Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients

Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment

JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.

LINC00618 facilitates growth and metastasis of hepatocellular carcinoma via elevating cholesterol synthesis by promoting NSUN2-mediated SREBP2 m5C modification

Dysregulation of cholesterol metabolism is an important feature of cancer development. There are limited reports on the involvement of lncRNAs in hepatocellular carcinoma (HCC) progression via the cholesterol metabolism pathway. The present study explored the effect of LINC00618 on HCC growth and metastasis, and elucidated the underlying mechanisms involved in cholesterol metabolism. Here, we found that LINC00618 expression was upregulated in cancerous tissues from 30 patients with HCC compared to that in adjacent normal tissues. High expression of LINC00618 was detected in metastatic HCC tissues. LINC00618 is predominantly localized in the nucleus and overexpression of LINC00618 facilitated HCC cell proliferation, migration and EMT progression by promoting cholesterol biosynthesis. Mechanistically, the 1-101nt region of LINC00618 bound to NSUN2. LINC00618 inhibited ubiquitin-proteasome pathway-induced NSUN2 degradation. NSUN2 stabilized by LINC00618 increased m5C modification of SREBP2 and promoted SREBP2 mRNA stability in a YBX1-dependent manner, thereby promoting cholesterol biosynthesis in HCC cells. Moreover, mouse HCC xenograft and lung metastasis models were established by subcutaneous and tail vein injections of MHCC97 cells transfected with or without sh-LINC00618. Silencing LINC00618 impeded HCC growth and metastasis. In conclusion, LINC00618 promoted HCC growth and metastasis by elevating cholesterol synthesis by stabilizing NSUN2 to enhance SREBP2 mRNA stability in an m5C-dependent manner.

Genetic profiling in radiotherapy: a comprehensive review

This comprehensive review explores the pivotal role of radiotherapy in cancer treatment, emphasizing the diverse applications of genetic profiling. The review highlights genetic markers for predicting radiation toxicity, enabling personalized treatment planning. It delves into the impact of genetic profiling on radiotherapy strategies across various cancer types, discussing research findings related to treatment response, prognosis, and therapeutic resistance. The integration of genetic profiling is shown to transform cancer treatment paradigms, offering insights into personalized radiotherapy regimens and guiding decisions in cases where standard protocols may fall short. Ultimately, the review underscores the potential of genetic profiling to enhance patient outcomes and advance precision medicine in oncology.

Long noncoding RNA UNC5B-AS1 suppresses cell proliferation by sponging miR-24-3p in glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most common primary CNS tumor, characterized by high mortality and heterogeneity. However, the related lncRNA signatures and their target microRNA (miRNA) for GBM are still mostly unknown. Therefore, it is critical that we discover lncRNA markers in GBM and their biological activities.

MATERIALS AND METHODS

GBM-related RNA-seq data were obtained from the Cancer Genome Atlas (TCGA) database. The "edger" R package was used for differently expressed lncRNAs (DELs) identification. Then, we forecasted prospective miRNAs that might bind to lncRNAs by Cytoscape software. Survival analysis of those miRNAs was examined by the starBase database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the miRNAs' target genes was conducted by the Gene Set Enrichment Analysis (GSEA) database and R software. Moreover, the proliferative ability of unc-5 netrin receptor B antisense RNA 1 (UNC5B-AS1) cells was evaluated by Cell Counting Kit-8 (CCK-8) analysis. Mechanistically, the regulatory interaction between UNC5B-AS1 and miRNA in GBM biological processes was studied using CCK-8 analysis.

RESULTS

Our results indicated that overexpression of UNC5B-AS1 has been shown to suppress GBM cell growth. Mechanistically, miR-24-3p in GBM was able to alleviate the anti-oncogenic effects of UNC5B-AS1 on cell proliferation.

CONCLUSION

The discovery of the novel UNC5B-AS1-miR-24-3p network suggests possible lncRNA and miRNA roles in the development of GBM, which may have significant ramifications for the analysis of clinical prognosis and the development of GBM medications.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients.

METHODS

We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model.

RESULTS

We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination.

CONCLUSIONS

We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

LINC02015 modulates the cell proliferation and apoptosis of aortic vascular smooth muscle cells by transcriptional regulation and protein interaction network

Long intergenic nonprotein coding RNA 2015 (LINC02015) is a long non-coding RNA that has been found elevated in various cell proliferation-related diseases. However, the functions and interactive mechanism of LINC02015 remain unknown. This study aimed to explore the role of LINC02015 in the cell proliferation and apoptosis of vascular smooth muscle cells (VSMCs) to explain the pathogenesis of aortic diseases. Ascending aorta samples and angiotensin-II (AT-II) treated primary human aortic VSMCs (HAVSMCs) were used to evaluate the LINC02015 expression. RNA sequencing, chromatin isolation by RNA purification sequencing, RNA pull-down, and mass spectrometry (MS) were applied to explore the potential interacting mechanisms. LINC02015 expression was found elevated in aortic dissection and AT-II-treated HAVSMCs. Cell proliferation and cell cycle were activated in HAVSMCs with LINC02015 knockdown. The cyclins family and caspase family were found to participate in regulating the cell cycle and apoptosis via the NF-κB signaling pathway. RXRA was discovered as a possible hub gene for LINC02015 transcriptional regulating networks. Besides, the protein interaction network of LINC02015 was revealed with candidate regulating molecules. It was concluded that the knockdown of LINC02015 could promote cell proliferation and inhibit the apoptosis of HAVSMCs through an RXRA-related transcriptional regulation network, which could provide a potential therapeutic target for aortic diseases.

T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes

Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1, using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.

