Prognostic and Predictive Value of a Long Non-coding RNA Signature in Glioma: A lncRNA Expression Analysis

Immune gene features and prognosis in colorectal cancer: insights from ssGSEA typing

BACKGROUND

Colorectal cancer (CRC) is a molecularly heterogeneous disease, and its treatment and prognosis vary greatly among subgroups. Therefore, it is necessary to identify prognostic factors associated with the biological heterogeneity of CRC in order to improve patients' survival expectations.

METHODS

We obtained and merged RNA-Seq data along with clinical details for colorectal cancer (CRC) from The Cancer Genome Atlas (TCGA) repository, and then performed immunocluster typing on all CRC specimens. We conducted differential expression gene (DEG) analysis, gene set enrichment analysis (GSEA), and tumor microenvironment (TME) analysis on CRC samples that were divided into high and low Immunity categories. Moreover, we pinpointed prognostic genes from immune-related gene (IRGs) sets, developed a prognostic risk model, and executed survival analysis, receiver operating characteristic (ROC) curve analysis, and independent prognostic analysis. Additionally, we assessed the risk for patients categorized into high- and low-risk groups based on the model. Lastly, we created a Nomogram to customize the prediction of survival outcomes in CRC patients.

RESULTS

CRC samples were divided into high and low Immunity groups based on the median value of the immunity score. Between the two groups, a total of 1550 DEGs were identified and 395 differentially expressed immune-related genes (DE-IRGs) were identified by intersection with 2483 IRGs. The DE-IRGs of the high Immunity group were dominated by Cytokine receptor interactions, chemokine signaling pathways and immune cell-mediated cytotoxicity, and molecule function of immune effector process. TME analysis showed that most of the 27 immune cells and functions were highly enriched in high Immunity group, whose Immune Score, Stromal Score and ESTIMATE Score were significantly higher. Subsequently, a prognostic risk model of CRC was constructed based on 12 prognostic genes, and the accuracy and reliability of the model prediction were verified. Finally, Nomogram enabled accurate individual prediction of the survival prognosis of CRC patients.

CONCLUSIONS

Our study develops an immune-related prognostic model and Nomogram that reliably predicts survival outcomes in CRC patients and enhances understanding of the tumor immunity and molecular mechanisms of CRC.

Development and approval of a Lasso score based on nutritional and inflammatory parameters to predict prognosis in patients with glioma

Objectives

Preoperative peripheral hematological indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and prognostic nutritional index (PNI), exhibit promise as prognostic markers for glioma. This study evaluated the prognostic value of a combined scoring system incorporating NLR, PLR, MLR, and PNI, and developed a nomogram to predict glioma prognosis.

Methods

Data on preoperative NLR, PLR, MLR, and PNI were collected from 380 patients with pathologically diagnosed glioma (266 in the training cohort, 114 in the validation cohort). The Least Absolute Shrinkage and Selection Operator (Lasso) was employed to select relevant hematological indicators and generate a Lasso score. A nomogram was constructed utilizing Cox regression and Lasso variable selection. This nomogram incorporated the Lasso score, age, pathological type, chemotherapy status, and Ki67 expression to predict overall survival (OS). Model performance was evaluated utilizing Harrell's c-index, calibration curves, DCA, and clinical utility (stratification into low-risk and high-risk groups), and verified utilizing the independent validation cohort.

Results

A total of 380 glioma patients were enrolled and separated into training (n = 266) and validation (n = 114) cohorts. The two cohorts demonstrated no significant differences in baseline characteristics. NLR, PLR, MLR, and PNI from the training dataset were utilized for Lasso calculation. Multivariable analysis indicated that age, pathological grade, chemotherapy status, Ki-67 expression, and the Lasso score were independent predictors of OS and were then included in the nomogram. The nomogram model based on the training cohort had a C index of 0.742 (95% CI: 0.700-0.783) and AUC values of 0.802, 0.775, and 0.815 for ROC curves at 1, 3, and 5 years after surgery. The validation cohort derived a similar C-index of 0.734 (95% CI: 0.671-0.798) and AUC values of 0.785, 0.778, and 0.767 at 1, 3, and 5 years, respectively. The nomogram demonstrated good calibration in both cohorts, indicating strong agreement between predicted and observed outcomes. The threshold probabilities for DCA at 1-, 3-, and 5-years post-surgery in the training and validation cohorts were 0.08~k0.74, 0.25~0.80, and 0.08~0.89, and 0.13~0.60, 0.28~0.81, and 0.25~0.88, respectively.

