The Prognostic Role of m6A-Related Genes in Paediatric Neuroblastoma Patients

Background and Purpose. N6-Methyladenosine (m6A) is the most abundant methylation modification form in eukaryotic mRNA. Nonetheless, the role of m6A-related genes in neuroblastoma (NB) is unclear. This study attempted to determine the prognostic role of m6A-related genes in NB patients. Methods. The gene expression data were extracted from the “Therapeutically Applicable Research to Generate Effective Treatments” (TARGET) database. The differentially expressed genes (DEGs) were identified, and the relationships between DEGs and m6A genes were explored. Then, the correlations among the m6A genes in neuroblastoma were investigated. Finally, the prognostic role of the m6A genes was studied, and the risk model was constructed. Results. 81 NB patients were extracted from the TARGET database. After comparing the gene expression between unfavorable and favorable outcome groups, 73 DEGs were identified, including 54 downregulated genes and 19 upregulated genes. In NB patients, we found that IGF2BP3, METTL14, and METTL16 are prognostic factors for disease-free survival (DFS) while IGF2BP3, METTL14, and METTL16 are prognostic factors for overall survival (OS). Besides, after the risk model construction, the OS between the two risk groups was drawn (log-rank p = 1.64 e − 08 , HR = 3.438 , 95% CI 2.24-5.278). The 1-, 3-, and 5-year time-dependent receiving operating characteristic (ROC) curves were also illustrated, and the areas under the receiver operating characteristic curves (AUCs) attained 0.75, 0.798, and 0.768, respectively. Conclusions. IGF2BP3, METTL14, and METTL16 were identified as the significant factors for DFS and OS in NB patients.

The 2022 Nucleic Acids Research database issue and the online molecular biology database collection

The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.

Identification of the 7-lncRNA Signature as a Prognostic Biomarker for Acute Myeloid Leukemia

A lot of evidence has emphasized the function of long noncoding RNAs (lncRNAs) in tumors’ development and progression. Nevertheless, there is still a lack of lncRNA biomarkers that can predict the prognosis of acute myeloid leukemia (AML). Our goal was to develop a lncRNA marker with prognostic value for the survival of AML. AML patients’ RNA sequencing data as well as clinical characteristics were obtained from the public TARGET database. Then, differentially expressed lncRNAs were identified in female and male AML samples. By adopting univariate and multivariate Cox regression analyses, AML patients’ survival was predicted by a seven-lncRNA signature. It was found that 95 abnormal expressed lncRNAs existed in AML. Then, the analysis of multivariate Cox regression showed that, among them, 7 (LINC00461, RP11-309M23.1, AC016735.2, RP11-61I13.3, KIAA0087, RORB-AS1, and AC012354.6) had an obvious prognostic value, and according to their cumulative risk scores, these 7 lncRNA signatures could independently predict the AML patients’ overall survival. Overall, the prognosis of AML patients could be predicted by a reliable tool, that is, seven-lncRNA prognostic signature.

PPARG, GNG12, and CD19 are Potential Independent Predictors of Central Nerve Recurrence in Childhood Acute Lymphoblastic Leukemia

Objective: To identify biomarkers that can predict the recurrence of the central nervous system (CNS) in children with acute lymphoblastic leukemia (ALL), and establish a prediction model. Materials and Methods: The transcriptome and clinical data collected by the Children's Oncology Group (COG) collaboration group in the Phase II study (use for test group) and Phase I study (use for validation group) of ALL in children were downloaded from the TARGET database. Transcriptome data were analyzed by bioinformatics method to identify core (hub) genes and establish a risk assessment model. Univariate Cox analysis was performed on each clinical data, and multivariate Cox regression analysis was performed on the obtained results and risk score. The children ALL phase I samples collected by the COG collaboration group in the TARGET database were used for verification. Results: A total of 1230 differentially expressed genes were screened out between the CNS relapsed and non-relapsed groups. Univariate multivariate Cox analysis of 10 hub genes identified showed that PPARG (HR=0.78, 95%CI=0.67-0.91, p=0.007), CD19 (HR=1.15, 95%CI=1.05-1.26, p=0.003) and GNG12 (HR=1.25, 95%CI=1.04-1.51, p=0.017) had statistical differences. The risk score was statistically significant in univariate (HR=3.06, 95%CI=1.30-7.19, p=0.011) and multivariate (HR=1.81, 95%CI=1.16-2.32, p=0.046) Cox regression analysis. The survival analysis results of the high and low-risk groups were different when the validation group was substituted into the model (p=0.018). In addition, the CNS involvement grading status at first diagnosis CNS3 vs. CNS1 (HR=5.74, 95%CI=2.01-16.4, p=0.001), T cell vs B cell (HR=1.63, 95% CI=1.06-2.49, p=0.026) were also statistically significant. Conclusions: PPARG, GNG12, and CD19 may be predictors of CNS relapse in childhood ALL.

