An Immune Gene-Related Five-lncRNA Signature for to Predict Glioma Prognosis

Immunological processes of enhancers and suppressors of long non-coding RNAs associated with brain tumors and inflammation

Immunological processes, such as inflammation, can both cause tumor suppression and cancer progression. Moreover, deregulated levels of long non-coding RNA (lncRNA) expression in the brain may cause inflammation and lead to the growth of tumors. Like other biological processes, the immune system's role in cancer is complicated, varies, and can help or hurt the cancer's maintenance. According to research, inflammation and brain cancer are correlated via several signaling pathways. A variety of lncRNAs have recently been revealed to influence cancer by modulating inflammatory pathways. As a result, lncRNAs have the potential to influence carcinogenesis, tumor formation, or tumor suppression via an increase or decrease in inflammation functions. Although the study and targeting of lncRNAs have made great progress in the treatment of cancer, there are definitely limitations and challenges. Using new technologies like nanocarriers and cell-penetrating peptides (CPPs) to target treatments without hurting healthy body tissues has shown to be very effective. In this review article, we have collected significantly related lncRNAs and their inhibitory or stimulating roles in inflammation and brain cancer for the first time. However, there are limitations, such as side effects and damage to normal tissues. With the advancement of new targeting technologies, these lncRNAs may be candidates for the specific targeting therapy of brain cancers by limiting inflammation or stimulating the immune system against them in the future.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

The potential of DEirlncRNAs: A novel approach to predict glioblastoma prognosis

Background

Despite tremendous evolution in therapies, the prognosis of glioblastoma (GBM) remains grim, which calls for innovative approaches to optimize chemotherapy efficacy and predict risk.

Methods

The transcriptome and clinical data of GBM were acquired from the Cancer Genome Atlas (TCGA), followed by the identification of differentially expressed immune-related long noncoding RNAs (DEirlncRNAs) with Pearson correlation and limma packet analyses. Survival-related DEirlncRNA pairs were screened with univariate Cox proportional hazard regression. Prognostic markers were obtained, and risk scores were calculated with Lasso regression and multivariate Cox risk regression analyses. The association of the prognostic risk model with immune cell infiltration was evaluated by comprehensively analyzing tumor-infiltrating immune cells with TIMER, XCELL, CIBERSORT, QUANTISEQ, and EPIC. Differences in half-maximal inhibitory concentration (IC50) values between the high- and low-risk groups were assessed with the Wilcoxon signed-rank test.

Results

A total of 276 DEirlncRNAs were identified, followed by the visualization of their expression patterns. Two prognosis-related DEirlncRNA pairs were screened, with high accuracy and reliability. The constructed prognostic risk model effectively distinguished between high- and low-risk patients, and significant differences were observed in survival outcomes between the high- and low-risk groups. Furthermore, risk scores were associated with tumor-infiltrating immune cells and DEirlncRNA expression. Additionally, the risk model had a correlation with the effectiveness of commonly used chemotherapeutic agents, providing clues into potential treatment responses.

Conclusions

In our study, a novel signature was constructed with paired DEirlncRNAs (regardless of their expression), which holds significant clinical predictive value and is a potential breakthrough for personalized management of GBM.

The landscape of immune checkpoint-related long non-coding RNAs core regulatory circuitry reveals implications for immunoregulation and immunotherapy responses

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

Nifuroxazide inhibits the growth of glioblastoma and promotes the infiltration of CD8 T cells to enhance antitumour immunity

INTRODUCTION

Glioblastoma is a primary intracranial tumour with extremely high disability and fatality rates among adults. Existing diagnosis and treatment methods have not significantly improved the overall poor prognosis of patients. Nifuroxazide, an oral antibiotic, has been reported to act as a tumour suppressor in a variety of tumours and to participate in the process of antitumour immunity. However, whether it can inhibit the growth of glioma is still unclear.

METHODS

We explored the potential mechanism of nifuroxazide inhibiting the growth of glioblastoma cells through in vitro and in vivo experiments.

