A novel chemokine-based signature for prediction of prognosis and therapeutic response in glioma

Development of stemness-related signature to optimize prognosis prediction and identify XMD8-85 as a novel therapeutic compound for glioma

Glioma is a highly invasive and aggressive type of brain cancer with poor treatment response. Stemness-related transcription factors form a regulatory network that sustains the malignant phenotype of gliomas. We conducted an integrated analysis of stemness-related transcription factors using The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, established the characteristics of stemness-related transcription factors, including Octamer-Binding Protein 4 (OCT4), Meis Homeobox 1 (MEIS1), E2F Transcription Factor 1 (E2F1), Transcription Factor CP2 Like 1 (TFCP2L1), and RUNX Family Transcription Factor 1 (RUNX1). The characteristic of stemness-related transcription factors was identified as an independent prognostic factor for glioma patients. Patients in the high-risk group have a worse prognosis than those in the low-risk group. The glioma microenvironment in the high-risk group exhibited a more active immune status. Single-cell level analysis revealed that stem cell-like cells exhibited stronger intercellular communication than glioma cells. Meanwhile, patients in different risk stratification exhibited varying sensitivities to immunotherapy and small molecule drug therapy. XMD8-85 was more effective in the high-risk group, and its antitumor effects were validated both in vivo and in vitro. Our results indicate that this prognostic feature will assist clinicians in predicting the prognosis of glioma patients, guiding immunotherapy and personalized treatment, as well as the potential clinical application of XMD8-85 in glioma treatment, and helping to develop effective treatment strategies.

Biomarkers of immunotherapy in glioblastoma

Glioblastoma (GBM) is the most common primary brain cancer, comprising half of all malignant brain tumors. Patients with GBM have a poor prognosis, with a median survival of 14-15 months. Current therapies for GBM, including chemotherapy, radiotherapy, and surgical resection, remain inadequate. Novel therapies are required to extend patient survival. Although immunotherapy has shown promise in other cancers, including melanoma and non-small lung cancer, its efficacy in GBM has been limited to subsets of patients. Identifying biomarkers of immunotherapy response in GBM could help stratify patients, identify new therapeutic targets, and develop more effective treatments. This article reviews existing and emerging biomarkers of clinical response to immunotherapy in GBM. The scope of this review includes immune checkpoint inhibitor and antitumoral vaccination approaches, summarizing the variety of molecular, cellular, and computational methodologies that have been explored in the setting of anti-GBM immunotherapies.

Histone acetylation risk model predicts prognosis and guides therapy selection in glioblastoma: implications for chemotherapy and anti-CTLA-4 immunotherapy

BACKGROUND

Glioblastoma is characterized by high aggressiveness, frequent recurrence, and poor prognosis. Histone acetylation-associated genes have been implicated in its occurrence and development, yet their predictive ability in glioblastoma prognosis remains unclear.

RESULTS

This study constructs a histone acetylation risk model using Cox and LASSO regression analyses to evaluate glioblastoma prognosis. We assessed the model's prognostic ability with univariate and multivariate Cox regression analyses. Additionally, immune infiltration was evaluated using ESTIMATE and TIMER algorithms, and the SubMAP algorithm was utilized to predict responses to CTLA4 inhibitor. Multiple drug databases were applied to assess drug sensitivity in high- and low-risk groups. Our results indicate that the histone acetylation risk model is independent and reliable in predicting prognosis.

CONCLUSIONS

Low-risk patients showed higher immune activity and longer overall survival, suggesting anti-CTLA4 immunotherapy suitability, while high-risk patients might benefit more from chemotherapy. This model could guide personalized therapy selection for glioblastoma patients.

Relationship between chemokine/chemokine receptor and glioma prognosis and outcomes: Systematic review and meta-analysis

BACKGROUND

Glioma is a primary tumor originating from the central nervous system, and despite ongoing efforts to improve treatment, its overall survival rate remains low. There are a limited number of reports regarding the clinical grading, prognostic impact, and utility of chemokines. Therefore, conducting a meta-analysis is necessary to obtain convincing and conclusive results.

METHODS

A comprehensive literature search was conducted using various databases, including PubMed, Web of Science, The Cochrane Library, Embase, Ovid Medline, CNKI, Wanfang Database, VIP, and CBM. The search encompassed articles published from the inception of the databases until March 2024. The estimated odds ratio (ORs), standard mean difference (SMDs), and hazard ratio (HR) with their corresponding 95% confidence intervals (95% CI) were calculated to assess the predictive value of chemokine and receptor levels in glioma risk. Additionally, heterogeneity tests and bias tests were performed to evaluate the reliability of the findings.

