Metabolism-Associated Molecular Classification of Colorectal Cancer.

The high heterogeneity of colorectal cancer (CRC) is the main clinical challenge for individualized therapies. Molecular classification will contribute to drug discovery and personalized management optimizing. Here, we aimed to characterize the molecular features of CRC by a classification system based on metabolic gene expression profiles. 435 CRC samples from the Genomic Data Commons data portal were chosen as training set while 566 sample in GSE39582 were selected as testing set. Then, a non-negative matrix factorization clustering was performed, and three subclasses of CRC (C1, C2, and C3) were identified in both training set and testing set. Results showed that subclass C1 displayed high metabolic activity and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high immune signatures as well as high expression of immune checkpoint genes. C2 had the worst prognosis among the three subtypes. Subclass C3 displayed intermediate metabolic activity, high gene mutation numbers and good prognosis. Finally, a 27-gene metabolism-related signature was identified for prognosis prediction. Our works deepened the understanding of metabolic hallmarks of CRC, and provided valuable information for "multi-molecular" based personalized therapies.

Identification of NEO1 as a prognostic biomarker and its effects on the progression of colorectal cancer.

BACKGROUND: Due to the high morbidity and poor clinical outcomes, early predictive and prognostic biomarker identification is desiderated in colorectal cancer (CRC). As a homologue of the Deleted in Colorectal Cancer (DCC) gene, the role of Neogenin-1 (NEO1) in CRC remained unveiled. This study was designed to probe into the effects and potential function of NEO1 in CRC. METHODS: Online databases, Gene Set Enrichment Analysis (GSEA), quantitative real-time PCR and western blotting were used to evaluate NEO1 expression in colorectal cancer tissues. Survival analysis was performed to predict the prognosis of CRC patients based on NEO1 expression level. Then, cell proliferation was detected by colony formation and Cell Counting Kit 8 (CCK-8) assays. CRC cell migration and invasion were examined by transwell assays. Finally, we utilized the Gene Set Variation Analysis (GSVA) and GSEA to dig the potential mechanisms of NEO1 in CRC. RESULTS: Oncomine database and The Cancer Genome Atlas (TCGA) database showed that NEO1 was down-regulated in CRC. Further results validated that NEO1 mRNA and protein expression were both significantly lower in CRC tumor tissues than in the adjacent tissues in our clinical samples. NEO1 expression was decreased with the progression of CRC. Survival and other clinical characteristic analyses exhibited that low NEO1 expression was related with poor prognosis. A gain-of-function study showed that overexpression of NEO1 restrained proliferation, migration and invasion of CRC cells while a loss-of-function showed the opposite effects. Finally, functional pathway enrichment analysis revealed that NEO1 low expression samples were enriched in inflammation-related signaling pathways, EMT and angiogenesis. CONCLUSION: A tumor suppressor gene NEO1 was identified and verified to be correlated with the prognosis and progression of CRC, which could serve as a prognostic biomarker for CRC patients.

Identification of PIGU as the Hub Gene Associated with KRAS Mutation in Colorectal Cancer by Coexpression Analysis.

Colorectal cancer (CRC) patients with KRAS mutation are refractory and usually have poor prognosis. We aimed to identify the hub gene associated with KRAS mutant CRCs. Weighted gene coexpression network analysis (WGCNA) was used to calculate the key module and the hub genes in GSE39582. Combined with the protein-protein interaction (PPI) network and survival analysis, the real hub gene was identified and further validated. With the highest module significance value and correlation coefficient, the blue module was selected as the key module, 19 genes were identified as the hub gene candidates. The above genes were significantly downregulated in KRAS mutant CRCs compared with the wild type. Four genes (AAR2, PSMA7, NELFCD, and PIGU) were further screened as the potential hub genes by the PPI network. Low expression of PIGU for KRAS mutant patients had a poor prognosis. Therefore, PIGU was identified as the hub gene. PIGU expression was also downregulated in other two CRC datasets. "MAPK SIGNALING PATHWAY" was enriched in PIGU lowly expressed samples. PIGU was identified and validated to be closely related to KRAS mutation. It could be a potential prognosis biomarker and a novel treatment target for KRAS mutant CRC patients.

[Study on the vacuum ultraviolet transmittance of barium fluoride crystals at different temperature].

Two VUV-grade BaF2 windows with 0.5 mm-thick and 1 mm-thick respectively were selected to study the transmittance variety with the temperature. The results show that the cutoff wavelength of BaF2 crystals will shift towards the long wave with the increase in temperature. In a certain temperature range, BaF2 crystals can depress 130.4 nm radiation well, and also has a high transmittance at 135.6 nm. Compared with the reported method in which SrF2 crystals can be applied to suppress 130.4 nm stray light by heating, BaF2 crystal can inhibit the 130. 4 nm emission line completely, and thus reduce the power consumption of the device at the same time. This indicates that BaF2 crystals can play an important role in the ionosphere optical remote sensing detection.