Survival analysis of immune-related lncRNA in low-grade glioma

Long Non-coding RNA Prader Willi/Angelman Region RNA 6 Suppresses Glioma Development by Modulating MicroRNA-106a-5p

As one of the most frequent intracranial tumors, glioma showed invasive development and poor prognosis. lncRNAs have been illustrated to serve as biomarkers in various cancers. Whether the long non-coding RNA Prader Willi/Angelman region RNA 6 (PWAR6) was involved in glioma development and the underlying mechanism was investigated. PWAR6 in glioma was evaluated by polymerase chain reaction and its clinical significance was assessed with a series of statistical analyses. The biological function of PWAR6 was investigated with the cell counting kit 8 and Transwell assay. The potential underlying mechanism was studied with the luciferase reporter assay. The significant downregulation of PWAR6 was observed in glioma, which showed a close relationship with the major clinicopathological features and poor prognosis of patients. PWAR6 restrained cell growth, migration and invasion of glioma, which was alleviated by the overexpression of microRNA-106a-5p (miR-106a-5p). PWAR6 functioned as a prognostic biomarker and tumor suppressor of glioma through regulating miR-106a-5p.

TET1 is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Papillary Thyroid Cancer

To investigate the function of ten-eleven translocation 1 (TET1) and its underlying mechanism in papillary thyroid cancer (PTC). Using the RNA-Seq data based on GDC TCGA, we analyzed the gene expression pattern of TET1 in PTC. Immunohistochemistry was carried out to assess the TET1 protein level. Then, its diagnostic and prognostic functions were determined by various bioinformatics approaches. Enrichment analysis was performed to explore the potential pathways in which TET1 is mainly involved. Finally, the immune cell infiltration analysis was conducted and the association of TET1 mRNA expression with the expression levels of immune checkpoints, tumor mutation burden (TMB) score, microsatellite instability (MSI) score, and cancer stem cells (CSC) score was examined. TET1 expression was lower in PTC tissues compared with that in normal tissues (P < 0.01). Besides, TET1 had a certain value in diagnosing PTC, and low-TET1 mRNA expression led to favorable disease-specific survival (DSS) (P < 0.01). The enrichment analysis revealed autoimmune thyroid disease and cytokine-cytokine receptor interaction were the consistent pathways in which TET1 participated. TET1 was negatively correlated with the Stromal score and Immune score. The different proportions of immune cell subtypes were observed between high- and low-TET1 expression groups. Interestingly, TET1 mRNA expression was inversely related to the expression levels of immune checkpoints, and TMB, MSI, and CSC scores. TET1 might be a robust diagnostic and prognostic biomarker for PTC. TET1 affected the DSS of PTC patients possibly through the regulation of immune-related pathways and tumor immunity.

The Role of Long Noncoding Ribonucleic Acids in Glioblastoma: What the Neurosurgeon Should Know

A significant proportion of the human transcriptome, long noncoding RNAs (lncRNAs) play pivotal roles in several aspects of glioblastoma (GBM) pathophysiology including proliferation, invasion, radiation and temozolomide resistance, and immune modulation. The majority of lncRNAs exhibit tissue- and tumor-specific expression, lending them to be attractive targets for therapeutic translation. In recent years, unprecedented progress has been made toward our understanding of lncRNA in GBM. In this review, we discuss the function of lncRNAs, including specific lncRNAs that have critical roles in key aspects of GBM pathophysiology, and potential clinical relevance of lncRNAs for patients with GBM.

Decapping enzyme 2 is a novel immune-related biomarker that predicts poor prognosis in glioma

This study analyzed sequencing and clinical data from the Cancer Genome Atlas (TCGA) and gene expression synthesis, and used Chinese glioma Genome Atlas (CGGA) data for external validation. The expression of DCP2 in normal brain and tumor tissue was compared. We analyzed the clinical and molecular characteristics and prognostic value of DCP2 in glioma. In addition, DCP2 expression levels were evaluated in 30 glioma tissue samples and upregulated in glioma samples compared to normal brain tissue (p < 0.001). Multivariate data analysis from TCGA showed that increased DCP2 expression was an independent risk factor for overall survival and prognosis of glioma patients. As indicated by the analysis of the TCGA data set. The expression level of DCP2 is closely related to tumor immunity, including tumor immune cell infiltration, immune score, and co-expression of multiple immune-related genes. In addition, DCP2 was positively correlated with IL-6 and IL-7. Glioma cell proliferation and invasion were evaluated using cell viability, colony formation, wound healing, and transwell assays.Apoptosis and cell cycle were detected by flow cytometry. DCP2 promoted the proliferation, invasion and migration of glioma cells T98G and U251, inhibited apoptosis and blocked the S phase of the cell cycle. As a result of the altered expression of DCP2, a new prognostic biomarker may be identified that can improve patient survival.These findings suggest DCP2 as a potential biomarker for the prognosis of glioma and a candidate immunotherapy target.

Tumor-Derived Biomarkers in Liquid Biopsy of Glioblastoma

There is a pressing clinical need for minimally invasive liquid biopsies to supplement imaging in the treatment of glioblastoma. Diagnostic imaging is often difficult to interpret and the medical community is divided on distinguishing among complete response, partial response, stable disease, and progressive disease. A minimally invasive liquid biopsy would supplement imaging and clinical findings and has the capacity to be helpful in several ways: 1) diagnosis, 2) selection of patients for specific treatments, 3) tracking of treatment response, and 4) prognostic value. The liquid biome is the combination of biological fluids including blood, urine, and cerebrospinal fluid that contain small amounts of tumor cells, DNA/RNA coding material, peptides, and metabolites. Within the liquid biome, 2 broad categories of biomarkers can exist: tumor-derived, which can be directly traced to the tumor, and tumor-associated, which can be traced back to the response of the body to disease. Although tumor-associated biomarkers are promising liquid biopsy candidates, recent advances in biomarker enrichment and detection have allowed concentration on a new class of biomarker: tumor-derived biomarkers. This review focuses on making the distinction between the 2 biomarker categories and highlights promising new direction.

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.

