Analysis Of Large-scale Gene Expression Data Research Articles

Discovery of a Carbamoyl Phosphate Synthetase 1-Deficient HCC Subtype With Therapeutic Potential Through Integrative Genomic and Experimental Analysis.

Metabolic reprogramming plays an important role in tumorigenesis. However, the metabolic types of different tumors are diverse and lack in-depth study. Here, through analysis of big databases and clinical samples, we identified a carbamoyl phosphate synthetase 1 (CPS1)-deficient hepatocellular carcinoma (HCC) subtype, explored tumorigenesis mechanism of this HCC subtype, and aimed to investigate metabolic reprogramming as a target for HCC prevention. A pan-cancer study involving differentially expressed metabolic genes of 7,764 tumor samples in 16 cancer types provided by The Cancer Genome Atlas (TCGA) demonstrated that urea cycle (UC) was liver-specific and was down-regulated in HCC. A large-scale gene expression data analysis including 2,596 HCC cases in 7 HCC cohorts from Database of HCC Expression Atlas and 17,444 HCC cases from in-house hepatectomy cohort identified a specific CPS1-deficent HCC subtype with poor clinical prognosis. In vitro and in vivo validation confirmed the crucial role of CPS1 in HCC. Liquid chromatography-mass spectrometry assay and Seahorse analysis revealed that UC disorder (UCD) led to the deceleration of the tricarboxylic acid cycle, whereas excess ammonia caused by CPS1 deficiency activated fatty acid oxidation (FAO) through phosphorylated adenosine monophosphate-activated protein kinase. Mechanistically, FAO provided sufficient ATP for cell proliferation and enhanced chemoresistance of HCC cells by activating forkhead box protein M1. Subcutaneous xenograft tumor models and patient-derived organoids were employed to identify that blocking FAO by etomoxir may provide therapeutic benefit to HCC patients with CPS1 deficiency. In conclusion, our results prove a direct link between UCD and cancer stemness in HCC, define a CPS1-deficient HCC subtype through big-data mining, and provide insights for therapeutics for this type of HCC through targeting FAO.

Latent network-based representations for large-scale gene expression data analysis

BackgroundWith the recent advancements in high-throughput experimental procedures, biologists are gathering huge quantities of data. A main priority in bioinformatics and computational biology is to provide system level analytical tools capable of meeting an ever-growing production of high-throughput biological data while taking into account its biological context. In gene expression data analysis, genes have widely been considered as independent components. However, a systemic view shows that they act synergistically in living cells, forming functional complexes and more generally a biological system.ResultsIn this paper, we propose LatNet, a signal transformation framework that, starting from an initial large-scale gene expression data, allows to generate new representations based on latent network-based relationships between the genes. LatNet aims to leverage system level relations between the genes as an underlying hidden structure to derive the new transformed latent signals. We present a concrete implementation of our framework, based on a gene regulatory network structure and two signal transformation approaches, to quantify latent network-based activity of regulators, as well as gene perturbation signals. The new gene/regulator signals are at the level of each sample of the input data and, thus, could directly be used instead of the initial expression signals for major bioinformatics analysis, including diagnosis and personalized medicine.ConclusionMultiple patterns could be hidden or weakly observed in expression data. LatNet helps in uncovering latent signals that could emphasize hidden patterns based on the relations between the genes and, thus, enhancing the performance of gene expression-based analysis algorithms. We use LatNet for the analysis of real-world gene expression data of bladder cancer and we show the efficiency of our transformation framework as compared to using the initial expression data.

Finding regulatory modules through large-scale gene-expression data analysis

The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Based on the iterative signature algorithm [Bergmann,S., Ihmels,J. and Barkai,N. (2002) Phys. Rev. E 67, 031902], we present an algorithm-the progressive iterative signature algorithm (PISA)-that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology database as a reference, found that the algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics.

Client-server environment for high-performance gene expression data analysis.

We have developed a platform independent, flexible and scalable Java environment for high-performance large-scale gene expression data analysis, which integrates various computational intensive hierarchical and non-hierarchical clustering algorithms. The environment includes a powerful client for data preparation and results visualization, an application server for computation and an additional administration tool. The package is available free of charge for academic and non-profit institutions.

Large-Scale Gene Expression Data Analysis: A New Challenge to Computational Biologists

The use of high-density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of each of the approximately 6000 genes in the yeast genome. In this survey I review three recent experiments related to transcriptional regulation and discuss the great challenge for computational biologists trying to extract functional information from such large-scale gene expression data.