COSMIC: a curated database of somatic variants and clinical data for cancer.

The Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic, is an expert-curated knowledgebase providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. The catalogue is accessed and used by thousands of cancer researchers and clinicians daily, allowing them to quickly access information from an immense pool of data curated from over 29 thousand scientific publications and large studies. Within the last 4 years, COSMIC has substantially expanded its utility by adding new resources: the Mutational Signatures catalogue, the Cancer Mutation Census, and Actionability. To improve data accessibility and interoperability, somatic variants have received stable genomic identifiers that are associated with their genomic coordinates in GRCh37 and GRCh38, and new export files with reduced data redundancy have been made available for download.

Planteome 2024 Update: Reference Ontologies and Knowledgebase for Plant Biology.

The Planteome project (https://planteome.org/) provides a suite of reference and crop-specific ontologies and an integrated knowledgebase of plant genomics data. The plant genomics data in the Planteome has been obtained through manual and automated curation and sourced from more than 40 partner databases and resources. Here, we report on updates to the Planteome reference ontologies, namely, the Plant Ontology (PO), Trait Ontology (TO), the Plant Experimental Conditions Ontology (PECO), and integration of species/crop-specific vocabularies from our partners, the Crop Ontology (CO) into the TO ontology graph. Currently, 11 CO vocabularies are integrated into the Planteome with the addition of yam, sorghum, and potato since 2018. In addition, the size of the annotation database has increased by 34%, and the number of bioentities (genes, proteins, etc.) from 125 plant taxa has increased by 72%. We developed new tools to facilitate user requests and improvements to the CO vocabularies, and to allow fast searching and browsing of PO terms and definitions. These enhancements and future changes to automate the TO-CO mappings and knowledge discovery tools ensure that the Planteome will continue to be a valuable resource for plant biology.

JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024.

The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.

Database resources of the national center for biotechnology information.

The National Center for Biotechnology Information (NCBI) produces a variety of online information resources for biology, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. NCBI provides search and retrieval operations for most of these data from 35 distinct databases. The E-utilities serve as the programming interface for the most of these databases. Resources receiving significant updates in the past year include PubMed, PMC, Bookshelf, RefSeq, SRA, Virus, dbSNP, dbVar, ClinicalTrials.gov, MMDB, iCn3D and PubChem. These resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.

South Africa's contribution of insect records on the BOLD system.

South Africa is the third most biodiverse country in the world and insects represent a large part of its faunal diversity, as is seen globally. With more than 65,000 described animal species in South Africa, insects represent 44,088 species. While there are still a lot of species yet to be identified, progress may be hindered by the few insect taxonomists available in South Africa and subsequently, the time-consuming nature and costs of the methods used during species identification. DNA barcoding, on the other hand, has become a valuable tool for documenting biodiversity with the use of a small DNA fragment such as cytochrome oxidase subunit 1 (COI). This paper aims to assess South Africa's contribution to the Barcode of Life Database (BOLD) as well as highlight the regions that are under-represented on BOLD. From the 4,984,215 Insecta records on BOLD, South Africa contributed 56,392 insect records, with only 16.85% of that total identified to species level. The Gauteng Province had the most represented insect samples submitted to BOLD with 63.57% followed by Kwazulu-Natal (15.74%), and Mpumalanga (5.73%). However, the Free State, Limpopo, and the Northern Cape provinces are all under-represented on BOLD. This is evident as both the Northern Cape and Limpopo provinces contain one or more biodiversity hotspots which in turn displays the high levels of biodiversity that could still be recorded on BOLD. Improving our understanding with regards to DNA barcoding data linked to geographical regions, as well as specific insect groups, can highlight the areas in need of more research.

Clan genomics: From OMIM phenotypic traits to genes and biology.

Clinical characterization of a patient phenotype has been the quintessential approach for elucidating a differential diagnosis and a hypothesis to explore a potential clinical diagnosis. This has resulted in a language of medicine and a semantic ontology, with both specialty- and subspecialty-specific lexicons, that can be challenging to translate and interpret. There is no 'Rosetta Stone' of clinical medicine such as the genetic code that can assist translation and interpretation of the language of genetics. Nevertheless, the information content embodied within a clinical diagnosis can guide management, therapeutic intervention, and potentially prognostic outlook of disease enabling anticipatory guidance for patients and families. Clinical genomics is now established firmly in medical practice. The granularity and informative content of a personal genome is immense. Yet, we are limited in our utility of much of that personal genome information by the lack of functional characterization of the overwhelming majority of computationally annotated genes in the haploid human reference genome sequence. Whereas DNA and the genetic code have provided a 'Rosetta Stone' to translate genetic variant information, clinical medicine, and clinical genomics provide the context to understand human biology and disease. A path forward will integrate deep phenotyping, such as available in a clinical synopsis in the Online Mendelian Inheritance in Man (OMIM) entries, with personal genome analyses.