LINC01711 promotes transforming growth factor-beta (TGF-β) induced invasion in glioblastoma multiforme (GBM) by acting as a competing endogenous RNA for miR-34a and promoting ZEB1 expression

GBM is the central nervous system's most aggressive and malignant tumor. TGF-β expression is elevated in GBM, and it promotes invasion and EMT. TGF-β regulates the expression of several lncRNAs, which promote glioma pathogenesis. Here we characterize the role of TGF-β-induced lncRNA- LINC01711 in glioma pathogenesis. We show that LINC01711 expression is significantly upregulated in GBM tissues and is associated with poor overall survival of GBM patients. Loss-of-function studies illustrate that LINC01711 promotes proliferation, migration, and invasion in GBM. In addition, LINC01711 depletion sensitizes glioma cells to Temozolomide (TMZ) induced apoptosis by inhibiting ZEB1 expression. LINC01711 functions as a competing endogenous RNA for miR-34a and promotes ZEB1 expression to regulate invasion. Our findings suggest that LINC01711 is an attractive therapeutic target for GBM.

Foreboding lncRNA markers of low-grade gliomas dependent on metabolism

At present, there is no systematic study on the signature of long-chain noncoding RNAs (lncRNAs) involved in metabolism that can fully predict the prognosis in patients with low-grade gliomas (LGGs). Therefore, consistent metabolic-related lncRNA signatures need to be established. The Cancer Genome Atlas (TCGA) was used to identify the expression profile of lncRNAs containing 529 LGGs samples. LncRNAs and genes related to metabolism are used to establish a network in the form of coexpression to screen lncRNAs related to metabolism. LncRNA was more clearly described by univariate Cox regression. Moreover, lncRNA signatures were explored by multivariate Cox regression and lasso regression. The risk score was established according to the signature and it was an unattached prognostic marker according to Cox regression analysis. Functional enrichment of lncRNAs was shown by employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Univariate Cox retrospective analysis showed that 543 metabolism-related lncRNAs were independent prognostic factors of LGG, and multivariate Cox regression analysis confirmed that 19 metabolism-related lncRNAs were prognostic genes of LGG. In the risk model, the low-risk group had a higher Overall survival (OS) than the high-risk group (P < .001). Univariate Cox regression analysis of risk score and clinical factors showed that risk score was an independent prognostic factor (P < .001, HR = 1.047, 95% CI: 1.038-1.056). Multivariate Cox results showed that risk score could predict the prognosis of LGG (P < .001, HR = 1.036, 95% CI: 1.026-1.045). ROC curve analysis showed that risk score could predict the prognosis of LGG. The areas of 1-year, 3-years, and 5 years are 0.891, 0.904 and 0.832. GO and KEGG analysis showed that metabolism-related lncRNAs was mainly concentrated in the pathways related to tumor metabolism. In order to find a more stable and reliable target for the treatment of LGG, we established 19 metabolic-related lncRNAs prognostic model, and determined that it can predict the prognosis of LGG patients. This provides a new solution approach to the poor prognosis of patients with LGG and may reverse the trend of LGG's transformation to high-grade gliomas.

A novel inflammation-related lncRNAs prognostic signature identifies LINC00346 in promoting proliferation, migration, and immune infiltration of glioma

In this study, a total of 13 inflammation-related lncRNAs with a high prognostic value were identified with univariate, multivariate Cox regression analysis, and LASSO analysis. LINC00346, which is one of the 13 lncRNAs identified, was positively associated with type 2 macrophage activation and the malignant degree of glioma. Fluorescence in situ hybridization (FISH) and immunohistochemical staining showed that LINC00346 was highly expressed in high-grade glioma, while type 2 macrophages key transcription factor STAT3 and surface marker CD204 were also highly expressed simultaneously. LINC00346 high-expression gliomas were more sensitive to the anti–PD-1 and anti-CTLA-4 therapy. LINC00346 was also associated with tumor proliferation and tumor migration validated by EdU, cell colony, formation CCK8, and transwell assays. These findings reveal novel biomarkers for predicting glioma prognosis and outline relationships between lncRNAs inflammation, and glioma, as well as possible immune checkpoint targets for glioma.

Identification and validation of an immune-related lncRNAs signature to predict the overall survival of ovarian cancer

Ovarian cancer (OC) is the most lethal gynecological cancer in women. Studies had reported that immune-related lncRNAs signatures were valuable in predicting the survival and prognosis of patients with various cancers. In our study, the prognostic value of immune-related lncRNAs was investigated in OC patients from TCGA-RNA-seq cohort (n=378) and HG-U133_Plus_2 cohort (n=590), respectively. Pearson correlation analysis was implemented to screen the immune-related lncRNA and then univariate Cox regression analysis was performed to explore their prognostic value in OC patients. Five prognostic immune-related lncRNAs were identified as prognostic lncRNAs. Besides, they were inputted into a LASSO Cox regression to establish and validate an immune-related lncRNA prognostic signature in TCGA-RNA-Seq cohort and HG-U133_Plus_2 cohort, respectively. Based on the best cut-off value of risk score, patients were divided into high- and low-risk groups. Survival analysis suggested that patients in the high-risk group had a worse overall survival (OS) than those in the low-risk group in both cohorts. The association between clinicopathological feathers and risk score was then evaluated by using stratification analysis. Moreover, we constructed a nomogram based on risk score, age and stage, which had a strong ability to forecast the OS of the OC patients. The influence of risk score on immune infiltration and immunotherapy response were assessed and the results suggested that patients with high-risk score might recruit multiple immune cells and stromal cells, leading to facilitating immune surveillance evasive. Ultimately, we demonstrated that the risk model was associated with chemotherapy response of multiple antitumor drugs, especially for paclitaxel, metformin and veliparib, which are commonly used in treating OC patients. In conclusion, we constructed a novel immune-related lncRNA signature, which had a potential prognostic value for OC patients and might facilitate personalized counselling for immunotherapy and chemotherapy.