RESULTS

This meta-analysis included a total of 36 studies, involving 2,480 patients diagnosed with glioma. The results revealed a significant association between the expression levels of CXCR4 (n = 8; OR = 22.28; 95 % CI = 11.47-43.30; p = 0.000), CXCL12 (n = 4; OR = 10.69; 95 % CI = 7.03-16.24; p = 0.000), CCL2 (n = 6; SMD = -0.83; 95 % CI = -0.98--0.67; p = 0.000), CXCL8 (n = 3; SMD = 0.75; 95 % CI = 0.47-1.04; p = 0.000), CXCR7 (n = 3; OR = 20.66; 95 % CI = 10.20-41.82; p = 0.000), CXCL10 (n = 2; SMD = 3.27; 95 % CI = 2.91-3.62; p = 0.000) and the risk of glioma. Additionally, a significant correlation was observed between CXCR4 (n = 8; OR = 4.39; 95 % CI = 3.04-6.32; p = 0.000), (n = 6; SMD = 1.37; 95 % CI = 1.09-1.65; p = 0.000), CXCL12 (n = 6; OR = 6.30; 95 % CI = 3.87-10.25; p = 0.000), (n = 5; ES = 2.25; 95 % CI = 1.15-3.34; p = 0.041), CCL2 (n = 3; OR = 9.65; 95 % CI = 4.55-20.45; p = 0.000), (n = 4; SMD = -1.47; 95 % CI = -1.68--1.26; p = 0.000), and CCL18 (n = 3; SMD = 1.62; 95 % CI = 1.30-1.93; p = 0.000) expression levels and high-grade glioma (grades 3-4). Furthermore, CXCR4 (HR = 2.38, 95 % CI = 1.66-3.40; p = 0.000) exhibited a strong correlation with poor overall survival (OS) rates in glioma patients.

CONCLUSION

The findings of this study showed a robust association between elevated levels of CXCR4, CXCL12, CCL2, CXCL8, CXCL10 and CXCR7 with a higher risk of glioma. Furthermore, the WHO grading system was validated by the strong correlation shown between higher expression of CXCR4, CXCL12, CCL2, and CCL18 and WHO high-grade gliomas (grades 3-4). Furthermore, the results of the meta-analysis suggested that CXCR4 might be a helpful biomarker for predicting the worse prognosis of glioma patients.

The war between the immune system and the tumor - using immune biomarkers as tracers

Nowadays, immunotherapy is one of the most promising anti-tumor therapeutic strategy. Specifically, immune-related targets can be used to predict the efficacy and side effects of immunotherapy and monitor the tumor immune response. In the past few decades, increasing numbers of novel immune biomarkers have been found to participate in certain links of the tumor immunity to contribute to the formation of immunosuppression and have entered clinical trials. Here, we systematically reviewed the oncogenesis and progression of cancer in the view of anti-tumor immunity, particularly in terms of tumor antigen expression (related to tumor immunogenicity) and tumor innate immunity to complement the cancer-immune cycle. From the perspective of integrated management of chronic cancer, we also appraised emerging factors affecting tumor immunity (including metabolic, microbial, and exercise-related markers). We finally summarized the clinical studies and applications based on immune biomarkers. Overall, immune biomarkers participate in promoting the development of more precise and individualized immunotherapy by predicting, monitoring, and regulating tumor immune response. Therefore, targeting immune biomarkers may lead to the development of innovative clinical applications.

Deciphering glycosylation-driven prognostic insights and therapeutic prospects in glioblastoma through a comprehensive regulatory model

The oncogenesis and development of glioblastoma multiforme have been linked to glycosylation modifications, which are common post-translational protein modifications. Abnormal glycosyltransferase development leads to irregular glycosylation patterns, which hold clinical significance for GB prognosis. By utilizing both single-cell and bulk data, we developed a scoring system to assess glycosylation levels in GB. Moreover, a glycosylation-based signature was created to predict GB outcomes and therapy responsiveness. The study led to the development of an glyco-model incorporating nine key genes. This risk assessment tool effectively stratified GB patients into two distinct groups. Extensive validation through ROC analysis, RMST, and Kaplan-Meier (KM) survival analysis emphasized the model's robust predictive capabilities. Additionally, a nomogram was constructed to predict survival rates at specific time intervals. The research revealed substantial disparities in immune cell infiltration between low-risk and high-risk groups, characterized by differences in immune cell abundance and elevated immune scores. Notably, the glyco-model predicted diverse responses to immune checkpoint inhibitors and drug therapies, with high-risk groups exhibiting a preference for immune checkpoint inhibitors and demonstrated superior responses to drug treatments. Furthermore, the study identified two potential drug targets and utilized Connectivity Map analysis to pinpoint promising therapeutic agents. Clofarabine and YM155 were identified as potent candidates for the treatment of high-risk GB. Our well-crafted glyco-model effectively discriminates patients by calculating the risk score, accurately predicting GB outcomes, and significantly enhancing prognostic assessment while identifying novel immunotherapeutic and chemotherapeutic strategies for GB treatment.

