an average age of 44.4. The total RNAs of these glioma specimens and 2 specimens of donated brain of normal adults were extracted. BioStarH140S microarray (including 8347 old genes and 5592 novel genes) were adopted and hybridized with probes which were prepared from the total RNAs. Differentially expressed genes between the normal tissues and glioma tissues were assayed after scanning cDNA microarray with ScanArray 4000. Northern hybridization, and in situ hybridization (ISH) were used to identify the functions of novel genes. Those differentially expressed genes were studied with Hierarchical method and molecular classification of glioma was preliminarily carried out. RESULTS: Among the 13 939 target genes, there were 1200 (8.61%) differentially expressed genes and 395 (2.83%) novel genes. 348 genes were up-regulated and 852 genes were down-regulated in glioma. The results of bioinformatical analysis, Northern hybridization and ISH revealed that those novel genes were highly associated with glioma. There were multiple genes which were relevance to classification by Hierarchical method, such as MAP gene, cytoskeleton and matrix motility genes, etc. Molecular classification of glioma with Hierarchical cluster was in accordance with pathology and revealed internal essence in tumorigenesis and development. CONCLUSION: Multiple genes play important roles in development of glioma. cDNA microarray technology is a powerful technique in screening for differentially expressed genes between two different kinds of tissues.

Adolescent↗

Feature selection for DNA methylation based cancer classification.

Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia.

Computational Biology↗

Pulmonary adenocarcinoma: classification and molecular biology.

Pulmonary adenocarcinoma is increasing in incidence. Current classification systems based purely on morphological features are of little clinical relevance. Recent descriptions of genetic abnormalities, particularly in K-ras, p53, and c-erb-B2, may form the basis of a new taxonomy having direct prognostic relevance. Study of these molecular lesions has also helped to define a new pathway of tumourigenesis in the lung parenchyma, through alveolar atypical hyperplasia (AAH) to clinical adenocarcinoma.

Adenocarcinoma↗

Epidemiology informing clinical practice: from bills of mortality to population laboratories.

The earliest observations on population patterns of disease and how they might inform medical practice probably occurred during the 17th century, and they continue to the present day, with increasing relevance to nutritional and infectious diseases, and cancer and other chronic diseases. Chronic-disease methods grew out of infectious-disease epidemiology, in which both field and laboratory methods are used. In diseases where intermediate biology was not initially observable (particularly cancer), record-based and interview-based epidemiology revealed some key exposures (e.g. smoking and radiation). With measurable intermediates (e.g. blood lipids), cardiovascular epidemiology also yielded inferences on causal pathways. Important changes that are remaking the field of epidemiology and will ultimately influence all aspects of medical practice include the following: high-throughput genotyping, allowing genetic and gene-environment causes of disease to be identified; high-throughput proteomics, which should allow the development of early-detection methods; new tools for the measurement of exposures; and a molecular basis for disease taxonomy. These new methods will allow a much better understanding of both the etiology and the intermediate stages of disease; however, new methods do not obviate the necessity for good study design, especially the need to be clear on the difference between observation and experiment. The greatest opportunities to inform medical practice come from the application of new methods to large-scale human observational studies, which include genetics, environment, early-detection markers, molecular classification of outcome, and treatment data. Improved molecular classification of disease will allow smaller, focused clinical trials to be undertaken and, ultimately, the tailoring of treatment to the biological profile of patient and disease.

Epidemiologic Methods↗

Breast cancer expression profiling: the impact of microarray testing on clinical decision making.

The available clinical prognostic tools show an obvious limitation in predicting the outcome of breast cancer patients, and pathological features cannot classify tumours accurately. Microarray-based molecular classification of breast tumours or selection of gene expression panels to improve risk prediction or treatment outcomes are thought to be theoretically superior to established clinical and pathological criteria, based on guidelines such as the St Gallen and National Institute of Health consensus, or which use specific prognostic tools, such as the Nottingham Prognostic Index or Adjuvant-Online algorithm. Although two diagnostic tests based on gene expression profiling of breast cancer are commercially available, a new molecular classification and molecular forecasting of breast cancer based on expression profiling cannot outperform the standard tumour diagnostic at present. This review focuses on some important problems in the practical application of molecular profiling of breast cancer for clinical purposes.

Breast Neoplasms↗

Molecular-pathogenetic classification of genetic disorders of the skeleton.