Identification of Prognostic LncRNAs Subtypes Predicts Prognosis and Immune Microenvironment for Glioma

Glioma is the most commonly occurring primary neuroepithelial neoplasm. Long noncoding RNAs (lncRNAs) are emerging as pivotal modulators of gene expression in the immune system and play critical roles in the growth, progression, and immune response of carcinomas. In this study, we performed univariate Cox regression analysis on survival data from TCGA and identified 20 prognostic lncRNAs. Moreover, we revealed that these prognosis-related lncRNAs (PRLnc) were dysregulated in glioma. Furthermore, we constructed a signature based on the expression levels of these prognosis-related lncRNAs based on 13 prognostic lncRNAs, including AGAP2-AS1, CYTOR, MIR155HG, LINC00634, HOTAIRM1, SNHG18, LINC01841, LINC01842, LINC01426, MIR9-3HG, TMEM220-AS1, LINC00641, LINC01270, and LINC01503. The Kaplan–Meier curves show that high-risk patients had a shorter survival time. Finally, the glioma samples were classified into 2 subgroups based on the median expression of prognosis-related lncRNAs in each sample. In summary, these findings suggest that PRLnc is associated with tumor-infiltrating immune cells in glioma and that subtype 2 patients may respond more positively to immunotherapy.

Long Non-coding RNA and mRNA Co-expression Network Reveals Novel Players in Pleomorphic Xanthoastrocytoma

Histological interpretation of the rare pleomorphic xanthoastrocytoma (PXA) has been the holy grail for treatment options. However, no stand-alone clinical interventions have been developed owing to the lack of gene expression profiling data in PXA/APXA patients. We first time report the comprehensive analyses of the coding as well as long non-coding RNA (lncRNA) signatures of PXA/APXA patients. Several genes such as IGFBP2, NF1, FOS, ERBB2, and lncRNAs such as NEAT1, HOTAIRM1, and GAS5 known to play crucial roles in glioma patients were also deregulated in PXA patients suggesting the commonality in the molecular signatures. PPI network, co-expression, and lncRNA-mRNA interaction studies unraveled hub genes (such as ERBB2, FOS, RPA1) and networks that may play a critical role in PXA biology. The most enriched pathways based on gene profiles were related to TLR, chemokine, MAPK, Rb, and PI3K-Akt signaling pathways. The lncRNA targets were enriched in glucuronidation, adipogenesis, TGF-beta signaling, EGF/EGFR signaling, and cell cycle pathways. Interestingly, several mRNAs like PARVG, and ABI2 were found to be targeted by multiple lncRNAs suggesting a tight control of their levels. Some of the most prominent lncRNA-mRNA pairs were LOC728730: MRPL9, XLOC_l2_011987: ASIC2, lnc-C1QTNF5-1: RNF26. Notably, several lncRNAs such as lnc-CETP-1, lnc-XRCC3-1, lnc-RPL31-1, lnc-USP13-1, and MAPKAPK5-AS1, and genes such as RPA1, NTRK3, and CNRP1 showed strong correlation to the progression-free survival of PXA patients suggesting their potential as novel biomarkers. Overall, the findings of this study may facilitate the development of a new realm of RNA biology in PXA that may have clinical significance in the future.

Comprehensive analyses indicated the association between m6A related long non-coding RNAs and various pathways in glioma

BACKGROUND

Glioma is one of the most malignant brain tumors and diseases. N6-methyladenosine modification (m6A) is the most abundant and prevalent internal chemical modification of mRNA and long non-coding RNAs (lncRNAs) in eukaryotes. Nevertheless, the correlated pathways and clinical utilization of m6A-related lncRNAs have not been fully evaluated in glioma.

METHODS

Public RNA-sequencing and clinical annotation data were retrieved from TCGA, CGGA and GEO database. Differential expression analysis and univariate Cox regression analysis were performed to identify the m6A-related and differentially expressed lncRNAs with prognostic function (m6A-DELPF). The consensus clustering was performed to identify the expression pattern of m6A-DELPF. LASSO Cox regression analysis was performed to construct the lncRNA-based signature. The CIBERSORT and ESTIMATE algorithms were performed to analyze immune infiltration and tumor microenvironment, respectively. Immunotherapy sensitivity analysis was performed using data from TCIA. The small molecule drugs prediction analysis was performed using The Connectivity Map (CMap) database and STITCH database. A competing endogenous RNAs (ceRNA) network was constructed based on miRcode, miRDB, miRTarBase, TargetScan database.

RESULTS

Two clusters (cluster1 and cluster2) were identified after unsupervised cluster analysis based on m6A-DELPF. Additionally, a 15-gene prognostic signature namely m6A-DELPFS was constructed. Analyses of epithelial-mesenchymal-transition score, tumor microenvironment, immune infiltration, clinical characterization analysis, and putative drug prediction were performed to confirm the clinical utility and efficacy of m6A-DELPFS. The potential mechanisms including tumor immune microenvironment of m6A-DELPF influence the initiation and progression of glioma. A clinically accessible nomogram was also constructed based on the m6A-DELPF and other survival-relevant clinical parameters. Two miRNAs and 114 mRNAs were identified as the downstream of seven m6A-related lncRNAs in a ceRNA network.

CONCLUSION

Our present research confirmed the clinical value of m6A related lncRNAs and their high correlation with tumor immunity, tumor microenvironment, tumor mutation burden and drug sensitivity in glioma.

The Involvement of Long Non-Coding RNAs in Glioma: From Early Detection to Immunotherapy

Glioma is a brain tumor that arises in the central nervous system and is categorized according to histology and molecular genetic characteristics. Long non-coding RNAs (lncRNAs) are RNAs longer than 200 nucleotides in length. They have been reported to influence significant events such as carcinogenesis, progression, and increased treatment resistance on glioma cells. Long non-coding RNAs promote cell proliferation, migration, epithelial-to-mesenchymal transition and invasion in glioma cells. Various significant advancements in transcriptomic profiling studies have enabled the identification of immune-related long non-coding RNAs as immune cell-specific gene expression regulators that mediates both stimulatory and suppressive immune responses, implying lncRNAs as potential candidates for improving immunotherapy efficacy against tumors and due to the lack of different diagnostic and treatments for glioma, lncRNAs are potential candidates to be used as future diagnostic, prognostic biomarker and treatment tools for glioma. This review’s primary purpose is to concentrate on the role of long non-coding RNAs in early glioma identification, treatment, and immunotherapy.

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.

