Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma

The GJB3 correlates with the prognosis, immune cell infiltration, and therapeutic responses in lung adenocarcinoma

Gap junction protein beta 3 (GJB3) has been reported as a tumor suppressor in most tumors. However, its role in lung adenocarcinoma (LUAD) remains unknown. The purpose of this study is to explore the role of GJB3 in the prognosis and tumor microenvironment of LUAD patients. The data used in this study were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and imvigor210 cohorts. We found that GJB3 expression was increased in LUAD patients and correlated with LUAD stages. LUAD patients with high GJB3 expression exhibited a worse prognosis. A total of 164 pathways were significantly activated in the GJB3 high group. GJB3 expression was positively associated with nine transcription factors and might be negatively regulated by hsa-miR-6511b-5p. Finally, we found that immune cell infiltration and immune checkpoint expression were different between the GJB3 high and GJB3 low groups. In summary. GJB3 demonstrated high expression levels in LUAD patients, and those with elevated GJB3 expression displayed unfavorable prognoses. Additionally, there was a correlation between GJB3 and immune cell infiltration, as well as immune checkpoint expression in LUAD patients.

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.

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis

Background

The bioinformatics analysis on glioma has been a hot point recently. The purpose of this study was to provide an overview of the research in this field using a bibliometric method.

Methods

The Web of Science Core Collection (WOSCC) database was used to search for literature related to the bioinformatics analysis of gliomas. Countries, institutions, authors, references, and keywords were analyzed using VOSviewer, CiteSpace, and Microsoft Excel software.

Result

China was the most productive country, while the USA was the most cited. Capital Medical University had the largest number of publications and citations. Institutions tend to collaborate more with other institutions in their countries rather than foreign ones. The most productive and most cited author was Jiang Tao. Two citation paths were identified, with literature in basic research journals often cited in clinical journals. Immune-related vocabularies appeared frequently in recent studies.

Conclusion

Glioma bioinformatics analyses spanned a wide range of fields. The international communication in this field urgently needs to be strengthened. Glioma bioinformatics approaches are developing from basic research to clinical applications. Recently, immune-related research has become a focus.

Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma

PURPOSE

Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients.

METHODS

In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA.

RESULTS

Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001).

CONCLUSION

In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.

Identification and verification of microRNA signature and key genes in the development of osteosarcoma with lung metastasis

BACKGROUND

Osteosarcoma (OS) is a heterogeneous malignant spindle cell tumor in children under the age of 20. This study aims to research the association between Solute Carrier Family 7 Member 8 (SLC7A8) as well as related genes and OS.

METHOD

OS and normal samples (GSE38698 and GSE85537) were downloaded from Gene Expression Omnibus dataset. The bioinformatics analysis was performed to distinguish 2 differentially expressed genes, prognostic candidate genes and functional enrichment pathway. Immunohistochemistry and quantitative real-time PCR were utilized for further study.

RESULTS

There were 5 DEMs and 10 differentially expressed genes in cancer tissues compared to normal tissues. According to the km-plot software, ARHGEF3, BSN, PQLC3, and SLC7A8 were significantly related to the overall survival of patients with OS. Furthermore, Multivariate analysis included that SLC7A8 was independent risk factors for OS patients. Furthermore, immunohistochemistry and quantitative real-time PCR outcomes indicated that the expression level of SLC7A8 and hsa-miR-506 was differentially expressed in lung metastasis OS tissues and non-metastasis tissues.

CONCLUSION

The prognostic model based on the miRNA-mRNA network could provide predictive significance for prognosis of OS patients, which would be worthy of clinical application. Our results concluded that SLC7A8 may play a key role in the development of OS.

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

BACKGROUND

To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD).

METHODS

The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis.

RESULTS

Patients in the high mRNAsi groups with poor prognosis were screened along with 144 immune-stemness genes. IL-6, FPR2, and RLN3 showed a higher degree in the PPI network. A total of 26 immune-stemness genes associated with prognosis were screened. Two clusters were obtained (cluster 1 and cluster 2). Survival analysis revealed that patients in cluster 2 had a poor prognosis. A total of 12 immune cell subpopulations exhibited significant differences between cluster 1 and cluster 2 (P < 0.05). A total of 10 immune checkpoint genes exhibited significantly higher expression in cluster 1 (P < 0.05) than in cluster 2. Further, the TMB value in cluster 2 was higher than that in cluster 1 (P < 0.05).

CONCLUSION

Immune-stemness genes, including L-6, FPR2, and RLN3, might play significant roles in LUAD development via cytokine-cytokine receptor interaction, neuroactive ligand‒receptor interaction, and the JAK‒STAT pathway. Immune-stemness genes were related to tumor-infiltrating immune cells, TMB, and expression of immune checkpoint gene.