EPRS1 correlates with malignant progression in hepatocellular carcinoma

BACKGROUND

Glutamyl-prolyl-tRNA synthetase 1 (EPRS1) is an aminoacyl-tRNA synthase involved in the pathology of cancer and other diseases. In this study, we investigated the carcinogenic function, potential mechanism, and clinical significance of EPRS1 in human hepatocellular carcinoma (HCC).

METHODS

The expression, clinical significance, and prognostic value of EPRS1 in HCC were assessed using the TCGA and GEO databases. The function of EPRS1 in HCC cells was detected by CCK-8, Transwell, and hepatosphere formation assays. Immunohistochemistry was used to explore the difference in EPRS1 levels in HCC tissues and peri-cancerous tissues. The mechanism of EPRS1 was studied using a proteomics method. Finally, cBioportal and MEXEPRSS were used to analyze the variations involved in the differential expression of EPRS1.

RESULTS

EPRS1 was frequently upregulated at the mRNA and protein levels in liver cancer. Increased EPRS1 correlated with shortened patient survival. EPRS1 could promote cancer cell proliferation, characteristics of cell stemness, and mobility. Mechanistically, EPRS1 played a carcinogenic role by upregulating several downstream proline-rich proteins, primarily LAMC1 and CCNB1. In addition, copy number variation could contribute to the high expression of EPRS1 in liver cancer.

CONCLUSION

Together, our data imply that enhanced EPRS1 contributes to the development of HCC by increasing the expression of oncogenes in the tumor microenvironment. EPRS1 may be a successful treatment target.

Expression analysis of novel long non-coding RNAs for invasive ductal and invasive lobular breast carcinoma cases

AIM

Long non-coding RNAs (LncRNAs) serve as important regulatory molecules of gene expression and protein functionality at multiple biological levels, and their deregulation plays a key role in tumorigenesis including in breast cancer metastasis. Therefore, in this study, we aim to compare the expression of novel lncRNAs in the landscape of invasive ductal carcinoma (IDC) and invasive lobular (ILC) carcinoma of breast.

MAIN METHODS

We have designed an in-silico approach to find the lncRNAs that regulate the breast cancer. Then, we used the clinical samples to carry out the verification of our in silico finding. In the present study, the tissues of breast cancer were deparaffinized. RNA was extracted by the TRIzole method. After synthesizing cDNA from the extracted RNA, expression levels of lncRNAs were analyzed by qPCR using primers specifically designed and validated for the targeted lncRNAs. In this study, breast biopsy materials from 41 female patients with IDC and 10 female patients with ILC were examined histopathological and expression changes of candidate lncRNAs were investigated in line with the findings. The results were analyzed using IBM SPSS Statistics 25 version.

RESULTS

The mean age of the cases was 53.78 ± 14.96. The minimum age was 29, while the maximum age was 87. While 27 of the cases were pre-menopausal, 24 cases were post-menopausal. The number of hormone receptor-positive cases was found to be 40, 35, and 27 for ER, PR, and cerb2/neu, respectively. While the expressions of LINC00501, LINC00578, LINC01209, LINC02015, LINC02584, ABCC5-AS1, PEX5L-AS2, SHANK2-AS3 and SOX2-OT showed significant differences (p < 0.05), the expressions of LINC01206, LINC01994, SHANK2-AS1, and TPRG1-AS2 showed no significant differences (p > 0.05). In addition, it was determined that the regulation of all lncRNAs could be able to involve in the development of cancer such as the NOTCH1, NFKB, and estrogen receptor signalings.

CONCLUSION

As a result, it was thought that the discovery of novel lncRNAs might be an important player in the diagnosis, prognosis and therapeutic development of breast cancer.

MTHFD2 suppresses glioblastoma progression via the inhibition of ERK1/2 phosphorylation

Glioblastoma (GBM) is a WHO grade 4 tumor and is the most malignant form of glioma. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), a mitochondrial enzyme involved in folate metabolism, has been reported to be highly expressed in several human tumors. However, little is known about the role of MTHFD2 in GBM. In this study, we aimed to explore the biological functions of MTHFD2 in GBM and identify the associated mechanisms. We performed experiments such as immunohistochemistry, Western blot, and transwell assays and found that MTHFD2 expression was lower in high-grade glioma than in low-grade glioma. Furthermore, a high expression of MTHFD2 was associated with a favorable prognosis, and MTHFD2 levels showed good prognostic accuracy for glioma patients. The overexpression of MTHFD2 could inhibit the migration, invasion, and proliferation of GBM cells, whereas its knockdown induced the opposite effect. Mechanistically, our findings revealed that MTHFD2 suppressed GBM progression independent of its enzymatic activity, likely by inducing cytoskeletal remodeling through the regulation of extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation, thereby influencing GBM malignance. Collectively, these findings uncover a potential tumor-suppressor role of MTHFD2 in GBM cells. MTHFD2 may act as a promising diagnostic and therapeutic target for GBM treatment.

Antipsychotic drug trifluoperazine as a potential therapeutic agent against nasopharyngeal carcinoma

BACKGROUND

Trifluoperazine (TFP) is a typical antipsychotic primarily used to treat schizophrenia. In this study, we aimed to evaluate whether TFP can be used as a therapeutic agent against nasopharyngeal carcinoma (NPC) and identify its underlying molecular mechanisms.

METHODS

We used NPC-TW01, TW03, TW04, and BM to assess the anticancer effects of TFP by using cytotoxicity, wound healing, colony formation, and cell invasion assays. An in vivo animal study was conducted. RNA sequencing combined with Ingenuity Pathways Analysis was performed to identify the mechanism by which TFP influences NPC cells.

