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PubMed2025

Integrated proteomic network analysis reveals PTPRC as a central hub protein orchestrating co-expression modules and metabolic dysregulation in renal carcinoma: PTPRC protein molecular action.

The occurrence of renal carcinoma is closely related to a variety of molecular mechanisms and metabolic disorders. PTPRC (protein tyrosine phosphatase receptor C), as an important regulatory protein, was studied to reveal the role of PTPRC in renal carcinoma through comprehensive proteomic network analysis, especially its core position in the coordination of co-expression modules and metabolic disorders. This study was the first to download and process multiple publicly available renal cancer transcriptome data to conduct differential gene expression analysis across datasets. Functional enrichment and disease ontology analysis were performed on the transcriptome of renal cancer, and weighted gene co-expression network (WGCNA) was constructed. The results showed that comprehensive principal component analysis revealed significant differences in the transcriptome of renal cancer, and functional annotation revealed specific pathways associated with renal cancer. WGCNA analysis identified tumor-associated co-expression modules, while multi-omics analysis further identified core regulatory networks including PTPRC. As a central hub protein, PTPRC plays an important coordinating role in the co-expression module and metabolic dysregulation of renal carcinoma. This discovery provides a new perspective for understanding the molecular mechanism of kidney cancer.

Humans↗

PubMed1992

Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism.

Analysis of metabolic networks using linear optimization theory allows one to quantify and understand the limitations imposed on the cell by its metabolic stoichiometry, and to understand how the flux through each pathway influences the overall behavior of metabolism. A stoichiometric matrix accounting for the major pathways involved in energy and mass transformations in the cell was used in our analysis. The auxiliary parameters of linear optimization, the so-called shadow prices, identify the intermediates and cofactors that cause the growth to be limited on each nutrient. This formalism was used to examine how well the cell balances its needs for carbon, nitrogen, and energy during growth on different substrates. The relative values of glucose and glutamine as nutrients were compared by varying the ratio of rates of glucose to glutamine uptakes, and calculating the maximum growth rate. The optimum value of this ratio is between 2-7, similar to experimentally observed ratios. The theoretical maximum growth rate was calculated for growth on each amino acid, and the amino acids catabolized directly to glutamate were found to be the optimal nutrients. The importance of each reaction in the network can be examined both by selectively limiting the flux through the reaction, and by the value of the reduced cost for that reaction. Some reactions, such as malic enzyme and glutamate dehydrogenase, may be inhibited or deleted with little or no adverse effect on the calculated cell growth rate.

Adenosine Triphosphate↗

PubMed2003

Artificial neural network analysis of malaria severity through aggregation and deformability parameters of erythrocytes.

The erythrocyte aggregation and deformability of blood samples obtained from normal subjects and malaria patients are determined by microscopic imaging and laser aggregometry techniques, and optical hemorheometer, respectively. By these techniques several parameters are determined but four parameters, aggregate sedimentation velocity (ASV), effective number of cells (ENC), process completion time (PCT) and mean filtration time (MFT), show significant variation in malaria patients compared to that of healthy subjects. For malaria severity analysis artificial neural network (ANN), based on feedforward-error back-propagation algorithm in a supervisory training mode is proposed. This network is first trained for different number of epochs ranging from 20 to 50 by set of patterns and at 30 epochs training session the minimum mean square error (MSE) between desired and actual output is obtained. By applying the same procedure the test patterns belonging to normal, non-severe, severe, and highly severe malaria (NSM, SM and HSM) are identified. The results show that malaria with high severity is classified accurately (100%). The success of classification for non-severe and mildly-severe malaria ranges from 60% to 80%.

Equipment Design↗

PubMed2004

Neural network analysis for evaluating cancer risk in thyroid nodules with an indeterminate diagnosis at aspiration cytology: identification of a low-risk subgroup.