Construction of an Immune-Related lncRNA Signature That Predicts Prognosis and Immune Microenvironment in Osteosarcoma Patients

Osteosarcoma is one of the most common bone tumors in teenagers. We hope to provide a reliable method to predict the prognosis of osteosarcoma and find potential targets for early diagnosis and precise treatment. To address this issue, we performed a detailed bioinformatics analysis based on the Cancer Genome Atlas (TCGA). A total of 85 osteosarcoma patients with gene expression data and clinicopathological features were included in this study, which was considered the entire set. They were randomly divided into a train set and a test set. We identified six lncRNAs (ELFN1-AS1, LINC00837, OLMALINC, AL669970.3, AC005332.4 and AC023157.3), and constructed a signature that exhibited good predictive ability of patient survival and metastasis. What’s more, we found that risk score calculated by the signature was positively correlated to tumor purity, CD4+ naive T cells, and negatively correlated to CD8+ T cells. Furthermore, we investigated each lncRNA in the signature and found that these six lncRNAs were associated with tumorigenesis and immune cells in the tumor microenvironment. In conclusion, we constructed and validated a signature, which had good performance in the prediction of survival, metastasis and immune microenvironment. Our study indicated possible mechanisms of these lncRNAs in the development of osteosarcoma, which may provide new insights into the precise treatment of osteosarcoma.

Identification of a pyroptosis-related lncRNA risk model for predicting prognosis and immune response in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is one of the most common malignant tumors and is diagnosed at an advanced stage with a poor prognosis worldwide. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.

Methods

Transcriptome data, miRNA-sequencing data, and clinical information were downloaded from the TCGA database. First, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Second, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB)-related genes was analyzed.

Results

A total of 5373 DEmRNAs, 1159 DElncRNAs, and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7 miRNAs, and 5 mRNAs was constructed, and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. The tumor tissues were classified into high- and low-risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis, and nomogram further indicated the risk model was a significant independent prognostic factor what had an excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cell naïve, T cell follicular helper, macrophage M1) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2).

Conclusions

This pyroptosis-related lncRNA risk model possessed good prognostic value, and the ability to predict the outcome of ICB immunotherapy in COAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02572-8.

Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme

Ferroptosis, a recently discovered regulated programmed cell death, is associated with tumorigenesis and progression in glioblastoma. Based on widely recognized ferroptosis-related genes (FRGs), the regulation of ferroptosis patterns and corresponding characteristics of immune infiltration of 516 GBM samples with GSE13041, TCGA-GBM, and CGGA-325 were comprehensively analyzed. Here, we revealed the expression, mutations, and CNV of FRGs in GBM. We identified three distinct regulation patterns of ferroptosis and found the hub genes of immunity and stemness among DEGs in three patterns. A prognostic model was constructed based on five FRGs and verified at the mRNA and protein level. The risk score can not only predict the prognosis but also the degree of immune infiltration and ICB responsiveness by functional annotation. The overall assessment of FRGs in GBM patients will guide the direction of improved research and develop new prognostic prediction tools.

Significance of a PTEN Mutational Status-Associated Gene Signature in the Progression and Prognosis of Endometrial Carcinoma

Background

PTEN mutations have been reported to be involved in the development and prognosis of endometrial carcinoma (EC). However, a prognostic gene signature associated with PTEN mutational status has not yet been developed. In this study, we generated a PTEN mutation-associated prognostic gene signature for EC.

Methods

We obtained the single-nucleotide variation and transcriptomic profiling data from The Cancer Genome Atlas database as training data and implemented the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm to establish a PTEN mutation-associated prognostic gene signature. The overall survival rates of the high-risk and low-risk groups were determined with the Kaplan-Meier (K-M) method, and the accuracy of risk score prediction was tested by using the receiver operating characteristic (ROC) curve.

Results

The K-M curves revealed that the EC patients with PTEN mutations augured favorable survival outcomes. Differential expression analysis between the EC patients with PTEN mutation and wild-type PTEN identified 224 differentially expressed genes (DEGs). Eighty-four DEGs that manifested prognostic value were fitted into the LASSO-Cox analysis, and a PTEN gene signature with seven mutation-associated prognostic genes that showed robust prognostic ability was constructed; this signature was then successfully validated in the other two datasets from the cBioPortal database as well as with 60 clinical specimens. Furthermore, the PTEN mutation-associated prognostic gene signature proved to be an independent prognostic predictor of EC. Remarkably, the EC patients in the high-risk group were characterized by higher tumor stages and grades as well as lower tumor mutation burden with respect to EC, with a poor survival outcome. Collectively, the PTEN mutation-associated prognostic gene signature that we developed could now be used as a favorable prognostic biomarker for EC.

Conclusion

In summary, we developed and validated a prognostic predictor for EC associated with PTEN mutational status that may be used as a favorable prognostic biomarker and therapeutic target for EC.

Bioinformatics analysis of C3 in brain low-grade gliomas as potential therapeutic target and promoting immune cell infiltration

Low-grade gliomas is the malignant nervous tumor with distinct biological and clinical characteristics. Despite advances in diagnostic and therapeutic methods, how to significantly elongate the survival of low-grade gliomas is still challengeable. Complement 3, as the critical component in the innate immune system, plays an essential role in local immune response and participating into regulation of the epithelial-mesenchymal transition and tumor microenvironment. In this study, we systematically determined the expression levels and immunological roles of C3 in low-grade gliomas using various public databases. Then, we further identified the impact of C3 expression on immune cell infiltration compared to normal tissue, indicating the effect of cellular microenvironment on overall survival of LGG patients. We obtained clinical characteristics, transcriptome, and survival of C3 in LGG from the TCGA, GEPIA2.0, and cBioportal databases. Two differentially expressed genes (DEGs) were obtained, DEGs compared to normal tissue (DEG_G1) and DEGs between C3 high expression and C3 low expression in LGG patients (DEG_G2). By performing the GO analysis and protein-protein interaction (PPI) network of DEG_G1, we have identified the top-ranked 10 hub genes, which are highly associated with regulation of cell cycle. The gene set enrichment analysis demonstrated that overexpression of C3 in LGG patient is positively correlated with regulation of cell cycle. The relative PPI analysis and GSEA of DEG_G2 were performed and analysis results indicated that higher expression of C3 in the LGG can activate immune-related pathways. Finally, immune cell infiltration analysis of C3 in the LGG patients was employed and clearly indicated that higher neutrophil infiltration can worsen the survival of the LGG patients with higher expression of C3. These results were confirmed by the Human Protein Atlas database, in which expression level of C3 protein in gliomas patients always higher. This investigation implied that C3 can be as diagnostic biomarker and potential targets of precise therapy for the LGG patients.