RESULTS

Our data revealed that TFP decreased NPC cell viability in a dose-dependent manner. The invasion and migration of NPC tumor cells were inhibited by TFP. An in vivo study also demonstrated the anticancer effects of TFP. RNA sequencing revealed several anticancer molecular mechanisms following TFP administration.

CONCLUSIONS

The antipsychotic drug TFP could be a potential therapeutic regimen for NPC treatment.

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.

SurviveAI: Long Term Survival Prediction of Cancer Patients Based on Somatic RNA-Seq Expression

Motivation

Prediction of cancer outcome is a major challenge in oncology and is essential for treatment planning. Repositories such as The Cancer Genome Atlas (TCGA) contain vast amounts of data for many types of cancers. Our goal was to create reliable prediction models using TCGA data and validate them using an external dataset.

Results

For 16 TCGA cancer type cohorts we have optimized a Random Forest prediction model using parameter grid search followed by a backward feature elimination loop for dimensions reduction. For each feature that was removed, the model was retrained and the area under the curve of the receiver operating characteristic (AUC-ROC) was calculated using test data. Five prediction models gave AUC-ROC bigger than 80%. We used Clinical Proteomic Tumor Analysis Consortium v3 (CPTAC3) data for validation. The most enriched pathways for the top models were those involved in basic functions related to tumorigenesis and organ development. Enrichment for 2 prediction models of the TCGA-KIRP cohort was explored, one with 42 genes (AUC-ROC = 0.86) the other is composed of 300 genes (AUC-ROC = 0.85). The most enriched networks for both models share only 5 network nodes: DMBT1, IL11, HOXB6, TRIB3, PIM1. These genes play a significant role in renal cancer and might be used for prognosis prediction and as candidate therapeutic targets.

Availability And Implementation

The prediction models were created and tested using Python SciKit-Learn package. They are freely accessible via a friendly web interface we called surviveAI at https://tinyurl.com/surviveai.

Folate enzyme MTHFD2 links one-carbon metabolism to unfolded protein response in glioblastoma

The mitochondrial folate enzyme methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) has shown oncogenic roles in various cancers and may have non-metabolic functions. This study investigated the role of MTHFD2 in glioblastoma pathogenesis. We find that MTHFD2 expression is enriched in gliomas by analysing public databases and clinical specimens. RNA interference (RNAi) and inhibitor of MTHFD2 hamper the proliferation of glioblastoma and induce apoptosis in cell lines, glioma stem-like cells (GSCs) and patient-derived xenografts (PDX). Metabolomic analyses show that MTHFD2 depletion suppresses the central carbon metabolic pathways, including glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid (TCA) cycle. GSEA reveals a novel non-metabolic function of MTHFD2 in association with the unfolded protein response (UPR). MTHFD2 depletion activates the PERK/eIF2α axis which contributes to translation inhibition and apoptosis; these effects are attenuated by a PERK inhibitor. Mechanistically, MTHFD2 may be linked to UPR via the post-transcriptionally regulation of chaperone protein GRP78. In conclusion, MTHFD2 could be a promising therapeutic target for glioblastoma. Besides its canonical role, MTHFD2 may contribute to glioblastoma pathogenesis via UPR, highlighting a newly identified functional link between one-carbon metabolism and cell stress response.

ATF4/MYC Regulates MTHFD2 to Promote NSCLC Progression by Mediating Redox Homeostasis

Purpose. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) has been reported to be overexpressed in non-small-cell lung cancer (NSCLC) and to correlate with malignant proliferation. However, the mechanism of high MTHFD2 expression in NSCLC has not been clarified. Methods. qPCR, western blot, and immunofluorescence experiments were used to measure the expression of related mRNAs and proteins. Cell apoptosis was measured by flow cytometry and TUNEL assays. The CCK-8 assay was used to determine cell viability. Flow cytometry was used to analyze the cell cycle. ROS, H2O2, MDA, SOD, and NADPH/NADP+ were evaluated by relevant assay kits. Transfection of siRNA or vectors was used to downregulate or upregulate gene expression. Dual-luciferase reporter gene assays were used to evaluate the regulated relationship between MTHFD2 and ATF4 or MYC. Results. MTHFD2 was highly expressed in NSCLC cells. Knockdown of MTHFD2 inhibited proliferation and increased apoptosis. Furthermore, oxidative factors significantly increased, while antioxidant factors significantly decreased in NSCLC cells with MTHFD2 knockdown, indicating that MTHFD2 was involved in NSCLC progression through the redox pathway. Although MTHFD2 was downregulated with ATF4 silencing, the dual-luciferase reporter assay suggested that ATF4 did not directly mediate MTHFD2 transcription. Further studies revealed that MYC had a transcriptional effect on MTHFD2 and was also regulated by ATF4. PCR, and western blotting experiments with ATF4 knockdown and MYC overexpression as well as ATF4 overexpression and MYC knockdown proved that ATF4 stimulated MTHFD2 through MYC mediation. Conclusions. ATF4 promoted high expression of MTHFD2 in NSCLC dependent on MYC.