TRAF3IP3 promotes glioma progression through the ERK signaling pathway

TRAF3IP3 was reportedly associated with poor prognosis in patients with melanoma; however, its role in glioma is unknown. We aimed to demonstrate the relationship between TRAF3IP3 and glioma and to investigate the potential role of TRAF3IP3 in glioma. Datasets were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We used the Wilcoxon rank-sum test to compared TRAF3IP3 expression in normal and glioma tissues. Kaplan–Meier analysis was performed to evaluate the correlation between TRAF3IP3 and patient survival rate. Gene set enrichment analysis (GSEA) was used to annotate the biological function of TRAF3IP3 in glioma. We also examined the effects of TRAF3IP3 on glioma progression, including characteristics such as cell proliferation, migration, and invasion, using cell proliferation, wound healing, and Transwell assays, respectively, paired with in vitro glioma cell lines and in vivo mouse xenograft models to determine the molecular mechanisms underlying these effects. High TRAF3IP3 expression in glioma tissues was associated with patients with neoplasm cancer tissue source site, and poorer overall survival (OS) (p = 0.03), which was validated using TCGA. GSEA revealed the enrichment of neuroactive ligand–receptor interactions, the olfactory pathway, proteasome pathway, cytokine–cytokine receptor interactions, and calcium signaling pathway in the TRAF3IP3 high-expression phenotype. TRAF3IP3 knockdown markedly suppressed the proliferation, migration, and invasion abilities of U251 glioma cells, whereas TRAF3IP3 overexpression notably promoted the progression of U118 cell tumors. Mechanistic studies revealed that TRAF3IP3 upregulated p-ERK expression in glioma cells. Notably, the ERK signaling pathway inhibitor U0126 drastically attenuated the effects of TRAF3IP3 on p-ERK and markedly blocked its tumor-promoting activity. TRAF3IP3 overexpression also promoted in vivo tumor growth in a nude mouse xenograft model. Collectively, TRAF3IP3 stimulates glioma cell proliferation, migration, and invasion, at least partly by activating the ERK signaling pathway. We hypothesize that TRAF3IP3 may participate in glioma development via the ERK signaling pathway and that elevated TRAF3IP3 expression may serve as a potential biomarker for glioma prognosis.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( $p<0.05$ ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

GPCR genes as a predictor of glioma severity and clinical outcome

OBJECTIVE

To undertake a comprehensive analysis of the differential expression of the G protein-coupled receptor (GPCR) genes in order to construct a GPCR gene signature for human glioma prognosis.

METHODS

This current study investigated several glioma transcriptomic datasets and identified the GPCR genes potentially associated with glioma severity.

RESULTS

A gene signature comprising 13 GPCR genes (nine upregulated and four downregulated genes in high-grade glioma) was developed. The predictive power of the 13-gene signature was tested in two validation cohorts and a strong positive correlation (Spearman's rank correlation test: ρ = 0.649 for the Validation1 cohort; ρ = 0.693 for the Validation2 cohort) was observed between the glioma grade and 13-gene based severity score in both cohorts. The 13-gene signature was also predictive of glioma prognosis based on Kaplan-Meier survival curve analyses and Cox proportional hazard regression analysis in four cohorts of patients with glioma.

CONCLUSIONS

Knowledge of GPCR gene expression in glioma may help researchers gain a better understanding of the pathogenesis of high-grade glioma. Further studies are needed to validate the association between these GPCR genes and glioma pathogenesis.

Identification of FABP7 as a Potential Biomarker for Predicting Prognosis and Antiangiogenic Drug Efficacy of Glioma

Objective

Glioma is a common malignant tumor of the central nervous system with extremely poor prognosis. An efficient molecular marker for diagnosis and treatment is urgently needed. Fatty acid binding protein 7(FABP7), which regulates intracellular lipid metabolism, is highly expressed in nervous system tumors, but its prognostic value remains undetermined. The present study investigated the relationship between FABP7 expression and prognosis in glioma patients by bioinformatics analysis, as well as immunohistochemically evaluating the effect of FABP7 expression on the efficacy of antiangiogenic drugs.

Methods

Gene expression and clinical data on patients with glioma were collected from the China Glioma Genome Atlas (CGGA) database, The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases. Levels of FABP7 expression and their association with the clinicopathologic characteristics of glioma patients were analyzed in the CGGA database. The relationships between FABP7 expression and clinical findings, such as survival and prognostic, were determined and used for nomogram construction. Mechanisms of action of FABP7 were assessed using GSEA software. FABP7 expression in the tissues of glioma patients treated with apatinib was evaluated immunohistochemically.