Conclusions

A nomogram incorporating a Lasso score effectively predicted prognosis in glioma patients. However, its performance did not significantly exceed that of standard clinical nomograms.

Bibliometric and visualization analysis in the field of epigenetics and glioma (2009-2024)

Introduction

Glioma represents the most prevalent primary malignant tumor in the central nervous system, a deeper understanding of the underlying molecular mechanisms driving glioma is imperative for guiding future treatment strategies. Emerging evidence has implicated a close relationship between glioma development and epigenetic regulation. However, there remains a significant lack of comprehensive summaries in this domain. This study aims to analyze epigenetic publications pertaining to gliomas from 2009 to 2024 using bibliometric methods, consolidate the extant research, and delineate future prospects for investigation in this critical area.

Methods

For the purpose of this study, publications spanning the years 2009 to 2024 were extracted from the esteemed Web of Science Core Collection (WoSCC) database. Utilizing advanced visualization tools such as CiteSpace and VOSviewer, comprehensive data pertaining to various aspects including countries, authors, author co-citations, countries/regions, institutions, journals, cited literature, and keywords were systematically visualized and analyzed.

Results

A thorough analysis was conducted on a comprehensive dataset consisting of 858 publications, which unveiled a discernible trend of steady annual growth in research output within this specific field. The nations of the United States, China, and Germany emerged as the foremost contributors to this research domain. It is noteworthy that von Deimling A and the Helmholtz Association were distinguished as prominent authors and institutions, respectively, in this corpus of literature. A rigorous keyword search and subsequent co-occurrence analysis were executed, ultimately leading to the identification of seven distinct clusters: "epigenetic regulation", "DNA repair", "DNA methylation", "brain tumors", "diffuse midline glioma (DMG)", "U-87 MG" and "epigenomics". Furthermore, an intricate cluster analysis revealed that the primary foci of research within this field were centered around the exploration of glioma pathogenesis and the development of corresponding treatment strategies.

Conclusion

This article underscores the prevailing trends and hotspots in glioma epigenetics, offering invaluable insights that can guide future research endeavors. The investigation of epigenetic mechanisms primarily centers on DNA modification, non-coding RNAs (ncRNAs), and histone modification. Furthermore, the pursuit of overcoming temozolomide (TMZ) resistance and the exploration of diverse emerging therapeutic strategies have emerged as pivotal avenues for future research within the field of glioma epigenetics.

Profiling hypoxia signaling reveals a lncRNA signature contributing to immunosuppression in high-grade glioma

Background

Hypoxic conditions in glioma are linked to tumor aggressiveness, poor prognosis, and treatment resistance. Long non-coding RNAs (lncRNAs) play key roles in the hypoxic and immune microenvironment of cancers, but their link to hypoxia-induced immunosuppression in high-grade glioma (HGG) is not well-studied.

Methods

Gene expression profiles from TCGA and CGGA, along with clinical and genomic data, were analyzed. Bioinformatics methods including Consensus Clustering, Pearson correlation, and Cox regression analyses were used. Cell proliferation was assessed using cell counting kit-8 and colony formation assays. Glioma-macrophage interactions were evaluated using a co-culture model.

Results

Hypoxia subtype clustering showed hypoxic stress correlates with worse HGG prognosis. Eight hypoxia-related lncRNAs (AP000695.4, OSMR-AS1, AC078883.3, RP11-545E17.3, LINC01057, LINC01503, TP73-AS1, and LINC00672) with prognostic value were identified, forming a risk signature that separated patients into distinct prognostic groups. Multivariate Cox regression confirmed the signature as an independent prognostic factor. High-risk patients had greater hypoxia, leading to an immunosuppressive environment and immunotherapy resistance via tumor-associated macrophages (TAMs). TP73-AS1 significantly influenced hypoxia-induced TAM infiltration and M2 polarization.