Hypoxia-related THBD+ macrophages as a prognostic factor in glioma: Construction of a powerful risk model

Glioma is a prevalent malignant tumour characterized by hypoxia as a pivotal factor in its progression. This study aims to investigate the impact of the most severely hypoxic cell subpopulation in glioma. Our findings reveal that the THBD+ macrophage subpopulation is closely associated with hypoxia in glioma, exhibiting significantly higher infiltration in tumours compared to non-tumour tissues. Moreover, a high proportion of THBD+ cells correlates with poor prognosis in glioblastoma (GBM) patients. Notably, THBD+ macrophages exhibit hypoxic characteristics and epithelial-mesenchymal transition features. Silencing THBD expression leads to a notable reduction in the proliferation and metastasis of glioma cells. Furthermore, we developed a THBD+ macrophage-related risk signature (THBDMRS) through machine learning techniques. THBDMRS emerges as an independent prognostic factor for GBM patients with a substantial prognostic impact. By comparing THBDMRS with 119 established prognostic features, we demonstrate the superior prognostic performance of THBDMRS. Additionally, THBDMRS is associated with glioma metastasis and extracellular matrix remodelling. In conclusion, hypoxia-related THBD+ macrophages play a pivotal role in glioma pathogenesis, and THBDMRS emerges as a potent and promising prognostic tool for GBM, contributing to enhanced patient survival outcomes.

Anoikis patterns via machine learning strategy and experimental verification exhibit distinct prognostic and immune landscapes in melanoma

BACKGROUND

Anoikis is a cell death programmed to eliminate dysfunctional or damaged cells induced by detachment from the extracellular matrix. Utilizing an anoikis-based risk stratification is anticipated to understand melanoma's prognostic and immune landscapes comprehensively.

METHODS

Differential expression genes (DEGs) were analyzed between melanoma and normal skin tissues in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data sets. Next, least absolute shrinkage and selection operator, support vector machine-recursive feature elimination algorithm, and univariate and multivariate Cox analyses on the 308 DEGs were performed to build the prognostic signature in the TCGA-melanoma data set. Finally, the signature was validated in GSE65904 and GSE22155 data sets. NOTCH3, PIK3R2, and SOD2 were validated in our clinical samples by immunohistochemistry.

RESULTS

The prognostic model for melanoma patients was developed utilizing ten hub anoikis-related genes. The overall survival (OS) of patients in the high-risk subgroup, which was classified by the optimal cutoff value, was remarkably shorter in the TCGA-melanoma, GSE65904, and GSE22155 data sets. Low-risk patients exhibited low immune cell infiltration and high expression of immunophenoscores and immune checkpoints. They also demonstrated increased sensitivity to various drugs, including dasatinib and dabrafenib. NOTCH3, PIK3R2, and SOD2 were notably associated with OS by univariate Cox analysis in the GSE65904 data set. The clinical melanoma samples showed remarkably higher protein expressions of NOTCH3 (P = 0.003) and PIK3R2 (P = 0.009) than the para-melanoma samples, while the SOD2 protein expression remained unchanged.

CONCLUSIONS

In this study, we successfully established a prognostic anoikis-connected signature using machine learning. This model may aid in evaluating patient prognosis, clinical characteristics, and immune treatment modalities for melanoma.

Prognostic impact of sodium fluorescein-guided microsurgery on cognitive function, neuropeptide dynamics, and short-term outcomes in brain glioma patients

This study conducted a retrospective analysis on 107 brain glioma patients treated from January 2018 to February 2020 to assess the impact of sodium fluorescein-guided microsurgery on postoperative cognitive function and short-term outcomes. Patients were divided into two groups: a control group (n=50 patients) undergoing routine surgery and a research group (n=57 patients) receiving sodium fluorescein-guided microsurgery. The study compared postoperative total resection rates, changes in cognitive scores, and neuropeptide levels in cerebrospinal fluid between the groups. The findings revealed that the research group experienced shorter surgical time and hospitalization duration, reduced blood loss, and higher total resection rates compared to the control group. Furthermore, the research group demonstrated improvements in cognitive scores and an increase in neuropeptide levels after surgery. There was no significant difference in the comparison of the incidence of postoperative complications between the two groups. The WHO classification and preoperative performance scores were independent prognostic factors for the evaluation of 3-year survival, highlighting the clinical significance of sodium fluorescein-guided microsurgery in improving quality of life and cognitive functions of patients without compromising their long-term survival outcomes.

Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of "Cuproptosis," a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the "CP-score," was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor's immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes

BACKGROUND

Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years.

RESULTS

We identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients' prognoses in both TCGA and GEO datasets.