Calmodulin as Ca2+-Dependent Interactor of FTO Dioxygenase

FTO is an N6-methyladenosine demethylase removing methyl groups from nucleic acids. Several studies indicate the creation of FTO complexes with other proteins. Here, we looked for regulatory proteins recognizing parts of the FTO dioxygenase region. In the Calmodulin (CaM) Target Database, we found the FTO C-domain potentially binding CaM, and we proved this finding experimentally. The interaction was Ca2+-dependent but independent on FTO phosphorylation. We found that FTO–CaM interaction essentially influences calcium-binding loops in CaM, indicating the presence of two peptide populations—exchanging as CaM alone and differently, suggesting that only one part of CaM interacts with FTO, and the other one reminds free. The modeling of FTO–CaM interaction showed its stable structure when the half of the CaM molecule saturated with Ca2+ interacts with the FTO C-domain, whereas the other part is disconnected. The presented data indicate calmodulin as a new FTO interactor and support engagement of the FTO protein in calcium signaling pathways.

Identification of Novel Biomarkers and Targets for Natural Products in Inhibiting Prostate Cancer

Background: Prostate cancer (PCa) is a common urinary system malignancy. The lack of specific and sensitive biomarkers for the diagnosis and prognosis of PCa makes it important to seek alternatives. Meanwhile, targeted PCa inhibitors are limited. Natural products that potentially target PCa may offer a useful approach.Methods: Expression profile datasets about PCa from GEO were analyzed. Core differential genes were identified by String and Cytoscape. GEPIA and HPA were utilized to further validate the key genes. The targets of natural products were obtained from the Drugbank, Therapeutic Target Database, BindingDB, PubChem, and chEMBL databases, and PCa therapeutic targets were generated from the GeneCards, OMIM, and PharmGkb databases. Cytoscape was also used to screen the core modules and disease-drug targets. Construction of molecular docking models of drug-core targets was performed by Autodock to confirm the accuracy of the targets.Results: Four identified biomarkers, CENPF, TPX2, TK1 and CCNB1 were verified by HPA. Five novel PCa biomarkers, RRM2, UBE2C, TOP2A, BIRC5 and ZWINT were also identified. All the nine markers indicated poor prognosis for PCa patients were verified by GEPIA. PCa carcinogenesis is found to be mainly associated with hepatic fibrosis pathway, ILK signaling, NRF2-mediated oxidative stress response and many others. Four key PCa targets for curcumin (EP300, RELA, EGFR, NFKB1), seven for taxol (PTEN, EGFR, ERBB2, TP53, KRAS, AR, AKT1) and two for ursolic acid (GSK3B, RELA) were identified by Cytoscape combined KEGG and verified by Autodock.Conclusions: The novel identified biomarkers in our study would be valuable for the diagnosis and prognosis of PCa. Key targets of curcumin, paclitaxel, and ursolic acid in PCa could lay a solid foundation for precise treatment and molecularly targeted therapy for PCa.