RESULTS

nifuroxazide can inhibit the proliferation of glioblastoma cells, promote G2 phase arrest, induce apoptosis, and inhibit epithelial-mesenchymal transition through the MAP3K1/JAK2/STAT3 pathway. Similarly, clinical sample analysis confirmed that MAP3K1 combined with STAT3 can affect the prognostic characteristics of patients with glioma. In addition, nifuroxazide can drive the M1 polarization of microglioma cells, inhibit the expression of CTLA4 and PD-L1 in tumour cells, and promote the infiltration of CD8 T cells to exert antitumour effects. Combination treatment with PD-L1 inhibitors can significantly prolong the survival time of mice.

CONCLUSION

we found that nifuroxazide can inhibit the growth of glioblastoma and enhance antitumour immunity. Thus, nifuroxazide is an effective drug for the treatment of glioblastoma and has great potential for clinical application.

Development of a novel angiogenesis-related lncRNA signature to predict the prognosis and immunotherapy of glioblastoma multiforme

Background

Long noncoding RNA (lncRNA) can regulate tumorigenesis, angiogenesis, proliferation, and other tumor biological behaviors, and is closely related to the growth and progression of glioma. The purpose of this research was to investigate the role of angiogenesis-related lncRNA in the prognosis and immunotherapy of glioblastoma multiforme (GBM).

Methods

Differential analysis was carried out to acquire angiogenesis-related differentially expressed lncRNAs (AR-DElncRNAs). The AR-DElncRNAs were then subjected to univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Based on the median risk score, patients were classified into high-risk and low-risk groups. Kaplan-Meier survival analysis was conducted to estimate the prognostic value of the prognostic model. In addition, a nomogram was built to predict individual survival probabilities by combining clinicopathological characteristics and a prognostic model. Furthermore, immune infiltration, immunotherapy, and drug sensitivity analyses were administered to investigate the differences between the high- and low-risk groups.

Results

We identified 3 lncRNAs (DGCR5, PRKAG2-AS1, and ACAP2-IT1) that were significantly associated with the survival of GBM patients from the 255 AR-DElncRNAs based on univariate Cox and LASSO analyses. Then, a prognostic model was structured according to these 3 lncRNAs, from which we found that high-risk GBM patients had a worse prognosis than that of low-risk patients. Moreover, the risk score was determined to be an independent prognostic factor [hazard ratio (HR) =1.444; 95% confidence interval (CI): 1.014-2.057; P<0.05]. The immune microenvironment analysis revealed that the immune score, stromal score, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score were significantly higher in the high-risk group than in the low-risk group. Neutrophils, macrophages, immature dendritic cells (iDCs), natural killer (NK) CD56dim cells, activated DCs (aDCs), and uncharacterized cells were different in the high- and low-risk groups. In addition, the high-risk group had a stronger sensitivity to immunotherapy. Furthermore, the sensitivity of 28 potential chemotherapeutic drugs differed significantly between the high- and low-risk groups.

Conclusions

A novel angiogenesis-related lncRNA signature could be used to predict the prognosis and treatment of GBM.

Development and validation of a prognostic gene expression signature for lower-grade glioma following surgery and adjuvant radiotherapy

BACKGROUND AND PURPOSE

Standard of care for lower-grade glioma (LGG) is maximal safe resection and risk-adaptive adjuvant therapy. While patients who benefit the most from adjuvant chemotherapy have been elucidated in prospective randomized studies, comparable insights for adjuvant radiotherapy (RT) are lacking. We sought to identify and validate patterns of gene expression that are associated with differential outcomes among LGG patients treated by RT from two large genomics databases.

MATERIALS AND METHODS

Patients from The Cancer Genome Atlas (TCGA) with LGG (WHO grade II-III glioma) treated by surgery and adjuvant RT were randomized 1:1 to a discovery cohort or an internal validation cohort. Using the discovery cohort only, associations between tumor RNA-seq expression and progression-free survival (PFS) as well as overall survival (OS) were evaluated with adjustment for clinicopathologic covariates. A Genomic Risk Score (GRS) was then constructed from the expression levels of top genes also screened for involvement in glioma carcinogenesis. The prognostic value of GRS was further assessed in the internal validation cohort of TCGA and a second distinct database, compiled by the Chinese Glioma Genome Association (CGGA).