Conclusions

We profiled hypoxic stress in HGG and developed an 8-lncRNA hypoxia-related signature predicting patient survival and immunotherapy response, emphasizing its role in hypoxia-induced immunosuppression.

Examining the function of macrophage oxidative stress response and immune system in glioblastoma multiforme through analysis of single-cell transcriptomics

Background

Glioblastoma (GBM), a prevalent malignant neoplasm within the neuro-oncological domain, has been a subject of considerable scrutiny. Macrophages, serving as the principal immunological constituents, profoundly infiltrate the microenvironment of GBM. However, investigations elucidating the intricate immunological mechanisms governing macrophage involvement in GBM at the single-cell level remain notably limited.

Methods

We conducted a comprehensive investigation employing single-cell analysis, aiming to redefine the intricate cellular landscape within both the core and peripheral regions of GBM tumors. Our analytical focus extended to the profound study of macrophages, elucidating their roles within the context of oxidative stress, intercellular information exchange, and cellular trajectories concerning GBM and its assorted subpopulations. We pursued the identification of GBM prognostic genes intricately associated with macrophages. Utilizing experimental research to investigate the relevance of MANBA in the context of GBM.

Results

Our investigations have illuminated the central role of macrophages in the intricate interplay among various subpopulations within the GBM microenvironment. Notably, we observed a pronounced intensity of oxidative stress responses within macrophages when compared to their GBM counterparts in other subpopulations. Moreover, macrophages orchestrated intricate cellular communication networks, facilitated by the SPP1-CD44 axis, both internally and with neighboring subpopulations. These findings collectively suggest the potential for macrophage polarization from an M1 to an M2 phenotype, contributing to immune suppression within the tumor microenvironment. Furthermore, our exploration unearthed GBM prognostic genes closely associated with macrophages, most notably MANBA and TCF12. Remarkably, MANBA appears to participate in the modulation of neuroimmune functionality by exerting inhibitory effects on M1-polarized macrophages, thereby fostering tumor progression. To bolster these assertions, experimental validations unequivocally affirmed the promotional impact of MANBA on GBM, elucidated through its capacity to curb cell proliferation, invasiveness, and metastatic potential.

Conclusion

These revelations represent a pivotal step towards unraveling the intricate immunological mechanisms governing the interactions between macrophages and diverse subpopulations within the GBM milieu. Furthermore, they lay the foundation for the development of an innovative GBM prognostic model, with MANBA at its epicenter, and underscore the potential for novel immunotherapeutic targets in the ongoing pursuit of enhanced treatment modalities for this formidable malignancy.

Impact of ferroptosis-related risk genes on macrophage M1/M2 polarization and prognosis in glioblastoma

Objective

To explore the effect impact of ferroptosis on macrophage polarization and patient prognosis in glioblastoma.

Methods

We screened ferroptosis-related risk from the public datasets of primary and recurrent glioblastoma, combined with reported ferroptosis genes, calculated the risk genes among the ferroptosis-related genes using the LASSO Cox regression model, and investigated the relationship between these ferroptosis-related risk genes in the tumor and the spectrum of infiltrating M1/M2 macrophages. Macrophages were analyzed using the CIBERSORTx deconvolution algorithm. Samples from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and a single-cell RNA sequencing dataset (GSE84465) were included. The expression levels of ferroptosis-related risk genes and molecular markers of M1 and M2 macrophages were detected by qPCR and western blot.

Results

A total of fourteen ferroptosis-related risk genes were obtained and the patients' risk scores were calculated. Compared with patients in the low-risk group, patients in the high-risk group had worse prognosis. The M1/M2 macrophage ratio and risk score were negatively correlated, indicating that the tumor microenvironment of glioblastoma in the high-risk group contained more M2 than M1 macrophages. In the single-cell RNA sequencing dataset, the risk score of ferroptosis-related genes in tumor cells was positively correlated with the proportion of high M2 macrophages. The expression of eight ferroptosis-related risk genes was increased in glioblastoma cell, which promoted the polarization of M1 macrophages to M2.

