Integrating transcriptomics, glycomics and glycoproteomics to characterize hepatitis B virus-associated hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) ranks as the third most common cause of cancer related death globally, representing a substantial challenge to global healthcare systems. In China, the primary risk factor for HCC is the hepatitis B virus (HBV). Aberrant serum glycoconjugate levels have long been linked to the progression of HBV-associated HCC (HBV-HCC). Nevertheless, few study systematically explored the dysregulation of glycoconjugates in the progression of HBV-associated HCC and their potency as the diagnostic and prognostic biomarker. METHODS: An integrated strategy that combined transcriptomics, glycomics, and glycoproteomics was employed to comprehensively investigate the dynamic alterations in glyco-genes, N-glycans, and glycoproteins in the progression of HBV- HCC. RESULTS: Bioinformatic analysis of Gene Expression Omnibus (GEO) datasets uncovered dysregulation of fucosyltransferases (FUTs) in liver tissues from HCC patients compared to adjacent tissues. Glycomic analysis indicated an elevated level of fucosylated N-glycans, especially a progressive increase in fucosylation levels on IgA1 and IgG2 determined by glycoproteomic analysis. CONCLUSIONS: The findings indicate that the abnormal fucosylation plays a pivotal role in the progression of HBV-HCC. Systematic and integrative multi-omic analysis is anticipated to facilitate the discovery of aberrant glycoconjugates in tumor progression.

A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial-Temporal Distribution of Users and Advanced Genetic Algorithms.

Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial-temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP's trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.

Dysregulation and prometastatic function of glycosyltransferase C1GALT1 modulated by cHP1BP3/ miR-1-3p axis in bladder cancer.

BACKGROUND: Abnormal glycosylation in a variety of cancer types is involved in tumor progression and chemoresistance. Glycosyltransferase C1GALT1, the key enzyme in conversion of Tn antigen to T antigen, is involved in both physiological and pathological conditions. However, the mechanisms of C1GALT1 in enhancing oncogenic phenotypes and its regulatory effects via non-coding RNA are unclear. METHODS: Abnormal expression of C1GALT1 and its products T antigen in human bladder cancer (BLCA) were evaluated with BLCA tissue, plasma samples and cell lines. Effects of C1GALT1 on migratory ability and proliferation were assessed in YTS-1 cells by transwell, CCK8 and colony formation assay in vitro and by mouse subcutaneous xenograft and trans-splenic metastasis models in vivo. Dysregulated circular RNAs (circRNAs) and microRNAs (miRNAs) were profiled in 3 pairs of bladder cancer tissues by RNA-seq. Effects of miR-1-3p and cHP1BP3 (circRNA derived from HP1BP3) on modulating C1GALT1 expression were investigated by target prediction program, correlation analysis and luciferase reporter assay. Functional roles of miR-1-3p and cHP1BP3 on migratory ability and proliferation in BLCA were also investigated by in vitro and in vivo experiments. Additionally, glycoproteomic analysis was employed to identify the target glycoproteins of C1GALT1. RESULTS: In this study, we demonstrated upregulation of C1GALT1 and its product T antigen in BLCA. C1GALT1 silencing suppressed migratory ability and proliferation of BLCA YTS-1 cells in vitro and in vivo. Subsets of circRNAs and miRNAs were dysregulated in BLCA tissues. miR-1-3p, which is reduced in BLCA tissues, inhibited transcription of C1GALT1 by binding directly to its 3'-untranslated region (3'-UTR). miR-1-3p overexpression resulted in decreased migratory ability and proliferation of YTS-1 cells. cHP1BP3 was upregulated in BLCA tissues, and served as an miR-1-3p "sponge". cHP1BP3 was shown to modulate migratory ability, proliferation, and colony formation of YTS-1 cells, and displayed tumor-suppressing activity in BLCA. Target glycoproteins of C1GALT1, including integrins and MUC16, were identified. CONCLUSIONS: This study reveals the pro-metastatic and proliferative function of upregulated glycosyltransferase C1GLAT1, and provides preliminary data on mechanisms underlying dysregulation of C1GALT1 via miR-1-3p / cHP1BP3 axis in BLCA.

Construction of a metastasis-associated ceRNA network reveals a prognostic signature in lung cancer.

BACKGROUND: Lung cancer is the most common cancer worldwide, and metastasis is the leading cause of lung cancer related death. However, the molecular network involved in lung cancer metastasis remains incompletely described. Here, we aimed to construct a metastasis-associated ceRNA network and identify a lncRNA prognostic signature in lung cancer. METHODS: RNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and gene set enrichment analysis (GSEA) were performed to investigate the function of these genes. Using Cox regression analysis, we found that a 6 lncRNA signature may serve as a candidate prognostic factor in lung cancer. Finally, we used Transwell assays with lung cancer cell lines to verify that LINC01010 acts as a tumor suppressor. RESULTS: We identified 1249 differentially expressed (DE) mRNAs, 440 DE lncRNAs and 26 DE miRNAs between nonmetastatic and metastatic lung cancer tissues. GO and KEGG analyses confirmed that the identified DE mRNAs are involved in lung cancer metastasis. Using bioinformatics tools, we constructed a metastasis-associated ceRNA network for lung cancer that includes 117 mRNAs, 23 lncRNAs and 22 miRNAs. We then identified a 6 lncRNA signature (LINC01287, SNAP25-AS1, LINC00470, AC104809.2, LINC00645 and LINC01010) that had the greatest prognostic value for lung cancer. Furthermore, we found that suppression of LINC01010 promoted lung cancer cell migration and invasion. CONCLUSIONS: This study might provide insight into the identification of potential lncRNA biomarkers for diagnosis and prognosis in lung cancer.