RESULTS

From TCGA, 289 patients with LGG received adjuvant RT alone (38 grade II, 30 grade III) or chemoradiotherapy (CRT) (51 grade II, 170 grade III) between 2009 and 2015. From CGGA, 178 patients with LGG received adjuvant RT alone (40 grade II, 13 grade III) or CRT (41 grade II, 84 grade III) between 2004 and 2016. The genes comprising GRS are involved in MAP kinase activity, T cell chemotaxis, and cell cycle transition: MAP3K15, MAPK10, CCL3, CCL4, and ADAMTS1. High GRS, defined as having a GRS in the top third, was significantly associated with poorer outcomes independent of age, sex, glioma histology, WHO grade, IDH mutation, 1p/19q co-deletion, and chemotherapy status in the discovery cohort (PFS HR 1.61, 95% CI 1.10-2.36, P=0.014; OS HR 2.74, 95% CI 1.68-4.47, P<0.001). These findings were replicated in the internal validation cohort (PFS HR 1.58, 95% CI 1.05-2.37, P=0.027; OS HR 1.84, 95% CI 1.13-3.00, P=0.015) and the CGGA external validation cohort (OS HR 1.72, 95% CI 1.27-2.34, P<0.001). Association between GRS and outcomes was observed only among patients who underwent RT, in both TCGA and CGGA.

CONCLUSION

This study successfully identified an expression signature of five genes that stratified outcomes among LGG patients who received adjuvant RT, with two rounds of validation leveraging independent genomics databases. Expression levels of the highlighted genes were associated with PFS and OS only among patients whose treatment included RT, but not among those with omission of RT, suggesting that expression of these genes may be predictive of radiation treatment response. While additional prospective studies are warranted, interrogation of these genes may be considered in the multidisciplinary management of LGG.

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.

Comprehensive Analysis of RELL2 as a Potential Biomarker Associated with Tumor Immune Infiltrating Cells in a Pan-Cancer Analysis

Background

Receptor expressed in lymphoid tissues-like 2 (RELL2), which is a member of RELT family, is closely associated with the plasma membrane and acts as a modulator for RELT signaling. Overexpression of RELL2 induces the activation of MAPK14/p38 cascade and apoptosis. However, whether RELL2 contributes to cancers remains unclear. Here, we examined its role in cancer patient prognosis and various tumors.

Methods

We used several bioinformatics methods, specifically gene set enrichment analysis (GSEA), ScanNeo, and ESTIMATE, to analyze the CCLE dataset, GTEx dataset, and TCGA dataset. We investigated the possible association of RELL2 with the microsatellite instability (MSI) of various tumors, tumor mutational burden (TMB), immune checkpoint, immune neoantigens, immune microenvironment, and patient prognosis.

Result

RELL2 is highly expressed in cancer compared with normal tissues. RELL2 expression is linked with worse progression-free interval and overall survival in numerous cancers. In most cancers, high RELL2 expression was related to a poor prognosis. RELL2 expression was significantly associated with the tumor microenvironment, MSI, and TMB. RELL2 expression is strongly associated with phenotypes that are of major clinical significance, particularly those associated with immune neoantigens and the expression profiles of immune checkpoint genes in pan-cancer. RELL2 expression strongly linked with the expressions of methyltransferases and DNA repair genes. It also significantly correlated with multiple signaling pathways through gene set enrichment analysis.

Conclusion

RELL2 may be a prognostic biomarker in pan-cancer and may have an important function in tumorigenesis and progression.

Genome Instability-Associated Long Non-Coding RNAs Reveal Biomarkers for Glioma Immunotherapy and Prognosis

Genome instability is a hallmark of tumors and is involved in proliferation, invasion, migration, and treatment resistance of many tumors. However, the relationship of genome instability with gliomas remains unclear. Here, we constructed genome instability-derived long non-coding RNA (lncRNA)-based gene signatures (GILncSig) using genome instability-related lncRNAs derived from somatic mutations. Multiple platforms were used to confirm that the GILncSig were closely related to patient prognosis and clinical characteristics. We found that GILncSig, the glioma microenvironment, and glioma cell DNA methylation-based stemness index (mDNAsi) interacted with each other to form a complex regulatory network. In summary, this study confirmed that GILncSig was an independent prognostic indicator for patients, distinguished high-risk and low-risk groups, and affected immune-cell infiltration and tumor-cell stemness indicators (mDNAsi) in the tumor microenvironment, resulting in tumor heterogeneity and immunotherapy resistance. GILncSig are expected to provide new molecular targets for the clinical treatment of patients with gliomas.

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.