Establishment of a lncRNA-Based Prognostic Gene Signature Associated With Altered Immune Responses in HCC

Hepatocellular carcinoma (HCC) is a common malignancy with higher mortality, and means are urgently needed to improve the prognosis. T cell exclusion (TCE) plays a pivotal role in immune evasion, and lncRNAs represent a large group of tumor development and progression modulators. Using the TCGA HCC dataset (n=374), we identified 2752 differentially expressed and 702 TCE-associated lncRNAs, of which 336 were in both groups. As identified using the univariate Cox regression analysis, those associated with overall survival (OS) were subjected to the LASSO-COX regression analysis to develop a prognosis signature. The model, which consisted of 11 lncRNAs and was named 11LNCPS for 11-lncRNA prognosis signature, was validated and performed better than two previous models. In addition to OS and TCE, higher 11LNCPS scores had a significant correlation with reduced infiltrations of CD8+ T cells and dendritic cells (DCs) and decreased infiltrations of Th1, Th2, and pro B cells. As expected, these infiltration alterations were significantly associated with worse OS in HCC. Analysis of published data indicates that HCCs with higher 11LNCPS scores were transcriptomically similar to those that responded better to PDL1 inhibitor. Of the 11LNCPS lncRNAs, LINC01134 and AC116025.2 seem more crucial, as their upregulations affected more immune cell types' infiltrations and were significantly associated with TCE, worse OS, and compromised immune responses in HCC. LncRNAs in the 11LNCPS impacted many cancer-associated biological processes and signaling pathways, particularly those involved in immune function and metabolism. The 11LNCPS should be useful for predicting prognosis and immune responses in HCC.

Radioresistance of Human Cancers: Clinical Implications of Genetic Expression Signatures

Although radiotherapy is given to more than 50% of cancer patients, little progress has been made in identifying optimal radiotherapy - drug combinations to improve treatment efficacy. Using molecular data from The Cancer Genome Atlas (TCGA), we extracted a total of 1016 cancer patients that received radiotherapy. The patients were diagnosed with head-and-neck (HNSC - 294 patients), cervical (CESC - 166 patients) and breast (BRCA - 549 patients) cancer. We analyzed mRNA expression patterns of 50 hallmark gene sets of the MSigDB collection, which we divided in eight categories based on a shared biological or functional process. Tumor samples were split into upregulated, neutral or downregulated mRNA expression for all gene sets using a gene set analysis (GSEA) pre-ranked analysis and assessed for their clinical relevance. We found a prognostic association between three of the eight gene set categories (Radiobiological, Metabolism and Proliferation) and overall survival in all three cancer types. Furthermore, multiple single associations were revealed in the other categories considered. To the best of our knowledge, our study is the first report suggesting clinical relevance of molecular characterization based on hallmark gene sets to refine radiation strategies.

Characterizing the Genomic Profile in High-Grade Gliomas: From Tumor Core to Peritumoral Brain Zone, Passing through Glioma-Derived Tumorspheres

Glioblastoma is an extremely heterogeneous disease. Treatment failure and tumor recurrence primarily reflect the presence in the tumor core (TC) of the glioma stem cells (GSCs), and secondly the contribution, still to be defined, of the peritumoral brain zone (PBZ). Using the array-CGH platform, we deepened the genomic knowledge about the different components of GBM and we identified new specific biomarkers useful for new therapies. We firstly investigated the genomic profile of 20 TCs of GBM; then, for 14 cases and 7 cases, respectively, we compared these genomic profiles with those of the related GSC cultures and PBZ biopsies. The analysis on 20 TCs confirmed the intertumoral heterogeneity and a high percentage of copy number alterations (CNAs) in GBM canonical pathways. Comparing the genomic profiles of 14 TC-GSC pairs, we evidenced a robust similarity among the two samples of each patient. The shared imbalanced genes are related to the development and progression of cancer and in metabolic pathways, as shown by bioinformatic analysis using DAVID. Finally, the comparison between 7 TC-PBZ pairs leads to the identification of PBZ-unique alterations that require further investigation.

Genome-wide profiling of alternative splicing in glioblastoma and their clinical value

Background

Alternative splicing (AS), one of the main post-transcriptional biological regulation mechanisms, plays a key role in the progression of glioblastoma (GBM). Systematic AS profiling in GBM is limited and urgently needed.

Methods

TCGA SpliceSeq data and the corresponding clinical data were downloaded from the TCGA data portal. Survival-related AS events were identified through Kaplan–Meier survival analysis and univariate Cox analysis. Then, splicing correlation network was constructed based on these AS events and associated splicing factors. LASSO regression followed by multivariate Cox analysis was performed to validate independent AS biomarkers and to construct a risk prediction model. Enrichment analysis was subsequently conducted to explore potential signaling pathways of these AS events.

Results

A total of 132 TCGA GBM samples and 45,610 AS events were included in our study, among which 416 survival-related AS events were identified. An AS correlation network, including 54 AS events and 94 splicing factors, was constructed, and further functional enrichment was performed. Moreover, the novel risk prediction model we constructed displayed moderate performance (the area under the curves were > 0.7) at both one, two and three years.

Conclusions

Survival-related AS events may be vital factors of both biological function and prognosis. Our findings in this study can deepen the understanding of the complicated mechanisms of AS in GBM and provide novel insights for further study. Moreover, our risk prediction model is ready for preliminary clinical applications. Further verification is required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08681-z.

Expression and Prognostic Value Identification of Methylenetetrahydrofolate Dehydrogenase 2 (MTHFD2) in Brain Low-Grade Glioma

Objective

This study aimed to reveal the potential function of methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) and emphasized its importance in brain low-grade glioma (LGG).

Methods

We firstly explored the differential expression of MTHFD2 mRNA in LGG and normal tissues, followed by correlation analysis of MTHFD2 mRNA expression with patient’s clinical characteristics. MTHFD2 protein expression in LGG and subcellular location were also evaluated. Then, survival analysis was performed to reveal the influence of MTHFD2 expression on the overall survival of patients, and Cox regression analysis was applied to predict the prognostic factor for overall survival of LGG. Finally, we performed functional analysis to reveal potential MTHFD2-associated pathways involved in LGG.

Results

We found that MTHFD2 was highly expressed in LGG patients (P<0.05), and MTHFD2 expression was related to patient’s age and IDH mutation status (all P<0.05). MTHFD2 protein was mainly localized to the mitochondria. Survival analysis showed that high expression of MTHFD2 desirably improved the prognosis of LGG patients (P<0.001), especially for those patients with age ≥45 years (P<0.05). But independent prognostic role of MTHFD2 in LGG was not observed. Pathway enrichment analysis indicated that MTHFD2 high expression significantly and positively participated in the pathway of one carbon pool by folate (all P<0.05).

