Characterization of Different Subtypes of Immune Cell Infiltration in Glioblastoma to Aid Immunotherapy

Eukaryotic initiation factor 3a promotes the development of diffuse large B-cell lymphoma through regulating cell proliferation

BACKGROUND

One-third of diffuse large B-cell lymphoma (DLBCL) patients suffer relapse after standard treatment. Eukaryotic initiation factor 3a (eIF3a) is a key player in the initial stage of translation, which has been widely reported to be correlated with tumorigenesis and therapeutic response. This study aimed to explore the biological role of eIF3a, evaluate its prognostic and therapeutic potential in DLBCL.

METHODS

RNA-seq datasets from GEO database were utilized to detect the expression and prognostic role of eIF3a in DLBCL patients. Protein level of eIF3a was estimated by western blot and immunohistochemical. Next, DLBCL cells were transfected with lentiviral vector either eIF3a-knockdown or empty to assess the biological role of eIF3a. Then, samples were divided into 2 clusters based on eIF3a expression and differentially expressed genes (DEGs) were identified. Function enrichment and mutation analysis of DEGs were employed to detect potential biological roles. Moreover, we also applied pan-cancer and chemosensitivity analysis for deep exploration.

RESULTS

eIF3a expression was found to be higher in DLBCL than healthy controls, which was associated with worse prognosis. The expression of eIF3a protein was significantly increased in DLBCL cell lines compared with peripheral blood mononuclear cells (PBMCs) from healthy donors. eIF3a knockdown inhibited the proliferation of DLBCL cells and the expression of proliferation-related proteins and increase cell apoptosis rate. Besides, 114 DEGs were identified which had a close linkage to cell cycle and tumor immune. eIF3a and DEGs mutations were found to be correlated to chemosensitivity and vital signal pathways. Pan-cancer analysis demonstrated that high eIF3a expression was associated with worse prognosis in several tumors. Moreover, eIF3a expression was found to be related to chemosensitivity of several anti-tumor drugs in DLBCL, including Vincristine and Wee1 inhibitor.

CONCLUSIONS

We firstly revealed the high expression and prognostic role of eIF3a in DLBCL, and eIF3a might promote the development of DLBCL through regulating cell proliferation and apoptosis. eIF3a expression was related to immune profile and chemosensitivity in DLBCL. These results suggest that eIF3a could serve as a potential prognostic biomarker and therapeutic target in DLBCL.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study

BACKGROUND

Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy.

METHODS

This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes.

RESULTS

The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies.

CONCLUSIONS

This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

Comparative transcriptomics analysis identifies crucial genes and pathways during goose spleen development

As the largest peripheral lymphoid organ in poultry, the spleen plays an essential role in regulating the body's immune capacity. However, compared with chickens and ducks, information about the age- and breed-related changes in the goose spleen remains scarce. In this study, we systematically analyzed and compared the age-dependent changes in the morphological, histological, and transcriptomic characteristics between Landes goose (LG; Anser anser) and Sichuan White goose (SWG; Anser cygnoides). The results showed a gradual increase in the splenic weights for both LG and SWG until week 10, while their splenic organ indexes reached the peak at week 6. Meanwhile, the splenic histological indexes of both goose breeds continuously increased with age, reaching the highest levels at week 30. The red pulp (RP) area was significantly higher in SWG than in LG at week 0, while the splenic corpuscle (AL) diameter was significantly larger in LG than in SWG at week 30. At the transcriptomic level, a total of 1710 and 1266 differentially expressed genes (DEGs) between week 0 and week 30 were identified in spleens of LG and SWG, respectively. Meanwhile, a total of 911 and 808 DEGs in spleens between LG and SWG were identified at weeks 0 and 30, respectively. Both GO and KEGG enrichment analysis showed that the age-related DEGs of LG or SWG were dominantly enriched in the Cell cycle, TGF-beta signaling, and Wnt signaling pathways, while most of the breed-related DEGs were enriched in the Neuroactive ligand-receptor interaction, Cytokine-cytokine receptor interaction, ECM-receptor interaction, and metabolic pathways. Furthermore, through construction of protein-protein interaction networks using significant DEGs, it was inferred that three hub genes including BUB1, BUB1B, and TTK could play crucial roles in regulating age-dependent goose spleen development while GRIA2, GRIA4, and RYR2 could be crucial for the breed-specific goose spleen development. These data provide novel insights into the splenic developmental differences between Chinese and European domestic geese, and the identified crucial pathways and genes are helpful for a better understanding of the mechanisms regulating goose immune functions.

Prognostic marker CXCL5 in glioblastoma polyformis and its mechanism of immune invasion

Glioblastoma multiforme (GBM) is the most aggressive brain cancer with a poor prognosis. Therefore, the correlative molecular markers and molecular mechanisms should be explored to assess the occurrence and treatment of glioma.WB and qPCR assays were used to detect the expression of CXCL5 in human GBM tissues. The relationship between CXCL5 expression and clinicopathological features was evaluated using logistic regression analysis, Wilcoxon symbolic rank test, and Kruskal-Wallis test. Univariate, multivariate Cox regression and Kaplan-Meier methods were used to assess CXCL5 and other prognostic factors of GBM. Gene set enrichment analysis (GSEA) was used to identify pathways associated with CXCL5. The correlation between CXCL5 and tumor immunoinfiltration was investigated using single sample gene set enrichment analysis (ssGSEA) of TCGA data. Cell experiments and mouse subcutaneous transplanted tumor models were used to evaluate the role of CXCL5 in GBM. WB, qPCR, immunofluorescence, and immunohistochemical assays showed that CXCL5 expression was increased in human GBM tissues. Furthermore, high CXCL5 expression was closely related to poor disease-specific survival and overall survival of GBM patients. The ssGSEA suggested that CXCL5 is closely related to the cell cycle and immune response through PPAR signaling pathway. GSEA also showed that CXCL5 expression was positively correlated with macrophage cell infiltration level and negatively correlated with cytotoxic cell infiltration level. CXCL5 may be associated with the prognosis and immunoinfiltration of GBM.