USP30-AS1 contributes to mitochondrial quality control in glioblastoma cells

Glioblastoma is the most serious type of brain cancer with poor prognosis. Here, using the publicly available glioma database, we identified that USP30-AS1, an antisense lncRNA locating on the opposite strand of USP30 locus, is upregulated in human gliomas, particularly in high grade glioma. High level of USP30-AS1 is correlated with poor survival in both primary and recurrent glioma patients. USP30-AS1 regulates mitochondrial homeostasis and mitophagy in glioblastoma cells. Knockdown of USP30-AS1 decreases mitochondrial protein expression and mitochondrial mass, promotes mitochondrial uncoupler-induced mitophagy. However, USP30-AS1 does not regulate USP30 expression in a cis-regulatory manner. In summary, this study proposed that USP30-AS1 may serve as a valuable prognostic marker for gliomas. USP3-AS1 is a negative regulator of mitophagy and the regulatory effect is USP30-independent. USP30-AS1 mediated repression of mitophagy may contribute to the loss of mitochondrial homeostasis and tumor development in glioma.

An immune-associated ten-long noncoding RNA signature for predicting overall survival in cervical cancer

BACKGROUND

Several immune-associated long non-coding RNA (lncRNA) signatures have been reported as prognostic models in different types of cancers; however, the immune-associated lncRNA signature for predicting overall survival (OS) in cervical cancer is unknown.

METHODS

The lncRNA expression profiles and clinical data of cervical cancer were acquired from The Cancer Genome Atlas (TCGA) dataset. Immune-associated genes were extracted from the Molecular Signatures Database (MSigDB), and the immune-associated lncRNAs were extracted for Cox regression analysis. Principal component analysis (PCA) was used to distinguish the high and low risk status of cervical cancer patients. Gene Set Enrichment Analysis (GSEA) was used for functional analyses.

RESULTS

Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to construct an immune-associated ten-lncRNA signature (containing AL021807.1, AL109976.1, LINC02446, MIR4458HG, AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) for predicting OS in cervical cancer. The signature segregated the cervical cancer patients into 2 groups (high-risk group and low-risk group). The Kaplan-Meier survival curves of AL021807.1, AL109976.1, LINC02446, and MIR4458HG were statistically significant (P<0.05) and the others (including AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) were not statistically significant (P>0.05). The Kaplan-Meier survival curves of the signature were statistically significant (P=1.134e-10), and the 5-year survival rate was 0.444 in the high-risk group [95% confidence interval (CI): 0.334 to 0.590] and 0.884 in the low-risk group (95% CI: 0.807 to 0.969). The area under curve (AUC) of the receiver operating characteristic (ROC) curve of the signature was 0.833. The concordance index (C-index) of the signature was 0.788 (95% CI: 0.730 to 0.846, P=1.884778e-22). The PCA successfully distinguished the high-risk group and low-risk group based on the signature. The GSEA showed that the signature-related protein coding genes (PCGs) may participate in immunologic biological processes and pathways.

CONCLUSIONS

This study revealed that the immune-associated ten-lncRNA signature is an independent factor for cervical cancer prognosis prediction, providing a bright future for immunotherapy of cervical cancer patients.

Integrated analyses of long noncoding RNAs and mRNAs in the progression of breast cancer

Objective

The objective was to explore the expression and potential functions of long noncoding RNA (lncRNA) and mRNAs in human breast cancer (BC).

Methods

Differentially expressed lncRNAs and mRNAs were identified and annotated in BC tissues by using the Agilent human lncRNA assay (Agilent Technologies, Santa Clara, CA, USA) and RNA sequencing. After identification of lncRNAs and mRNAs through quantitative reverse transcription polymerase chain reaction, we conducted a series of functional experiments to confirm the effects of knockdown of one lncRNA, TCONS_00029809, on the progression of BC.

Results

We discovered 238 lncRNAs and 200 mRNAs that were differentially expressed in BC tissues and para-carcinoma tissue. We showed that differentially expressed mRNAs were related to biological adhesion and biological regulation and mainly enriched in cytokine-cytokine receptor interaction, metabolic pathways, and PI3K-Akt signaling pathway. We created a protein–protein interaction network to analyze the proteins enriched in these pathways. We demonstrated that silencing of TCONS_00029809 remarkably inhibited proliferation, invasion, and migration of BC cells, and accelerated their apoptosis.

Conclusions

We identified a large number of differentially expressed lncRNAs and mRNAs, which provide data useful in understanding BC carcinogenesis. The lncRNA TCONS_00029809 may be involved in the development of BC.

A robust signature of immune-related long non-coding RNA to predict the prognosis of bladder cancer

Background

Bladder cancer is the second most common malignant tumor in the urogenital system. The research investigated the prognostic role of immune‐related long non‐coding RNA (lncRNA) in bladder cancer.

Methods

We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single‐sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis. Multivariate Cox analysis was used to establish the risk score model. Then we established and evaluated the nomogram. Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis.

Results

Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune‐related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high‐ and low‐risk cohorts. Patients in the low‐risk cohort had better survival outcomes than those in the high‐risk cohort. The nomogram was established based on the indicators including age, gender, tumor‐node‐metastases stage, and risk score. The model's predictive performance was confirmed by the receiver operating characteristic curve analysis, concordance index method, calibration curve, and decision curve analysis. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9‐lncRNA signature was involved in the modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway.

Conclusions

Our study uncovered the prognostic value of immune‐related lncRNAs for bladder cancer and showed that they may regulate tumor pathogenesis in various ways.

Exosomal lncRNA LINC01711 facilitates metastasis of esophageal squamous cell carcinoma via the miR-326/FSCN1 axis

Esophageal cancer is a malignant tumor with a five-year survival rate of less than 20%. Early diagnosis and exploration of esophageal cancer pathogenesis are of great significance for the treatment and prognosis of esophageal cancer. Long non-coding RNA (lncRNA) plays a vital role in the occurrence and development of different types of tumors. However, the role of exosome LncRNA in esophageal squamous cell carcinoma (ESCC) is rarely reported. In this study, we detected high expression of lncRNA LINC01711 in ESCC tissues and was associated with poor prognosis. Silencing LINC01711 can inhibit the proliferation, migration, invasion, and growth of ESCC cell lines, and induce apoptosis. Linc01711 was identified as a competitive endogenous RNA that suppressed miR-326, and up-regulated the expression of fascin actin-bundling protein 1 (FSCN1). Besides, in vivo experiments showed that the administration of exosome-derived LINC01711 (LINC01711-Exo) promoted the growth of tumors in nude mice. In general, exosomal LINC01711 promoted the proliferation, migration, and invasion of esophageal cancer cells by up-regulating FSCN1 and down-regulating miR-326, thus improved the occurrence and development of ESCC.

