Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma

SMG9 is a novel prognostic-related biomarker in glioma correlating with ferroptosis and immune infiltrates

Background

Glioma is the most frequent type of malignancy that may damage the brain with high morbidity and mortality rates and patients' prognoses are still dismal. Ferroptosis, a newly uncovered mode of programmed cell death, may be triggered to destroy glioma cells. Nevertheless, the significance of ferroptosis-related genes (FRGs) in predicting prognosis in glioma individuals is still a mystery.

Methods

The CGGA (The Chinese Glioma Atlas), GEO (Gene Expression Omnibus), and TCGA (The Cancer Genome Atlas) databases were all searched to obtain the glioma expression dataset. First, TCGA was searched to identify differentially expressed genes (DEGs). This was followed by a machine learning algorithm-based screening of the glioma's most relevant genes. Additionally, these genes were subjected to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. The chosen biological markers were then submitted to single-cell, immune function, and gene set enrichment analysis (GSEA). In addition, we performed functional enrichment and Mfuzz expression profile clustering on the most promising biological markers to delve deeper into their regulatory mechanisms and assess their clinical diagnostic capacities.

Results

We identified 4444 DEGs via differential analysis and 564 FRGs from the FerrDb database. The two were subjected to intersection analysis, which led to the discovery of 143 overlapping genes. After that, glioma biological markers were identified in fourteen genes by the use of machine learning methods. In terms of its use for clinical diagnosis, SMG9 stands out as the most significant among these biomarkers.

Conclusion

In light of these findings, the identification of SMG9 as a new biological marker has the potential to provide information on the mechanism of action and the effect of the immune milieu in glioma. The promise of SMG9 in glioma prognosis prediction warrants more study.

Quantitative relationships of FAM50B and PTCHD3 methylation with reduced intelligence quotients in school aged children exposed to lead: Evidence from epidemiological and in vitro studies

At present, the application of DNA methylation (DNAm) biomarkers in environmental health risk assessment (EHRA) is more challenging due to the unclearly quantitative relationship between them. We aimed to explore the role of FAM50B and PTCHD3 at the level of signaling pathways, and establish the quantitative relationship between them and children's intelligence quotients (IQs). DNAm of target regions was measured in multiple cell models and was compared with the human population data. Then the dose-response relationships of lead exposure with neurotoxicity and DNAm were established by benchmark dose (BMD) model, followed by potential signaling pathway screening. Results showed that there was a quantitative linear relationship between children's IQs and FAM50B/PTCHD3 DNAm (DNAm between 51.40 % - 78.78 % and 31.41 % - 74.19 % for FAM50B and PTCHD3, respectively), and this relationship was more significant when children's IQs > 90. The receiver operating characteristic (ROC) and calibration curves showed that FAM50B/PTCHD3 DNAm had a satisfying accuracy and consistency in predicting children's IQs, which was confirmed by sensitivity analysis of gender and CpG site grouping data. In cell experiments, there was also a quantitative linear relationship between FAM50B DNAm and reactive oxygen species (ROS) production, which was mediated by PI3K-AKT signaling pathway. In addition, the lead BMD of ROS was close to that of FAM50B DNAm, suggesting that FAM50B DNAm was a suitable biomarker for the risk assessments of adverse outcomes induced by lead. Taken collectively, these results suggest that FAM50B/PTCHD3 can be applied to EHRA and the prevention/intervention of adverse effects of lead on children's IQs.

Interaction of a Novel Alternatively Spliced Variant of HSD11B1L with Parkin Enhances the Carcinogenesis Potential of Glioblastoma: Peiminine Interferes with This Interaction

Glioblastoma (GBM) is a primary brain tumor of unknown etiology. It is extremely aggressive, incurable and has a short average survival time for patients. Therefore, understanding the precise molecular mechanisms of this diseases is essential to establish effective treatments. In this study, we cloned and sequenced a splice variant of the hydroxysteroid 11-β dehydrogenase 1 like gene (HSD11B1L) and named it HSD11B1L-181. HSD11 B1L-181 was specifically expressed only in GBM cells. Overexpression of this variant can significantly promote the proliferation, migration and invasion of GBM cells. Knockdown of HSD11B1L-181 expression inhibited the oncogenic potential of GBM cells. Furthermore, we identified the direct interaction of parkin with HSD11B1L-181 by screening the GBM cDNA expression library via yeast two-hybrid. Parkin is an RBR E3 ubiquitin ligase whose mutations are associated with tumorigenesis. Small interfering RNA treatment of parkin enhanced the proliferative, migratory and invasive abilities of GBM. Finally, we found that the alkaloid peiminine from the bulbs of Fritillaria thunbergii Miq blocks the interaction between HSD11B1L-181 and parkin, thereby lessening carcinogenesis of GBM. We further confirmed the potential of peiminine to prevent GBM in cellular, ectopic and orthotopic xenograft mouse models. Taken together, these findings not only provide insight into GBM, but also present an opportunity for future GBM treatment.

Identification of the prognostic signature based on genomic instability-related alternative splicing in colorectal cancer and its regulatory network

Background: Colorectal cancer (CRC) is a heterogeneous disease with many somatic mutations defining its genomic instability. Alternative Splicing (AS) events, are essential for maintaining genomic instability. However, the role of genomic instability-related AS events in CRC has not been investigated.

Methods: From The Cancer Genome Atlas (TCGA) program, we obtained the splicing profiles, the single nucleotide polymorphism, transcriptomics, and clinical information of CRC. Combining somatic mutation and AS events data, a genomic instability-related AS signature was constructed for CRC. Mutations analyses, clinical stratification analyses, and multivariate Cox regression analyses evaluated this signature in training set. Subsequently, we validated the sensitivity and specificity of this prognostic signature using a test set and the entire TCGA dataset. We constructed a nomogram for the prognosis prediction of CRC patients. Differentially infiltrating immune cells were screened by using CIBERSORT. Inmmunophenoscore (IPS) analysis was used to evaluate the response of immunotherapy. The AS events-related splicing factors (SF) were analyzed by Pearson’s correlation. The effects of SF regulating the prognostic AS events in proliferation and migration were validated in Caco2 cells.

Results: A prognostic signature consisting of seven AS events (PDHA1-88633-ES, KIAA1522-1632-AP, TATDN1-85088-ES, PRMT1-51042-ES, VEZT-23786-ES, AIG1-77972-AT, and PHF11-25891-AP) was constructed. Patients in the high-risk score group showed a higher somatic mutation. The genomic instability risk score was an independent variable associated with overall survival (OS), with a hazard ratio of a risk score of 1.537. The area under the curve of receiver operator characteristic curve of the genomic instability risk score in predicting the OS of CRC patients was 0.733. Furthermore, a nomogram was established and could be used clinically to stratify patients to predict prognosis. Patients defined as high-risk by this signature showed a lower proportion of eosinophils than the low-risk group. Patients with low risk were more sensitive to anti-CTLA4 immunotherapy. Additionally, HSPA1A and FAM50B were two SF regulating the OS-related AS. Downregulation of HSPA1A and FAM50B inhibited the proliferation and migration of Caco2 cells.

Conclusion: We constructed an ideal prognostic signature reflecting the genomic instability and OS of CRC patients. HSPA1A and FAM50B were verified as two important SF regulating the OS-related AS.

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903-1.000) for local recurrence; 0.864 (0.726-0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or "radiomic risk score", increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.