Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets.

BACKGROUND: This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. METHODS: Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. RESULTS: The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. CONCLUSION: These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

Development and Initial Validation of the Macrophage Activation Syndrome/Primary Hemophagocytic Lymphohistiocytosis Score, a Diagnostic Tool that Differentiates Primary Hemophagocytic Lymphohistiocytosis from Macrophage Activation Syndrome.

OBJECTIVE: To develop and validate a diagnostic score that assists in discriminating primary hemophagocytic lymphohistiocytosis (pHLH) from macrophage activation syndrome (MAS) related to systemic juvenile idiopathic arthritis. STUDY DESIGN: The clinical, laboratory, and histopathologic features of 362 patients with MAS and 258 patients with pHLH were collected in a multinational collaborative study. Eighty percent of the population was assessed to develop the score and the remaining 20% constituted the validation sample. Variables that entered the best fitted model of logistic regression were assigned a score, based on their statistical weight. The MAS/HLH (MH) score was made up with the individual scores of selected variables. The cutoff in the MH score that discriminated pHLH from MAS best was calculated by means of receiver operating characteristic curve analysis. Score performance was examined in both developmental and validation samples. RESULTS: Six variables composed the MH score: age at onset, neutrophil count, fibrinogen, splenomegaly, platelet count, and hemoglobin. The MH score ranged from 0 to 123, and its median value was 97 (1st-3rd quartile 75-123) and 12 (1st-3rd quartile 11-34) in pHLH and MAS, respectively. The probability of a diagnosis of pHLH ranged from <1% for a score of <11 to >99% for a score of ≥123. A cutoff value of ≥60 revealed the best performance in discriminating pHLH from MAS. CONCLUSION: The MH score is a powerful tool that may aid practitioners to identify patients who are more likely to have pHLH and, thus, could be prioritized for functional and genetic testing.

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines.

Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Availability and Implementation: Source code: https://github.com/SBU-BMI/region-templates/ . Contact: teodoro@unb.br. Supplementary information: Supplementary data are available at Bioinformatics online.

Free energies and forces in helix-coil transition of homopolypeptides under stretching.

We show here that constant velocity steered molecular dynamics (SMD) simulations of alpha-helices in a vacuum present a well defined plateau in the force-extension relationship for homopolypeptides having more than (approximately) twenty residues. With the processes being far away from equilibrium, the energies strongly depend on the stretching velocity. Importantly, for a given velocity variation, the energy variation depends also on the helix sequence. Additionally, our observations show that homopolypeptides made of ten different amino acids (Ala, Cys, Gln, Ile, Leu, Met, Phe, Ser, Thr and Val) present a linear helix-coil transition.