Leveraging Methylation Alterations to Discover Potential Causal Genes Associated With the Survival Risk of Cervical Cancer in TCGA Through a Two-Stage Inference Approach

Extrachromosomal circular DNAs in prostate adenocarcinoma: global characterizations and a novel prediction model

Background

The role of focal amplifications and extrachromosomal circular DNA (eccDNA) is still uncertain in prostate adenocarcinoma (PRAD). Here, we first mapped the global characterizations of eccDNA and then investigate the characterization of eccDNA-amplified key differentially expressed encoded genes (eKDEGs) in the progression, immune response and immunotherapy of PRAD.

Methods

Circular_seq was used in conjunction with the TCGA-PRAD transcriptome dataset to sequence, annotate, and filter for eccDNA-amplified differentially expressed coding genes (eDEGs) in PRAD and para-cancerous normal prostate tissues. Afterwards, risk models were created and eKDEGs linked to the PRAD prognosis were identified using Cox and Lasso regression analysis. The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration.

Results

In this research, there was no significant difference in the size, type, and chromosomal distribution of eccDNA in PRAD and para-cancerous normal prostate tissues. However, 4,290 differentially expressed eccDNAs were identified and 1,981 coding genes were amplified. Following that, 499 eDEGs were tested in conjunction with the transcriptome dataset from TCGA-PRAD. By using Cox and Lasso regression techniques, ZNF330 and PITPNM3 were identified as eKDEGs of PRAD, and a new PRAD risk model was conducted based on this. Survival analysis showed that the high-risk group of this model was associated with poor prognosis and validated in external data. Immune infiltration analysis showed that the model risks affected immune cell infiltration in PRAD, not only mediating changes in immune cell function, but also correlating with immunophenotyping. Furthermore, the high-risk group was negatively associated with anti-CTLA-4/anti-PD-1 response and mutational burden. In addition, Tumor Immune Dysfunction and Exclusion analyses showed that high-risk group was more prone to immune escape. Drug sensitivity analyses identified 10 drugs, which were instructive for PRAD treatment.

Conclusion

ZNF330 and PITPNM are the eKDEGs for PRAD, which can be used as potential new prognostic markers. The two-factor combined risk model can effectively assess the survival and prognosis of PRAD patients, but also can predict the different responses of immunotherapy to PRAD patients, which may provide new ideas for PRAD immunotherapy.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

BACKGROUND

The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.

METHODS

We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.

RESULTS

We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.

CONCLUSION

Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Functions, Mechanisms, and therapeutic applications of the inositol pyrophosphates 5PP-InsP5 and InsP8 in mammalian cells

Water-soluble myo-inositol phosphates have long been characterized as second messengers. The signaling properties of these compounds are determined by the number and arrangement of phosphate groups on the myo-inositol backbone. Recently, higher inositol phosphates with pyrophosphate groups were recognized as signaling molecules. 5-Diphosphoinositol 1,2,3,4,6-pentakisphosphate (5PP-InsP5) is the most abundant isoform, constituting more than 90% of intracellular inositol pyrophosphates. 5PP-InsP5 can be further phosphorylated to 1,5-bisdiphosphoinositol 2,3,4,6-tetrakisphosphate (InsP8). These two molecules, 5PP-InsP5 and InsP8, are present in various subcellular compartments, where they participate in regulating diverse cellular processes such as cell death, energy homeostasis, and cytoskeletal dynamics. The synthesis and metabolism of inositol pyrophosphates are subjected to tight regulation, allowing for their highly specific functions. Blocking the 5PP-InsP5/InsP8 signaling pathway by inhibiting the biosynthesis of 5PP-InsP5 demonstrates therapeutic benefits in preclinical studies, and thus holds promise as a therapeutic approach for certain diseases treatment, such as metabolic disorders.

