The Role of m5C-Related lncRNAs in Predicting Overall Prognosis and Regulating the Lower Grade Glioma Microenvironment

Molecular and clinical characterization of ANG expression in gliomas and its association with tumor-related immune response

Background

Angiogenin (ANG) has been widely reported as a crucial molecular regulator in multiple malignancies. However, its role in gliomagenesis remains unclear. This study aimed to investigate the molecular and clinical characterization of ANG expression at transcriptome level and the association with glioma-related immune response.

Methods

A total of 301 glioma samples with mRNA microarray data (CGGA301) was obtained from the official website of CGGA project for yielding preliminary results, followed by validation in two independent RNAseq datasets, including TCGA with 697 samples and CGGA325 with 325 patients. Moreover, CGGA single-cell RNAseq (scRNAseq) data were analyzed to identify differential and dynamic ANG expression in different cells. Immunohistochemistry was performed to evaluate ANG protein expression across different WHO grades in a tissue microarray (TMA). Figure generation and statistical analysis were conducted using R software.

Results

ANG expression was associated with clinical features, malignant phenotypes, and genomic alterations. Based on significantly correlated genes of ANG, subsequent gene ontology (GO) and gene set enrichment analysis (GSEA) concordantly pointed to the significant association of ANG in immune-related biological processes. Moreover, ANG showed robust correlations with canonical immune checkpoint molecules, including PD1 signaling, CTLA4, TIM3, and B7H3. Gene sets variation analysis (GSVA) found that ANG was particularly associated with activities of macrophages and antigen presentation cells (APCs) in both LGG and GBM across different datasets. Furthermore, the higher-ANG milieu seemed to recruit monocyte-macrophage lineage and dendritic cells into the glioma microenvironment. According to scRNAseq analysis, ANG was mainly expressed by neoplastic cells and tumor-associated macrophages (TAMs) and was correlated with the initiation and progression of tumor cells and the polarization of TAMs. Finally, Kaplan-Meier plots demonstrated that higher expression of ANG was significantly correlated with shorter survival in gliomas. Cox regression analysis further confirmed ANG as an independent predictor of prognosis for gliomas of all three datasets.

Conclusion

ANG is significantly correlated with a range of malignant and aggressive characteristics in gliomas and reveals considerable prognostic value for glioma patients. ANG seems to be primarily associated with immune activities of macrophages and APCs in gliomas. Furthermore, ANG is mainly expressed in neoplastic cells and TAMs and is involved in the initiation and progression of neoplastic cells as well as macrophage polarization.

Deciphering glioma epitranscriptome: focus on RNA modifications

Gliomas are highly malignant tumors accounting for the majority of brain neoplasms. They are characterized by nuclear atypia, high mitotic rate and cellular polymorphism that often contributes to aggressiveness and resistance to standard therapy. They often associate with challenging treatment approaches and poor outcomes. New treatment strategies or regimens to improve the efficacy of glioma treatment require a deeper understanding of glioma occurrence and development as well as elucidation of their molecular biological characteristics. Recent studies have revealed RNA modifications as a key regulatory mechanism involved in tumorigenesis, tumor progression, immune regulation, and response to therapy. The present review discusses research advances on several RNA modifications involved in glioma progression and tumor microenvironment (TME) immunoregulation as well as in the development of adaptive drug resistance, summarizing current progress on major RNA modification targeting strategies.

Identification and validation of m5c-related lncRNA risk model for ovarian cancer

Ovarian cancer (OC) is one of the common malignant tumors that seriously threaten women's health, and there is a lack of clinical prognostic predictors, while m5c and lncRNA have been shown to be predictive of multiple cancers, including OC. Therefore, our goal was to construct a risk model for OC based on m5c-related lncRNA.340 m5c-related lncRNA were identified and a novel risk model of OC ground on nine m5C-related lncRNA was constructed using LASSO-COX regression analysis. Kaplan-Meier analysis showed there was a significant difference in prognosis between risk groups. We established a nomogram which was a good predictor of overall survival. In addition, GSEA was enriched in multiple pathways and immune function analysis suggested that immune infiltration varies depending on the risk group. In vitro experiments show that AC005562.1, a key lncRNA of the risk model, is highly expressed in OC cells and promotes OC cell proliferation. Finally, we further explored the potential biological markers of m5c-related lncRNA in OC with WGCNA analysis and established a ceRNA network. In conclusion,we have developed a reliable m5c-related prediction model and performed systematic validation and exploration of various aspects. These results can be used for the assessment of OC prognosis and the discovery of novel biomarkers.