Thyroid nodules with a predominant follicular structure are often diagnosed as indeterminate at fine-needle aspiration biopsy (FNAB). We studied 453 patients with a thyroid nodule diagnosed as indeterminate at FNAB by using a feed-forward artificial neural network (ANN) analysis to integrate cytologic and clinical data, with the goal of subgrouping patients into a high-risk and in a low-risk category. Three hundred seventy-one patients were used to train the network and 82 patients were used to validate the model. The cytologic smears were blindly reviewed and classified in a high-risk and a low-risk subgroup on the basis of standard criteria. Neural network analysis subdivided the 371 lesions of the first series into a high-risk group (cancer rate of approximately 33% at histology) and a low-risk group (cancer rate of 3%). Only cytologic parameters contributed to this classification. Analysis of the receiver operating characteristic (ROC) curves demonstrated that the ANN model discriminated with higher sensitivity and specificity between benign and malignant nodules compared to standard cytologic criteria (p < 0.001). This value did not show degradation when ANN predictions were applied to the validation series of 82 nodules. In conclusion, neural network analysis of cytologic data may be a useful tool to refine the risk of cancer in patients with lesions diagnosed as indeterminate by FNAB.

Adult↗

PubMed2006

Differentiation of Listeria monocytogenes serovars by using artificial neural network analysis of Fourier-transformed infrared spectra.

A classification system based on Fourier transform infrared (FTIR) spectroscopy combined with artificial neural network analysis was designed to differentiate 12 serovars of Listeria monocytogenes using a reference database of 106 well-defined strains. External validation was performed using a test set of another 166 L. monocytogenes strains. The O antigens (serogroup) of 164 strains (98.8%) could be identified correctly, and H antigens were correctly determined in 152 (91.6%) of the test strains. Importantly, 40 out of 41 potentially epidemic serovar 4b strains were unambiguously identified. FTIR analysis is superior to PCR-based systems for serovar differentiation and has potential for the rapid, simultaneous identification of both species and serovar of an unknown Listeria isolate by simply measuring a whole-cell infrared spectrum.

Bacterial Typing Techniques↗

PubMed2005

Gene network analysis in plant development by genomic technologies.

The analysis of the gene regulatory networks underlying development is of central importance for a better understanding of the mechanisms that control the formation of the different cell-types, tissues or organs of an organism. The recent invention of genomic technologies has opened the possibility of studying these networks at a global level. In this paper, we summarize some of the recent advances that have been made in the understanding of plant development by the application of genomic technologies. We focus on a few specific processes, namely flower and root development and the control of the cell cycle, but we also highlight landmark studies in other areas that opened new avenues of experimentation or analysis. We describe the methods and the strategies that are currently used for the analysis of plant development by genomic technologies, as well as some of the problems and limitations that hamper their application. Since many genomic technologies and concepts were first developed and tested in organisms other than plants, we make reference to work in non-plant species and compare the current state of network analysis in plants to that in other multicellular organisms.

Cell Cycle↗

PubMed2025

Exploring prognostic genes in the immune microenvironment of acute myeloid leukemia via weighted gene co-expression network analysis.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous blood cancer that arises from transformed myeloid precursor cells in a compromised bone marrow microenvironment. This environment is essential for AML initiation, progression, and relapse. Alongside oncogenic changes in hematopoietic cells, immunological dysregulation also contributes to leukemogenesis. The present study is aimed to identify prognostic genes in stromal and immune cells associated with AML using the weighted gene co-expression network analysis (WGCNA). METHODS: Gene expression profiles were retrieved from The Cancer Genome Atlas database, and immune and stromal cell scores were calculated using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) method. These scores helped identify differentially expressed genes (DEGs), which were then used to create gene clusters through WGCNA. To explore the functions of genes linked to AML subtypes, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. A protein-protein interaction network was developed to identify hub genes. The top 18 hub genes were identified using the cytoHubba plug-in in Cytoscape software, and survival analysis was conducted with the Gene Expression Profiling Interactive Analysis 2 online tool. RESULTS: A total of 1097 DEGs were identified, with 601 being upregulated and 496 downregulated. WGCNA analysis indicated that the gray module, comprising 165 genes, had the strongest association with AML subtypes (Cor > 0.3; P < .05). Gene Ontology enrichment analysis demonstrated that the 18 identified hub genes were predominantly associated with neutrophil activation, immune response, secretory granule membrane, and pattern recognition receptor activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the DEGs were mainly involved in pathways related to phagosome, lysosome, tuberculosis, leishmaniasis, and neutrophil extracellular trap formation. Kaplan-Meier survival analysis of the top 18 hub genes indicated that ITGAM, IL10, and CD163 were significantly correlated with survival outcomes in AML. CONCLUSION: Key stromal and immune-related genes influencing AML patient outcomes were identified, highlighting their potential as therapeutic targets. These discoveries provide deeper insights into the molecular mechanisms driving AML pathogenesis and subtype differentiation.

