Phytanoyl-CoA 2-Hydroxylase-Interacting Protein-Like Gene Is a Therapeutic Target Gene for Glioblastoma Multiforme

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

BACKGROUND

Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM.

METHODS

A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023.

RESULTS

From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts.

CONCLUSION

We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs

BACKGROUND

Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability.

METHODS

We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs.

RESULTS

Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer's disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015).

CONCLUSIONS

The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

Proteomics separates adult-type diffuse high-grade gliomas in metabolic subgroups independent of 1p/19q codeletion and across IDH mutational status

High-grade adult-type diffuse gliomas are malignant neuroepithelial tumors with poor survival rates in combined chemoradiotherapy. The current WHO classification is based on IDH1/2 mutational and 1p/19q codeletion status. Glioma proteome alterations remain undercharacterized despite their promise for a better molecular patient stratification and therapeutic target identification. Here, we use mass spectrometry to characterize 42 formalin-fixed, paraffin-embedded (FFPE) samples from IDH-wild-type (IDHwt) gliomas, IDH-mutant (IDHmut) gliomas with and without 1p/19q codeletion, and non-neoplastic controls. Based on more than 5,500 quantified proteins and 5,000 phosphosites, gliomas separate by IDH1/2 mutational status but not by 1p/19q status. Instead, IDHmut gliomas split into two proteomic subtypes with widespread perturbations, including aerobic/anaerobic energy metabolism. Validations with three independent glioma proteome datasets confirm these subgroups and link the IDHmut subtypes to the established proneural and classic/mesenchymal subtypes in IDHwt glioma. This demonstrates common phenotypic subtypes across the IDH status with potential therapeutic implications for patients with IDHmut gliomas.

The landscape of hervRNAs transcribed from human endogenous retroviruses across human body sites

BACKGROUND

Human endogenous retroviruses (HERVs), the remnants of ancient retroviruses, account for 8% of the human genome, but most have lost their transcriptional abilities under physiological conditions. However, mounting evidence shows that several expressed HERVs do exert biological functions. Here, we systematically characterize physiologically expressed HERVs and examine whether they may give insight into the molecular fundamentals of human development and disease.

RESULTS

We systematically identify 13,889 expressed HERVs across normal body sites and demonstrate that they are expressed in body site-specific patterns and also by sex, ethnicity, and age. Analyzing cis-ERV-related quantitative trait loci, we find that 5435 hervRNAs are regulated by genetic variants. Combining this with a genome-wide association study, we elucidate that the dysregulation of expressed HERVs might be associated with various complex diseases, particularly neurodegenerative and psychiatric diseases. We further find that physiologically activated hervRNAs are associated with histone modifications rather than DNA demethylation.

CONCLUSIONS

Our results present a locus-specific landscape of physiologically expressed hervRNAs, which represent a hidden layer of genetic architecture in development and disease.

Spatial Proteomic Analysis of Isogenic Metastatic Colorectal Cancer Cells Reveals Key Dysregulated Proteins Associated with Lymph Node, Liver, and Lung Metastasis

Metastasis is the primary cause of colorectal cancer (CRC) death. The liver and lung, besides adjacent lymph nodes, are the most common sites of metastasis. Here, we aimed to study the lymph nodes, liver, and lung CRC metastasis by quantitative spatial proteomics analysis using CRC cell-based models that recapitulate these metastases. The isogenic KM12 cell system composed of the non-metastatic KM12C cells, liver metastatic KM12SM cells, and liver and lung metastatic KM12L4a cells, and the isogenic non-metastatic SW480 and lymph nodes metastatic SW620 cells, were used. Cells were fractionated to study by proteomics five subcellular fractions corresponding to cytoplasm, membrane, nucleus, chromatin-bound proteins, and cytoskeletal proteins, and the secretome. Trypsin digested extracts were labeled with TMT 11-plex and fractionated prior to proteomics analysis on a Q Exactive. We provide data on protein abundance and localization of 4710 proteins in their different subcellular fractions, depicting dysregulation of proteins in abundance and/or localization in the most common sites of CRC metastasis. After bioinformatics, alterations in abundance and localization for selected proteins from diverse subcellular localizations were validated via WB, IF, IHC, and ELISA using CRC cells, patient tissues, and plasma samples. Results supported the relevance of the proteomics results in an actual CRC scenario. It was particularly relevant that the measurement of GLG1 in plasma showed diagnostic ability of advanced stages of the disease, and that the mislocalization of MUC5AC and BAIAP2 in the nucleus and membrane, respectively, was significantly associated with poor prognosis of CRC patients. Our results demonstrate that the analysis of cell extracts dilutes protein alterations in abundance in specific localizations that might only be observed studying specific subcellular fractions, as here observed for BAIAP2, GLG1, PHYHIPL, TNFRSF10A, or CDKN2AIP, which are interesting proteins that should be further analyzed in CRC metastasis.

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Background

Aberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.

Methods

Methylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).

Results

We identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HR_indirect = 1.473, P = 0.001, Proportion mediated, 69.10%).