The lncRNA epigenetics: The significance of m6A and m5C lncRNA modifications in cancer

Most of our transcribed RNAs are represented by non-coding sequences. Long non-coding RNAs (lncRNAs) are transcripts with no or very limited protein coding ability and a length >200nt. They can be epigenetically modified. N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G) and 2’-O-methylation (Nm) are some of the lncRNAs epigenetic modifications. The epigenetic modifications of RNA are controlled by three classes of enzymes, each playing a role in a specific phase of the modification. These enzymes are defined as “writers”, “readers” and “erasers”. m6A and m5C are the most studied epigenetic modifications in RNA. These modifications alter the structure and properties, thus modulating the functions and interactions of lncRNAs. The aberrant expression of several lncRNAs is linked to the development of a variety of cancers and the epigenetic signatures of m6A- or m5C-related lncRNAs are increasingly recognized as potential biomarkers of prognosis, predictors of disease stage and overall survival. In the present manuscript, the most up to date literature is reviewed with the focus on m6A and m5C modifications of lncRNAs and their significance in cancer.

Genetic variants in m5C modification core genes are associated with the risk of Chinese pediatric acute lymphoblastic leukemia: A five-center case-control study

Objective

To explore the functions of the polymorphisms in 5-methylcytosine (m5C) modification-related coding genes on the susceptibility of pediatric acute lymphoblastic leukemia (ALL).

Methods

Case-control study and multinomial logistic regression analysis were performed to construct models to evaluate the susceptibility of pediatric ALL. The relationship between five functional SNPs in m5C modification-coding genes and pediatric ALL risk was analyzed. Genotyping of 808 cases and 1,340 healthy samples from South China was identified using a TaqMan assay; odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the relationship between the five selected SNPs and pediatric ALL susceptibility.

Results

Among the five analyzed SNPs, NOL1 rs3764909 and NSUN4 rs10252 variants significantly increased the susceptibility of pediatric ALL, while NSUN3 rs7653521, NSUN5 rs1880948, and NSUN6 rs3740102 variants were not associated with the risk of ALL. Stratification analyses demonstrated that NOL1 rs3764909 C>A exhibited a significant association with increased pediatric ALL risk in subgroups of common B ALL, pre-B ALL, T-cell ALL, low and middle risk, other gene fusion types, non-gene fusion, hypodiploid, normal diploid, primitive lymphocytes in marrow < 5% on week 12, and minimal residual disease (MRD) <0.01% on week 12 after induced therapy; NSUN4 rs10252 G>A was related to increased risk of ALL children in subgroups of age ≥ 120 months, normal white blood cell (WBC) number, middle risk, non-gene fusion, MRD ≥ 0.01 on days 15-19, and primitive lymphocytes in marrow < 5% on day 33 after induced therapy. Compared with the reference haplotype CAGTA, children who harbored haplotypes CCGTG and ACATA were remarkably related to increased ALL susceptibility. rs3764909 and rs10252 varieties of alleles were not associated with MRD levels after the selected chemotherapeutics.

Conclusions

In conclusion, NOL1 rs3764909 and NSUN4 rs10252 variants were enhanced by pediatric ALL risk and were suggested to be potential biomarkers for pediatric ALL.

Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma

Background

Anoikis resistance (AR) plays an important role in the process of metastasis, which is an important factor affecting the risk stage of neuroblastoma (NB). This study aims to construct an anoikis-related prognostic model and analyze the characteristics of hub genes, important pathways and tumor microenvironment of anoikis-related subtypes of NB, so as to provide help for the clinical diagnosis, treatment and research of NB.

Methods

We combined transcriptome data of GSE49710 and E-MTAB-8248, screened anoikis-related genes (Args) closely related to the prognosis of NB by univariate cox regression analysis, and divided the samples into anoikis-related subtypes by consistent cluster analysis. WGCNA was used to screen hub genes, GSVA and GSEA were used to analyze the differentially enriched pathways between anoikis-related subtypes. We analyzed the infiltration levels of immune cells between different groups by SsGSEA and CIBERSORT. Lasso and multivariate regression analyses were used to construct a prognostic model. Finally, we analyzed drug sensitivity through the GDSC database.

Results

721 cases and 283 Args were included in this study. All samples were grouped into two subtypes with different prognoses. The analyses of WGCNA, GSVA and GSEA suggested the existence of differentially expressed hub genes and important pathways in the two subtypes. We further constructed an anoikis-related prognostic model, in which 15 Args participated. This model had more advantages in evaluating the prognoses of NB than other commonly used clinical indicators. The infiltration levels of 9 immune cells were significantly different between different risk groups, and 13 Args involved in the model construction were correlated with the infiltration levels of immune cells. There was a relationship between the infiltration levels of 6 immune cells and riskscores. Finally, we screened 15 drugs with more obvious effects on NB in high-risk group.

Conclusion

There are two anoikis-related subtypes with different prognoses in the population of NB. The anoikis-related prognostic model constructed in this study can accurately predict the prognoses of children with NB, and has a good guiding significance for clinical diagnosis, treatment and research of NB.