Conclusion

We investigated the fourteen ferroptosis-related risk genes in glioblastoma for the first time, and clarified the impact of ferroptosis-related risk genes on M1/M2 macrophage polarization and patient prognosis.

Construction and experimental validation of a signature for predicting prognosis and immune infiltration analysis of glioma based on disulfidptosis-related lncRNAs

Backgrounds

Disulfidptosis, a newly discovered mechanism of programmed cell death, is believed to have a unique role in elucidating cancer progression and guiding cancer therapy strategies. However, no studies have yet explored this mechanism in glioma.

Methods

We downloaded data on glioma patients from online databases to address this gap. Subsequently, we identified disulfidptosis-related genes from published literature and verified the associated lncRNAs.

Results

Through univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) regression algorithms analyses, we identified 10 lncRNAs. These were then utilized to construct prognostic prediction models, culminating in a risk-scoring signature. Reliability and validity tests demonstrated that the model effectively discerns glioma patients' prognosis outcomes. We also analyzed the relationship between the risk score and immune characteristics, and identified several drugs that may be effective for high-risk patients. In vitro experiments revealed that LINC02525 could enhances glioma cells' migration and invasion capacities. Additionally, knocking down LINC02525 was observed to promote glioma cell disulfidptosis.

Conclusion

This study delves into disulfidptosis-related lncRNAs in glioma, offering novel insights into glioma therapeutic strategies.

Mutation-derived, genomic instability-associated lncRNAs are prognostic markers in gliomas

Background

Gliomas are the most commonly-detected malignant tumors of the brain. They contain abundant long non-coding RNAs (lncRNAs), which are valuable cancer biomarkers. LncRNAs may be involved in genomic instability; however, their specific role and mechanism in gliomas remains unclear. LncRNAs that are related to genomic instability have not been reported in gliomas.

Methods

The transcriptome data from The Cancer Genome Atlas (TCGA) database were analyzed. The co-expression network of genomic instability-related lncRNAs and mRNA was established, and the model of genomic instability-related lncRNA was identified by univariate Cox regression and LASSO analyses. Based on the median risk score obtained in the training set, we divided the samples into high-risk and low-risk groups and proved the survival prediction ability of genomic instability-related lncRNA signatures. The results were verified in the external data set. Finally, a real-time quantitative polymerase chain reaction assay was performed to validate the signature.

Results

The signatures of 17 lncRNAs (LINC01579, AL022344.1, AC025171.5, LINC01116, MIR155HG, AC131097.3, LINC00906, CYTOR, AC015540.1, SLC25A21.AS1, H19, AL133415.1, SNHG18, FOXD3.AS1, LINC02593, AL354919.2 and CRNDE) related to genomic instability were identified. In the internal data set and Gene Expression Omnibus (GEO) external data set, the low-risk group showed better survival than the high-risk group (P < 0.001). In addition, this feature was identified as an independent risk factor, showing its independent prognostic value with different clinical stratifications. The majority of patients in the low-risk group had isocitrate dehydrogenase 1 (IDH1) mutations. The expression levels of these lncRNAs were significantly higher in glioblastoma cell lines than in normal cells.

Conclusions

Our study shows that the signature of 17 lncRNAs related to genomic instability has prognostic value for gliomas and could provide a potential therapeutic method for glioblastoma.

LncRNA as potential biomarker and therapeutic target in glioma

Glioma is the most frequent type of malignant tumor in the central nervous system, accounting for about 80% of primary malignant brain tumors, usually with a poor prognosis. A number of studies have been conducted on the molecular abnormalities in glioma to further understand its pathogenesis, and it has been found that lncRNAs (long non-coding RNA) play a key role in angiogenesis, tumor growth, infiltration and metastasis of glioma. Since specific lncRNAs have an aberrant expression in brain tissue, cerebrospinal fluid as well as peripheral circulation of glioma patients, they are considered to be potential biomarkers. This review focuses on the biological characteristics of lncRNA and its value as a biomarker for glioma diagnosis and prognosis. Moreover, in view of the role of lncRNAs in glioma proliferation and chemoradiotherapy resistance, we discussed the feasibility for lncRNAs as therapeutic targets. Finally, the persisting deficiencies and future prospects of using lncRNAs as clinical biomarkers and therapeutic targets were concluded.

