Increased Expression of Sema3C Indicates a Poor Prognosis and Is Regulated by miR-142-5p in Glioma

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci

Backgrounds

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Methods

A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes.

Results

Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable.

Conclusion

We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder

BACKGROUND

Integrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse data.

METHODS

We propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT) to utilize intra- and inter-omics information comprehensively. Additionally, attention mechanisms with confidence learning are incorporated for enhanced feature representation and trustworthy prediction.

RESULTS

Extensive experiments were conducted on four benchmark datasets to evaluate the effectiveness of our proposed model, including BRCA, ROSMAP, LGG, and KIPAN. Our model significantly improved most evaluation measurements and consistently surpassed the state-of-the-art methods. Ablation studies showed that the auxiliary classifiers significantly boosted classification accuracy in the ROSMAP and LGG datasets. Moreover, the attention mechanisms and confidence evaluation block contributed to improvements in the predictive accuracy and generalizability of our model.

CONCLUSIONS

The proposed framework exhibits superior performance in disease classification and biomarker discovery, establishing itself as a robust and versatile tool for analyzing multi-layer biological data. This study highlights the significance of elaborated designed deep learning methodologies in dissecting complex disease phenotypes and improving the accuracy of disease predictions.

Integrated comparison of the mRNAome in cartilage, synovium, and macrophages in osteoarthritis

The precise molecular mechanisms associated with osteoarthritis (OA), the most common musculoskeletal disorder, are poorly understood. There are currently no effective treatments to prevent the initiation and progression of the disease. In recent years, the development of mRNAome has made it possible to identify new mechanisms and therapeutic targets. However, the differentially expressed genes screened by different microarrays are not completely the same. In order to avoid this shortcoming, we integrate the different genes from different tissues and data sets, and select the commonly expressed genes for further studies.

Association of Immune Semaphorins with COVID-19 Severity and Outcomes

Semaphorins have recently been recognized as crucial modulators of immune responses. In the pathogenesis of COVID-19, the activation of immune responses is the key factor in the development of severe disease. This study aimed to determine the association of serum semaphorin concentrations with COVID-19 severity and outcomes. Serum semaphorin concentrations (SEMA3A, -3C, -3F, -4D, -7A) were measured in 80 hospitalized adult patients with COVID-19 (moderate (n = 24), severe (n = 32), critical, (n = 24)) and 40 healthy controls. While SEMA3C, SEMA3F and SEMA7A serum concentrations were significantly higher in patients with COVID-19, SEMA3A was significantly lower. Furthermore, SEMA3A and SEMA3C decreased with COVID-19 severity, while SEMA3F and SEMA7A increased. SEMA4D showed no correlation with disease severity. Serum semaphorin levels show better predictive values than CRP, IL-6 and LDH for differentiating critical from moderate/severe COVID-19. SEMA3F and SEMA7A serum concentrations were associated with the time to recovery, requirement of invasive mechanical ventilation, development of pulmonary thrombosis and nosocomial infections, as well as with in-hospital mortality. In conclusion, we provide the first evidence that SEMA3A, SEMA3C, SEMA3F and SEMA7A can be considered as new biomarkers of COVID-19 severity.

Emerging roles and mechanisms of semaphorins activity in cancer

Semaphorins are regulatory molecules that are linked to the modulation of several cancer processes, such as angiogenesis, cancer cell invasiveness and metastasis, tumor growth, as well as cancer cell survival. Semaphorin (SEMA) activity depends on the cancer histotypes and their particularities. In broad terms, the effects of SEMAs result from their interaction with specific receptors/co-receptors - Plexins, Neuropilins and Integrins - and the subsequent effects upon the downstream effectors (e.g. PI3K/AKT, MAPK/ERK). The present article serves as an integrative review work, discussing the broad implications of semaphorins in cancer, focusing on cell proliferation/survival, angiogenesis, invasion, metastasis, stemness, and chemo-resistance/response whilst highlighting their heterogeneity as a family. Herein, we emphasized that semaphorins are largely implicated in cancer progression, interacting with the tumor microenvironment components. Whilst some SEMAs (e.g. SEMA3A, SEMA3B) function widely as tumor suppressors, others (e.g. SEMA3C) act as pro-tumor semaphorins. The differences observed in terms of the biological structure of SEMAs and the particularities of each cancer histotypes require that each semaphorin be viewed as a unique entity, and its roles must be researched accordingly. A more in-depth and comprehensive view of the molecular mechanisms that promote and sustain the malignant behavior of cancer cells is of utmost importance.

Circ_0000020 elevates the expression of PIK3CA and facilitates the malignant phenotypes of glioma cells via targeting miR-142-5p

BACKGROUND

Multiple circular RNAs (circRNAs) have been recently described as crucial oncogenic factors or tumor suppressors. This study aimed to investigate the role of circ_0000020 in glioma progression.

METHODS

Circ_0000020 and miR-142-5p expressions in glioma samples were assessed through qRT-PCR, and then the association between pathological indexes and circ_0000020 expressions was analyzed. Functional experiment was performed with human glioma cell lines U251 and U87. Gain-of-function and loss-of-function models were established. CCK-8 assay was used to detect glioma cell proliferation. Transwell assay was used to examine glioma cell migration and invasion. The regulatory relationships among circ_0000020, miR-142-5p and phosphatidylinositol 3-kinase C (PIK3CA) were investigated by bioinformatics analysis, luciferase reporter assay, qRT-PCR and Western blot. In vivo tumorigenesis assay was performed with nude mice to further validate the demonstrations of in vitro experiments.

RESULTS

Circ_0000020 expression in glioma samples was remarkably increased compared with that in normal brain tissues and its high expression was associated with unfavorable pathological indexes. Circ_0000020 overexpression remarkably accelerated proliferation, migration and invasion of glioma cells. Accordingly, circ_0000020 knockdown suppressed the malignant phenotypes of glioma cells. Circ_0000020 overexpression significantly reduced miR-142-5p expression by sponging it, and circ_0000020 could enhance the expression of PIK3CA, which was a target gene of miR-142-5p.

CONCLUSIONS

Circ_0000020 promotes glioma progression via miR-142-5p/PIK3CA axis.