Identification and Validation of a m5C RNA Modification-Related Gene Signature for Predicting Prognosis and Immunotherapeutic Efficiency of Gastric Cancer

Background

5-methylcytosine (m5C) is a major site of RNA methylation modification, and its abnormal modification is associated with the development of gastric cancer (GC). This study aimed to explore the value of m5C-related genes on the prognosis of GC patients through bioinformatics.

Methods

First, m5C-related genes were obtained by nonnegative matrix factorization (NMF) analysis and differentially expressed analysis. The m5C-related model was established and validated in distinct datasets. Moreover, a differential analysis of risk scores according to clinical characteristics was performed. The enrichment analysis was carried out to elucidate the underlying molecular mechanisms. Furthermore, we calculated the differences in immunotherapy and chemotherapy sensitivity between the high- and low-risk groups. Finally, we validated the expression levels of identified model genes by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

A total of five m5C-related subtypes of GC patients in the TCGA database were identified. The m5C-related model was constructed based on APOD, ASCL2, MFAP2, and CREB3L3. Functional enrichment revealed that the m5C-related model might involve in the cell cycle and cell adhesion. Moreover, the high-risk group had a higher abundance of stromal and immune cells in malignant tumor tissues and a lower tumor purity than the low-risk group. The patients in the high-risk group were more sensitive to chemotherapy and had better sensitivity to CTLA4 inhibitors. Furthermore, qRT-PCR results from our specimens verified an over-expression of ASCL2, CREB3L3, and MFAP2 in the cancer cells compared with the normal cells.

Conclusion

A total of five GC subtypes were identified, and a risk model was constructed based on m5C modification.

RNA Modifications Meet Tumors

RNA modifications occur through the whole process of gene expression regulation, including transcription, translation, and post-translational processes. They are closely associated with gene expression, RNA stability, and cell cycle. RNA modifications in tumor cells play a vital role in tumor development and metastasis, changes in the tumor microenvironment, drug resistance in tumors, construction of tumor cell-cell "internet", etc. Several types of RNA modifications have been identified to date and have various effects on the biological characteristics of different tumors. In this review, we discussed the function of RNA modifications, including N 6-methyladenine (m6A), 5-methylcytosine (m5C), N 7-methyladenosine (m7G), N 1-methyladenosine (m1A), pseudouridine (Ψ), and adenosine-to-inosine (A-to-I), in the microenvironment and therapy of solid and liquid tumors.

The role of RNA modification in the generation of acquired drug resistance in glioma

Glioma is the most common malignant tumor in the central nervous system. The clinical treatment strategy is mainly surgery combined with concurrent temozolomide chemotherapy, but patients can develop drug resistance during treatment, which severely limits its therapeutic efficacy. Epigenetic regulation at the RNA level is plastic and adaptable, and it can induce a variety of tumor responses to drugs. The regulators of RNA modification include methyltransferases, demethylases, and methylation binding proteins; these are also considered to play an important role in the development, prognosis, and therapeutic response of gliomas, which provides a basis for finding new targets of epigenetic drugs and resetting the sensitivity of tumor cells to temozolomide. This review discusses the relationship between the development of adaptive drug resistance and RNA modification in glioma and summarizes the progress of several major RNA modification strategies in this field, especially RNA m6A modification, m5C modification, and adenosine-to-inosine editing.

Identification of Prognostic Signature of Necroptosis-Related lncRNAs and Molecular Subtypes in Glioma

Background

In tumor progression and epigenetic regulation, long non-coding RNA (lncRNA) and necroptosis are crucial regulators. However, in glioma microenvironment, the role of necroptosis-related lncRNAs (NRLs) remains unknown.

Method

In this study, the RNA-seq and clinical annotation of glioma patients were analyzed using the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. To investigate prognosis and tumor microenvironment of NRLs in gliomas, we conducted a prediction model based on the training cohort. The accuracy of the model was verified in the verification cohort.

Results

A signature composed of 13 NRLs was identified, and all glioma patients were divided into two groups. We found that each group has unique survival outcomes, biological behaviors, and immune infiltrating status. The necroptosis-related lncRNA signature (NRLS) model was found to be an independent risk factor in multivariate Cox analysis. Immunosuppressive microenvironment was positively correlated with the high-risk group. Due to significantly different IC50 between risk groups, NRLS could be used as a guide for chemotherapeutic treatment. Further, the entire cohort was divided into two clusters depending on NRLs. Consensus clustering method and the risk scoring system were basically similar. Survival probability was higher in Cluster 2, while Cluster 1 has stronger immunologic infiltration.

Conclusion

The predictive signature could be a prognostic factor independently and serve to detect the role of NRLs in glioma immunotherapy response.

Recurrence- and Malignant Progression-Associated Biomarkers in Low-Grade Gliomas and Their Roles in Immunotherapy

Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.