Genetic disorders of the skeleton (skeletal dysplasias and dysostoses) are a large and disparate group of diseases whose unifying features are malformation, disproportionate growth, and deformation of the skeleton or of individual bones or groups of bones. To cope with the large number of different disorders, the "Nosology and Classification of the Osteochondrodysplasias," based on clinical and radiographic features, has been designed and revised periodically. Biochemical and molecular features have been partially implemented in the Nosology, but the rapid accumulation of knowledge on genes and proteins cannot be easily merged into the clinical-radiographic classification. We present here, as a complement to the existing Nosology, a classification of genetic disorders of the skeleton based on the structure and function of the causative genes and proteins. This molecular-pathogenetic classification should be helpful in recognizing metabolic and signaling pathways relevant to skeletal development, in pointing out candidate genes and possible therapeutic targets, and more generally in bringing the clinic closer to the basic science laboratory and in promoting research in this field.

Bone Diseases, Developmental↗

Genetic diversity of BVDV: consequences for classification and molecular epidemiology.

Genetic typing of bovine viral diarrhoea virus (BVDV) is important for the precise classification of viruses as well as for the development of molecular epidemiology. BVDV isolates were usually typed based on comparison of genomic sequences from the 5'-untranslated region (5'-UTR), N(pro) and E2 region. Recently we have identified 11 genetic groups (subgenotypes) of BVDV-1. Our further experiments confirmed a new subgenotype, BVDV-1k, isolated from cattle in Switzerland. BVDV isolates from India were typed as BVDV-1b whereas BVDV-1c is a predominant subgenotype in Australia. The results of genetic typing of BVDV indicate that distribution of subgenotypes has no relationship to the geographic origin of viral isolates.

Animals↗

Insights into a complex group of neoplastic disease: advances in histopathologic classification and molecular pathology of salivary gland cancer.

Cancers of major and minor salivary glands represent a histopathologic challenge in two major respects. The first challenge is the complexity of morphologic features and overlapping of histologic patterns in the different tumor entities many of which are relatively rare. The number of separate tumor entities to be considered in differential diagnosis has greatly increased in the two latest WHO classification systems 12 (Table I). The second challenge is prognostication based on histopathology. The clinical experience is that behavior of some salivary gland carcinomas does not correlate well with their histopathologic classification, and that tumors classified within the same category may exhibit quite different clinical outcomes. However, recent advances in histopathological classification have been combined with new tools in immunohistochemical diagnosis and prognostication including cell-proliferation markers, myoepithelial antigens, matrix metalloproteinases, steroid receptors, growth factors and their receptors. These have improved our possibilities for more specific choices in the treatment of a variety of salivary gland carcinomas. This paper will give an overview on recent developments in histopathological classification, prognostication, and molecular pathology of salivary gland cancer.

Humans↗

Neural networks for molecular sequence classification.

A neural network classification method has been developed as an alternative approach to the search/organization problem of large molecular databases. Two artificial neural systems have been implemented on a Cray supercomputer for rapid protein/nucleic acid sequence classifications. The neural networks used are three-layered, feed-forward networks that employ back-propagation learning algorithm. The molecular sequences are encoded into neural input vectors by applying an n-gram hashing method or a SVD (singular value decomposition) method. Once trained with known sequences in the molecular databases, the neural system becomes an associative memory capable of classifying unknown sequences based on the class information embedded in its neural interconnections. The protein system, which classifies proteins into PIR (Protein Identification Resource) superfamilies, showed a 82% to a close to 100% sensitivity at a speed that is about an order of magnitude faster than other search methods. The pilot nucleic acid system, which classifies ribosomal RNA sequences according to phylogenetic groups, has achieved a 100% classification accuracy. The system could be used to reduce the database search time and help organize the molecular sequence databases. The tool is generally applicable to any databases that are organized according to family relationships.

Base Sequence↗

Genomics and proteomics: emerging technologies in clinical cancer research.

Fueled by the complete genomic data acquired from the human genome project and the desperate clinical need of comprehensive analytical tools to study a heterogeneous disease like cancer, genomic and proteomic technologies have evolved rapidly, accelerating the rate and number of discoveries in clinical cancer research. These discoveries include mechanistic understanding of cancer biology as well as the identification of biomarkers supporting early detection, molecular classification of tumors, molecular predictors of metastasis, treatment response, and prognosis. While the technical advances have been significant, clinical researchers and practicing physicians are now confronted with the challenges of understanding technically and statistically complex data sets, translating this complex information to fit clinical contexts and incorporating it into clinical studies. In this review, we will summarize the available technologies and associated bioinformatics, discuss studies that are clinically relevant, and discuss the limitations we are still facing. We will present a framework for future directions of these technologies and how we believe they should be applied in clinical studies.