Conclusion

High expression of MTHFD2 was observed in LGG, which was favorable for the overall survival of LGG patients. Our results assumed that MTHFD2 high expression might play a pivotal role in LGG through positively regulating pathway of one carbon pool by folate.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Identification of ubiquitination-related genes in human glioma as indicators of patient prognosis

Ubiquitination is a dynamic and reversible process of a specific modification of target proteins catalyzed by a series of ubiquitination enzymes. Because of the extensive range of substrates, ubiquitination plays a crucial role in the localization, metabolism, regulation, and degradation of proteins. Although the treatment of glioma has been improved, the survival rate of patients is still not satisfactory. Therefore, we explore the role of ubiquitin proteasome in glioma. Survival-related ubiquitination related genes (URGs) were obtained through analysis of the Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA). Cox analysis was performed to construct risk model. The accuracy of risk model is verified by survival, Receiver operating characteristic (ROC) and Cox analysis. We obtained 36 differentially expressed URGs and found that 25 URGs were related to patient prognosis. We used the 25 URGs to construct a model containing 8 URGs to predict glioma patient risk by Cox analysis. ROC showed that the accuracy rate of this model is 85.3%. Cox analysis found that this model can be used as an independent prognostic factor. We also found that this model is related to molecular typing markers. Patients in the high-risk group were enriched in multiple tumor-related signaling pathways. In addition, we predicted TFs that may regulate the risk model URGs and found that the risk model is related to B cells, CD4 T cells, and neutrophils.

Identification of Novel Transcriptome Signature as a Potential Prognostic Biomarker for Anti-Angiogenic Therapy in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is the most common and devastating type of primary brain tumor, with a median survival time of only 15 months. Having a clinically applicable genetic biomarker would lead to a paradigm shift in precise diagnosis, personalized therapeutic decisions, and prognostic prediction for GBM. Radiogenomic profiling connecting radiological imaging features with molecular alterations will offer a noninvasive method for genomic studies of GBM. To this end, we analyzed over 3800 glioma and GBM cases across four independent datasets. The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases were employed for RNA-Seq analysis, whereas the Ivy Glioblastoma Atlas Project (Ivy-GAP) and The Cancer Imaging Archive (TCIA) provided clinicopathological data. The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) was used for proteomic analysis. We identified a simple three-gene transcriptome signature—SOCS3, VEGFA, and TEK—that can connect GBM’s overall prognosis with genes’ expression and simultaneously correlate radiographical features of perfusion imaging with SOCS3 expression levels. More importantly, the rampant development of neovascularization in GBM offers a promising target for therapeutic intervention. However, treatment with bevacizumab failed to improve overall survival. We identified SOCS3 expression levels as a potential selection marker for patients who may benefit from early initiation of angiogenesis inhibitors.

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.

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub (https://github.com/hanjunwei-lab/crmSubpathway).

A SEMA3 Signaling Pathway-Based Multi-Biomarker for Prediction of Glioma Patient Survival

Glioma is a lethal central nervous system tumor with poor patient survival prognosis. Because of the molecular heterogeneity, it is a challenge to precisely determine the type of the tumor and to choose the most effective treatment. Therefore, novel biomarkers are essential to improve the diagnosis and prognosis of glioma tumors. Class 3 semaphorin proteins (SEMA3) play an important role in tumor biology. SEMA3 transduce their signals by using neuropilin and plexin receptors, which functionally interact with the vascular endothelial growth factor-mediated signaling pathways. Therefore, the aim of this study was to explore the potential of SEMA3 signaling molecules for prognosis of glioma patient survival. The quantitative real-time PCR method was used to evaluate mRNA expression of SEMA3(A-G), neuropilins (NRP1 and NRP2), plexins (PLXNA2 and PLXND1), cadherins (CDH1 and CDH2), integrins (ITGB1, ITGB3, ITGA5, and ITGAV), VEGFA and KDR genes in 59 II-IV grade glioma tissues. Seven genes significantly associated with patient overall survival were used for multi-biomarker construction, which showed 64%, 75%, and 68% of accuracy of predicting the survival of 1-, 2-, and 3-year glioma patients, respectively. The results suggest that the seven-gene signature could serve as a novel multi-biomarker for more accurate prognosis of a glioma patient’s outcome.

A Nuclear Long Non-Coding RNA LINC00618 Accelerates Ferroptosis in a Manner Dependent upon Apoptosis

Ferroptosis is primarily caused by intracellular iron catalytic activity and lipid peroxidation. The potential interplay between ferroptosis and apoptosis remains poorly understood. Here, we show that the expression of a nuclear long non-coding RNA (lncRNA), LINC00618, is reduced in human leukemia and strongly increased by vincristine (VCR) treatment. Furthermore, LINC00618 promotes apoptosis by increasing the levels of BCL2-Associated X (BAX) and cleavage of caspase-3. LINC00618 also accelerates ferroptosis by increasing the levels of lipid reactive oxygen species (ROS) and iron, two surrogate markers of ferroptosis, and decreasing the expression of solute carrier family 7 member 11 (SLC7A11). Interestingly, VCR-induced ferroptosis and apoptosis are promoted by LINC00618, and LINC00618 accelerates ferroptosis in a manner dependent upon apoptosis. LINC00618 attenuates the expression of lymphoid-specific helicase (LSH), and LSH enhances the transcription of SLC7A11 after the recruitment to the promoter regions of SLC7A11, further inhibiting ferroptosis. Knowledge of these mechanisms demonstrates that lncRNAs related to ferroptosis and apoptosis are critical to leukemogenesis and chemotherapy.