A Potential Immune-Related Long Non-coding RNA Prognostic Signature for Ovarian Cancer

Ovarian cancer (OC), the most lethal gynecologic malignancy, ranks fifth in cancer deaths among women, largely because of late diagnosis. Recent studies suggest that the expression levels of immune-related long non-coding RNAs (lncRNAs) play a significant role in the prognosis of OC; however, the potential of immune-related lncRNAs as prognostic factors in OC remains unexplored. In this study, we aimed to identify a potential immune-related lncRNA prognostic signature for OC patients. We used RNA sequencing and clinical data from The Cancer Genome Atlas and the Gene Expression Omnibus database to identify immune-related lncRNAs that could serve as useful biomarkers for OC diagnosis and prognosis. Univariate Cox regression analysis was used to identify the immune-related lncRNAs with prognostic value. Functional annotation of the data was performed through the GenCLiP310 website. Seven differentially expressed lncRNAs (AC007406.4, AC008750.1, AL022341.2, AL133351.1, FAM74A7, LINC02229, and HOXB-AS2) were found to be independent prognostic factors for OC patients. The Kaplan-Meier curve indicated that patients in the high-risk group had a poorer survival outcome than those in the low-risk group. The receiver operating characteristic curve revealed that the predictive potential of the immune-related lncRNA signature for OC was robust. The prognostic signature of the seven lncRNAs was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the signature of the seven lncRNAs was an independent prognostic factor for OC patients. Finally, we constructed a nomogram model and a competing endogenous RNA network related to the lncRNA prognostic signature. In conclusion, our study reveals novel immune-related lncRNAs that may serve as independent prognostic factors in OC.

Identification and validation of a novel eight mutant-derived long non-coding RNAs signature as a prognostic biomarker for genome instability in low-grade glioma

Long non-coding RNAs (lncRNAs) comprise an integral part of the eukaryotic transcriptome. Alongside proteins, lncRNAs modulate lncRNA-based gene signatures of unstable transcripts, play a crucial role as antisense lncRNAs to control intracellular homeostasis and are implicated in tumorigenesis. However, the role of genomic instability-associated lncRNAs in low-grade gliomas (LGG) has not been fully explored. In this study, lncRNAs expression and somatic mutation profiles in low-grade glioma genome were used to identify eight novel mutant-derived genomic instability-associated lncRNAs including H19, FLG-AS1, AC091932.1, AC064875.1, AL138767.3, AC010273.2, AC131097.4 and ISX-AS1. Patients from the LGG gene mutagenome atlas were grouped into training and validation sets to test the performance of the signature. The genomic instability-associated lncRNAs signature (GILncSig) was then validated using multiple external cohorts. A total of 59 novel genomic instability-associated lncRNAs in LGG were used for least absolute shrinkage and selection operator (Lasso), single and multifactor Cox regression analysis using the training set. Furthermore, the independent predictive role of risk features in the training and validation sets were evaluated through survival analysis, receiver operating feature analysis and construction of a nomogram. Patients with IDH1 mutation status were grouped into two different risk groups based on the GILncSig score. The low-risk group showed a relatively higher rate of IDH1 mutations compared with patients in the high-risk group. Furthermore, patients in the low-risk group had better prognosis compared with patients in the high-risk group. In summary, this study reports a reliable prognostic prediction signature and provides a basis for further investigation of the role of lncRNAs on genomic instability. In addition, lncRNAs in the signature can be used as new targets for treatment of LGG.

Long non-coding RNAs in brain tumors: roles and potential as therapeutic targets

Brain tumors are associated with adverse outcomes despite improvements in radiation therapy, chemotherapy, and photodynamic therapy. However, treatment approaches are evolving, and new biological phenomena are being explored to identify the appropriate treatment of brain tumors. Long non-coding RNAs (lncRNAs), a type of non-coding RNA longer than 200 nucleotides, regulate gene expression at the transcriptional, post-transcriptional, and epigenetic levels and are involved in a variety of biological functions. Recent studies on lncRNAs have revealed their aberrant expression in various cancers, with distinct expression patterns associated with their instrumental roles in cancer. Abnormal expression of lncRNAs has also been identified in brain tumors. Here, we review the potential roles of lncRNAs and their biological functions in the context of brain tumors. We also summarize the current understanding of the molecular mechanisms and signaling pathways related to lncRNAs that may guide clinical trials for brain tumor therapy.

The role of M6A modification in the regulation of tumor-related lncRNAs

N⁶-methyladenosine (m⁶A) is the most abundant modification in eukaryotic cells, and it regulates RNA transcription, processing, splicing, degradation, and translation. Long non-coding RNAs (lncRNAs), as transcriptional products with no or limited protein coding ability more than 200 nt in length, play an important role in epigenetic modification, mRNA transcription, splicing, stability, translation, and other biological functions. Extensive studies have shown that both m⁶A modification and lncRNAs are involved in the pathogenesis of various diseases, such as kinds of cancers, heart failure, Alzheimer’s disease, periodontitis, human abdominal aortic aneurysm, and obesity. To date, m⁶A modification has been identified as an important biological function in enrichment and regulation of lncRNAs. In this review, we summarize the role of m⁶A modification in the regulation and function of tumor-related lncRNAs. Moreover, we discuss the potential applications and possible future directions in the field.

Integrated analysis of immune-related long noncoding RNAs as diagnostic biomarkers in psoriasis

Background

Psoriasis is a chronic immune-mediated inflammatory dermatosis. Long noncoding RNAs (lncRNAs) play an important role in immune-related diseases. This study aimed to identify potential immune-related lncRNA biomarkers for psoriasis.

Methods

We screened differentially expressed immune-related lncRNAs biomarkers using GSE13355 (skin biopsy samples of 180 cases) from Gene Expression Omnibus (GEO). Moreover, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA) were performed to explore biological mechanisms in psoriasis. In addition, we performed LASSO logistic regression to identify potential diagnostic lncRNAs and further verify the diagnostic value and relationship with drug response using two validation sets: GSE30999 (skin biopsy samples of 170 cases) and GSE106992 (skin biopsy samples of 192 cases). Furthermore, we estimated the degree of infiltrated immune cells and investigated the correlation between infiltrated immune cells and diagnostic lncRNA biomarkers.