The Identification of Zinc-Finger Protein 433 as a Possible Prognostic Biomarker for Clear-Cell Renal Cell Carcinoma.

Clear-cell renal cell carcinoma (ccRCC) is the most common and aggressive form of all urological cancers, with poor prognosis and high mortality. At late stages, ccRCC is known to be mainly resistant to chemotherapy and radiotherapy. Therefore, it is urgent and necessary to identify biomarkers that can facilitate the early detection of ccRCC in patients. In this study, the levels of transcripts of ccRCC from The Cancer Genome Atlas (TCGA) dataset were used to identify prognostic biomarkers in this disease. Analyzing the data obtained indicated that the KRAB-ZNF protein is significantly suppressed in clear-cell carcinomas. Furthermore, ZNF433 is differentially expressed in ccRCC in a stage- and histological-grade-specific manner. In addition, ZNF433 expression was correlated with metastasis, with greater node involvement associated with lower ZNF433 expression (p < 0.01) and with a more unsatisfactory overall survival outcome (HR, 0.45; 95% CI, 0.33-0.6; p = 8.5 × 10-8). Since ccRCC is characterized by mutations in proteins that alter epigenetic modifications and /or chromatin remodeling, we examined the expression of ZNF433 transcripts in ccRCC with wildtype and mutated forms of BAP1, KDMC5, MTOR, PBRM1, SETD2, and VHL. Analysis revealed that ZNF433 expression was significantly reduced in ccRCC with mutations in the BAP1, SETD2, and KDM5C genes (p < 0.05). In addition, the ZNF433 promoter region was highly methylated, and hypermethylation was significantly associated with mRNA suppression (p < 2.2 × 10-16). In silico analysis of potential ZNF target genes found that the largest group of target genes are involved in cellular metabolic processes, which incidentally are particularly impaired in ccRCC. It was concluded from this study that gene expression of ZNF433 is associated with cancer progression and poorer prognosis, and that ZNF433 behaves in a manner that suggests that it is a prognostic marker and a possible tumor-suppressor gene in clear-cell renal cell carcinoma.

Karyotype coding: The creation and maintenance of system information for complexity and biodiversity.

The mechanism of biological information flow is of vital importance. However, traditional research surrounding the genetic code that follows the central dogma to a phenotype faces challengers, including missing heritability and two-phased evolution. Here, we propose the karyotype code, which by organizing genes along chromosomes at once preserves species genome information and provides a platform for other genetic and nongenetic information to develop and accumulate. This specific genome-level code, which exists in all living systems, is compared to the genetic code and other organic codes in the context of information management, leading to the concept of hierarchical biological codes and an 'extended' definition of adaptor where the adaptors of a code can be not only molecular structures but also, more commonly, biological processes. Notably, different levels of a biosystem have their own mechanisms of information management, and gene-coded parts inheritance preserves "parts information" while karyotype-coded system inheritance preserves the "system information" which organizes parts information. The karyotype code prompts many questions regarding the flow of biological information, including the distinction between information creation, maintenance, modification, and usage, along with differences between living and non-living systems. How do biological systems exist, reproduce, and self-evolve for increased complexity and diversity? Inheritance is mediated by organic codes which function as informational tools to organize chemical reactions, create new information, and preserve frozen accidents, transforming historical miracles into biological routines.

Analyzing the impact of ATF3 in tumorigenesis and immune cell infiltration of ovarian tumor: a bioinformatics study.

ATF3 is an essential transcription activator in regulating cancer-related genetic expression. To identify the role of ATF3 in ovarian tumor, we investigated the correlation between ATF3 expression and the clinicopathological properties using multiple database. The cBioPortal and GEPIA database displayed the clinical information of ovarian patients harboring or without harboring ATF3 mutation. Furthermore, we assessed the relationship between survival and ATF3 expression level using Kaplan-Meier plotter, which reveals that the ovarian patients with higher expression of ATF3 suffered the worse overall survival and progression-free survival. The differentially expressed genes were analyzed using gene ontology, protein-protein interaction network, and gene set enrichment analysis to identify the hub gene and critical pathways, significantly affecting the tumorigenesis of ovarian tumor. Finally, we assessed the correlation between ATF3 and immune cell infiltration using Tumor Immunoassay Resource (TIMER) database. The results demonstrated that higher expression has a positive correlation with macrophage infiltration, expression for M1- and M2-type macrophages. Our study suggests that ATF3 can regulate the cell cycle and heme-related oxidative phosphorylation process, and it may be a critical factor to regulate the macrophage cell to be infiltrated into ovarian cancer. ATF3 can be used as a biomarker for diagnosis and therapy of ovarian tumor.