Comparison of false-discovery rates of various decoy databases

Background The target-decoy strategy effectively estimates the false-discovery rate (FDR) by creating a decoy database with a size identical to that of the target database. Decoy databases are created by various methods, such as, the reverse, pseudo-reverse, shuffle, pseudo-shuffle, and the de Bruijn methods. FDR is sometimes over- or under-estimated depending on which decoy database is used because the ratios of redundant peptides in the target databases are different, that is, the numbers of unique (non-redundancy) peptides in the target and decoy databases differ. Results We used two protein databases (the UniProt Saccharomyces cerevisiae protein database and the UniProt human protein database) to compare the FDRs of various decoy databases. When the ratio of redundant peptides in the target database is low, the FDR is not overestimated by any decoy construction method. However, if the ratio of redundant peptides in the target database is high, the FDR is overestimated when the (pseudo) shuffle decoy database is used. Additionally, human and S. cerevisiae six frame translation databases, which are large databases, also showed outcomes similar to that from the UniProt human protein database. Conclusion The FDR must be estimated using the correction factor proposed by Elias and Gygi or that by Kim et al. when (pseudo) shuffle decoy databases are used.

Contraceptive and infertility target DataBase (CITDBase): A contraceptive drug development tool for targeting and analysis of human reproductive specific tissues

The long and challenging drug development process begins with discovery biology for the selection of an appropriate target for a specific indication. Target is a broad term that can be applied to a range of biological entities such as proteins, genes and RNAs. Although there are numerous databases available for mining biological entities, publicly available searchable, downloadable databases to aid in target selection for a specific disease or indication (e.g. developing contraceptives and infertility treatments) are limited. We report the development of the Contraceptive Infertility Target DataBase (CITDBase: https://www.citdbase.org), which provides investigators an interface to mine existing transcriptomic and proteomic resources to identify high quality contraceptive/infertility targets. The creation of similar individualized databases is applicable to the identification of targets for other diseases and conditions.

Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption

Predicting system energy consumption accurately and adjusting dynamic operating parameters of the HVAC system in advance is the basis of realizing the model predictive control (MPC). In recent years, the LSTM network had made remarkable achievements in the field of load forecasting. This paper aimed to evaluate the potential of using an attentional-based LSTM network (A-LSTM) to predict HVAC energy consumption in practical applications. To evaluate the application potential of the A-LSTM model in real cases, the training set and test set used in experiments are the real energy consumption data collected by Kitakyushu Science Research Park in Japan. Pearce analysis was first carried out on the source data set and built the target database. Then five baseline models (A-LSTM, LSTM, RNN, DNN, and SVR) were built. Besides, to optimize the super parameters of the model, the Tree-structured of Parzen Estimators (TPE) algorithm was introduced. Finally, the applications are performed on the target database, and the results are analyzed from multiple perspectives, including model comparisons on different sizes of the training set, model comparisons on different system operation modes, graphical examination, etc. The results showed that the performance of the A-LSTM model was better than other baseline models, it could provide accurate and reliable hourly forecasting for HVAC energy consumption.

miR-339-5p Inhibits Autophagy to Reduce the Resistance of Laryngeal Carcinoma on Cisplatin via Targeting TAK1

Laryngeal carcinoma is a malignant disease with high morbidity and mortality. Several studies have indicated that miRNA dysfunction involves in the development of laryngeal carcinoma. In this study, the connection of miR-339-5p and laryngeal carcinoma was investigated, and qRT-PCR, CCK-8, and flow cytometry assay were used to observe the function of miR-339-5p on laryngeal carcinoma. Besides, the target database, dual-luciferase reporter assay, and western blot were used to explore the regulation mechanism of miR-339-5p on the progression of laryngeal carcinoma. The results showed that miR-339-5p was significantly downregulated in cisplatin-resistant cells of laryngeal carcinoma, and miR-339-5p upregulation could weaken the resistance of laryngeal carcinoma cells on cisplatin. Moreover, miR-339-5p could directly react with 3 ′ -UTR of TAK1, and TAK1 could reverse the effects of miR-339-5p on the progression of autophagy. In conclusion, this study suggests that miR-339-5p can inhibit the autophagy to decrease the cisplatin resistance of laryngeal carcinoma via targeting TAK1.