Results

FABP7 was highly expressed in glioma samples, with higher FABP7 expression associated with poorer patient prognosis and more advanced clinicopathological features. Bioinformatics analysis, including survival, receiver operating characteristic curve, and univariate and multivariate Cox analyses, showed that FABP7 was independently prognostic of outcomes in glioma patients. GSEA analysis showed that FABP7 was associated with angiogenesis, with FABP7 having correlation coefficients > 0.4 with seven factors in the angiogenic pathway, POSTN, TIMP1, PDGFA, FGFR1, S100A4, COL5A2, and STC1, and the expression of these factors related to patient prognosis. Immunohistochemistry showed that FABP7 expression was higher in glioma patients with poor survival after apatinib treatment.

Conclusions

High FABP7 expression is associated with poor prognosis in glioma patients. FABP7, which is important for glioma angiogenesis, may serve as an independent prognostic predictor in glioma patients.

Identification of Paxillin as a Prognostic Factor for Glioblastoma via Integrated Bioinformatics Analysis

Glioblastoma (GBM) is the most prevalent and aggressive type of brain tumor in the central nervous system. Clinical outcomes for patients with GBM are unsatisfactory. Here, we aimed to identify novel, reliable prognostic factors for GBM. Cox and interactive analyses were used to identify hub genes from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas datasets. After validation using various cohorts, survival analysis, meta-analysis, and prognostic analysis were performed. Coexpression and enrichment analyses were performed to elucidate the biological pathways of hub genes involved in GBM. ESTIMATE and CIBERSORT methods were applied to analyze the association of hub genes with the tumor microenvironment (TME). Paxillin (PXN) was identified as a hub gene with a high expression in GBM. PXN expression was negatively correlated with overall survival, progression-free survival, and disease-free survival in patients with GBM. Meta-analysis and Cox analysis revealed that PXN could act as an independent prognostic factor in GBM. In addition, PXN was significantly coexpressed with signal transducer and activator of transcription 3 and transforming growth factor β1 and participated in focal adhesion, extracellular matrix/receptor interactions, and the phosphatidylinositol 3-kinase/AKT signaling pathway. The results of ESTIMATE and CIBERSORT analyses revealed that PXN was implicated in TME alterations, particularly the infiltration of regulatory T cells, activated memory T cells, and activated natural killer cells. PXN may be a reliable prognostic factor for GBM. Further studies are needed to validate these findings.

Identification of an Immune-Related Prognostic Risk Model in Glioblastoma

Background: Glioblastoma (GBM) is the most common and malignant type of brain tumor. A large number of studies have shown that the immunotherapy of tumors is effective, but the immunotherapy effect of GBM is not poor. Thus, further research on the immune-related hub genes of GBM is extremely important.

Methods: The GBM highly correlated gene clusters were screened out by differential expression, mutation analysis, and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) and proportional hazards model (COX) regressions were implemented to construct prognostic risk models. Survival, receiver operating characteristic (ROC) curve, and compound difference analyses of tumor mutation burden were used to further verify the prognostic risk model. Then, we predicted GBM patient responses to immunotherapy using the ESTIMATE algorithm, GSEA, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.

Results: A total of 834 immune-related differentially expressed genes (DEGs) were identified. The five hub genes (STAT3, SEMA4F, GREM2, MDK, and SREBF1) were identified as the prognostic risk model (PRM) screened out by WGCNA and LASSO analysis of DEGs. In addition, the PRM has a significant positive correlation with immune cell infiltration of the tumor microenvironment (TME) and expression of critical immune checkpoints, indicating that the poor prognosis of patients is due to TIDE.

Conclusion: We constructed the PRM composed of five hub genes, which provided a new strategy for developing tumor immunotherapy.

Identification of Prognostic Biomarkers of Glioblastoma Based on Multidatabase Integration and Its Correlation with Immune-Infiltration Cells

Background

Glioblastoma (GBM) is the most malignant of all known intracranial tumors; meanwhile, most patients have a poor prognosis. In order to improve the poor prognosis of GBM patients as much as possible, it is specifically significant to identify biomarkers related to the gene diagnosis and gene therapy.

Methods

In this study, a total of 343 GBM specimens and 259 nontumor specimens were collected from four Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database; then, we analyzed the differentially expressed genes (DEGs) from the above data. Through Venn diagram analysis, 54 common upregulated DEGs and 22 common downregulated DEGs were triumphantly recognized.