Identification of a 5-lncRNA-Based Signature for Immune Characteristics and Prognosis of Lung Squamous Cell Carcinoma and Verification of the Function of lncRNA SPATA41

Lung squamous cell carcinoma (LUSC) is one of the most lethal cancers worldwide. Traditional tumor-node-metastasis (TNM) staging system has many insufficiencies in predicting immune characteristics, overall survival (OS), and prognosis of LUSC. LncRNA is currently found involved in tumor development and effectively predicts tumor prognosis. We screened potential tumor-related lncRNAs for immune characteristics and constructed a nomogram combining lncRNA and traditional clinical indicators for prognosis prediction. We obtained the large-scale gene expression profiles of samples from 492 LUSC patients in The Cancer Genome Atlas database. SPATA41, AL034550.2, AP003721.2, AC106786.1, and AC078889.1 were finally screened to construct a 5-lncRNA-based signature. The risk score of the signature divided patients into subgroups of high-risk and low-risk with significant differences in OS. Their area under the curve (AUC) reached more than 0.70 in 1, 3, and 5 years. In addition, compared with the high-risk subgroup, the low-risk subgroup exhibited a remarkably favorable prognosis and TME score, along with a higher immune infiltration score and lower TIDE score. The signature also significantly related to chemotherapy response, especially in cisplatin, vinorelbine, and paclitaxel. Importantly, the nomogram we constructed had good reliability with the assessment of the calibration chart and consistency index (c-index). GO and KEGG enrichment analysis indicated that co-expression mRNAs of the 5 lncRNAs were mainly focused on RNA splicing, DNA replication, and protein serine/threonine kinase activity. Functional assays demonstrated that SPATA41, one of the five OS-related lncRNAs, regulated invasion, migration, proliferation, and programmed death in vitro. In summary, our 5-lncRNA-based signature has a good performance in predicting immune characteristics and prognosis of LUSC patients.

Clinical Characterization and Prognostic Value of TPM4 and Its Correlation with Epithelial–Mesenchymal Transition in Glioma

Tropomyosin 4 (TPM4) has been reported as an oncogenic gene across different malignancies. However, the role of TPM4 in glioma remains unclear. This study aimed to determine the clinical characterization and prognostic value of TPM4 in gliomas. Transcriptome expression and clinical information were collected from the CGGA and TCGA datasets, which included 998 glioma patients. ScRNA-seq data were obtained from CGGA. R software was utilized for statistical analyses. There was a positive correlation between TPM4 and WHO grades. IDH-wildtype and mesenchymal subtype gliomas were accompanied by TPM4 upregulation. GO and GSEA analysis suggested that TPM4 was profoundly associated with epithelial-to-mesenchymal transition (EMT). Subsequent GSVA revealed a robust correlation between TPM4 and three signaling pathways of EMT (hypoxia, TGF-β, PI3K/AKT). Furthermore, TPM4 showed a synergistic effect with mesenchymal biomarkers, particularly with N-cadherin, Slug, Snail, TWIST1, and vimentin. ScRNA-seq analysis suggested that higher TPM4 was mainly attributed to tumor cells and macrophages and associated with tumor cell progression and macrophage polarization. Finally, high TPM4 was significantly associated with unfavorable outcomes. In conclusion, our findings indicate that TPM4 is significantly correlated with more malignant characteristics of gliomas, potentially through involvement in EMT. TPM4 could predict worse survival for patients with glioma.