Identification of a cullin5-RING E3 ligase transcriptome signature in glioblastoma multiforme

Glioblastoma multiforme (GBM) is the deadliest type of brain tumor. The median survival time for patients with GBM is only 15 months, even following maximal surgical resection and chemotherapy and radiation therapy. A genetic biomarker could enable a paradigm shift in precise diagnosis, personalized therapeutics and prognosis. In this study, we employed the Chinese Glioma Genome Atlas, The Cancer Genome Atlas, and the Ivy Glioblastoma Atlas Project databases for RNA sequencing (RNA-seq) analysis and clinicopathological studies. We demonstrated that elevated expression of the RNF7, TCEB1, SOCS1 and SOCS3 genes, which encode components of cullin5-RING E3 ligase (CRL5), predict unfavorable GBM prognoses. In GBM and glioma cases carrying IDH1 mutations, SOCS1 and SOCS3 methylation was increased and their expression was downregulated. This study has thus identified a simple transcriptome signature for GBM prognosis.

Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

BACKGROUND

Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.

RESULTS

In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.

CONCLUSIONS

PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

More Than a Metabolic Enzyme: MTHFD2 as a Novel Target for Anticancer Therapy?

The bifunctional methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) is a mitochondrial one-carbon folate metabolic enzyme whose role in cancer was not known until recently. MTHFD2 is highly expressed in embryos and a wide range of tumors but has low or absent expression in most adult differentiated tissues. Elevated MTHFD2 expression is associated with poor prognosis in both hematological and solid malignancy. Its depletion leads to suppression of multiple malignant phenotypes including proliferation, invasion, migration, and induction of cancer cell death. The non-metabolic functions of this enzyme, especially in cancers, have thus generated considerable research interests. This review summarizes current knowledge on both the metabolic functions and non-enzymatic roles of MTHFD2. Its expression, potential functions, and regulatory mechanism in cancers are highlighted. The development of MTHFD2 inhibitors and their implications in pre-clinical models are also discussed.

Cancer Stem Cell Chemotherapeutics Assay for Prospective Treatment of Recurrent Glioblastoma and Progressive Anaplastic Glioma: A Single-Institution Case Series

BACKGROUND: Chemotherapy-resistant cancer stem cells (CSC) may lead to tumor recurrence in glioblastoma (GBM). The poor prognosis of this disease emphasizes the critical need for developing a treatment stratification system to improve outcomes through personalized medicine. METHODS: We present a case series of 12 GBM and 2 progressive anaplastic glioma cases from a single Institution prospectively treated utilizing a CSC chemotherapeutics assay (ChemoID) guided report. All patients were eligible to receive a stereotactic biopsy and thus undergo ChemoID testing. We selected one of the most effective treatments based on the ChemoID assay report from a panel of FDA approved chemotherapy as monotherapy or their combinations for our patients. Patients were evaluated by MRI scans and response was assessed according to RANO 1.1 criteria. RESULTS: Of the 14 cases reviewed, the median age of our patient cohort was 49 years (21–63). We observed 6 complete responses (CR) 43%, 6 partial responses (PR) 43%, and 2 progressive diseases (PD) 14%. Patients treated with ChemoID assay-directed therapy, in combination with other modality of treatment (RT, LITT), had a longer median overall survival (OS) of 13.3 months (5.4-NA), compared to the historical median OS of 9.0 months (8.0–10.8 months) previously reported. Notably, patients with recurrent GBM or progressive high-grade glioma treated with assay-guided therapy had a 57% probability to survive at 12 months, compared to the 27% historical probability of survival observed in previous studies. CONCLUSIONS: The results presented here suggest that the ChemoID Assay has the potential to stratify individualized chemotherapy choices to improve recurrent and progressive high-grade glioma patient survival.

Importance of the Study

Glioblastoma (GBM) and progressive anaplastic glioma are the most aggressive brain tumor in adults and their prognosis is very poor even if treated with the standard of care chemoradiation Stupp's protocol. Recent knowledge pointed out that current treatments often fail to successfully target cancer stem cells (CSCs) that are responsible for therapy resistance and recurrence of these malignant tumors. ChemoID is the first and only CLIA (clinical laboratory improvements amendment) -certified and CAP (College of American Pathologists) -accredited chemotherapeutic assay currently available in oncology clinics that examines patient's derived CSCs susceptibility to conventional FDA (Food and Drugs Administration) -approved drugs. In this study we observed that although the majority of our patients (71.5%) presented with unfavorable prognostic predictors (wild type IDH-1/2 and unmethylated MGMT promoter), patients treated with ChemoID assay-directed therapy had an overall response rate of 86% and increased median OS of 13.3 months compared to the historical median OS of 9.1 months (8.1–10.1 months) previously reported [1] suggesting that the ChemoID assay may be beneficial in personalizing treatment strategies.

Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Introduction: Glioblastoma multiforme (GBM) is the most common deadly brain malignancy and lacks effective therapies. Immunotherapy acts as a promising novel strategy, but not for all GBM patients. Therefore, classifying these patients into different prognostic groups is urgent for better personalized management.

Materials and Methods: The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the fraction of 22 types of immune-infiltrating cells, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune infiltration-related prognostic scoring system (IIRPSS). Additionally, a quantitative predicting survival nomogram was also established based on the immune risk score (IRS) derived from the IIRPSS. Moreover, we also preliminarily explored the differences in the immune microenvironment between different prognostic groups.

