Changhong Yang

FBXO protein family plays an essential role in the ubiquitination process acting as E3 ligases, which may contribute to the progression of cancers. However, the molecular functions of FBXOs in hepatocellular carcinoma (HCC) remain incompletely understood. Here, we investigated the overlapping genes between the FBXOs and differentially expressed genes (DEGs) of HCC identified by utilizing The Cancer Genome Atlas (TCGA) dataset, then, a prognostic model with effective predictive capacity was constructed based on the uni-cox and LASSO regression analyses. To elucidate the underlying mechanism of the FBXO model genes, KEGG analysis was carried out. Drug metabolism-cytochrome P450 and retinol metabolism were revealed as the potential pathway, which Increased the credibility of subsequent drug prediction research. Meanwhile, patients divided by the prognostic model showed a different immune infiltrating status and we also found FBXO model genes may ubiquitinate P53, inducing TP53 more prone to mutations, thereby promoting the occurrence and development of tumors. Consistent with these findings, the result of immunohistochemistry (IHC) validated an elevated expression of these model genes in HCC tissues than in the adjacent tissues. The primary aim of this investigation is to formulate a prognostic model while exploring the underlying mechanisms associated with FBXO genes in HCC. These findings offer initial research perspectives on the involvement of FBXO genes in HCC and contribute to the discovery of dependable biomarkers for the management, prognostication, and early detection of HCC in patients.

Necroptosis, pro-inflammatory programmed necrosis, has been reported to exert momentous roles in pancreatic cancer (PC). Herein, the objective of this study is to construct a necroptosis-related prognostic model for detecting pancreatic cancer. In this study, the intersection between necroptosis-related genes and differentially expressed genes (DEGs) of pancreatic ductal adenocarcinoma (PDAC) was obtained based on GeneCards database, GEO database (GSE28735 and GSE15471), and verified using The Cancer Genome Atlas (TCGA). Next, a prognostic model with Cox and LASSO regression analysis, and divided the patients into high-risk and low-risk groups. Subsequently, the Kaplan–Meier (KM) survival curve and the receiver operating characteristic (ROC) curves were generated to assess the predictive ability of overall survival (OS) of PC patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict the potential biofunction and possible mechanical pathways. The EMTome database and an immune analysis were applied to further explore underlying mechanism. Finally, clinical samples of PDAC patients were utilized to verify the expression of model genes via immunohistochemistry (IHC), and the normal human pancreatic ductal cell line, hTERT-HPNE as well as human pancreatic ductal carcinoma cell lines, PANC-1 and PL45, were used to identify the levels of model genes by Western blot (WB) and immunofluorescence (IF) in vitro. The results showed that 13 necroptosis-related DEGs (NRDEGs) were screened based on GEO database, and finally four of five prognostic genes, including KRT7, KRT19, IGF2BP3, CXCL5, were further identified by TCGA to successfully construct a prognostic model. Univariate and multivariate Cox analysis ultimately confirmed that this prognostic model has independent prognostic significance, KM curve suggested that the OS of low-risk group was longer than high-risk group, and the area under receiver (AUC) of ROC for 1, 3, 5 years was 0.733, 0.749 and 0.667, respectively. A GO analysis illustrated that model genes may participate in cell–cell junction, cadherin binding, cell adhesion molecule binding, and neutrophil migration and chemotaxis, while KEGG showed involvement in PI3K-Akt signaling pathway, ECMreceptor interaction, IL-17 signaling pathway, TNF signaling pathway, etc. Moreover, our results showed KRT7 and KRT19 were closely related to EMT markers, and EMTome database manifested that KRT7 and KRT19 are highly expressed in both primary and metastatic pancreatic cancer, declaring that model genes promoted invasion and metastasis potential through EMT. In addition, four model genes were positively correlated with Th2, which has been reported to take part in promoting immune escape, while model genes except CXCL5 were negatively correlated with TFH cells, indicating that model genes may participate in immunity. Additionally, IHC results showed that model genes were higher expressed in PC tissues than that in adjacent tumor tissues, and WB and IF also suggested that model genes were more highly expressed in PANC-1 and PL45 than in hTERT-HPNE. Tracing of a necroptosis-related prognostic model for pancreatic carcinoma reveals its invasion and metastasis potential through EMT and immunity. The construction of this model and the possible mechanism of necroptosis in PDAC was preliminarily explored to provide reliable new biomarkers for the early diagnosis, treatment, and prognosis for pancreatic cancer patients.