High Expression of CISD2 in Relation to Adverse Outcome and Abnormal Immune Cell Infiltration in Glioma

Glioma is a serious disease burden globally, with high mortality and recurrence rates. CDGSH iron sulfur domain 2 (CISD2) is an evolutionarily conserved protein that is involved in several cancers. However, its role in the prognosis and immune infiltration in glioma remains unclear. In our research, RNA-seq matrix and clinicopathological relevant data for CISD2 were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Human Protein Atlas was used to verify the CISD2 protein level in glioma, and STRING was used to establish relative coexpression gene network. The Kaplan-Meier plotter was adopted to analyze the effect of CISD2 on prognosis. The connection between CISD2 expression and immune infiltration was analyzed using single-sample GSEA (ssGSEA), TIMER, and GEPIA. In contrast to normal tissues, CISD2 expression was significantly higher in glioma tissues, and CISD2 presented a certain diagnostic value in distinguishing glioma tissues from normal tissues. Furthermore, the CISD2 level was correlated with age, histologic grade, histological type, isocitrate dehydrogenase (IDH) status, 1p/19q codeletion status, and primary therapy outcome of glioma, while high CISD2 mRNA expression was correlated with grave overall survival. Multivariate analysis demonstrated that CISD2 was an independent risk factor for patients with glioma. Functional enrichment analysis indicated that CISD2 could regulate proliferation, immune reaction, and mitochondrial function. The results from the ssGSEA and TIMER databases confirmed that CISD2 acts a prominent role in immune cell infiltration in the tumor microenvironment, especially in low-grade glioma (LGG). Furthermore, CISD2 expression was observably correlated to M2 polarization in macrophages with glioma progression. This is the first research to investigate the immune role of CISD2 in glioma. CISD2 may be an innovative prognostic biomarker and can act as a potential target for future therapy for glioma.

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.

Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N⁶-methyladenosine (m⁶A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).

Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m⁶A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.

Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m⁶A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

A Novel Prognostic Tool for Glioma Based on Enhancer RNA-Regulated Immune Genes

Background: Gliomas are the most malignant tumors of the nervous system. Even though their survival outcome is closely affected by immune-related genes (IRGs) in the tumor microenvironment (TME), the corresponding regulatory mechanism remains poorly characterized.

Methods: Specific enhancer RNAs (eRNAs) can be found in tumors, where they control downstream genes. The present study aimed to identify eRNA-regulated IRGs, evaluate their influence on the TME, and use them to construct a novel prognostic model for gliomas.

Results: Thirteen target genes (ADCYAP1R1, BMP2, BMPR1A, CD4, DDX17, ELN, FGF13, MAPT, PDIA2, PSMB8, PTPN6, SEMA6C, and SSTR5) were identified and integrated into a comprehensive risk signature, which distinguished two risk subclasses. Discrepancies between these subclasses were compared to explore potential mechanisms attributed to eRNA-regulated genes, including immune cell infiltration, clinicopathological features, survival outcomes, and chemotherapeutic drug sensitivity. Furthermore, the risk signature was used to construct a prognostic tool that was evaluated by calibration curve, clinical utility, Harrell’s concordance index (0.87; 95% CI: 0.84–0.90), and time-dependent receiver operator characteristic curves (AUCs: 0.93 and 0.89 at 3 and 5 years, respectively). The strong reliability and robustness of the established prognostic tool were validated in another independent cohort. Finally, potential subtypes were explored in patients with grade III tumors.

Conclusion: Overall, eRNAs were associated with immune-related dysfunctions in the TME. Targeting of IRGs regulated by eRNAs could improve immunotherapeutic/therapeutic outcomes.

Identification of Immune-Related lncRNA Prognostic Signature and Molecular Subtypes for Glioblastoma

Background

Glioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature.

Methods

Univariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated.

Results

A total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM.

Conclusion

Construction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.

The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Long non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

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.

Identification and Validation of a Novel Immune-Related lncRNA Signature for Bladder Cancer

Purpose

We aimed to construct an immune-related long noncoding ribonucleic acids (irlncRNA) signature to evaluate the prognosis of patients without specific expression level of these irlncRNA.