Evolving Insights Into the Biological Function and Clinical Significance of Long Noncoding RNA in Glioblastoma

Glioblastoma (GBM) is one of the most prevalent and aggressive cancers worldwide. The overall survival period of GBM patients is only 15 months even with standard combination therapy. The absence of validated biomarkers for early diagnosis mainly accounts for worse clinical outcomes of GBM patients. Thus, there is an urgent requirement to characterize more biomarkers for the early diagnosis of GBM patients. In addition, the detailed molecular basis during GBM pathogenesis and oncogenesis is not fully understood, highlighting that it is of great significance to elucidate the molecular mechanisms of GBM initiation and development. Recently, accumulated pieces of evidence have revealed the central roles of long noncoding RNAs (lncRNAs) in the tumorigenesis and progression of GBM by binding with DNA, RNA, or protein. Targeting those oncogenic lncRNAs in GBM may be promising to develop more effective therapeutics. Furthermore, a better understanding of the biological function and underlying molecular basis of dysregulated lncRNAs in GBM initiation and development will offer new insights into GBM early diagnosis and develop novel treatments for GBM patients. Herein, this review builds on previous studies to summarize the dysregulated lncRNAs in GBM and their unique biological functions during GBM tumorigenesis and progression. In addition, new insights and challenges of lncRNA-based diagnostic and therapeutic potentials for GBM patients were also introduced.

The current state of glioma data registries

Background

The landscape of glioma research has evolved in the past 20 years to include numerous large, multi-institutional, database efforts compiling either clinical data on glioma patients, molecular data on glioma specimens, or a combination of both. While these strategies can provide a wealth of information for glioma research, obtaining information regarding data availability and access specifications can be challenging.

Methods

We reviewed the literature for ongoing clinical, molecular, and combined database efforts related to glioma research to provide researchers with a curated overview of the current state of glioma database resources.

Results

We identified and reviewed a total of 20 databases with data collection spanning from 1975 to 2022. Surveyed databases included both low- and high-grade gliomas, and data elements included over 100 clinical variables and 12 molecular data types. Select database strengths included large sample sizes and a wide variety of variables available, while limitations of some databases included complex data access requirements and a lack of glioma-specific variables.

Conclusions

This review highlights current databases and registries and their potential utility in clinical and genomic glioma research. While many high-quality resources exist, the fluid nature of glioma taxonomy makes it difficult to isolate a large cohort of patients with a pathologically confirmed diagnosis. Large, well-defined, and publicly available glioma datasets have the potential to expand the reach of glioma research and drive the field forward.

A putative role for lncRNAs in epigenetic regulation of memory

The central dogma of molecular genetics is defined as encoded genetic information within DNA, transcribed into messenger RNA, which contain the instructions for protein synthesis, thus imparting cellular functionality and ultimately life. This molecular genetic theory has given birth to the field of neuroepigenetics, and it is now well established that epigenetic regulation of gene transcription is critical to the learning and memory process. In this review, we address a potential role for a relatively new player in the field of epigenetic crosstalk - long non-coding RNAs (lncRNAs). First, we briefly summarize epigenetic mechanisms in memory formation and examine what little is known about the emerging role of lncRNAs during this process. We then focus discussions on how lncRNAs interact with epigenetic mechanisms to control transcriptional programs under various conditions in the brain, and how this may be applied to regulation of gene expression necessary for memory formation. Next, we explore how epigenetic crosstalk in turn serves to regulate expression of various individual lncRNAs themselves. To highlight the importance of further exploring the role of lncRNA in epigenetic regulation of gene expression, we consider the significant relationship between lncRNA dysregulation and declining memory reserve with aging, Alzheimer's disease, and epilepsy, as well as the promise of novel therapeutic interventions. Finally, we conclude with a discussion of the critical questions that remain to be answered regarding a role for lncRNA in memory.