Large-scale analysis reveals spatiotemporal circadian patterns of cilia transcriptomes in the primate brain.

Cilia are dynamic subcellular systems, with core structural and functional components operating in a highly coordinated manner. Since many environmental stimuli sensed by cilia are circadian in nature, it is reasonable to speculate that genes encoding cilia structural and functional components follow rhythmic circadian patterns of expression. Using computational methods and the largest spatiotemporal gene expression atlas of primates, we identified and analyzed the circadian rhythmic expression of cilia genes across 22 primate brain areas. We found that around 73% of cilia transcripts exhibited circadian rhythmicity across at least one of 22 brain regions. In 12 brain regions, cilia transcriptomes were significantly enriched with circadian oscillating transcripts, as compared to the rest of the transcriptome. The phase of the cilia circadian transcripts deviated from the phase of the majority of the background circadian transcripts, and transcripts coding for cilia basal body components accounted for the majority of cilia circadian transcripts. In addition, adjacent or functionally connected brain nuclei had large overlapping complements of circadian cilia genes. Most remarkably, cilia circadian transcripts shared across the basal ganglia nuclei and the prefrontal cortex peaked in these structures in sequential fashion that is similar to the sequential order of activation of the basal ganglia-cortical circuitry in connection with movement coordination, albeit on completely different timescales. These findings support a role for the circadian spatiotemporal orchestration of cilia gene expression in the normal physiology of the basal ganglia-cortical circuit and motor control. Studying orchestrated cilia rhythmicity in the basal ganglia-cortical circuits and other brain circuits may help develop better functional models, and shed light on the causal effects cilia functions have on these circuits and on the regulation of movement and other behaviors.

Immune checkpoint B7-H3 protein expression is associated with poor outcome and androgen receptor status in prostate cancer.

BACKGROUND: Novel immune checkpoint-based immunotherapies may benefit specific groups of prostate cancer patients who are resistant to other treatments. METHODS: We analyzed by immunohistochemistry the expression of B7-H3, PD-L1/B7-H1, and androgen receptor (AR) in tissue samples from 120 prostate adenocarcinoma patients treated with radical prostatectomy in Spain, and from 206 prostate adenocarcinoma patients treated with radical prostatectomy in Norway. RESULTS: B7-H3 expression correlated positively with AR expression and was associated with biochemical recurrence in the Spanish cohort, but PD-L1 expression correlated with neither of them. Findings for B7-H3 were validated in the Norwegian cohort, where B7-H3 expression correlated positively with Gleason grade, surgical margins, seminal vesicle invasion, and CAPRA-S risk group, and was associated with clinical recurrence. High B7-H3 expression in the Norwegian cohort was also consistent with positive AR expression. CONCLUSION: These results suggest distinct clinical relevance of the two immune checkpoint proteins PD-L1 and B7-H3 in prostate cancer. Our findings highlight B7-H3 as an actionable novel immune checkpoint protein in prostate cancer.

Neuronal ApoE upregulates MHC-I expression to drive selective neurodegeneration in Alzheimer's disease.

Selective neurodegeneration is a critical causal factor in Alzheimer's disease (AD); however, the mechanisms that lead some neurons to perish, whereas others remain resilient, are unknown. We sought potential drivers of this selective vulnerability using single-nucleus RNA sequencing and discovered that ApoE expression level is a substantial driver of neuronal variability. Strikingly, neuronal expression of ApoE-which has a robust genetic linkage to AD-correlated strongly, on a cell-by-cell basis, with immune response pathways in neurons in the brains of wild-type mice, human ApoE knock-in mice and humans with or without AD. Elimination or over-expression of neuronal ApoE revealed a causal relationship among ApoE expression, neuronal MHC-I expression, tau pathology and neurodegeneration. Functional reduction of MHC-I ameliorated tau pathology in ApoE4-expressing primary neurons and in mouse hippocampi expressing pathological tau. These findings suggest a mechanism linking neuronal ApoE expression to MHC-I expression and, subsequently, to tau pathology and selective neurodegeneration.