Leukemia, Myeloid, Acute↗

PubMed1996

Mathematical modeling of AVM physiology using compartmental network analysis: theoretical considerations and preliminary in vivo validation using a previously developed animal model.

The development of computer modeling technique of cerebral arteriovenous malformations using circuit network analysis, validated with a previously developed animal model is presented. Such a malformation and its vascular connections are rendered into a complex system of interconnecting tubes, which is then simulated by an analogous electrical circuit using commercially available computer software. This methodology was tested using a swine model, of which a detailed computer model was constructed from anatomic and angiographic measurements of the cranial vessels. Flow conditions, before and after creation of the in vivo model, were predicted from the computer model and compared with previously reported in vivo measurements. Detailed analysis of flow within the CAVM nidus was also performed. There was a good correlation between the computer and in vivo models regarding changes in flow and pressure drop across the rete. Flow mapping within the nidus showed localized directional flow that was determined by global inputs, consistent with functional compartmentalization. This method of computer modeling appears promising for studying clinically relevant aspects of cerebral arteriovenous malformation pathophysiology. To our knowledge it is the first computer model to demonstrate functional compartmentalization.

Animals↗

PubMed2004

Sense-making in the wake of September 11th: a network analysis of lay understandings.

The objective of this research was to document and explore British university students' immediate understanding of the events of September 11th. A network analysis of lay causal perceptions procedure was employed to capture the social perceptions and sense-making of respondents at a time when they and the world struggled to impose meaning and coherence on the events. The study also examined the possible effects of 'belief in a just world' and 'right-wing authoritarianism' on the pattern of perceived causes. The results suggest that most participants perceive cultural and religious differences, the history of conflict in the Middle East, unfairness and prejudice as being the distal causes of the individual agent's emotions and actions. There is also some evidence that right-wing authoritarianism and belief in a just world have an interactive effect on the strength of the perceived link between some of these causes.

Adult↗

PubMed1999

Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound.

BACKGROUND: Our purpose was to determine the diagnostic potential of a new, computerized method of interpreting transrectal ultrasound (TRUS) information by artificial neural network analysis (ANNA). This method was developed to resolve the current dilemma of visual differentiation between benign and malignant tissue on TRUS. To train and objectively evaluate ANNA, a new precise method of computerized virtual correlation of preoperative ultrasound findings and radical prostatectomy histopathology was devised. After training with this pathologically confirmed digitized TRUS information, ANNA was tested in a blinded study. METHODS: Following radical prostatectomy, 289 pathology whole-mount sections of 61 patients were correlated digitally with the corresponding TRUS slices. Specific selection of TRUS areas unequivocally identified on the correlated digitized pathohistology resulted in 553 pathology-confirmed representations (samples). Of these, 53 were used for training and 500 were subjected to blind analysis by ANNA. RESULTS: ANNA classified 378 (99%) of the 381 benign pathology-confirmed samples correctly as benign. The false-positive rate was 1% (n = 3). Of the 119 pathology-confirmed malignant samples, 94 (79%) were classified correctly; 25 (21%) were falsely classified as normal. Out of all 119 cancers, ANNA classified 60 (71%) of the hypoechoic cancers as malignant and 24 (29%) as benign. Surprisingly, 34 (97%) of the isoechoic cancers were correctly classified by ANNA, missing only one sample. CONCLUSIONS: The introduction of ANNA enhanced the accuracy of TRUS prostate cancer identification. Although not all malignant areas were detected, cancer was detected in each patient. The ability to detect isoechoic cancerous lesions appears to be the essential innovation over conventional TRUS interpretation.

