Cancer CRC: A Comprehensive Cancer Core Transcriptional Regulatory Circuit Resource and Analysis Platform.

A core transcriptional regulatory circuit (CRC) is a group of interconnected auto-regulating transcription factors (TFs) that form loops and can be identified by super-enhancers (SEs). Studies have indicated that CRCs play an important role in defining cellular identity and determining cellular fate. Additionally, core TFs in CRCs are regulators of cell-type-specific transcriptional regulation. However, a global view of CRC properties across various cancer types has not been generated. Thus, we integrated paired cancer ATAC-seq and H3K27ac ChIP-seq data for specific cell lines to develop the Cancer CRC (http://bio.liclab.net/Cancer_crc/index.html). This platform documented 94,108 cancer CRCs, including 325 core TFs. The cancer CRC also provided the "SE active core TFs analysis" and "TF enrichment analysis" tools to identify potentially key TFs in cancer. In addition, we performed a comprehensive analysis of core TFs in various cancer types to reveal conserved and cancer-specific TFs.

FADS1 promotes the progression of laryngeal squamous cell carcinoma through activating AKT/mTOR signaling.

Metabolic abnormality is the major feature of laryngeal squamous cell carcinoma (LSCC), however, the underlying mechanism remain largely elusive. Fatty acid desaturase 1 (FADS1), as the key rate-limiting enzyme of polyunsaturated fatty acids (PUFAs), catalyzes dihomo-gamma-linolenic acid (DGLA) to arachidonic acid (AA). In this study, we reported that the expression of FADS1 was upregulated in LSCC, high FADS1 expression was closely associated with the advanced clinical features and poor prognosis of the recurrent LSCC patients after chemotherapy. Liquid chromatograph-mass spectrometry (LC-MS) analysis revealed that FADS1 overexpression induced greater conversion of DGLA to AA, suggesting an increased activity of FADS1. Similarly, the level of prostaglandin E2 (PGE2), a downstream metabolite of AA, was also elevated in cancerous laryngeal tissues. Functional assays showed that FADS1 knockdown suppressed the proliferation, migration and invasion of LSCC cells, while FADS1 overexpression had the opposite effects. Bioinformatic analysis based on microarray data found that FADS1 could activate AKT/mTOR signaling. This hypothesis was further validated by both in vivo and in vitro assays. Hence, our data has supported the viewpoint that FADS1 is a potential promoter in LSCC progression, and has laid the foundation for further functional research on the PUFA dietary supplementation interventions targeting FADS1/AKT/mTOR pathway for LSCC prevention and treatment.

MPINet: metabolite pathway identification via coupling of global metabolite network structure and metabolomic profile.

High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet's effectiveness and reliability for analyzing metabolomic data across multiple different application fields.