Impact of beta-2 microglobulin expression on the survival of glioma patients via modulating the tumor immune microenvironment.

AIMS: High immune cell infiltration in gliomas establishes an immunosuppressive tumor microenvironment, which in turn promotes resistance to immunotherapy. Hence, it is important to identify novel targets associated with high immune cell infiltration in gliomas. Our previous study showed that serum levels of beta-2 microglobulin (B2M) in lower-grade glioma patients were lower than those in glioblastoma patients. In the present study, we focused on exploring the roles of B2M in glioma immune infiltration. METHODS: A large cohort of patients with gliomas from the TCGA, CGGA, and Gravendeel databases was included to explore differential expression patterns and potential roles of B2M in gliomas. A total of 103 glioma tissue samples were collected to determine the distributions of B2M protein levels by immunofluorescent assays. Kaplan-Meier survival analysis and meta-analysis were used for survival analysis. GO(Gene-ontology) enrichment analysis, co-expression analysis, KEGG(Kyoto Encyclopedia of Genes and Genomes) pathway analysis, and immune infiltration analysis were performed to explore roles and related mechanisms of B2M in glioma. RESULTS: We found that both B2M mRNA and protein levels were abnormally upregulated in glioma samples compared with those from normal brain tissue. B2M expression was correlated with tumor grade and was downregulated in IDH1 mutant samples. Furthermore, B2M was a moderately sensitive indicator for predicting the mesenchymal molecular subtype of gliomas. Interestingly, glioma patients with lower B2M expression had remarkably longer survival times than those with higher B2M expression. Moreover, meta-analysis showed that B2M was an independent predictive marker in glioma patients. The results of GO enrichment analysis revealed that B2M contributed to immune cell infiltration in glioma patients. In addition, results of KEGG pathway analysis and co-expression analysis suggested that B2M may mediate glioma immune infiltration via chemokines. CONCLUSIONS: We conclude that B2M levels are critical for the survival times of glioma patients, at least in part due to mediating high immune infiltration.

Ferroptosis-related gene signature predicts prognosis and immunotherapy in glioma.

AIMS: Glioma is a highly invasive brain tumor, which makes prognosis challenging and renders patients resistant to various treatments. Induction of cell death is promising in cancer therapy. Ferroptosis, a recently discovered regulated cell death, can be induced for killing glioma cells. However, the prognostic prediction of ferroptosis-related genes (FRGs) in glioma remains elusive. METHODS: The mRNA expression profiles and gene variation and corresponding clinical data of glioma patients and NON-TUMOR control were downloaded from public databases. Risk score based on a FRGs signature was constructed in REMBRANDT cohort and validated in other datasets including CGGA-693, CGGA-325, and TCGA. RESULTS: Our results demonstrated that the majority of FRGs was differentially expressed among GBM, LGG, and NON-TUMOR groups (96.6%). Furthermore, the glioma patients with low-risk score exhibited a more satisfactory clinical outcome. The better prognosis was also validated in the glioma patients with low-risk score no matter to which grade they were affiliated. Functional analysis revealed that the high-risk score group was positively correlated with the enrichment scores for immune checkpoint blockade-related positive signatures, indicating the critical role of glioma immunotherapy via risk score. CONCLUSION: A novel FRGs-related risk score can predict prognosis and immunotherapy in glioma patients.

Identification of a five gene signature to predict time to biochemical recurrence after radical prostatectomy.