Biomedical Research↗

PCR and single-strand conformational polymorphism for recognition of medically important opportunistic fungi.

The application of PCR technology to molecular diagnostics holds great promise for the early identification of medically important pathogens. PCR has been shown to be useful for the detection of the presence of fungal DNA in both laboratory and clinical samples. Considerable interest has been focused on the utility of selecting universal primers, those that recognize constant regions among most, if not all, medically important fungi. Once an amplicon, or piece of amplified DNA determined by the unique pair of oligonucleotide primers, has been generated, several different methods may be used to distinguish between genera and between species. The two major approaches have utilized differences in restriction enzyme digestion patterns or hybridization with specific probe. We report the application of single-strand conformational polymorphism (SSCP) as a technique to delineate the differences between fungal species and/or genera. Minor sequence variations in small single-stranded DNA cause subtle changes in conformation, allowing these strands to be separated on polyacrylamide gels by SSCP. We used a 197-bp fragment amplified from the 18S rRNA gene, common to all medically important fungi. After amplification, the fragments were denatured and run on an acrylamide-glycerol gel at room temperature or 4 degrees C for 4.5 or 4 h, respectively. Under room temperature conditions, the SSCP patterns for Candida albicans, Candida tropicalis, and Candida parapsilosis were identical and all strains within each species demonstrated the same pattern. These patterns differed markedly from those of the genus Aspergillus. The SSCP patterns of major and minor bands at room temperature permitted distinction between strains of Aspergillus fumigatus and Aspergillus flavus. There also was consistency of the SSCP banding pattern among different strains of the same Aspergillus species. The SSCP patterns for other medically important opportunistic fungi, such as Cryptococcus neoformans, Pseudallescheria boydii, and Rhizopus arrhizus, were sufficiently unique to permit distinction from those of C. albicans and A. fumigatus. We conclude that the technique of PCR-SSCP provides a novel method by which to recognize and distinguish medically important opportunistic fungi and which has potential applications to molecular diagnosis, taxonomic classification, molecular epidemiology, and elucidation of mechanisms of antifungal drug resistance.

Aspergillus↗

sWGS Identifies a Copy-Number-High Subset of TP53-mutated Multiple-Classifier Endometrial Carcinomas With Adverse Clinicopathological Features.

TP53-mutated "multiple-classifier" endometrial carcinomas represent a diagnostically challenging subgroup within current molecular classification algorithms. Although these tumors are assigned to POLE-mutated or mismatch repair-deficient categories according to current ESGO/FIGO-based algorithms, their biological heterogeneity remains incompletely characterized. Herein, we retrospectively analyzed TP53-mutated multiple-classifier endometrial carcinomas identified through routine molecular profiling at our institution between 2022 and 2025 using an integrated histopathological, immunohistochemical, targeted sequencing, and shallow whole-genome sequencing approach. Copy-number alteration-high (CNA-high) status was defined as ≥5 large-scale genomic alterations, corresponding to copy-number gains or losses ≥3 Mb within a single chromosomal arm excluding whole-arm alterations. Among 33 analyzable TP53-mutated multiple-classifier endometrial carcinomas, sWGS identified 12 CNA-high tumors (36.4%) and 21 CNA-low tumors (63.6%). CNA-high tumors were more frequently non-endometrioid, high-grade, and advanced-stage according to FIGO 2023. They showed higher TP53 variant allele frequencies (VAF) and higher TP53 VAF-to-tumor-cellularity ratios. After a median follow-up of 12.8 months, recurrences (6/33; 18.2%) and disease-related deaths (3/33; 9.1%) were observed in the CNA-high subgroup, whereas no recurrence or disease-related death was observed among CNA-low patients. These findings indicate that TP53-mutated multiple-classifier endometrial carcinomas comprise biologically distinct subsets that are not fully captured by current 4-tier TCGA-based molecular classification and ESGO-based risk stratification. In this cohort, sWGS identified a CNA-high group with adverse clinicopathological features and clinical events suggesting a potentially more aggressive clinical course. Integration of genome-wide copy-number profiling may therefore refine the biological interpretation of TP53 alterations in multiple-classifier endometrial carcinomas and warrants validation in larger multicenter cohorts.

TP53↗