Results

On the basis of the degree of formation communication in protein-protein interaction network (PPIN), the 10 upregulated central genes were ranked, incorporating LOX, IGFBP3, CD44, TIMP1, FN1, VEGFA, POSTN, COL1A1, COL1A2, and COL3A1. By combining the expression levels and the clinical features of GBM, we found that four hub genes (TIMP1, FN1, POSTN, and LOX) were significantly upregulated and related to poor prognosis of GBM. Meanwhile, univariate and multivariate Cox regression analysis suggested that TIMP1 could be one of the independent prognostic factors for GBM patients. Furthermore, TIMP1 was particularly correlated with the immune marker of macrophage M1, macrophage M2, neutrophils, tumor-associated macrophage, and Tregs. We then analyzed the role of TIMP1 in GBM cancer cell lines by relevant experiments, which indicated that TIMP1 knockdown resulted in the decreased cell proliferation, migration, and invasion.

Conclusions

Taken together, these findings demonstrated that TIMP1 might be a new biomarker to determine prognosis and immune infiltration of GBM patients.

Identification of novel prognostic targets in glioblastoma using bioinformatics analysis

Background

Glioblastoma (GBM) is the most malignant grade of glioma. Highly aggressive characteristics of GBM and poor prognosis cause GBM-related deaths. The potential prognostic biomarkers remain to be demonstrated. This research builds up predictive gene targets of expression alterations in GBM utilizing bioinformatics analysis.

Methods and results

The microarray datasets (GSE15824 and GSE16011) associated with GBM were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GBM and non-tumor tissues. In total, 719 DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for function enrichment analysis. Furthermore, we constructed protein–protein Interaction (PPI) network among DEGs utilizing Search Tool for the Retrieval of Interacting Genes (STRING) online tool and Cytoscape software. The DEGs of degree > 10 was selected as hub genes, including 73 upregulated genes and 21 downregulated genes. Moreover, MCODE application in Cytoscape software was employed to identify three key modules involved in GBM development and prognosis. Additionally, we used the Gene expression profiling and interactive analyses (GEPIA) online tool to further confirm four genes involving in poor prognosis of GBM patients, including interferon-gamma-inducible protein 30 (IFI30), major histocompatibility complex class II-DM alpha (HLA-DMA), Prolyl 4-hydroxylase beta polypeptide (P4HB) and reticulocalbin-1 (RCN1). Furthermore, the correlation analysis indicated that the expression of IFI30, an acknowledged biomarker in glioma, was positively correlated with HLA-DMA, P4HB and RCN1. RCN1 expression was positively correlated with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation of four prognostic markers in GBM tissues.

Conclusions

Analysis of multiple datasets combined with global network information and experimental verification presents a successful approach to uncover the risk hub genes and prognostic markers of GBM. Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of GBM.

Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature

Background

Melanoma is a highly heterogeneous and aggressive cutaneous malignancy. Ferroptosis, a new pathway of cell death depending on the intracellar iron, has been shown to be significantly associated with apoptosis of a number of tumors, including melanoma. Nevertheless, the relationship between ferroptosis-related genes (FRGs) and the melanoma patients’ prognosis needs to be explored.

Methods

Download expression profiles of FRGs and clinical data from The Cancer Genome Atlas (TCGA) database. 70% data were randomly selected from the TCGA database and utilized the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model to create a prognostic model, and the remaining 30% was used to validate the predictive power of the model. In addition, GSE65904 and GSE22153 date sets as the verification cohort to testify the predictive ability of the signature.

Results

We identified nine FRGs relating with melanoma patients’ overall survival (OS) and established a prognostic model based on their expression. During the research, patients were divided into group of high-risk and low-risk according to the results of LASSO regression analysis. Survival time was significantly longer in the low-risk group than that of in the high-risk group (P < 0.001). Enrichment analysis of different risk groups demonstrated that the reasons for the difference were related to immune-related pathways, and the degree of immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group.

Conclusions

The FRG prognostic model we established can predict the prognosis of melanoma patients and may further guide subsequent treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08475-y.

Characterization of the Immune Cell Infiltration Landscape and a New Prognostic Score in Glioblastoma

Glioblastoma (GBM) is the most aggressive, malignant primary brain tumor, which has abundant tumor-infiltrating immune cells and stroma in the tumor microenvironment (TME). So far, the TME landscape of GBM has not been elucidated. GBM samples were retrieved from TCGA and GEO databases. We used ESTIMATE and CIBERSORT algorithms to calculate risk score associated with TME, and immune cell infiltration (ICI) score of each patient is calculated by PCA. GSEA analysis is explored for each subgroup. Finally, the patient prognosis in different ICI score subgroup is determined. Two ICI clusters are determined in 208 GBM patients, and 207 differentially expressed genes (DGEs) are found between ICI clusters. And then, two gene clusters were determined. Finally, we obtained ICI score for each patient using principal component analysis (PCA). Patients were divided into high and low ICI score subgroups by setting the median as cutoff. Through GSEA, we found ECM-receptor interaction, mTOR signaling pathway, pathways in cancer, TGF-beta signaling pathway, and other immunosuppressive pathway related genes in the low ICI score group. Furthermore, patients with high ICI score group have more better prognosis. Targeting the stroma in GBM may be an effective new therapeutic approach, and the ICI score is an effective potential prognostic classifier of GBM.