Results: There was a total of 310 appropriate GBM samples (239 from TCGA and 71 from CGGA) included in further analyses after CIBERSORT filtering and data processing. The IIRPSS consisting of 17 types of immune cell fractions was constructed in TCGA cohort, the patients were successfully classified into different prognostic groups based on their immune risk score (p = 1e-10). What's more, the prognostic performance of the IIRPSS was validated in CGGA cohort (p = 0.005). The nomogram also showed a superior predicting value. (The predicting AUC for 1-, 2-, and 3-year were 0.754, 0.813, and 0.871, respectively). The immune microenvironment analyses reflected a significant immune response and a higher immune checkpoint expression in high-risk immune group.

Conclusion: Our study constructed an IIRPSS, which maybe valuable to help clinicians select candidates most likely to benefit from immunological checkpoint inhibitors (ICIs) and laid the foundation for further improving personalized immunotherapy in patients with GBM.

Kernel Differential Subgraph Analysis to Reveal the Key Period Affecting Glioblastoma

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

Targeting Ephrin Receptor Tyrosine Kinase A2 with a Selective Aptamer for Glioblastoma Stem Cells

Despite the benefits associated with radiotherapy and chemotherapy for glioblastoma (GBM) treatment, most patients experience a relapse following initial therapy. Recurrent or progressive GBM usually does not respond anymore to standard therapy, and this is associated with poor patient outcome. GBM stem cells (GSCs) are a subset of cells resistant to radiotherapy and chemotherapy and play a role in tumor recurrence. The targeting of GSCs and the identification of novel markers are crucial issues in the development of innovative strategies for GBM eradication. By differential cell SELEX (systematic evolution of ligands by exponential enrichment), we have recently described two RNA aptamers, that is, the 40L sequence and its truncated form A40s, able to bind the cell surface of human GSCs. Both aptamers were selective for stem-like growing GBM cells and are rapidly internalized into target cells. In this study, we demonstrate that their binding to cells is mediated by direct recognition of the ephrin type-A receptor 2 (EphA2). Functionally, the two aptamers were able to inhibit cell growth, stemness, and migration of GSCs. Furthermore, A40s was able to cross the blood-brain barrier (BBB) and was stable in serum in in vitro experiments. These results suggest that 40L and A40s represent innovative potential therapeutic tools for GBM.

A robust two-gene signature for glioblastoma survival prediction

Glioblastoma multiforme (GBM) is a highly malignant brain tumor. We explored the prognostic gene signature in 443 GBM samples by systematic bioinformatics analysis, using GSE16011 with microarray expression and corresponding clinical data from Gene Expression Omnibus as the training set. Meanwhile, patients from The Chinese Glioma Genome Atlas database (CGGA) were used as the test set and The Cancer Genome Atlas database (TCGA) as the validation set. Through Cox regression analysis, Kaplan-Meier analysis, t-distributed Stochastic Neighbor Embedding algorithm, clustering, and receiver operating characteristic analysis, a two-gene signature (GRIA2 and RYR3) associated with survival was selected in the GSE16011 dataset. The GRIA2-RYR3 signature divided patients into two risk groups with significantly different survival in the GSE16011 dataset (median: 0.72, 95% confidence interval [CI]: 0.64-0.98, vs median: 0.98, 95% CI: 0.65-1.61 years, logrank test P < .001), the CGGA dataset (median: 0.84, 95% CI: 0.70-1.18, vs median: 1.21, 95% CI: 0.95-2.94 years, logrank test P = .0017), and the TCGA dataset (median: 1.03, 95% CI: 0.86-1.24, vs median: 1.23, 95% CI: 1.04-1.85 years, logrank test P = .0064), validating the predictive value of the signature. And the survival predictive potency of the signature was independent from clinicopathological prognostic features in multivariable Cox analysis. We found that after transfection of U87 cells with small interfering RNA, GRIA2 and RYR3 influenced the biological behaviors of proliferation, migration, and invasion of glioblastoma cells. In conclusion, the two-gene signature was a robust prognostic model to predict GBM survival.

The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity

BACKGROUND

The prevalence of obesity and type 2 diabetes mellitus (T2DM) has become the most serious global public health issue. In recent years, there has been increasing attention to the role of long noncoding RNAs (lncRNAs) in the occurrence and development of obesity and T2DM. The aim of this work was to find new lncRNAs as potential predictive biomarkers or therapeutic targets for obesity and T2DM.

METHODS

In this study, we identified significant differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) between adipose tissue of individuals with obesity and T2DM and normal adipose tissue (absolute log₂FC ≥ 1 and FDR < 0.05). Then, the lncRNA-miRNA interactions predicted by miRcode were further screened with a threshold of MIC > 0.2. Simultaneously, the mRNA-miRNA interactions were explored by miRWalk 2.0. Finally, a ceRNA network consisting of lncRNAs, miRNAs, and mRNAs was established by integrating lncRNA-miRNA interactions and mRNA-miRNA interactions.

RESULTS

Upon comparing adipose tissue from individuals with obesity and T2DM and normal adipose tissues, 364 significant DEmRNAs, including 140 upregulated and 224 downregulated mRNAs, were identified in GSE104674; in addition, 231 significant DEmRNAs, including 146 upregulated and 85 downregulated mRNAs, were identified in GSE133099. GO and KEGG analyses have shown that downregulated DEmRNAs in GSE104674 and GSE133099 were associated with obesity- and T2DM-related biological pathways, such as lipid metabolism, AMPK signaling, and insulin resistance. Furthermore, 28 significant DElncRNAs, including 14 upregulated and 14 downregulated lncRNAs, were found. Based on the predicted lncRNA-miRNA and mRNA-miRNA relationships, we constructed a competitive endogenous RNA (ceRNA) network, including five lncRNAs, ten miRNAs, and 15 mRNAs. KEGG-GSEA analysis revealed that four lncRNAs (FLG-AS1, SNAI3-AS1, AC008147.0, and LINC02015) in the ceRNA network were related to the biological pathways of metabolic diseases.