Mechanisms of Long Non-Coding RNAs in Biological Characteristics and Aerobic Glycolysis of Glioma

Glioma is the most common and aggressive tumor of the central nervous system. The uncontrolled proliferation, cellular heterogeneity, and diffusive capacity of glioma cells contribute to a very poor prognosis of patients with high grade glioma. Compared to normal cells, cancer cells exhibit a higher rate of glucose uptake, which is accompanied with the metabolic switch from oxidative phosphorylation to aerobic glycolysis. The metabolic reprogramming of cancer cell supports excessive cell proliferation, which are frequently mediated by the activation of oncogenes or the perturbations of tumor suppressor genes. Recently, a growing body of evidence has started to reveal that long noncoding RNAs (lncRNAs) are implicated in a wide spectrum of biological processes in glioma, including malignant phenotypes and aerobic glycolysis. However, the mechanisms of diverse lncRNAs in the initiation and progression of gliomas remain to be fully unveiled. In this review, we summarized the diverse roles of lncRNAs in shaping the biological features and aerobic glycolysis of glioma. The thorough understanding of lncRNAs in glioma biology provides opportunities for developing diagnostic biomarkers and novel therapeutic strategies targeting gliomas.

Long Non-Coding RNA SNHG6 Supports Glioma Progression Through Upregulation of Notch1, Sox2, and EMT

Gliomas, particularly the advanced grade glioblastomas, have poor 5-year survival rates and worse outcomes. lncRNAs and EMT have been extensively studied in gliomas but the disease progression remains poorly understood. SNHG6 has been shown to affect glioma cell proliferation but its effect on EMT of glioma cells along with its effect on disease progression is not known. We screened four glioma cell lines; H4, A172, U87MG, and SW088 and grouped them based on high vs. low SNHG6 expression. Transfections with SNHG6 specific siRNA resulted in induction of apoptosis of high SNHG6 expressing A172 and U87MG cells. This was accompanied by inhibition of EMT and downregulation of EMT-modulating factor Notch1, β-catenin activity and the cancer stem cell marker Sox2. The regulation was not found to be reciprocal as silencing of Notch1 and Sox2 failed to affect SNHG6 levels. The levels of SNHG6 and Notch1 were also found elevated in Grade IV glioma patients (n = 4) relative to Grade II glioma patients (n = 5). These results identify SNHG6 and Notch1 as valid targets for glioma therapy.

Development and Validation of an Immune-Related Long Non-coding RNA Prognostic Model in Glioma

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune processes of glioma. Immune related lncRNAs (IRlncRs) may be a critical prognosis in patients with glioma. The current study aimed to construct a glioma immune-related prognosis model by IRlncRs.

Methods: Transcriptome RNA-sequencing data of glioma were obtained from The Cancer Genome Atlas (TCGA) and an immune‑related risk score (IRRS) model was constructed by Lasso and multivariate Cox regression analysis. Receiver Operating Characteristic (ROC) curves were used to assess the sensitivity and specificity of the prognosis on IRRS. A predictive nomogram and a time-dependent ROC curve was performed in training and validation cohort. We explored the relationships between survival‑related IRlncRs (sIRlncRs) and clinicopathologic parameters. Functional annotation of the sIRlncRs was investigated by gene set enrichment analysis (GSEA) and principal component analysis (PCA). The relationships between IRRS model and immune cell infiltration and co-expression network analysis among the sIRlncRs were performed for molecular mechanism study.

Results: A total of 10 sIRlncRs were enrolled to build IRRS model. The IRRS was identified as an independent prognostic factor and correlated with the overall survival (AUC =0.880). The nomogram was constructed successfully with IRRS, age and grade as variables. Immune cell infiltration analysis indicated that B cells, neutrophil, dendritic and macrophage cells were positively correlated with IRRS. PCA and GSEA illustrated that the lncRNA signature enrolled the IRRS model was closely related to immune status. Additionally, co-expression network showed that there was a strong correlation between 10 sIRlncRs at the transcriptional level.

Conclusion: We successfully constructed a remarkable clinical model of sIRlncRs with potential prognostic value for glioma patients, which provides an insight into immunological research and treatment strategies of glioma.