Aged↗

PubMed2002

Metabolic network analysis of an adipoyl-7-ADCA-producing strain of Penicillium chrysogenum: elucidation of adipate degradation.

An adipoyl-7-ADCA-producing, recombinant strain of Penicillium chrysogenum was characterized by metabolic network analysis, with special focus on the degradation of adipate and determination of the metabolic fluxes. Degradation of the side-chain precursor, adipate, causes an undesired consumption of adipate in the production of 7-ADCA. Using (13)C-labeled glucose and measurement of metabolite labeling patterns, it was shown that adipate was degraded by beta-oxidation to succinyl-CoA and acetyl-CoA. The labeling analysis indicated that degradation of adipate was taking place in the microbodies and the formed acetyl-CoA was metabolized in the glyoxylate shunt. This hypothesis was further substantiated by an enzyme assay, which showed activity of the key enzyme in the glyoxylate shunt. Flux estimations in two chemostat cultures, one with and one without adipate in the feed, revealed that degradation of adipate replaces the net anaplerotic reaction from pyruvate to oxaloacetate. Thus, with a combination of labeling experiments and enzyme assays, the pathway of adipate degradation was elucidated, and the effect of adipate degradation on the primary metabolism was quantified.

Acetyl Coenzyme A↗

PubMed2003

Carcinogenesis and chemotherapy viewed from the perspective of stoichiometric network analysis (SNA): what can the biological system of the elements contribute to an understanding of tumour induction by elemental chemical noxae (e.g., Ni2+ , Cd2+ ) and to an understanding of chemotherapy?

The biological application of stoichiometric network analysis (SNA) permits an understanding of tumour induction, carcinogenesis, and chemotherapy. Starting from the Biological System of the Elements, which provides a comprehensive treatment of the functions and distributions of chemical (trace) elements in biology, an attempt is made to interrelate the essential feature of biology and--regrettably--of tumour genesis by superimposing SNA reasoning on common features of all crucial biological processes. For this purpose, aspects, effects and drawbacks of autocatalysis (identical reproduction which can occur either under control or without control [in tumours]) are linked with the known facts about element distributions in living beings and about interference of metals with tumours (in terms of both chemotherapy and carcinogenesis). The essential role of autocatalysis in biology and the drawbacks of either controlled or spontaneous cell division can be used to understand crucial aspects of carcinogenesis and chemotherapy because SNA describes and predicts effects of autocatalysis, including phase effects that may be due to some kind of intervention. The SNA-based classifications of autocatalytic networks in cell biology are outlined here to identify new approaches to chemotherapy.

Cadmium↗

PubMed2002

Artificial neural network analysis for predicting pathological stage of clinically localized prostate cancer in the Japanese population.

BACKGROUND: Although prostate cancer has been prevalent in Japan, there has been no particular model for predicting the pathological stage in the Japanese population. We examined whether artificial neural network analysis (ANNA), which is a relatively new diagnostic tool in prostate cancer, can be one of the predictive methods for predicting organ confinement, compared with the traditional logistic regression model, in the Japanese population for the first time. METHODS: The study population comprised 178 men who underwent radical prostatectomy at our institutions between October 1992 and May 1999. As additional pretreatment parameters to the preoperative serum PSA level, clinical TNM classification and biopsy Gleason score, the percentage of number of cores exhibiting traces of tumor, maximum tumor length in biopsy cores, PSA density and patient age were used. The predictive ability of ANNA with several parameters for a set of 36 randomly selected test data was compared with those of logistic regression analysis and 'Partin Tables' by area under the receiver operating characteristics (ROC) curve analysis. RESULTS: Of 178 patients, 97 (54.5%) had organ-confined disease but 81 (45.5%) had locally advanced disease. With three parameters, the area under the ROC curve of ANNA (0.825 +/- 0.071) was larger than those for logistic regression (0.782 +/- 0.079) and Partin Tables (0.756 +/- 0.087), but not to a significant extent (P = 0.690 and 0.541). Although the expansion of the parameters did not increase the difference in area under the ROC curve between the best ANNA and logistic regression (0.899 +/- 0.053 and 0.873 +/- 0.065, respectively), the difference between the best ANNA and Partin Tables did not reach but approached statistical significance (P = 0.157). CONCLUSION: Although more modeling optimization is necessary to improve the predictive accuracy and generalizability of ANNA, we suggest that there is the possibility for this new predictive method to evolve in the analysis of clinical staging of prostate cancer.