CONCLUSIONS

Through ceRNA network analysis, our study identified four new lncRNAs that may be used as potential biomarkers and therapeutic targets of obesity and T2DM, thus laying a foundation for future clinical studies.

Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma

Glioblastoma (GBM) is the most common brain tumor with significant morbidity and mortality. Autophagy plays a vital role in GBM development and progression. We aimed to establish an autophagy-related multigene expression signature for individualized prognosis prediction in patients with GBM. Differentially expressed autophagy-related genes (DE-ATGs) in GBM and normal samples were screened using TCGA. Univariate and multivariate Cox regression analyses were performed on DE-ATGs to identify the optimal prognosis-related genes. Consequently, NRG1 (HR=1.142, P=0.008), ITGA3 (HR=1.149, P=0.043), and MAP1LC3A (HR=1.308, P=0.014) were selected to establish the prognostic risk score model and validated in the CGGA validation cohort. GSEA revealed that these genes were mainly enriched in cancer- and autophagy-related KEGG pathways. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P= 6.955×10^-5). The autophagy signature was identified as an independent prognostic factor. Finally, a prognostic nomogram including the autophagy signature, age, pharmacotherapy, radiotherapy, and IDH mutation status was constructed, and TCGA/CGGA-based calibration plots indicated its excellent predictive performance. The autophagy-related three-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for GBM. The prognostic nomogram could assist individualized survival prediction and improve treatment strategies.

Microarray expression profiles and bioinformatics analysis of mRNAs, lncRNAs, and circRNAs in the secondary temozolomide-resistant glioblastoma

Temozolomide is a first line anti-tumor drug used for the treatment of patients with Glioblastoma multiforme (GBM). However, the drug resistance to temozolomide limits its clinical application. Therefore, novel strategies to overcome chemoresistance are desperately needed for improved treatment of human GBM. Here, we simultaneously detected, for the first time, the expression profiles of mRNAs, lncRNAs, and circRNAs in three pairs of secondary temozolomide-resistant glioblastoma (STRG) and matched primary glioblastoma tissues by microarrays. Using these data, we discovered a total of 92 mRNA, 299 lncRNAs and 53 circRNAs were altered in human glioma tissue after chemotherapy with temozolomide. The functions of differentially expressed lncRNAs, circRNAs were annotated by analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The results showed that the highest enriched GO terms of the upregulated lncRNAs were embryonic forelimb morphogenesis (BP), extracellular space (CC), and serine-type endopeptidase activity (MF). Meanwhile, GO:0035360(BP), PRC1 complex (CC), and ubiquitin-protein transferase activity (MF) were the highest enriched GO terms targeted by downregulated lncRNAs. The NF-kappa B signaling pathway were significantly enriched in the STRG. However, circRNAs highest enriched GO term was viral process, chromosome, and protein transporter activity, respectively. KEGG pathway analysis showed that circRNAs in the network were enriched in ErbB signaling pathway. Furthermore, we also predicted the potential role of these differentially expressed ncRNAs and constructed a network of lncRNAs-mRNAs and circRNAs-miRNAs to show their interactions. After a series of bioinformatics analyses, we found that low expression of NONHSAT163779 and high expression of circ_0043949 are closely related to the chemoresistance of STRG. Our findings revealed the alteration of expression patterns of mRNAs, lncRNAs, and circRNAs in the secondary temozolomide-resistant glioblastoma for the first time. NONHSAT163779 and hsa_circ_0043949 might be potential therapeutic targets and prognostic biomarkers for the treatment of glioblastoma.

Role of alternative splicing signatures in the prognosis of glioblastoma

BACKGROUND

Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking.

METHODS

The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan-Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM.

RESULTS

A total of 2355 survival-associated AS events were identified. The splicing prognostic model revealed that patients in the high-risk group have worse survival rates than those in the low-risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = -.506).

CONCLUSIONS

Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM.

A Serum Proteomic Signature Predicting Survival in Patients with Glioblastoma

Purpose

Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning.

Material/Methods

In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates.

Results

Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid.

Conclusion

These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.

A Risk Classification System With Five-Gene for Survival Prediction of Glioblastoma Patients

Objective: Glioblastoma (GBM) is the most common and fatal primary brain tumor in adults. It is necessary to identify novel and effective biomarkers or risk signatures for GBM patients. Methods: Differentially expressed genes (DEGs) between GBM and low-grade glioma (LGG) in TCGA samples were screened out and weight correlation network analysis (WGCNA) was performed to confirm WHO grade-related genes. Five genes were selected via multivariate Cox proportional hazards regression analysis and were used to construct a risk signature. A nomogram composed of the risk signature and clinical characters (age, radiotherapy, and chemotherapy experience) was established to predict 1, 3, 5-year survival rate for GBM patients. Results: One hundred ninety-four DEGs in blue gene module were found to be positively related to WHO grade via WGCNA. Five genes (DES, RANBP17, CLEC5A, HOXC11, POSTN) were selected to construct a risk signature for GBM via R language. This risk signature was identified to independently predict the outcome of GBM patients, as well as stratified by IDH1 status, MGMT promoter status, and radio-chemotherapy. The nomogram was established which combined the risk signature with clinical factors. The results of c-index, ROC curve and calibration plot revealed the nomogram showing a good accuracy for predicting 1, 3, or 5-year survival of GBM patients. Conclusion: The risk signature with five genes could serve as an independent factor for predicting the prognosis of patients with GBM. Moreover, the nomogram with the risk signature and clinical traits proved to perform better for predicting 1, 3, 5-year survival rate.