Laryngeal squamous cell cancer (LSCC) is a common malignant tumor with a high degree of malignancy, and its etiology remains unclear. Therefore, screening potential biomarkers is necessary to facilitate the treatment and diagnosis of LSCC. Robust rank aggregation (RRA) analysis was used to integrate two gene expression datasets of LSCC patients from the Gene Expression Omnibus (GEO) database and identify differentially expressed genes (DEGs) between LSCC and nonneoplastic tissues. A gene coexpression network was constructed using weighted gene correlation network analysis (WGCNA) to explore potential associations between the module genes and clinical features of LSCC. Combining differential gene expression analysis and survival analysis, we screened potential hub genes, including CDK1, SPC24, HOXB7, and SELENBP1. Subsequently, western blotting and immunohistochemistry were used to test the protein levels in clinical specimens to verify our findings. Finally, four candidate diagnostic and prognostic biomarkers (CDK1, SPC24, HOXB7, and SELENBP1) were identified. We propose, for the first time, that SPC24 is a gene that may associate with LSCC malignancy and is a novel therapeutic target. These findings may provide important mechanistic insight of LSCC.

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variants, there is no collection of postzygotic mosaic variants in noncancer disease-related and healthy individuals. Here, we present MosaicBase, a comprehensive database that includes 6698 mosaic variants related to 266 noncancer diseases and 27,991 mosaic variants identified in 422 healthy individuals. Genomic and phenotypic information of each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations for any genomic region. By analyzing the variants collected in MosaicBase, we find that mosaic variants that directly contribute to disease phenotype show features distinct from those of variants in individuals with mild or no phenotypes, in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population. MosaicBase is publicly available at http://mosaicbase.com/ or http://49.4.21.8:8000.

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variant collections, there are no collections of mosaic variants in noncancer diseases and asymptomatic individuals. Here, we present MosaicBase (http://mosaicbase.cbi.pku.edu.cn/ or http://49.4.21.8:8000/), a comprehensive database that includes 6,698 mosaic variants related to 269 noncancer diseases and 27,991 mosaic variants identified in 422 asymptomatic individuals. The genomic and phenotypic information for each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations within any genomic region. By analyzing the variants collected in MosaicBase, we found that mosaic variants that directly contribute to disease phenotype showed features distinct from those of variants in individuals with a mild or no phenotype in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with strong genetic contributions. To provide a comprehensive resource for the genetic evidence of ASD, we have updated the Autism KnowledgeBase (AutismKB) to version 2.0. AutismKB 2.0 integrates multiscale genetic data on 1379 genes, 5420 copy number variations and structural variations, 11 669 single-nucleotide variations or small insertions/deletions (SNVs/indels) and 172 linkage regions. In particular, AutismKB 2.0 highlights 5669 de novo SNVs/indels due to their significant contribution to ASD genetics and includes 789 mosaic variants due to their recently discovered contributions to ASD pathogenesis. The genes and variants are annotated extensively with genetic evidence and clinical evidence. To help users fully understand the functional consequences of SNVs and small indels, we provided comprehensive predictions of pathogenicity with iFish, SIFT, Polyphen etc. To improve user experiences, the new version incorporates multiple query methods, including simple query, advanced query and batch query. It also functionally integrates two analytical tools to help users perform downstream analyses, including a gene ranking tool and an enrichment analysis tool, KOBAS. AutismKB 2.0 is freely available and can be a valuable resource for researchers.