Comprehensive omics analyses profile genesets related with tumor heterogeneity of multifocal glioblastomas and reveal LIF/CCL2 as biomarkers for mesenchymal subtype

Rationale: Around 10%-20% patients with glioblastoma (GBM) are diagnosed with more than one tumor lesions or multifocal GBM (mGBM). However, the understanding on genetic, DNA methylomic, and transcriptomic characteristics of mGBM is still limited.

Methods: In this study, we collected nine tumor foci from three mGBM patients followed by whole genome sequencing, whole genome bisulfite sequencing, RNA sequencing, and immunohistochemistry. The data were further examined using public GBM databases and GBM cell line.

Results: Analysis on genetic data confirmed common features of GBM, including gain of chr.7 and loss of chr.10, loss of critical tumor suppressors, high frequency of PDGFA and EGFR amplification. Through profiling DNA methylome of individual tumor foci, we found that promoter methylation status of genes involved in detection of chemical stimulus, immune response, and Hippo/YAP1 pathway was significantly changed in mGBM. Although both CNV and promoter methylation alteration were involved in heterogeneity of different tumor foci from same patients, more CNV events than promoter hypomethylation events were shared by different tumor foci, implying CNV were relatively earlier than promoter methylation alteration during evolution of different tumor foci from same mGBM. Moreover, different tumor foci from same mGBM assumed different molecular subtypes and mesenchymal subtype was prevalent in mGBM, which might explain the worse prognosis of mGBM than single GBM. Interestingly, we noticed that LIF and CCL2 was tightly correlated with mesenchymal subtype tumor focus in mGBM and predicted poor survival of GBM patients. Treatment with LIF and CCL2 produced mesenchymal-like transcriptome in GBM cells.

Conclusions: Together, our work herein comprehensively profiled multi-omics features of mGBM and emphasized that components of extracellular microenvironment, such as LIF and CCL2, contributed to the evolution and prognosis of tumor foci in mGBM patients.

Diagnostic and prognostic value of MATN3 expression in gastric carcinoma: TCGA database mining

Backgrounds

Globally, the high morbidity and mortality of gastric carcinoma (GC) have been one of the great challenges facing humanity. However, the early diagnosis of GC is still unknown. Matrilin-3 (MATN3) is a member of the extracellular matrix (ECM) protein family. Previous studies have reported a correlation between the expression of MATN3 and bone disease. However, the role of MATN3 in GC has not been reported in depth, which can have a possible far-reaching implication for GC.

Methods

We explored the diagnostic and prognostic value and pathway enrichment of MATN3 expression in GC. Limma package conducted by R was used to analysis the difference expression data of MATN3 from The Cancer Genome Atlas (TCGA). The receiver operating characteristic (ROC) curve analysis was used to estimate the diagnostic value of MATN3 expression. univariate and multivariate analysis were used to assess the prognostic value of MATN3, and gene set enrichment analysis (GSEA) to identify the enriched signaling pathways.

Results

MATN3 was found to be significantly higher in GC tissue samples. GC patients with high MATN3 expression had poor prognosis. Then, GSEA showed that the gene sets were correlated with signaling pathways including ECM receptor interaction, hypertrophic cardiomyopathy (HCM), and glycosaminoglycan biosynthesis chondroitin sulfate, among others.

Conclusions

The study suggests that MATN3 can serve as a potential diagnostic and prognostic biomarker for GC.

Comprehensive Analysis of CD163 as a Prognostic Biomarker and Associated with Immune Infiltration in Glioblastoma Multiforme

Background

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignancy in adults with high aggression. The prognosis of GBM patients is poor. There is a critical need for novel biomarkers for the prognosis and therapy of GBM.

Methods

Differentially expressed genes (DEGs) in GBM were screened using TCGA cohort. Univariate and multivariate Cox regression analyses were performed on DEGs to identify the optimal prognosis-related genes. qRT-PCR was performed to verify the result.