CONCLUSION

This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.

Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning

Background

Hepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA.

Methods

Based on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments.

Results

The multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells.

Conclusion

This study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Identified RP2 as a prognostic biomarker for glioma, facilitating glioma pathogenesis mainly via regulating tumor immunity

Glioma is the most common primary intracranial tumor in the central nervous system, with a high degree of malignancy and poor prognosis, easy to recur, difficult to cure. The mutation of Retinitis Pigmentosa 2 (RP2) can cause retinitis pigmentosa, it is a prognostic factor of osteosarcoma, however, its role in glioma remains unclear. Based on the data from TCGA and GTEx, we identified RP2 as the most related gene for glioma by WGCNA, and used a series of bioinformatics analyses including LinkedOmics, GSCA, CTD, and so on, to explore the expression of RP2 in glioma and the biological functions it is involved in. The results showed that RP2 was highly expressed in glioma, and its overexpression could lead to poor prognosis. In addition, the results of enrichment analysis showed that RP2 was highly correlated with cell proliferation and immune response. And then, we found significant enrichment of Macrophages among immune cells. Furthermore, our experiments have confirmed that Macrophages can promote the development of glioma by secreting or influencing the secretion of some cytokines. Moreover, we investigated the influence of RP2 on the immunotherapy of glioma and the role of m6A modification in the influence of RP2 on glioma. Ultimately, we determined that RP2 is an independent prognostic factor that is mainly closely related to immune for glioma.

A novel 7-chemokine-genes predictive signature for prognosis and therapeutic response in renal clear cell carcinoma

Background: Renal clear cell carcinoma (ccRCC) is one of the most prevailing type of malignancies, which is affected by chemokines. Chemokines can form a local network to regulate the movement of immune cells and are essential for tumor proliferation and metastasis as well as for the interaction between tumor cells and mesenchymal cells. Establishing a chemokine genes signature to assess prognosis and therapy responsiveness in ccRCC is the goal of this effort.

Methods: mRNA sequencing data and clinicopathological data on 526 individuals with ccRCC were gathered from the The Cancer Genome Atlas database for this investigation (263 training group samples and 263 validation group samples). Utilizing the LASSO algorithm in conjunction with univariate Cox analysis, the gene signature was constructed. The Gene Expression Omnibus (GEO) database provided the single cell RNA sequencing (scRNA-seq) data, and the R package “Seurat” was applied to analyze the scRNA-seq data. In addition, the enrichment scores of 28 immune cells in the tumor microenvironment (TME) were calculated using the “ssGSEA” algorithm. In order to develop possible medications for patients with high-risk ccRCC, the “pRRophetic” package is employed.

Results: High-risk patients had lower overall survival in this model for predicting prognosis, which was supported by the validation cohort. In both cohorts, it served as an independent prognostic factor. Annotation of the predicted signature’s biological function revealed that it was correlated with immune-related pathways, and the riskscore was positively correlated with immune cell infiltration and several immune checkpoints (ICs), including CD47, PDCD1, TIGIT, and LAG-3, while it was negatively correlated with TNFRSF14. The CXCL2, CXCL12, and CX3CL1 genes of this signature were shown to be significantly expressed in monocytes and cancer cells, according to scRNA-seq analysis. Furthermore, the high expression of CD47 in cancer cells suggested us that this could be a promising immune checkpoint. For patients who had high riskscore, we predicted 12 potential medications.

Conclusion: Overall, our findings show that a putative 7-chemokine-gene signature might predict a patient’s prognosis for ccRCC and reflect the disease’s complicated immunological environment. Additionally, it offers suggestions on how to treat ccRCC using precision treatment and focused risk assessment.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma

Introduction

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.

Methods and results

In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents.

Conclusion

Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Potent predictive CpG signature for temozolomide response in non-glioma-CpG island methylator phenotype glioblastomas with methylated MGMT promoter

Aim: We aimed to identify potent CpG signatures predicting temozolomide (TMZ) response in glioblastomas (GBMs) that do not have the glioma-CpG island methylator phenotype (G-CIMP) but have a methylated promoter of MGMT (me MGMT). Materials & methods: Different datasets of non-G-CIMP me MGMT GBMs with molecular and clinical data were analyzed. Results: A panel of 77 TMZ efficacy-related CpGs and a seven-CpG risk signature were identified and validated for distinguishing differential outcomes to radiotherapy plus TMZ versus radiotherapy alone in non-G-CIMP me MGMT GBMs. An integrated classification scheme was also proposed for refining a MGMT-based TMZ-guiding approach in all G-CIMP-GBMs. Conclusion: The CpG signatures may serve as promising predictive biomarker candidates for guiding optimal TMZ usage in non-G-CIMP me MGMT GBMs.