Clinical characterization, genetic profiling, and immune infiltration of TOX in diffuse gliomas

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach

Background

In the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination.

Methods

Download glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes.

Results

The expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model.

Conclusions

Our study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning

Background

Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS.

Methods

We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR).

Results

Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS.

Conclusion

In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

Cellular Components of the Tumor Environment in Gliomas-What Do We Know Today?

A generation ago, the molecular properties of tumor cells were the focus of scientific interest in oncology research. Since then, it has become increasingly apparent that the tumor environment (TEM), whose major components are non-neoplastic cell types, is also of utmost importance for our understanding of tumor growth, maintenance and resistance. In this review, we present the current knowledge concerning all cellular components within the TEM in gliomas, focusing on their molecular properties, expression patterns and influence on the biological behavior of gliomas. Insight into the TEM of gliomas has expanded considerably in recent years, including many aspects that previously received only marginal attention, such as the phenomenon of phagocytosis of glioma cells by macrophages and the role of the thyroid-stimulating hormone on glioma growth. We also discuss other topics such as the migration of lymphocytes into the tumor, phenotypic similarities between chemoresistant glioma cells and stem cells, and new clinical approaches with immunotherapies involving the cells of TEM.

Effects of Dendrimer-microRNA Nanoformulations against Glioblastoma Stem Cells

Glioblastoma is a rapidly progressing tumor quite resistant to conventional treatment. These features are currently assigned to a self-sustaining population of glioblastoma stem cells. Anti-tumor stem cell therapy calls for a new means of treatment. In particular, microRNA-based treatment is a solution, which in turn requires specific carriers for intracellular delivery of functional oligonucleotides. Herein, we report a preclinical in vitro validation of antitumor activity of nanoformulations containing antitumor microRNA miR-34a and microRNA-21 synthetic inhibitor and polycationic phosphorus and carbosilane dendrimers. The testing was carried out in a panel of glioblastoma and glioma cell lines, glioblastoma stem-like cells and induced pluripotent stem cells. We have shown dendrimer-microRNA nanoformulations to induce cell death in a controllable manner, with cytotoxic effects being more pronounced in tumor cells than in non-tumor stem cells. Furthermore, nanoformulations affected the expression of proteins responsible for interactions between the tumor and its immune microenvironment: surface markers (PD-L1, TIM3, CD47) and IL-10. Our findings evidence the potential of dendrimer-based therapeutic constructions for the anti-tumor stem cell therapy worth further investigation.

Effect of N6-methyladenosine (m6A) regulator-related immunogenes on the prognosis and immune microenvironment of breast cancer

Background

Breast cancer is one of the most common malignant tumor and the prognosis remains unsatisfying. Various studies demonstrate that m6A modulators are new predictors of prognosis in immune microenvironment. We aimed to identify several m6A regulator-related immunogenes and explore the relationship between m6A regulator-related immunogenes and breast cancer prognosis as well as the tumor immune microenvironment (TIME).

Methods

RNA sequencing data and clinical information on 21 m6A regulators in 1,047 breast cancer samples were downloaded from The Cancer Genome Atlas (TCGA), and immune gene data were downloaded from InnateDB. Kaplan-Meier survival analysis was conducted with log-rank test using the survival package. An m6A-related immunogene-prognostic signature was then constructed, followed by immune infiltration and checkpoint analyses.

Results

A risk prognostic signature of m6A regulator-related immunogenes, including TOX, PSME2, MCTS1, NFKBIE, SH3BP4, RSPH1, JAK1, MLLT4, and PTGES3, was constructed. Furthermore, univariate and multivariate Cox regression analyses suggested that the tumor stage and risk score could be independent prognostic factors for patients with breast cancer. Immune infiltration analysis showed that the infiltration levels of T cells, memory B cells, activated NK cells, and macrophages between the high- and low-risk groups were significantly different. In addition, checkpoint analyses demonstrated that the levels of immune checkpoint genes, such as those of LAG3, PDCD1, CTLA4, and HAVCR2, were downregulated in the high-risk group compared to those in the low-risk group.

Conclusions

Our findings suggest that the m6A regulator-related risk prognostic signature can predict the prognosis of breast cancer and that it is related to the immune microenvironment.

Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.

A Novel Thrombosis-Related Signature for Predicting Survival and Drug Compounds in Glioblastoma

Glioblastoma is the most common primary tumor in the central nervous system, and thrombosis-associated genes are related to its occurrence and progression. Univariate Cox and LASSO regression analysis were utilized to develop a new prognostic signature based on thrombosis-associated genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and HALLMARK were used for functional annotation of risk signature. ESTIMATE, MCP-counter, xCell, and TIMER algorithms were used to quantify immune infiltration in the tumor microenvironment. Genomics of Drug Sensitivity in Cancer (GDSC) was used for selecting potential drug compounds. Risk signature based on thrombosis-associated genes shows moderate performance in prognosis prediction. The functional annotation of the risk signature indicates that the signaling pathways related to the cell cycle, apoptosis, tumorigenesis, and immune suppression are rich in the high-risk group. Somatic mutation analysis shows that tumor-suppressive gene TP53 and oncogene PTEN have higher expression in low-risk and high-risk groups, respectively. Potential drug compounds are explored in risk score groups and show higher AUC values in the low-risk score group. A nomogram with valuable prognostic factors exhibits high sensitivity in predicting the survival outcome of GBM patients. Our research screens out multiple thromboses-associated genes with remarkable clinical significance in GBM and further develops a meaningful prognostic risk signature predicting drug sensitivity and survival outcome.

TNFSF13 Is a Novel Onco-Inflammatory Marker and Correlates With Immune Infiltration in Gliomas

Existing therapeutic strategies for gliomas are restricted; hence, exploration for novel diagnostic indicator and treatment is essential. Here, we performed bioinformatic analyses for TNFSF13 (also known as APRIL), a proliferation-inducing ligand of the tumor necrosis factor (TNF) superfamily, aiming to assess its potential for predicting glioma patient’s prognosis and targeted therapy. TNFSF13 expression was upregulated in the increase of tumor grades based on Xiangya cohort. In high TNFSF13 gliomas, somatic mutation was proved to correlate with amplification of EGFR and deletion of CDKN2A; while mutation of IDH1 was more frequently observed in low TNFSF13 group. We also confirmed the positive correlation between TNFSF13 and infiltrating immune and stromal cells in glioma microenvironment. Further, TNFSF13 was found to be involved in immunosuppression via diverse immunoregulation pathways and was associated with other immune checkpoints and inflammation. Single-cell sequencing revealed an abundant expression of TNFSF13 in neoplastic cells and M2 macrophages, which TNFSF13 might potentially regulate the cell communication via IL-8, C3, and CD44. Lastly, TNFSF13 mediated the activities of transcription factors including FOXO3, MEIS2, and IRF8. Our analyses demonstrated the relevance between TNFSF13 and glioma progress and indicated the potential of TNFSF13 as a novel diagnostic onco-inflammatory biomarker and immunotherapy target of gliomas.

Novel Immune Infiltrating Cell Signature Based on Cell Pair Algorithm Is a Prognostic Marker in Cancer

Tumor-infiltrating immune cells (TIICs) have become an important source of markers for predicting the clinical outcomes of cancer patients. However, measurements of cellular heterogeneity vary due to the frequently updated reference genomes and gene annotations. In this study, we systematically collected and evaluated the infiltration pattern of 65 immune cells. We constructed the Immune Cell Pair (ICP) score based on the cell pair algorithm in 3,715 samples and across 12 independent cancer types, among which, the ICP score from six cancer types was further validated in 2,228 GEO samples. An extensive tumorigenic and immunogenomic analysis was subsequently conducted. As a result, the ICP score showed a robust reliability and efficacy in predicting the survival of patients with gliomas, in pan-cancer samples, and six independent cancer types. Notably, the ICP score was correlated with the genomic alteration features in gliomas. Moreover, the ICP score exhibited a remarkable association with multiple immunomodulators that could potentially mediate immune escape. Finally, the ICP score predicted immunotherapeutic responses with a high sensitivity, allowing a useful tool for predicting the overall survival and guiding immunotherapy for cancer patients.