Results

A total of 5216 DEGs, including 2785 upregulated and 2458 downregulated genes, were obtained. Enrichment analysis revealed that these DEGs were mainly involved in the p53 signaling pathway and cell cycle, immune response, and MAPK signaling pathways. Moreover, the top 50 DEGs were associated with drug resistance or drug sensitivity. Prognosis analysis revealed that GBM patients with a high expression of CD163 and CHI3L2 had a poor overall survival, prognosis-free survival, and disease-specific survival. The univariate and multivariate analyses revealed that CD163 and age were independent factors affecting the prognosis of GBM patients. A validation study revealed that CD163 was upregulated in GBM tissues and associated with poor overall survival. Moreover, further analysis revealed that CD163 showed significant correlation with immune cells, immune biomarkers, chemokines, and chemokine receptors. We also identified several CD163-associated kinase, miRNA, and transcription factor targets in GBM, including LCK, miR-483, and ELF1.

Conclusions

In conclusion, our study suggested CD163 as a prognostic biomarker and associated it with immune infiltration in GBM.

The Impact of Epigenetic Modifications on Adaptive Resistance Evolution in Glioblastoma

Glioblastoma (GBM) is a highly lethal cancer that is universally refractory to the standard multimodal therapies of surgical resection, radiation, and chemotherapy treatment. Temozolomide (TMZ) is currently the best chemotherapy agent for GBM, but the durability of response is epigenetically dependent and often short-lived secondary to tumor resistance. Therapies that can provide synergy to chemoradiation are desperately needed in GBM. There is accumulating evidence that adaptive resistance evolution in GBM is facilitated through treatment-induced epigenetic modifications. Epigenetic alterations of DNA methylation, histone modifications, and chromatin remodeling have all been implicated as mechanisms that enhance accessibility for transcriptional activation of genes that play critical roles in GBM resistance and lethality. Hence, understanding and targeting epigenetic modifications associated with GBM resistance is of utmost priority. In this review, we summarize the latest updates on the impact of epigenetic modifications on adaptive resistance evolution in GBM to therapy.

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

MOTIVATION

Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly.

RESULTS

To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at: https://github.com/mskspi/PathCNN.

Screening and identification of LMNB1 and DLGAP5, two key biomarkers in gliomas

Glioma is the most common primary cancer in the central nervous system. Despite advances in surgery, radiotherapy and chemotherapy over the past decades, the prognosis of glioblastoma patients remains poor. We aim to identify robust gene signatures to better understand the complex molecular mechanisms and to discover potential novel molecular biomarkers for glioma. By exploring GSE16011, GSE4290 and GSE50161 data in Gene Expression Omnibus (GEO) database, we screened out 380 differentially expressed genes between non-tumor and glioma tissues, and further selected 30 hub genes through the Molecular Complex Detection (MCODE) plug-in in Cytoscape. In addition, LMNB1 and DLGAP5 were selected for further analyses due to their high expression in gliomas and were verified by using our cohort. Our study confirmed that LMNB1 and DLGAP5 were up-regulated in gliomas, and patients with high expression of LMNB1 or DLGAP5 had poor survival rate. Furthermore, silence of LMNB1 and DLGAP5 inhibited the proliferation of glioma cells. Together, LMNB1 and DLGAP5 were two potentially novel molecular biomarkers for diagnosis and prognosis of glioma.

Heterogeneity Analysis of Esophageal Squamous Cell Carcinoma in Cell Lines, Tumor Tissues and Patient-Derived Xenografts

Esophageal Squamous Cell Carcinoma (ESCC) is the predominant type of Esophageal Cancer (EC), accounting for nearly 88% of EC incidents worldwide. Importantly, it is also a life-threatening cancer for patients diagnosed in advanced stages, with only a 20% 5-year survival rate due to a limited number of actionable targets and therapeutic options. Increasing evidence has shown that inter-tumor and intra-tumor heterogeneity are widely distributed across ESCC tumor tissues. In our work, multi-omics data from ESCC cell lines, tumor tissue, normal tissue and Patient-Derived Xenograft (PDX) tissues were analyzed to investigate the heterogeneity among ESCC samples at the DNA, RNA, and protein level. We identified enrichment of ECM-receptor interaction and Focal adhesion pathways from the subset of protein-coding genes with non-silent mutations in ESCC patients. We also found that TP53, TTN, KMT2D, CSMD3, DNAH5, MUC16 and DST are the most frequently mutated genes in ESCC patient samples. Out of the identified genes, TP53 is the most frequently mutated, with 84 distinct non-silent mutation variants. We observed that p.R248Q, p.R175G/H, and p.R273C/H are the most common TP53 mutation variants. The diversity of TP53 mutations reveal its importance in ESCC progression and may also provide promising targets for precision therapeutics. Additionally, we identified the Olfactory transduction as the top signaling pathway, enriched from genes uniquely expressed in The Cancer Genome Atlas (TCGA)-ESCC patient tumor tissues, which may provide implications for the exact roles of the corresponding genes in ESCC. Cyclic nucleotide-gated channel subunit beta 1(CNGB1), a gene belonging to the Olfactory transduction pathway, was found exclusively overexpressed in ESCC. Expression of CNGB1 could serve as a marker, indicating potential diagnostic or therapeutic value. Finally, we investigated heterogeneity in the context of the ESCC PDX model, which is an emerging tool used to predict drug response and recapitulate tumor behavior in vivo. We observed trans-species heterogeneity in as high as 75% of the identified proteins, indicating that the ambiguity of proteins should be addressed by specific strategies to avoid drawing false conclusions. The identification and characterization of gene mutation and expression heterogeneity across different ESCC datasets, including various novel TP53 mutations, ECM-receptor interaction, Focal adhesion, and Olfactory transduction pathways (CNGB1), provide researchers with evidence and implications for accurate research and precision therapeutic development.