Aged↗

PubMed2024

Comparison of the antibiotic resistance mechanisms in a gram-positive and a gram-negative bacterium by gene networks analysis.

Nowadays, the emergence of some microbial species resistant to antibiotics, both gram-positive and gram-negative bacteria, is due to changes in molecular activities, biological processes and their cellular structure in order to survive. The aim of the gene network analysis for the drug-resistant Enterococcus faecium as gram-positive and Salmonella Typhimurium as gram-negative bacteria was to gain insights into the important interactions between hub genes involved in key molecular pathways associated with cellular adaptations and the comparison of survival mechanisms of these two bacteria exposed to ciprofloxacin. To identify the gene clusters and hub genes, the gene networks in drug-resistant E. faecium and S. Typhimurium were analyzed using Cytoscape. Subsequently, the putative regulatory elements were found by examining the promoter regions of the hub genes and their gene ontology (GO) was determined. In addition, the interaction between milRNAs and up-regulated genes was predicted. RcsC and D920_01853 have been identified as the most important of the hub genes in S. Typhimurium and E. faecium, respectively. The enrichment analysis of hub genes revealed the importance of efflux pumps, and different enzymatic and binding activities in both bacteria. However, E. faecium specifically increases phospholipid biosynthesis and isopentenyl diphosphate biosynthesis, whereas S. Typhimurium focuses on phosphorelay signal transduction, transcriptional regulation, and protein autophosphorylation. The similarities in the GO findings of the promoters suggest common pathways for survival and basic physiological functions of both bacteria, including peptidoglycan production, glucose transport and cellular homeostasis. The genes with the most interactions with milRNAs include dpiB, rcsC and kdpD in S. Typhimurium and EFAU004_01228, EFAU004_02016 and EFAU004_00870 in E. faecium, respectively. The results showed that gram-positive and gram-negative bacteria have different mechanisms to survive under antibiotic stress. By deciphering their intricate adaptations, we can develop more effective therapeutic approaches and combat the challenges posed by multidrug-resistant bacteria.

Anti-Bacterial Agents↗

PubMed2003

Social network analysis for health and social interventions among Kenyan scavenging street children.

Street children are a high priority for health policy and service planning in Kenya. Poverty, wars, famine and disease have resulted in street children having a persistent presence in African cities and towns. The Maastricht Social Network Analysis (MSNA) was implemented as the core instrument in a battery to measure the health status of the street children. Owing to the absence of census data of street children in Kenya and the difficulty in tracking this mobile population, we implemented a mixed snowball and convenience sampling design to recruit research subjects. Three hundred street and orphanage children, and 100 primary school children as a control group, were included in the study. The MSNA provided a social diagnosis that complements the clinical diagnosis of the health status of the sample. Only one main methodological question is presented: is the MSNA applicable to describe the personal social networks of (1). children and of (2). people living in a Kenyan culture? Qualitative field observations, key informant interviews and focus groups inform the adaptation of the MSNA instrument, and improve its face validity. A case series analysis is presented. The main result is that the street children population consists of distinct subgroups defined by the UNICEF classification as 'on' and 'of' the street and by gender. Street children networks have some notable deficiencies depending on the subgroup. Constant across the groups is the deficiency of service providers in their networks. The conclusion is that the MSNA is a suitable instrument for obtaining a social diagnosis and gathering other useful information that helps in understanding the social and health backgrounds, status and daily experiences of Kenyan scavenging street children. Applying the MSNA protocol was successful in the diagnosis and interpretation of the findings.