Results

A total of 394 differentially expressed genes (DEGs) were extracted from gene expression profile. GO and KEGG analysis of target genes found that immune-related lncRNAs were primarily associated with epidermis development, skin development, collagen-containing extracellular matrix, and glycosaminoglycan binding and mainly enriched in cytokine-cytokine receptor interaction and influenza A and chemokine signaling pathway. We found that LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1, CARMN, CCDC18-AS1, EPB41L4A-AS, and LINC01214 exhibited well diagnostic efficacy. The ROC and ROC CI were 0.944 (0.907–0.982), 0.953 (0.919–0.987), 0.822 (0.758–0.887), 0.854 (0.797–0.911), 0.957(0.929–0.985), 0.894 (0.846–0.942), and 0.964 (0.937–0.991) for LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1,CARMN, CCDC18-AS1, EPB41L4A-AS1, and LINC01214. LINC01137, LINC01215, and LINC01214 were correlated with drug response. LINC01137, CCDC18-AS1, and CARMN were positively correlated with activated memory CD4 T cell, activated myeloid dendritic cell (DC), neutrophils, macrophage M1, and T follicular helper (Tfh) cells, while negatively correlated with T regulatory cell (Treg). LINC01215, MAPKAPK5-AS1, TPT1-AS1, EPB41L4A-AS, and LINC01214 were negatively correlated with activated memory CD4 T cell, activated myeloid DC, neutrophils, macrophage M1, and Tfh, while positively correlated with Treg.

Conclusions

These findings indicated that these immune-related lncRNAs may be used as potential diagnostic and predictive biomarkers for psoriasis.

Immunogenomic Profiling Demonstrate AC003092.1 as an Immune-Related eRNA in Glioblastoma Multiforme

Enhancer RNAs, a type of long non-coding RNAs (lncRNAs), play a critical role in the occurrence and development of glioma. RNA-seq data from 161 glioblastoma multiforme (GBM) samples were acquired from The Cancer Genome Atlas database. Then, 70 eRNAs were identified as prognosis-related genes, which had significant relations with overall survival (log-rank test, p < 0.05). AC003092.1 was demonstrated as an immune-related eRNA by functional enrichment analysis. We divided samples into two groups based on AC003092.1 expression: AC003092.1 High (AC003092.1_H) and AC003092.1 Low (AC003092.1_L) and systematically analyzed the influence of AC003092.1 on the immune microenvironment by single-sample gene-set enrichment analysis and CIBERSORTx. We quantified AC003092.1 and TFPI2 levels in 11 high-grade gliomas, 5 low-grade gliomas, and 7 GBM cell lines. Our study indicates that AC003092.1 is related to glioma-immunosuppressive microenvironment, and these results offer innovative sights into GBM immune therapy.

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.

Analyses of metastasis-associated genes in IDH wild-type glioma

Background

Glioma is the most common malignant tumor of the brain. The existence of metastatic tumor cells is an important cause of recurrence even after radical glioma resection.

Methods

Single-cell sequencing data and high-throughput data were downloaded from GEO database and TCGA/CGGA database. By means of PCA and tSNE clustering methods, metastasis-associated genes in glioma were identified. GSEA explored possible biological functions that these metastasis-associated genes may participate in. Univariate and multivariate Cox regression were used to construct a prognostic model.

Results

Glioma metastatic cells and metastasis-associated genes were identified. The prognostic model based on metastasis-associated genes had good sensitivity and specificity for the prognosis of glioma. These genes may be involved in signal pathways such as cellular protein catabolic process, p53 signaling pathway, transcriptional misregulation in cancer and JAK-STAT signaling pathway.

Conclusion

This study explored glioma metastasis-associated genes through single-cell sequencing data mining, and aimed to identify prognostic metastasis-associated signatures for glioma and may provide potential targets for further cancer research.

Autophagy-related long noncoding RNAs can predict prognosis in patients with bladder cancer

We investigated whether autophagy-related long noncoding RNAs (lncRNAs) can predict prognosis in bladder cancer. We obtained bladder cancer lncRNA data from The Cancer Genome Atlas and autophagy-related genes from the Human Autophagy Database. Fifteen autophagy-related lncRNAs with prognostic significance were identified. Multivariate Cox analysis was used to construct a risk score model, which divided bladder cancer patients into high-risk and low-risk groups. We found that patients in the low-risk group had better survival than those in the high-risk group. Subgroup analysis showed that patients in the high-risk group also had worse OS than that in the low-risk group in subgroups based on age, gender, clinical stage, and TNM stage. We next established a nomogram according to the results of multivariate Cox regression, which included age, gender, clinical stage, TNM stage, and risk score. The area under the curve for 3- and 5-year overall survival predicted by the nomogram were 0.711 and 0.719, respectively. Bioinformatics analysis demonstrated that the 15 identified lncRNAs are involved in the cell cycle, DNA replication, cell adhesion, cancer pathway, WNT signaling pathway, and oxidative stress. These findings confirm that autophagy-related lncRNAs are predictive of prognosis in bladder cancer patients and may affect tumor progression.

Exploring the Roles of lncRNAs in GBM Pathophysiology and Their Therapeutic Potential

Glioblastoma (GBM) remains the most devastating primary central nervous system malignancy with a median survival of around 15 months. The past decades of research have not yielded significant advancements in the treatment of GBM. In that same time, a novel class of molecules, long non-coding RNAs (lncRNAs), has been found to play a multitude of roles in cancer and normal biology. The increased accessibility of next generation sequencing technologies and the advent of lncRNA-specific microarrays have facilitated the study of lncRNA etiology. Molecular and computational methods can be applied to predict lncRNA function. LncRNAs can serve as molecular decoys, scaffolds, super-enhancers, or repressors. These molecules can serve as phenotypic switches for GBM cells at the expression and/or epigenetic levels. LncRNAs can affect stemness/differentiation, proliferation, invasion, survival, DNA damage response, and chromatin dynamics. Aberrant expression of these transcripts may facilitate therapy resistance, leading to tumor recurrence. LncRNAs could serve as novel theragnostic or prognostic biomarkers in GBM and other cancers. RNA-based therapeutics may also be employed to target lncRNAs as a novel route of treatment for primary or recurrent GBM. In this review, we explore the roles of lncRNAs in GBM pathophysiology and posit their novel therapeutic potential for GBM.

