Genomic instability in lower-grade glioma: Prediction of prognosis based on lncRNA and immune infiltration

Identification of PATL1 as a prognostic and immunotherapeutic predictive factor for nasal-type natural killer/T-cell lymphoma and head and neck squamous cell carcinoma

This research examines the function of protein associated with topoisomerase II homolog 1 (PATL1) in nasal-type natural killer/T-cell lymphoma (NKTCL) and head and neck squamous cell carcinoma (HNSCC). We analyzed bulk RNA-seq data from NKTCL, nasal polyps, and normal nasal mucosa, identifying 439 differentially expressed genes. Machine learning algorithms highlighted PATL1 as a hub gene. PATL1 exhibited significant upregulation in NKTCL and HNSCC tumor samples in comparison to normal tissues, showing high diagnostic accuracy (AUC = 1.000) for NKTCL. Further analysis of local hospital data identified PATL1 as an independent prognostic risk factor for NKTCL. Data analysis of TCGA and GEO datasets revealed that high PATL1 expression correlated with poorer prognosis in HNSCC patients (p < 0.05). We also constructed a PATL1-based nomogram, which emerged as an independent prognostic predictor for HNSCC after addressing missing values. Additionally, we found a strong correlation between PATL1 and various immune cell infiltrates (e.g., activated.CD4 T cell), and a significant association with the expression of 37 immune checkpoints genes (e.g., CTLA4, PDCD1) and 20 N6-methyladenosine-related genes (e.g., ZC3H13, METTL3) (all p < 0.05). Both TCIA and TIDE algorithms suggested that PATL1 could potentially predict immunotherapy efficacy (p < 0.05). Cellular experiments demonstrated that transfection with a silencing plasmid of PATL1 significantly inhibited the malignant behaviors of SNK6 and FaDu cell lines(p < 0.05). In conclusion, our findings suggest that PATL1 may serve as a valuable prognostic and predictive biomarker in NKTCL and HNSCC, highlighting its significant role in these cancers.

Stratification of glioma based on stemness scores in bulk and single-cell transcriptomes

BACKGROUND

Brain tumours are known to have a high mortality and morbidity rate due to their localised and frequent invasive growth. The concept that glioma resistance could originate from the dissimilarity in the vulnerability of clonogenic glial stem cells to chemotherapeutic drugs and radiation has driven the scientific community to reexamine the comprehension of glioma growth and strategies that target these cells or modify their stemness.

METHODS

Based on the enrichment scores of 12 stemness signatures, we identified glioma subtypes in both tumour bulks and single cells by clustering analysis. Furthermore, we comprehensively compared molecular and clinical features among the glioma subtypes.

RESULTS

Consistently, in seven different datasets, hierarchical clustering uncovered three subtypes of glioma, termed Stem-H, Stem-M, and Stem-L, with high, medium, and low stemness signatures, respectively. Stem-H and Stem-L exhibited the most unfavorable and favourable overall and disease-free survival, respectively. Stem-H showed the highest enrichment scores of the EMT, invasion, proliferation, differentiation, and metastasis processes signatures, while Stem-L displayed the lowest. Stem-H harboured a greater proportion of late-stage tumours compared to Stem-L. Moreover, Stem-H manifested higher tumour mutation burden, DNA damage repair and cell cycle activity, intratumour heterogeneity, and a more frequent incidence of TP53 and EGFR mutations than Stem-L. In contrast, Stem-L had higher O6-Methylguanine-DNA Methyltransferase (MGMT) methylation levels.

CONCLUSION

The classification of glioma based on stemness may offer new insights into the biology of the tumour, as well as more accurate clinical management of the disease.