Child↗

PubMed1985

A combined Bodian-Nissl stain for improved network analysis in neuronal cell culture.

Bodian and Nissl procedures were combined to stain dissociated mouse spinal cord cells cultured on coverslips. The Bodian technique stains fine neuronal processes in great detail as well as an intracellular fibrillar network concentrated around the nucleus and in proximal neurites. The Nissl stain clearly delimits neuronal cytoplasm in somata and in large dendrites. A combination of these techniques allows the simultaneous depiction of neuronal perikarya and all afferent and efferent processes. Costaining with little background staining by either procedure suggests high specificity for neurons. This procedure could be exploited for routine network analysis of cultured neurons.

Acetates↗

PubMed2006

Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot and mouth disease (FMD) epidemic in the UK.

Aggregated movement data do not take into account the relative position of the units within a higher-level structure. Social network analysis (SNA) and graph theory provide a tool to organise and analyse relational data overcoming the limitations of standard methods where the position of individuals/observations does not affect the result of the analysis. Some recorded movements of cattle and sheep during the initial phase of the 2001 foot and mouth disease (FMD) outbreak in the UK, before the ban on animal movements was imposed, are analysed descriptively using SNA. With the data available, a directed dichotomized network with 653 nodes and 797 arches was analysed. Most of the 10 nodes with the highest betweenness (3 farms, 4 markets and 3 dealers) were identified as key players in the initial spread of the infection. Three groups of nodes with distinctive proportion of k < or = 2 neighbours would result in three different theoretical outbreak dimensions assuming that the infection is only disseminated by the movements included in the network: no spread, spread up to 7% and around 25%. There are three hierarchical clusters with 308, 215 and 130 nodes, respectively. Farms in cluster 1 appear to be more similar in their movement patterns to non-farm holdings than to farms in clusters 2 and 3. Relative betweenness, k-neighbours and structural equivalence using hierarchical clustering were able to identify key actors in the evolution of the initial phases of the FMD outbreak such as markets, dealers and farms with atypical movement patterns. Holdings with high betweenness, large number of k < or = 2 neighbours and with movement pattern as in cluster 1 should be targeted in disease control activities once primary actors like markets, dealers and slaughter houses have been contained.

Animals↗

PubMed2006

Multivariate network analysis of fiber tract integrity in Alzheimer's disease.

Axonal and dendritic integrity is affected early in Alzheimer's disease (AD). Studies using region of interest or voxel-based analysis of diffusion tensor imaging data found significant decline of fractional anisotropy, a marker of fiber tract integrity, in selected white matter areas. We applied a multivariate network analysis based on principal component analysis to fractional anisotropy maps derived from diffusion-weighted scans from 15 AD patients, and 14 elderly healthy controls. Fractional anisotropy maps were obtained from an EPI diffusion sequence using parallel imaging to reduce distortion artifacts. We used high-dimensional image warping to control for partial volume effects due to white matter atrophy in AD. We found a significant regional pattern of fiber changes (p < 0.01) indicating that the integrity of intracortical projecting fiber tracts (including corpus callosum, cingulum and fornix, and frontal, temporal and occipital lobe white matter areas) was reduced, whereas extracortical projecting fiber tracts, including the pyramidal and extrapyramidal systems and somatosensory projections, were relatively preserved in AD. Effects of a univariate analysis were almost entirely contained within the multivariate effect. Our findings illustrate the use of a multivariate approach to fractional anisotropy data that takes advantage of the highly organized structure of anisotropy maps, and is independent of multiple comparison correction and partial volume effects. In agreement with post-mortem evidence, our study demonstrates dissociation between intracortical and extracortical projecting fiber systems in AD in the living human brain.

Aged↗