BACKGROUND: Identification of novel biomarkers associated with high-risk prostate cancer or biochemical recurrence can drive improvement in detection, prognosis, and treatment. However, studies can be limited by small sample sizes and sparse clinical follow-up data. We utilized a large sample of prostate specimens to identify a predictive model of biochemical recurrence following radical prostatectomy and we validated this model in two external data sets. METHODS: We analyzed prostate specimens from patients undergoing radical prostatectomy at Hartford Hospital between 2008 and 2011. RNA isolated from formalin-fixed paraffin-embedded prostates was hybridized to a custom Affymetrix microarray. Regularized (least absolute shrinkage and selection operator [Lasso]) Cox regression was performed with cross-validation to identify a model that incorporated gene expression and clinical factors to predict biochemical recurrence, defined as postoperative prostate-specific antigen (PSA) > 0.2 ng/ml or receipt of triggered salvage treatment. Model performance was assessed using time-dependent receiver operating curve (ROC) curves and survival plots. RESULTS: A total of 606 prostate specimens with gene expression and both pre- and postoperative PSA data were available for analysis. We identified a model that included Gleason grade and stage as well as five genes (CNRIP1, endoplasmic reticulum protein 44 [ERP44], metaxin-2 [MTX2], Ras homolog family member U [RHOU], and OXR1). Using the Lasso method, we determined that the five gene model independently predicted biochemical recurrence better than a model that included Gleason grade and tumor stage alone. The time-dependent ROCAUC for the five gene signature including Gleason grade and tumor stage was 0.868 compared to an AUC of 0.767 when Gleason grade and tumor stage were included alone. Low and high-risk groups displayed significant differences in their recurrence-free survival curves. The predictive model was subsequently validated on two independent data sets identified through the Gene Expression Omnibus. The model included genes (RHOU, MTX2, and ERP44) that have previously been implicated in prostate cancer biology. CONCLUSIONS: Expression of a small number of genes is associated with an increased risk of biochemical recurrence independent of classical pathological hallmarks.

Correlation of Tim-3 expression with chemokine levels for predicting the prognosis of patients with glioblastoma.

Glioblastoma (GBM) immunotherapy, which blocks the checkpoint inhibitor molecule T cell immunoglobulin domain and mucin domain-3 (Tim-3), has potential therapeutic applications. However, not all patients do benefit from the targeted therapy. This study aimed to explore Tim-3 expression correlated chemokine profiles and immune cell infiltration and investigate their potential as prognostic markers of glioblastoma (GBM) immunotherapy. We analyzed transcriptional data of GBM from TCGA database, to measure Tim-3 expression by R package DESeq2 analysis and observed differentially expressed genes in GBM samples with high Tim-3 expression levels. We also probed the relative gene enrichment pathways. Tim-3 expression was evident in biological processes including the recruitment of immune cells. We also identified some chemokines related to Tim-3 expression. The expression levels of CCL18, CXCL13 and CCL7 were significantly higher in GBM tissues with high Tim-3 expression than in GBM tissues with low Tim-3 expression. In addition, exploring the relationship between immune cell infiltration and Tim-3 expression suggested that Tim-3 expression was positively related to significant immune cell infiltration.

A retrospective analysis of GSE84010: Cell adhesion molecules might contribute to bevacizumab resistance in glioblastoma.

Bevacizumab (BEV) is an anti-angiogenesis antibody which has shown favorable therapeutic effects on some solid tumors. However, many clinical trials showed that BEV could only improve PFS instead of OS in glioblastoma (GBM) patients. However, some studies indicate that specific molecular subtypes of GBM could still benefit from combination treatment of BEV and Stupp protocol. Through the subgroup analysis of GSE84010 dataset, we found the neural and proneural subgroup can benefit from the administration of BEV in terms of OS, which is statistically significant. The further KEGG pathway enrichment analysis showed cell adhesion molecules (CAMs) pathway was enriched, and the expression of ITGAM has a predictive value for prognosis. These findings can provide some hints for future administration of BEV in newly diagnosed GBM patients.

Development of alternative splicing signature in lung squamous cell carcinoma.

Increasing evidence demonstrated that alternative splicing (AS) plays a vital role in tumorigenesis and clinical outcome of patient. However, systematical analysis of AS in lung squamous cell carcinoma (LUSC) is lacking and greatly necessary. Thus, this study was to systematically estimate the function of AS events served as prognostic indicators in LUSC. Among 31,345 mRNA AS events in 9633 genes, we detected 1996 AS in 1409 genes which have significant connection with overall survival (OS) of LUSC patients. Then, prognostic model based on seven types of AS events was established and we further constructed a combined prognostic model. The Kaplan-Meier curve results suggested that seven types of AS signatures and the combined prognostic model could exhibit robust performance in predicting prognosis. Patients in the high-risk group had significantly shorter OS than those in the low-risk group. The ROC showed all prognostic models had high accuracy and powerful predictive performance with different AUC ranging from 0.837 to 0.978. Moreover, the combined prognostic model had highest performance in risk stratification and predictive accuracy than single prognostic models and had higher accuracy than other mRNA model. Finally, a significant correlation network between survival-related AS genes and prognostic splicing factors (SFs) was established. In conclusion, our study provided several potential prognostic AS models and constructed splicing network between AS and SFs in LUSC, which could be used as potential indicators and treatment targets for LUSC patients.