A multi-omics analysis-based model to predict the prognosis of low-grade gliomas

Lower-grade gliomas (LGGs) exhibit highly variable clinical behaviors, while classic histology characteristics cannot accurately reflect the authentic biological behaviors, clinical outcomes, and prognosis of LGGs. In this study, we carried out analyses of whole exome sequencing, RNA sequencing and DNA methylation in primary vs. recurrent LGG samples, and also combined the multi-omics data to construct a prognostic prediction model. TCGA-LGG dataset was searched for LGG samples. 523 samples were used for whole exome sequencing analysis, 532 for transcriptional analysis, and 529 for DNA methylation analysis. LASSO regression was used to screen genes with significant association with LGG survival from the frequently mutated genes, differentially expressed genes, and differentially methylated genes, whereby a prediction model for prognosis of LGG was further constructed and validated. The most frequently mutated diver genes in LGGs were IDH1 (77%), TP53 (48%), ATRX (37%), etc. Top significantly up-regulated genes were C6orf15, DAO, MEOX2, etc., and top significantly down-regulated genes were DMBX1, GPR50, HMX2, etc. 2077 genes were more and 299 were less methylated in recurrent vs. primary LGG samples. Thirty-nine genes from the above analysis were included to establish a prediction model of survival, which showed that the high-score group had a very significantly shorter survival than the low-score group in both training and testing sets. ROC analysis showed that AUC was 0.817 for the training set and 0.819 for the testing set. This study will be beneficial to accurately predict the survival of LGGs to identify patients with poor prognosis to take specific treatment as early, which will help improve the treatment outcomes and prognosis of LGG.

Oligomer-Aβ42 suppress glioma progression via potentiating phagocytosis of microglia

AIMS

Glioma is characterized by an immunosuppressed environment and a poor prognosis. The accumulation of Amyloid β (Aβ) leads to an active environment during the early stages of Alzheimer's disease (AD). Aβ is also present in glioma tissues; however, the biological and translational implications of Aβ in glioma are elusive.

METHODS

Immunohistochemical (IHC) staining, Kaplan-Meier (KM) survival analysis and Cox regression analysis on a cohort of 79 patients from our institution were performed to investigate the association between Aβ and the malignancy of glioma. Subsequently, the potential of oligomer-Aβ42 (OAβ42) to inhibit glioma growth was investigated in vivo and in vitro. Immunofluorescence staining and phagocytosis assays were performed to evaluate the activation of microglia. Finally, RNA-seq was utilized to identify the predominant signaling involved in this process and in vitro studies were performed to validate them.

RESULTS

A positive correlation between Aβ and a favorable prognosis was observed in glioma. Furthermore, OAβ42 suppressed glioma growth by enhancing the phagocytic activity of microglia. Insulin-like growth factor 1 (IGF-1) secreted by OAβ42-activated microglia was essential in the engulfment process.

CONCLUSION

Our study proved an anti-glioma effect of Aβ, and microglia could serve as a cellular target for treating glioma with OAβ42.

Prognosis and immune response of a cuproptosis-related lncRNA signature in low grade glioma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in low grade glioma (LGG) at present. In this study, data on low grade glioma patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied univariate/multivariate, and LASSO regression algorithms, finally identified 11 lncRNAs for constructing prognosis prediction models, and constructed a risk scoring model. The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of LGG patients. Furthermore, the analyses of immunotherapy, immune microenvironment, as well as functional enrichment were also performed. Finally, we verified the expression of these six prognostic key lncRNAs using real-time polymerase chain reaction (RT-PCR). In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in LGG and aims to open up new directions for LGG therapy.

CDC20 in and out of mitosis: a prognostic factor and therapeutic target in hematological malignancies

Cell division cycle 20 homologue (CDC20) is a well-known regulator of cell cycle, as it controls the correct segregation of chromosomes during mitosis. Many studies have focused on the biological role of CDC20 in cancer development, as alterations of its functionality have been linked to genomic instability and evidence demonstrated that high CDC20 expression levels are associated with poor overall survival in solid cancers. More recently, novel CDC20 functions have been demonstrated or suggested, including the regulation of apoptosis and stemness properties and a correlation with immune cell infiltration. Here, we here summarize and discuss the role of CDC20 inside and outside mitosis, starting from its network of interacting proteins. In the last years, CDC20 has also attracted more interest in the blood cancer field, being overexpressed and showing an association with prognosis both in myeloid and lymphoid malignancies. Preclinical findings showed that selective CDC20 and APC/CCDC20/APC/CCDH1 inhibitors, namely Apcin and proTAME, are effective against lymphoma and multiple myeloma cells, resulting in mitotic arrest and apoptosis and synergizing with clinically-relevant drugs. The evidence and hypothesis presented in this review provide the input for further biological and chemical studies aiming to dissect novel potential CDC20 roles and targeting strategies in hematological malignancies.