Glioma: bridging the tumor microenvironment, patient immune profiles and novel personalized immunotherapy

Glioma is the most common primary brain tumor, characterized by a consistently high patient mortality rate and a dismal prognosis affecting both survival and quality of life. Substantial evidence underscores the vital role of the immune system in eradicating tumors effectively and preventing metastasis, underscoring the importance of cancer immunotherapy which could potentially address the challenges in glioma therapy. Although glioma immunotherapies have shown promise in preclinical and early-phase clinical trials, they face specific limitations and challenges that have hindered their success in further phase III trials. Resistance to therapy has been a major challenge across many experimental approaches, and as of now, no immunotherapies have been approved. In addition, there are several other limitations facing glioma immunotherapy in clinical trials, such as high intra- and inter-tumoral heterogeneity, an inherently immunosuppressive microenvironment, the unique tissue-specific interactions between the central nervous system and the peripheral immune system, the existence of the blood-brain barrier, which is a physical barrier to drug delivery, and the immunosuppressive effects of standard therapy. Therefore, in this review, we delve into several challenges that need to be addressed to achieve boosted immunotherapy against gliomas. First, we discuss the hurdles posed by the glioma microenvironment, particularly its primary cellular inhabitants, in particular tumor-associated microglia and macrophages (TAMs), and myeloid cells, which represent a significant barrier to effective immunotherapy. Here we emphasize the impact of inducing immunogenic cell death (ICD) on the migration of Th17 cells into the tumor microenvironment, converting it into an immunologically "hot" environment and enhancing the effectiveness of ongoing immunotherapy. Next, we address the challenge associated with the accurate identification and characterization of the primary immune profiles of gliomas, and their implications for patient prognosis, which can facilitate the selection of personalized treatment regimens and predict the patient's response to immunotherapy. Finally, we explore a prospective approach to developing highly personalized vaccination strategies against gliomas, based on the search for patient-specific neoantigens. All the pertinent challenges discussed in this review will serve as a compass for future developments in immunotherapeutic strategies against gliomas, paving the way for upcoming preclinical and clinical research endeavors.

Integrative analysis of single-cell transcriptomics reveals age-associated immune landscape of glioblastoma

Glioblastoma (GBM) is the most malignant tumor in center nervous system. Clinical statistics revealed that senior GBM patients had a worse overall survival (OS) comparing with that of patients in other ages, which is mainly related with tumor microenvironment including tumor-associated immune cells in particular. However, the immune heterogeneity and age-related prognosis in GBM are under studied. Here we developed a machine learning-based method to integrate public large-scale single-cell RNA sequencing (scRNA-seq) datasets to establish a comprehensive atlas of immune cells infiltrating in cross-age GBM. We found that the compositions of the immune cells are remarkably different across ages. Brain-resident microglia constitute the majority of glioblastoma-associated macrophages (GAMs) in patients, whereas dramatic elevation of extracranial monocyte-derived macrophages (MDMs) is observed in GAMs of senior patients, which contributes to the worse prognosis of aged patients. Further analysis suggests that the increased MDMs arisen from excessive recruitment and proliferation of peripheral monocytes not only lead to the T cell function inhibition in GBM, but also stimulate tumor cells proliferation via VEGFA secretion. In summary, our work provides new cues for the correlational relationship between the immune microenvironment of GBM and aging, which might be insightful for precise and effective therapeutic interventions for senior GBM patients.

Comprehensive analysis of hypoxia-related genes for prognosis value, immune status, and therapy in osteosarcoma patients

Osteosarcoma is a common malignant bone tumor in children and adolescents. The overall survival of osteosarcoma patients is remarkably poor. Herein, we sought to establish a reliable risk prognostic model to predict the prognosis of osteosarcoma patients. Patients ' RNA expression and corresponding clinical data were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus databases. A consensus clustering was conducted to uncover novel molecular subgroups based on 200 hypoxia-linked genes. A hypoxia-risk models were established by Cox regression analysis coupled with LASSO regression. Functional enrichment analysis, including Gene Ontology annotation and KEGG pathway analysis, were conducted to determine the associated mechanisms. Moreover, we explored relationships between the risk scores and age, gender, tumor microenvironment, and drug sensitivity by correlation analysis. We identified two molecular subgroups with significantly different survival rates and developed a risk model based on 12 genes. Survival analysis indicated that the high-risk osteosarcoma patients likely have a poor prognosis. The area under the curve (AUC) value showed the validity of our risk scoring model, and the nomogram indicates the model's reliability. High-risk patients had lower Tfh cell infiltration and a lower stromal score. We determined the abnormal expression of three prognostic genes in osteosarcoma cells. Sunitinib can promote osteosarcoma cell apoptosis with down-regulation of KCNJ3 expression. In summary, the constructed hypoxia-related risk score model can assist clinicians during clinical practice for osteosarcoma prognosis management. Immune and drug sensitivity analysis can provide essential insights into subsequent mechanisms. KCNJ3 may be a valuable prognostic marker for osteosarcoma development.

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.