Expression analysis of progesterone‑regulated miRNAs in cells derived from human glioblastoma

Glioblastomas (GBMs) are the most frequent and malignant type of brain tumor. It has been reported that progesterone (P4) regulates the progression of GBMs by modifying the expression of genes that promote cell proliferation, migration and invasion; however, it is not fully understood how these processes are regulated. It is possible that P4 mediates some of these effects through changes in the microRNA (miRNA) expression profile in GBM cells. The present study investigated the effects of P4 on miRNAs expression profile in U-251MG cells derived from a human GBM. U-251MG cells were treated for 6 h with P4, RU486 (an antagonist of the intracellular progesterone receptor), the combined treatment (P4+RU486) and cyclodextrin (vehicle) and then a miRNA microarray analysis conducted. The expression analysis revealed a set of 190 miRNAs with differential expression in the treatments of P4, RU486 and P4+RU486 in respect to the vehicle and P4 in respect to P4+RU486, of which only 16 were exclusively regulated by P4. The possible mRNA targets of the miRNAs regulated by P4 could participate in the regulation of proliferation, cell cycle progression and cell migration of GBMs. The present study provided insight for understanding epigenetic modifications regulated by sex hormones involved in GBM progression, and for identifying potential therapeutic strategies for these brain tumors.

Novel LncRNA OXCT1-AS1 indicates poor prognosis and contributes to tumorigenesis by regulating miR-195/CDC25A axis in glioblastoma

BACKGROUND

Long noncoding RNAs (lncRNAs) contribute to multiple biological processes in human glioblastoma (GBM). However, identifying a specific lncRNA target remains a challenge. In this study, bioinformatics methods and competing endogenous RNA (ceRNA) network regulatory rules were used to identify GBM-related lncRNAs and revealed that OXCT1 antisense RNA 1 (OXCT1-AS1) is a potential therapeutic target for the treatment of glioma.

METHODS

Based on the Gene Expression Omnibus (GEO) dataset, we identified differential lncRNAs, microRNAs and mRNAs and constructed an lncRNA-associated ceRNA network. The novel lncRNA OXCT1-AS1 was proposed to function as a ceRNA, and its potential target miRNAs were predicted through the database LncBase Predicted v.2. The expression patterns of OXCT1-AS1 in glioma and normal tissue samples were measured. The effect of OXCT1-AS1 on glioma cells was checked using the Cell Counting Kit 8 assay, cell colony formation assay, Transwell assay and flow cytometry in vitro. The dual-luciferase activity assay was performed to investigate the potential mechanism of the ceRNA network. Finally, orthotopic mouse models of glioma were created to evaluate the influence of OXCT1-AS1 on tumour growth in vivo.

RESULTS

In this study, it was found that the expression of lncRNA OXCT1-AS1 was upregulated in both The Cancer Genome Atlas (TCGA) GBM patients and GBM tissue samples, and high expression of OXCT1-AS1 predicted a poor prognosis. Suppressing OXCT1-AS1 expression significantly decreased GBM cell proliferation and inhibited cell migration and invasion. We further investigated the potential mechanism and found that OXCT1-AS1 may act as a ceRNA of miR-195 to enhance CDC25A expression and promote glioma cell progression. Finally, knocking down OXCT1-AS1 notably attenuated the severity of glioma in vivo.

CONCLUSION

OXCT1-AS1 inhibits glioma progression by regulating the miR-195-5p/CDC25A axis and is a specific tumour marker and a novel potential therapeutic target for glioma treatment.