Zinc finger protein 330 regulates Ramie mosaic virus infection in the whitefly Bemisia tabaci MED

BACKGROUND

The whitefly, Bemisia tabaci (Gennadius) is one of the most economically important pests that cause serious damage to agricultural production by transmitting plant pathogenic viruses. Approximately 90% of the virus species transmitted by the whitefly are members of the genus begomovirus. Ramie mosaic virus (RaMoV) is a new bipartite begomovirus that causes severe damage to ramie and several other economic crops in China. In previous studies, we have demonstrated that RaMoV had no obvious direct or indirect effects on B. tabaci. However, whether B. tabaci affects RaMoV infection and the molecular mechanisms of their interaction remain unclear.

RESULTS

Here, we identified a zinc finger protein 330 (ZNF330) in B. tabaci MED interacted with the coat protein (CP) of RaMoV by the yeast two-hybrid assay. Then the interaction between ZNF330 and RaMoV CP was further verified by glutathione S-transferase (GST) pull-down assay. The expression of ZNF330 gene was continuously induced after RaMoV infection. ZNF330 negatively regulated RaMoV replication in the B. tabaci MED. Furthermore, the longevity and fecundity of RaMoV-infected female adults were significantly decreased after silencing of ZNF330.

CONCLUSIONS

Our results indicated that the ZNF330 protein was involved in the negative regulation of RaMoV replication in the B. tabaci MED. High viral accumulation caused by ZNF330 silencing is detrimental to fecundity and longevity of the B. tabaci MED. These findings provided a new insight into identifying the binding partners in whitefly with viral CP and fully understanding the complex interactions between begomoviruses and their whitefly vector. © 2023 Society of Chemical Industry.

A Novel Fatty Acid Metabolism-Associated Risk Model for Prognosis Prediction in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults, with an unfavourable outcome and a high rate of recurrence due to its heterogeneity. Dysregulation of fatty acid metabolism plays a crucial role in the development of several tumours. However, the value of fatty acid metabolism (FAM) in the progression of AML remains unclear. In this study, we obtained RNA sequencing and corresponding clinicopathological information from the TCGA and GEO databases. Univariate Cox regression analysis and subsequent LASSO Cox regression analysis were utilized to identify prognostic FAM-related genes and develop a potential prognostic risk model. Kaplan-Meier analysis was used for prognostic significances. We also performed ROC curve to illustrate that the risk model in prognostic prediction has good performance. Moreover, significant differences in immune infiltration landscape were found between high-risk and low-risk groups using ESTIMATE and CIBERSOT algorithms. In the end, differential expressed genes (DEGs) were analyzed by gene set enrichment analysis (GSEA) to preliminarily explore the possible signaling pathways related to the prognosis of FAM and AML. The results of our study may provide potential prognostic biomarkers and therapeutic targets for AML patients, which is conducive to individualized precision therapy.

Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models

Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.

SYDE1 Acts as an Oncogene in Glioma and has Diagnostic and Prognostic Values

Objectives: Gliomas remain one of serious public health problems worldwide which demand further and deeper investigation. The aim of this study was to explore the association between synapse defective protein 1 homolog 1 (SYDE1) and gliomas via public database analysis and in vitro validation to determine the potential diagnostic and prognostic values. Methods and Results: Compared with healthy brain tissues, there was a significant increase in SYDE1 expression in glioma tissues. Additionally, SYDE1 exhibited higher expression levels in glioma patients with unfavorable clinicopathological factors. In vitro knockdown of SYDE1 in glioma cell lines A172 inhibited their migrative and invasive ability but not the proliferative ability. GO and KEGG pathway analysis of the top 100 genes coexpressed with SYDE1 showed enrichments of tumor-associated terms. Further bioinformatic analysis revealed that the SNHG16/hsa-miR-520e/SYDE1 axis might be involved in glioma development. Conclusions: SYDE1 is expressed at higher levels in gliomas than in healthy brains, and can promote metastasis and invasion but not proliferation of gliomas. Furthermore, SYDE1 has values in the diagnosis and prognosis prediction of gliomas.