Long noncoding RNA DLGAP1-AS1 promotes the progression of glioma by regulating the miR-1297/EZH2 axis

Dysregulated lncRNAs have been implicated in a plethora of tumors, including glioma. One such oncogenic lncRNAs that has been reported in several cancers is the lncRNA DLGAP1 antisense RNA 1 (DLGAP1-AS1). This study seeks to characterize the expression of DLGAP1-AS1 in glioma tissues, which we found to be raised in both glioma samples and cell lines. Functional experiments revealed that DLGAP1-AS1 promoted in vitro glioma cell invasion, migration and proliferation. DLGAP1-AS1 was found to function as a miR-1297 sponge, based on information from luciferase reporter assays, RNA pull-down assays and publicly available online databases. miR-1297 was in turn found to functionally target EZH2. DLGAP1-AS1 modulated EZH2 expressions through miR-1297 sponging. Glioma progression appears to be supported DLGAP1-AS1 -promoted activation of the miR-1297/EZH2 axis. The components of this axis may function as therapeutic targets for glioma.

Prognostic and Predictive Value of m6A "Eraser" Related Gene Signature in Gastric Cancer

Background

N6-methyladenosine (m6A) RNA modification plays a critical role in gastric cancer (GC). However, the relationship between the m6A “eraser”, FTO, and ALKBH5, and the prognosis of GC still remains unclear. This study aimed to evaluate the effect of FTO and ALKBH5 on the prognosis of patients and their potential roles in GC.

Materials and Methods

A total of 738 GC samples with clinical information obtained from two independent datasets were included and divided into training set and testing set. Differential expression analysis of the m6A “eraser” related genes was performed. The LASSO Cox regression model was constructed to analyze the m6A “eraser” related risk genes. The univariate and multivariate Cox regression model were employed to identify the independent prognostic factors. Kaplan-Meier method was used for survival analysis. A nomogram model was then carried out to predict the prognosis of GC patients. Additionally, GO and KEGG analyses were conducted to identify the potential role of the m6A “eraser” related genes in GC. The relative proportion of 22 different genotypes in immune infiltrating cells was calculated by CIBERSORT algorithm.

Results

In total, nine m6A “eraser” related risk genes and risk scores were obtained and calculated. Patients in high-risk group demonstrated significantly worse prognosis than those in low-risk group. Age, stage, and risk score were considered as independent prognostic factors. The nomogram model constructed accurately predicted the 3-year and 5-year overall survival (OS) of patients. Furthermore, m6A “eraser” might play a functional role in GC. The expression of m6A “eraser” leads to changes in tumor immune microenvironment.

Conclusions

FTO and ALKBH5 showed association with the prognosis of GC. The m6A “eraser” related genes, which is considered as a reliable prognostic and predictive tool, assists in predicting the OS in GC patients.

A Five Immune-Related lncRNA Signature as a Prognostic Target for Glioblastoma

Background: A variety of regulatory approaches including immune modulation have been explored as approaches to either eradicate antitumor response or induce suppressive mechanism in the glioblastoma microenvironment. Thus, the study of immune-related long noncoding RNA (lncRNA) signature is of great value in the diagnosis, treatment, and prognosis of glioblastoma.

Methods: Glioblastoma samples with lncRNA sequencing and corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) database. Immune-lncRNAs co-expression networks were built to identify immune-related lncRNAs via Pearson correlation. Based on the median risk score acquired in the training set, we divided the samples into high- and low-risk groups and demonstrate the survival prediction ability of the immune-related lncRNA signature. Both principal component analysis (PCA) and gene set enrichment analysis (GSEA) were used for immune state analysis.

Results: A cohort of 151 glioblastoma samples and 730 immune-related genes were acquired in this study. A five immune-related lncRNA signature (AC046143.1, AC021054.1, AC080112.1, MIR222HG, and PRKCQ-AS1) was identified. Compared with patients in the high-risk group, patients in the low-risk group showed a longer overall survival (OS) in the training, validation, and entire TCGA set (p = 1.931e-05, p = 1.706e-02, and p = 3.397e-06, respectively). Additionally, the survival prediction ability of this lncRNA signature was independent of known clinical factors and molecular features. The area under the ROC curve (AUC) and stratified analyses were further performed to verify its optimal survival predictive potency. Of note, the high-and low-risk groups exhibited significantly distinct immune state according to the PCA and GSEA analyses.

Conclusions: Our study proposes that a five immune-related lncRNA signature can be utilized as a latent indicator of prognosis and potential therapeutic approach for glioblastoma.