A pilot radiogenomic study of DIPG reveals distinct subgroups with unique clinical trajectories and therapeutic targets

An adequate understanding of the relationships between radiographic and genomic features in diffuse intrinsic pontine glioma (DIPG) is essential, especially in the absence of universal biopsy, to further characterize the molecular heterogeneity of this disease and determine which patients are most likely to respond to biologically-driven therapies. Here, a radiogenomics analytic approach was applied to a cohort of 28 patients with DIPG. Tumor size and imaging characteristics from all available serial MRIs were evaluated by a neuro-radiologist, and patients were divided into three radiographic response groups (partial response [PR], stable disease [SD], progressive disease [PD]) based on MRI within 2 months of radiotherapy (RT) completion. Whole genome and RNA sequencing were performed on autopsy tumor specimens. We report several key, therapeutically-relevant findings: (1) Certain radiologic features on first and subsequent post-RT MRIs are associated with worse overall survival, including PD following irradiation as well as present, new, and/or increasing peripheral ring enhancement, necrosis, and diffusion restriction. (2) Upregulation of EMT-related genes and distant tumor spread at autopsy are observed in a subset of DIPG patients who exhibit poorer radiographic response to irradiation and/or higher likelihood of harboring H3F3A mutations, suggesting possible benefit of upfront craniospinal irradiation. (3) Additional genetic aberrations were identified, including DYNC1LI1 mutations in a subgroup of patients with PR on post-RT MRI; further investigation into potential roles in DIPG tumorigenesis and/or treatment sensitivity is necessary. (4) Whereas most DIPG tumors have an immunologically “cold” microenvironment, there appears to be a subset which harbor a more inflammatory genomic profile and/or higher mutational burden, with a trend toward improved overall survival and more favorable radiographic response to irradiation, in whom immunotherapy should be considered. This study has begun elucidating relationships between post-RT radiographic response with DIPG molecular profiles, revealing radiogenomically distinct subgroups with unique clinical trajectories and therapeutic targets.

Bioinformatics analysis identifies COL1A1, THBS2 and SPP1 as potential predictors of patient prognosis and immunotherapy response in gastric cancer

Background: The present study aimed to use bioinformatics tools to explore pivotal genes associated with the occurrence of gastric cancer (GC) and assess their prognostic significance, and link with clinicopathological parameters. We also investigated the predictive role of COL1A1, THBS2, and SPP1 in immunotherapy.

Materials and methods: We identified differential genes (DEGs) that were up- and down-regulated in the three datasets (GSE26942, GSE13911, and GSE118916) and created protein–protein interaction (PPI) networks from the overlapping DEGs. We then investigated the potential functions of the hub genes in cancer prognosis using PPI networks, and explored the influence of such genes in the immune environment.

Results: Overall, 268 overlapping DEGs were identified, of which 230 were up-regulated and 38 were down-regulated. CytoHubba selected the top ten hub genes, which included SPP1, TIMP1, SERPINE1, MMP3, COL1A1, BGN, THBS2, CDH2, CXCL8, and THY1. With the exception of SPP1, survival analysis using the Kaplan–Meier database showed that the levels of expression of these genes were associated with overall survival. Genes in the most dominant module explored by MCODE, COL1A1, THBS2, and SPP1, were primarily enriched for two KEGG pathways. Further analysis showed that all three genes could influence clinicopathological parameters and immune microenvironment, and there was a significant correlation between COL1A1, THBS2, SPP1, and PD-L1 expression, thus indicating a potential predictive role for GC response to immunotherapy.

Conclusion: ECM–receptor interactions and focal adhesion pathways are of great significance in the progression of GC. COL1A1, THBS2, and SPP1 may help predict immunotherapy response in GC 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.

RPN2 is targeted by miR-181c and mediates glioma progression and temozolomide sensitivity via the wnt/β-catenin signaling pathway

Accumulating evidence indicates that the dysregulation of the miRNAs/mRNA-mediated carcinogenic signaling pathway network is intimately involved in glioma initiation and progression. In the present study, by performing experiments and bioinformatics analysis, we found that RPN2 was markedly elevated in glioma specimens compared with normal controls, and its upregulation was significantly linked to WHO grade and poor prognosis. Knockdown of RPN2 inhibited tumor proliferation and invasion, promoted apoptosis, and enhanced temozolomide (TMZ) sensitivity in vitro and in vivo. Mechanistic investigation revealed that RPN2 deletion repressed β-catenin/Tcf-4 transcription activity partly through functional activation of glycogen synthase kinase-3β (GSK-3β). Furthermore, we showed that RPN2 is a direct functional target of miR-181c. Ectopic miR-181c expression suppressed β-catenin/Tcf-4 activity, while restoration of RPN2 partly reversed this inhibitory effect mediated by miR-181c, implying a molecular mechanism in which TMZ sensitivity is mediated by miR-181c. Taken together, our data revealed a new miR-181c/RPN2/wnt/β-catenin signaling axis that plays significant roles in glioma tumorigenesis and TMZ resistance, and it represents a potential therapeutic target, especially in GBM.