Conclusions

We identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

Sequence of proteome profiles in preclinical and symptomatic Alzheimer's disease

Proteome profile changes in Alzheimer's disease (AD) brains have been reported. However, it is unclear whether they represent a continuous process, or whether there is a sequential involvement of distinct proteins. To address this question, we used mass spectrometry. We analyzed soluble, dispersible, sodium dodecyl sulfate, and formic acid fractions of neocortex homogenates (mainly Brodmann area 17-19) from 18 pathologically diagnosed preclinical AD, 17 symptomatic AD, and 18 cases without signs of neurodegeneration. By doing so, we identified four groups of AD-related proteins being changed in levels in preclinical and symptomatic AD cases: early-responding, late-responding, gradually-changing, and fraction-shifting proteins. Gene ontology analysis of these proteins and all known AD-risk/causative genes identified vesicle endocytosis and the secretory pathway-related processes as an early-involved AD component. In conclusion, our findings suggest that subtle changes involving the secretory pathway and endocytosis precede severe proteome changes in symptomatic AD as part of the preclinical phase of AD. The respective early-responding proteins may also contribute to synaptic vesicle cycle alterations in symptomatic AD.

The Ser19Stop single nucleotide polymorphism (SNP) of human PHYHIPL affects the cerebellum in mice

The HapMap Project is a major international research effort to construct a resource to facilitate the discovery of relationships between human genetic variations and health and disease. The Ser19Stop single nucleotide polymorphism (SNP) of human phytanoyl-CoA hydroxylase-interacting protein-like (PHYHIPL) gene was detected in HapMap project and registered in the dbSNP. PHYHIPL gene expression is altered in global ischemia and glioblastoma multiforme. However, the function of PHYHIPL is unknown. We generated PHYHIPL Ser19Stop knock-in mice and found that PHYHIPL impacts the morphology of cerebellar Purkinje cells (PCs), the innervation of climbing fibers to PCs, the inhibitory inputs to PCs from molecular layer interneurons, and motor learning ability. Thus, the Ser19Stop SNP of the PHYHIPL gene may be associated with cerebellum-related diseases.

An integrated analysis of enhancer RNAs in glioma and a validation of their prognostic values

Glioma, a highly aggressive neuroepithelial malignant brain tumor, is associated with high disability and recurrence rates. Enhancer RNA (eRNA) plays a significant role in tumor proliferation and metastasis; however, their functions in gliomas need further evaluation. We used the computational pipeline, PreSTIGE, to predict tissue-specific enhancer-derived RNAs and the underlying regulatory genes. Using data retrieved from the TCGA and CGGA databases, a LASSO regression analysis and multiCox proportional hazards regression analyses were performed to determine the hub eRNAs associated with glioma prognosis. Quantitative reverse transcription PCR was performed on the glioma samples to evaluate the expression characteristics of the identified hub eRNAs. To construct a risk signature, we selected three eRNAs, including CRNDE, MRPS31P5, and LINC00844, for their significant prognostic values. The predictive value of the risk signature was validated using the CGGA and Rembrandt cohorts. Apart from the risk signature, the nomogram performed well at predicting OS in glioma patients. An eRNA-target gene regulatory network was established, which we evaluated using a target gene enrichment analysis. Pathway and gene ontology (GO) analyses demonstrated that the risk signature is associated with mRNA processing and spliceosome in glioma. Furthermore, we found that hub eRNAs potentially regulate the expressions of numerous splicing factors, such as MOV10 and SEC31B, and are correlated with prognosis-associated alteration splicing (AS). In conclusion, we established a risk signature that comprises three eRNAs, which can accurately be utilized as targets to predict prognosis in glioma patients.

Development of a prognostic model of glioma based on immune-related genes

Glioma is the most common type of primary brain cancer, and the prognosis of most patients with glioma, and particularly that of patients with glioblastoma, is poor. Tumor immunity serves an important role in the development of glioma. However, immunotherapy for glioma has not been completely successful, and thus, comprehensive examination of the immune-related genes (IRGs) of glioma is required. In the present study, differentially expressed genes (DEGs) and differentially expressed IRGs (DEIRGs) were identified using the edgeR package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used for functional enrichment analysis of DEIRGs. Survival-associated IRGs were selected via univariate Cox regression analysis. A The Cancer Genome Atlas prognostic model and GSE43378 validation model were established using lasso-penalized Cox regression analysis. Based on the median risk score value, patients were divided into high-risk and low-risk groups for clinical analysis. Receiver operating characteristic curve and nomogram analyses were used to assess the accuracy of the models. Reverse transcription-quantitative PCR was performed to measure the expression levels of relevant genes, such as cyclin-dependent kinase 4 (CDK4), interleukin 24 (IL24), NADPH oxidase 4 (NOX4), bone morphogenetic protein 2 (BMP2) and baculoviral IAP repeat containing 5 (BIRC5). A total of 3,238 DEGs, including 1,950 upregulated and 1,288 downregulated DEGs, and 97 DEIRGs, including 60 upregulated and 37 downregulated DEIRGs, were identified. ‘Neuroactive ligand-receptor interaction’ and ‘Cytokine-cytokine receptor interaction’ were the most significantly enriched pathways according to KEGG pathway analysis. A prognostic model and a validation prognostic model were created for glioma, including 15 survival-associated IRGs (FCER1G, NOX4, TRIM5, SOCS1, APOBEC3C, BIRC5, VIM, TNC, BMP2, CMTM3, IL24, JAG1, CALCRL, HNF4G and CDK4). Furthermore, multivariate Cox regression analysis results suggested that age, high WHO Grade by histopathology, wild type isocitrate dehydrogenase 1 and high risk score were independently associated with poor overall survival. The infiltration of B cells, CD8⁺ T cells, dendritic cells, macrophages and neutrophils was positively associated with the prognostic risk score. In the present study, several clinically significant survival-associated IRGs were identified, and a prognosis evaluation model of glioma was established.

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.