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Background

Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD).

Methods

RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method.

Results

A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance (P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis.

Conclusions

We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

Non-coding RNAs in Brain Tumors, the Contribution of lncRNAs, circRNAs, and snoRNAs to Cancer Development-Their Diagnostic and Therapeutic Potential

Brain tumors are one of the most frightening ailments that afflict human beings worldwide. They are among the most lethal of all adult and pediatric solid tumors. The unique cell-intrinsic and microenvironmental properties of neural tissues are some of the most critical obstacles that researchers face in the diagnosis and treatment of brain tumors. Intensifying the search for potential new molecular markers in order to develop new effective treatments for patients might resolve this issue. Recently, the world of non-coding RNAs (ncRNAs) has become a field of intensive research since the discovery of their essential impact on carcinogenesis. Some of the most promising diagnostic and therapeutic regulatory RNAs are long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and small nucleolar RNAs (snoRNAs). Many recent reports indicate the important role of these molecules in brain tumor development, as well as their implications in metastasis. In the following review, we summarize the current state of knowledge about regulatory RNAs, namely lncRNA, circRNAs, and snoRNAs, and their impact on the development of brain tumors in children and adults with particular emphasis on malignant primary brain tumors-gliomas and medulloblastomas (MB). We also provide an overview of how these different ncRNAs may act as biomarkers in these tumors and we present their potential clinical implications.

Immune-Related lncRNA Risk Signatures Predict Survival of IDH Wild-Type and MGMT Promoter Unmethylated Glioblastoma

Introduction

Glioblastoma is the most malignant grade of glioma, and it is also the most common primary tumor in the brain. Immunotherapy is a kind of precise tumor treatment. However, there are limited studies about immune-related lncRNA. This study is aimed at analyzing immune-related lncRNAs in glioblastoma and screening out prognostic factors, providing new potential targets for glioblastoma immunology research. Material and Methods. Gene expression data and clinical data of IDH wild-type with MGMT promoter unmethylated glioblastoma were acquired from the TCGA and CGGA databases. Immune-related lncRNAs were identified with the help of data from the InnateDB database. Immune prognostic factors were recognized by Cox regression analysis. GSEA analysis pursued their potential functions.

Results

We found 318 immune-related lncRNAs. Among them, there were 137 immune-related lncRNAs that were upregulated and 181 that were downregulated. 15 prognostic lncRNAs were identified by Cox regression, and a total of 6 molecules were included in the following risk scoring model. GSEA showed that these lncRNAs participated in functions such as protein digestion and absorption and the PPAR signaling pathway.

Conclusion

There are limited studies about immune regulation mechanisms of lncRNA in IDH wild-type with MGMT promoter unmethylated glioblastoma. The identified immune-related lncRNAs in glioblastoma might contribute new targets and research directions for immunological molecular studies of glioblastoma.

Collagen Type III Alpha 1 chain regulated by GATA-Binding Protein 6 affects Type II IFN response and propanoate metabolism in the recurrence of lower grade glioma

Some studies suggested the prognosis value of immune gene in lower grade glioma (LGG). Recurrence in LGG is a tough clinical problem for many LGG patients. Therefore, prognosis biomarker is required. Multivariate prognosis Cox model was constructed and then calculated the risk score. And differential expressed transcription factors (TFs) and differential expressed immune genes (DEIGs) were co‐analysed. Besides, significant immune cells/pathways were identified by single sample gene set enrichment analysis (ssGSEA). Moreover, gene set variation analysis (GSVA) and univariate Cox regression were applied to filter prognostic signalling pathways. Additionally, significant DEIG and immune cells/pathways, and significant DEIG and pathways were co‐analysed. Further, differential enriched pathways were identified by GSEA. In sum, a scientific hypothesis for recurrence LGG including TF, immune gene and immune cell/pathway was established. In our study, a total of 536 primary LGG samples, 2,498 immune genes and 318 TFs were acquired. Based on edgeR method, 2,164 DEGs, 2,498 DEIGs and 31 differentials expressed TFs were identified. A total of 106 DEIGs were integrated into multivariate prognostic model. Additionally, the AUC of the ROC curve was 0.860, and P value of Kaplan‐Meier curve < 0.001. GATA6 (TF) and COL3A1 (DEIG) were selected (R = 0.900, P < 0.001, positive) as significant TF‐immune gene links. Type II IFN response (P < 0.001) was the significant immune pathway. Propanoate metabolism (P < 0.001) was the significant KEGG pathway. We proposed that COL3A1 was positively regulated by GATA6, and by effecting type II IFN response and propanoate metabolism, COL3A1 involved in LGG recurrence.

Coding of Glioblastoma Progression and Therapy Resistance through Long Noncoding RNAs

Glioblastoma is the most aggressive and lethal primary brain malignancy, with an average patient survival from diagnosis of 14 months. Glioblastoma also usually progresses as a more invasive phenotype after initial treatment. A major step forward in our understanding of the nature of glioblastoma was achieved with large-scale expression analysis. However, due to genomic complexity and heterogeneity, transcriptomics alone is not enough to define the glioblastoma “fingerprint”, so epigenetic mechanisms are being examined, including the noncoding genome. On the basis of their tissue specificity, long noncoding RNAs (lncRNAs) are being explored as new diagnostic and therapeutic targets. In addition, growing evidence indicates that lncRNAs have various roles in resistance to glioblastoma therapies (e.g., MALAT1, H19) and in glioblastoma progression (e.g., CRNDE, HOTAIRM1, ASLNC22381, ASLNC20819). Investigations have also focused on the prognostic value of lncRNAs, as well as the definition of the molecular signatures of glioma, to provide more precise tumor classification. This review discusses the potential that lncRNAs hold for the development of novel diagnostic and, hopefully, therapeutic targets that can contribute to prolonged survival and improved quality of life for patients with glioblastoma.

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Objective

Despite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG.

Methods

RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell’s concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER.

Results

Noted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan–Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4⁺ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8⁺T cells were significant higher in high-risk group in both TCGA and CGGA cohorts.

Conclusion

The present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.