Modification Patterns of DNA Methylation-Related lncRNAs Regulating Genomic Instability for Improving the Clinical Outcomes and Tumour Microenvironment Characterisation of Lower-Grade Gliomas

Background: DNA methylation is an important epigenetic modification that affects genomic instability and regulates gene expression. Long non-coding RNAs (lncRNAs) modulate gene expression by interacting with chromosomal modifications or remodelling factors. It is urgently needed to evaluate the effects of DNA methylation-related lncRNAs (DMlncRNAs) on genome instability and further investigate the mechanism of action of DMlncRNAs in mediating the progression of lower-grade gliomas (LGGs) and their impact on the immune microenvironment.

Methods: LGG transcriptome data, somatic mutation profiles and clinical features analysed in the present study were obtained from the CGGA, GEO and TCGA databases. Univariate, multivariate Cox and Lasso regression analyses were performed to establish a DMlncRNA signature. The KEGG and GO analyses were performed to screen for pathways and biological functions associated with key genes. The ESTIMATE and CIBERSORT algorithms were used to determine the level of immune cells in LGGs and the immune microenvironment fraction. In addition, DMlncRNAs were assessed using survival analysis, ROC curves, correlation analysis, external validation, independent prognostic analysis, clinical stratification analysis and qRT-PCR.

Results: We identified five DMlncRNAs with prognostic value for LGGs and established a prognostic signature using them. The Kaplan–Meier analysis revealed 10-years survival rate of 10.10% [95% confidence interval (CI): 3.27–31.40%] in high-risk patients and 57.28% (95% CI: 43.17–76.00%) in low-risk patients. The hazard ratio (HR) and 95% CI of risk scores were 1.013 and 1.009–1.017 (p &lt; 0.001), respectively, based on the univariate Cox regression analysis and 1.009 and 1.004–1.013 (p &lt; 0.001), respectively, based on the multivariate Cox regression analysis. Therefore, the five-lncRNAs were identified as independent prognostic markers for patients with LGGs. Furthermore, GO and KEGG analyses revealed that these lncRNAs are involved in the prognosis and tumorigenesis of LGGs by regulating cancer pathways and DNA methylation.

Conclusion: The findings of the study provide key information regarding the functions of lncRNAs in DNA methylation and reveal that DNA methylation can regulate tumour progression through modulation of the immune microenvironment and genomic instability. The identified prognostic lncRNAs have high potential for clinical grouping of patients with LGGs to ensure effective treatment and management.

PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma

Lower-grade glioma (LGG) is a diffuse infiltrative tumor of the central nervous system, which lacks targeted therapy. We investigated the role of Podocan-like 1 (PODNL1) methylation in LGG clinical outcomes using the TCGA-LGG transcriptomics dataset. We identified four PODNL1 CpG sites, cg07425555, cg26969888, cg18547299, and cg24354933, which were associated with unfavorable overall survival (OS) and disease-free survival (DFS) in univariate and multivariate analysis after adjusting for age, gender, tumor-grade, and IDH1-mutation. In multivariate analysis, the OS and DFS hazard ratios ranged from 0.44 to 0.58 (p < 0.001) and 0.62 to 0.72 (p < 0.001), respectively, for the four PODNL1 CpGs. Enrichment analysis of differential gene and protein expression and analysis of 24 infiltrating immune cell types showed significantly increased infiltration in LGGs and its histological subtypes with low-methylation levels of the PODNL1 CpGs. High PODNL1 expression and low-methylation subgroups of the PODNL1 CpG sites were associated with significantly increased PD-L1, PD-1, and CTLA4 expressions. PODNL1 methylation may thus be a potential indicator of immune checkpoint blockade response, and serve as a biomarker for determining prognosis and immune subtypes in LGG.