Immune Infiltrating Cells-Derived Risk Signature Based on Large-scale Analysis Defines Immune Landscape and Predicts Immunotherapy Responses in Glioma Tumor Microenvironment

The glioma tumor microenvironment (TME), composed of several noncancerous cells and biomolecules is known for its complexity of cancer-immune system interaction. Given that, novel risk signature is required for predicting glioma patient responses to immunotherapy. In this study, we systematically evaluated the TME infiltration pattern of 2877 glioma samples. TME phenotypes were determined using the Partitioning Around Medoid method. Machine learning including SVM-RFE and Principal component analysis (PCA) were used to construct a TME scoring system. A total of 857 glioma samples from four datasets were used for external validation of the TME-score. The correlation of TME phenotypes and TME-scores with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in glioma patients was determined. Immunohistochemistry staining for the M2 macrophage marker CD68 and CD163, mast cell marker CD117, neutrophil marker CD66b, and RNA sequencing of glioma samples from the XYNS cohort were performed. Two distinct TME phenotypes were identified. High TME-score correlated with a high number of immune infiltrating cells, elevated expression of immune checkpoints, increased mutation rates of oncogenes, and poor survival of glioma patients. Moreover, high TME-score exhibited remarkable association with multiple immunomodulators that could potentially mediate immune escape of cancer. Thus, the TME-score showed the potential to predict the efficacy of anti-PD-1 immunotherapy. Univariate and multivariate analyses demonstrated the TME-score to be a valuable prognostic biomarker for gliomas. Our study demonstrated that TME could potentially influence immunotherapy efficacy in melanoma patients whereas its role in immunotherapy of glioma patients remains unknown. Therefore, a better understanding of the TME landscape in gliomas would promote the development of novel immunotherapy strategies against glioma.

HOXA5 Is Recognized as a Prognostic-Related Biomarker and Promotes Glioma Progression Through Affecting Cell Cycle

Glioma is malignant tumor derives from glial cells in the central nervous system. High-grade glioma shows aggressive growth pattern, and conventional treatments, such as surgical removal and chemo-radiotherapy, archive limitation in the interference of this process. In this work, HOXA5, from the HOX family, was identified as a glioma cell proliferation-associated factor by investigating its feature in the TCGA and CGGA data set. High HOXA5 expression samples contain unfavorable clinical features of glioma, including IDH wild type, un-methylated MGMT status, non-codeletion 1p19q status, malignant molecular subtype. Survival analysis indicates that high HOXA5 expression samples are associated with worse clinical outcome. The CNVs and SNPs profile difference further confirmed the enrichment of glioma aggressive related biomarkers. In the meantime, the activation of DNA damage repair-related pathways and TP53-related pathways is also related to HOXA5 expression. In cell lines, U87MG and U251, by interfering HOXA5 expression significantly inhibit glioma progression and apoptosis, and cell cycle is arrested at the G2/M phase. Collectively, increased HOXA5 expression can promote glioma progression via affecting glioma cell proliferation.

The molecular feature of macrophages in tumor immune microenvironment of glioma patients

Background

Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth.

Methods

Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore.

Results

Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate.

Conclusion

Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.

m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning

Background

Cutaneous melanoma (CM) is one of the most life-threatening primary skin cancers and is prone to distant metastases. A widespread presence of posttranscriptional modification of RNA, 5-methylcytosine (m5C), has been observed in human cancers. However, the potential mechanism of the tumorigenesis and prognosis in CM by dysregulated m5C-related regulators is obscure.

Methods

We use comprehensive bioinformatics analyses to explore the expression of m5C regulators in CM, the prognostic implications of the m5C regulators, the frequency of the copy number variant (CNV), and somatic mutations in m5C regulators. Additionally, the CM patients were divided into three clusters for better predicting clinical features and outcomes via consensus clustering of m5C regulators. Then, the risk score was established via Lasso Cox regression analysis. Next, the prognosis value and clinical characteristics of m5C-related signatures were further explored. Then, machine learning was used to recognize the outstanding m5C regulators to risk score. Finally, the expression level and clinical value of USUN6 were analyzed via the tissue microarray (TMA) cohort.