Methods

The raw transcriptome data were downloaded from The Cancer Genome Atlas (TCGA), irlncRNAs were filtered out using an online immune related gene database and coexpression analysis, differently expressed irlncRNA (DEirlncRNA) pairs were identified by univariate analysis. The areas under curve (AUC) were compared and the Akaike information criterion (AIC) values of receiver operating curve (ROC) was counted, the most optimal model was constructed to divide bladder cancer patients into high- and low-risk groups usingõ the cut-off point of ROC. Then, we evaluated them from multiple perspectives, such as survival time, clinic-pathological characteristics, immune-related cells infiltrating, chemotherapeutics efficacy and immune checkpoint inhibitors.

Results

14 DEirlncRNA pairs were included in this signature. Patients in high-risk groups demonstrated apparent shorter survival time, more aggressive clinic-pathological characteristics, different immune-related cells infiltrating status, lower chemotherapeutics efficacy.

Conclusion

The irlncRNA signature demonstrated a promising prediction value for bladder cancer patients and was important in guiding clinical treatment.

The Roles Played by Long Non-Coding RNAs in Glioma Resistance

Glioma originates in the central nervous system and is classified based on both histological features and molecular genetic characteristics. Long non-coding RNAs (lncRNAs) are longer than 200 nucleotides and are known to regulate tumorigenesis and tumor progression, and even confer therapeutic resistance to glioma cells. Since oncogenic lncRNAs have been frequently upregulated to promote cell proliferation, migration, and invasion in glioma cells, while tumor-suppressive lncRNAs responsible for the inhibition of apoptosis and decrease in therapeutic sensitivity in glioma cells have been generally downregulated, the dysregulation of lncRNAs affects many features of glioma patients, and the expression profiles associated with these lncRNAs are needed to diagnose the disease stage and to determine suitable therapeutic strategies. Accumulating studies show that the orchestrations of oncogenic lncRNAs and tumor-suppressive lncRNAs in glioma cells result in signaling pathways that influence the pathogenesis and progression of glioma. Furthermore, several lncRNAs are related to the regulation of therapeutic sensitivity in existing anticancer therapies, including radiotherapy, chemotherapy and immunotherapy. Consequently, we undertook this review to improve the understanding of signaling pathways influenced by lncRNAs in glioma and how lncRNAs affect therapeutic resistance.

A Prognostic DNA Damage Repair Genes Signature and Its Impact on Immune Cell Infiltration in Glioma

Objective

Glioma is the most frequent type of malignant cerebral tumors. DNA damage repair genes (DDRGs) play a crucial role in the development of cancer. In this study, we constructed a DDRGs signature and investigated the potential mechanisms involved in this disease.

Methods

RNA sequence data, microarray data, and corresponding clinical information of gliomas were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO). Subsequently, we identified candidate genes by differential analysis and Cox regression analysis. The least absolute shrinkage and selection operator Cox regression model was utilized to construct a DDRGs signature using TCGA training dataset. According to this signature, patients with glioma were divided into low- and high-risk groups. The predictive ability of the signature was validated by prognostic analysis, receiver operating characteristic curves, principal component analysis, and stratification analysis in TCGA testing and CGGA verification datasets. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the immune microenvironment of glioma. Moreover, we conducted GSEA to determine the functions and pathways in the low- and high-risk groups. Finally, a nomogram was constructed by combining the signature and other clinical features.

Results

A total of 1,431 samples of glioma (592 from TCGA, 686 from the CGGA, and 153 from the GEO) and 23 samples of normal brain tissue from the GEO were analyzed in this study. There were 51 prognostic differentially expressed DDRGs. Additionally, five DDRGs (CDK4、HMGB2、WEE1、SMC3 and GADD45G) were selected to construct a DDRGs signature for glioma, stratifying patients into low- and high-risk groups. The survival analysis showed that the DDRGs signature could differentiate the outcome of the low- and high-risk groups, showing that high-risk gliomas were associated with shorter overall survival. The immune microenvironment analysis revealed that more immunosuppressive cells, such as tumor associated macrophages and regulatory T cells, were recruited in the high-risk group. GSEA also showed that high-risk glioma was correlated with the immune and extracellular matrix pathways.

Conclusion

The five DDRGs signature and its impact on the infiltration of immunosuppressive cells could precisely predict the prognosis and provide guidance on the treatment of glioma.

A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Background

Colorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.

Methods

The clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.

Results

A total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.

Conclusion

Eight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

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.