Results

We found that m5C regulators were dysregulated in CM, with a high frequency of somatic mutations and CNV alterations of the m5C regulatory gene in CM. Furthermore, 16 m5C-related proteins interacted with each other frequently, and we divided CM patients into three clusters to better predicting clinical features and outcomes. Then, five m5C regulators were selected as a risk score based on the LASSO model. The XGBoost algorithm recognized that NOP2 and NSUN6 were the most significant risk score contributors. Immunohistochemistry has verified that low expression of USUN6 was closely correlated with CM progression.

Conclusion

The m5C-related signatures can be used as new prognostic biomarkers and therapeutic targets for CM, and NSUN6 might play a vital role in tumorigenesis and malignant progression.

A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes

Objective

To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.

Methods

The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model.

Results

339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage.

Conclusion

The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.

Ferroptosis-Related Gene Signature Promotes Ovarian Cancer by Influencing Immune Infiltration and Invasion

Ovarian cancer is a kind of gynecological malignancy with high mortality. Ferroptosis is a new type of iron-dependent cell death characterized by the formation of lipid peroxides and excessive accumulation of reactive oxygen species. Studies have shown that ferroptosis modulates tumor genesis, progression, and invasion, including ovarian cancer. Based on the mRNA expression data from TCGA, we construct a scoring system using consensus clustering analysis, univariate Cox regression analysis, and least absolute selection operator. Then, we systematically evaluate the relationship between score and clinical characteristics of ovarian cancer. The result from the prediction of biofunction pathways shows that score serves as an independent prognostic marker for ovarian cancer and affects tumor progression by modulating tumor metastasis. Moreover, immunocytes such as activated CD4 T cell, activated CD8 T cell, regulatory T cells, macrophage, and stromal cells, including adipocytes, epithelial cells, and fibroblast infiltrate more in the tumor microenvironment in a high-score group, indicating ferroptosis can also affect tumor immune landscape. Critically, four potentially sensitive drugs, including staurosporine, epothilone B, DMOG, and HG6-64-1 based on the scores, are predicted, and DMOG is recognized as a novel targeted drug for ovarian cancer. In general, we construct the scoring system based on ferroptosis-related genes that can predict the prognosis of ovarian cancer patients and propose that ferroptosis may affect ovarian cancer progression by mediating tumor metastasis and immune landscape. Novel drugs to target ovarian cancer are also predicted.

The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning

Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM were applied for validating clustering results. Somatic mutation and copy number variation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of risk scores. Monocytes were associated with glioma patients’ survival and exhibited high predictive value. The prognostic value of risk score in glioma was validated by the abundant expression of immune checkpoint and metabolic profile. Additionally, high risk score was positively associated with the expression of immunogenic and antigen presenting factors, which indicated high immune infiltration. A prognostic model based on risk score demonstrated high accuracy rate of receiver operating characteristic curves. Compared with previous studies, our research dissected functional roles of monocytes from large-scale analysis. Findings of our analyses strongly support an immune modulatory and prognostic role of monocytes in glioma progression. Notably, monocyte could be an effective predictor for therapy responses of glioma patients.

B2M overexpression correlates with malignancy and immune signatures in human gliomas

Because of the limited treatment strategy of gliomas, the key of diagnosis and treatment is finding new molecular biomarkers. Here, we explored the potential of β2-microglobulin (B2M) to serve as a hopeful candidate for immunotherapy or diagnostic biomarker in gliomas. The genomic profiles, clinical characteristics, and immune signatures were analyzed based on TCGA and CGGA databases. We carried out the whole statistical analyses using R project. High B2M expression correlated with worse prognosis. Somatic mutations of gliomas with high B2M expression are associated with PTEN deletion and EGFR amplification. Isocitrate dehydrogenase (IDH) mutations accounted for 82% in gliomas with low B2M expression. In addition, B2M positively correlated with ESTIMATE scores, interacted with infiltrating immune and stromal cell types. B2M also suppressed anti-tumor immunity through immune related processes. Meanwhile, B2M was associated with immune checkpoint molecules and inflammatory activities. Finally, functional annotation of the identified B2M related genes verified that B2M was a potential candidate for immunotherapy. We confirmed that B2M played a critical role in tumor progression, patient prognosis and immunotherapy of gliomas.