Prognostic implications of immune-related eight-gene signature in pediatric brain tumors

Deciphering Natural Killer Cell Cytotoxicity Against Medulloblastoma in vitro and in vivo: Implications for Immunotherapy

Purpose

Medulloblastoma (MB) is the most prevalent paediatric brain tumour. Despite improvements in patient survival with current treatment strategies, the quality of life of these patients remains poor owing to the sequelae and relapse risk. An alternative, or, in addition to the current standard treatment, could be considered immunotherapy, such as Natural Killer cells (NK). NK cells are cytotoxic innate lymphoid cells that play a major role in cancer immunosurveillance. To date, the mechanism of cytotoxicity of NK cells, especially regarding the steps of adhesion, conjugation, cytotoxic granule polarisation in the cell contact area, perforin and granzyme release in two and three dimensions, and therapeutic efficacy in vivo have not been precisely described.

Materials and Methods

Each step of NK cytotoxicity against the three MB cell lines was explored using confocal microscopy for conjugation, Elispot for degranulation, flow cytometry, and luminescence assays for target cell necrosis and lysis and mediators released by cytokine array, and then confirmed in a 3D spheroid model. Medulloblastoma-xenografted mice were treated with NK cells. Their persistence was evaluated by flow cytometry, and their efficacy in tumour growth and survival was determined. In addition, their effects on the tumour transcriptome were evaluated.

Results

NK cells showed variable affinities for conjugation with MB target cells depending on their subgroup and cytokine activation. Chemokines secreted during NK and MB cell co-culture are mainly associated with angiogenesis and immune cell recruitment. NK cell cytotoxicity induces MB cell death in both 2D and 3D co-culture models. NK cells initiated an inflammatory response in a human MB murine model by modulating the MB cell transcriptome.

Conclusion

Our study confirmed that NK cells possess both in vitro and in vivo cytotoxic activity against MB cells and are of interest for the development of immunotherapy.

Comprehensive Analysis of TICRR in Hepatocellular Carcinoma Based on Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is one of the leading cause of cancer-associated death in the world. However, due to the complexity of HCC, it is urgent for us to find a reliable and accurate biomarker for HCC gene therapy.TopBP1-interacting checkpoint and replication regulator (TICRR), known as Treslin in vertebrate and sld3 in yeast, is involved in the tumorigenesis, progression, matastasis, diagnosis, and predicting prognosis of HCC. Disappointingly, the mechanism of TICRR expression in HCC is still not described in detail and requires further analysis. In this study, TCGA ( www.tcga-data.nci.nih.gov/tcga/ ) datasets and GEO ( www.ncbi.nlm.nih.gov/geo ) datasets were used to analyze the expression of TICRR in HCC, the relevance of TICRR mRNA expression and clinicopathological characteristics in patients with HCC, and the relationship between TICRR expression and immune infiltration level in Patients with HCC. Based on MethSurv database, the impact of TICRR in patients with HCC was investigated. In addition, GO/KEGG enrichment analysis of TICRR co-expression was performed using the R package. TICRR was found drastically highly expressed in a variety of cancer types including HCC.ROC curve analysis showed that TICRR had higher accuracy in predicting HCC compared with AFP. The expression level of TICRR was marked positively correlated with tumor stage and prognosis in Patients with HCC.GO/KEGG enrichment analysis showed that TICRR was associated with cell division and cell cycle as well as p53 signaling pathway. In addition, patients with high TICRR methylation of cg05841809, cg09403165, and cg03312532 CpG sites were significantly correlated with poor prognosis of HCC. This study demonstrated that increased TICRR expression in HCC might play an important role in the tumorigenesis, progression, diagnosis, and predicting prognosis of HCC. Therefore, TICRR might be used as a promising diagnostic and prognostic biomarker for HCC gene therapy.

CEP55, serving as a diagnostic marker gene for osteosarcoma, triggers the JAK2-STAT3-MMPs axis

Background

Osteosarcoma (OS) stands as the prevailing form of primary bone cancer in clinical practice. Lack of effective treatment options and an overall poor prognosis are caused by the disease's exceptionally rare occurrence and unclear rationale.

Objective

This study's goal is to determine diagnostic marker genes involved in the progression of OS and investigate related pathways and mechanisms with the purpose of offering effective methods for OS diagnostics and therapy.

Methods

The Gene Expression Omnibus database provided the gene microarray data. Core genes were identified through differential expression analysis and WGCNA. Three techniques for machine learning, random forest, least absolute shrinkage and selection operator regression, and support vector machine recursive feature elimination, were used to further screen the core genes and obtain diagnostic marker genes for OS. The specificity and sensitivity of the diagnostic marker genes for OS diagnosis were evaluated using receiver operating characteristic curves. Western blotting analysis was used for preliminary validation of the diagnostic marker genes and their related pathways.

Results

Two diagnostic marker genes were identified through screening, including CEP55 and VWF. Receiver operating characteristic curves have been utilized to assess the diagnostic and therapeutic effects of CEP55 and VWF on OS. Western blotting analysis preliminarily validated the overexpression of CEP55 in OS and its capacity to control MMP2 and MMP9 levels by activating the JAK2/STAT3 signaling pathway.

Conclusion

At the first time, this research shows that CEP55 and VWF are more powerful diagnostic and predictive indicators for OS. CEP55 holds the capacity to activate the JAK2/STAT3 signaling pathway and modulate MMP2 and MMP9 levels, thereby positioning it as a promising target in OS treatment.

Multi-omics analysis of N6-methyladenosine reader IGF2BP3 as a promising biomarker in pan-cancer

Background

Insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) has been reported to exhibit an oncogenic effect as an RNA-binding protein (RBP) by promoting tumor cell proliferation, migration and invasion in several tumor types. However, a pan-cancer analysis of IGF2BP3 is not currently available, and the exact roles of IGF2BP3 in prognosis and immunology in cancer patients remain enigmatic. The main aim of this study was to provide visualization of the systemic prognostic landscape of IGF2BP3 in pan-cancer and to uncover the potential relationship between IGF2BP3 expression in the tumor microenvironment and immune infiltration profile.

Methods

Raw data on IGF2BP3 expression were obtained from GTEx, CCLE, TCGA, and HPA data portals. We have investigated the expression patterns, diagnostic and prognostic significance, mutation landscapes, functional analysis, and functional states of IGF2BP3 utilizing multiple databases, including HPA, TISIDB, cBioPortal, GeneMANIA, GESA, and CancerSEA. Moreover, the relationship of IGF2BP3 expression with immune infiltrates, TMB, MSI and immune-related genes was evaluated in pan-cancer. IGF2BP3 with drug sensitivity analysis was performed from the CellMiner database. Furthermore, the expression of IGF2BP3 in different grades of glioma was detected by immunohistochemical staining and western blot.

Results

We found that IGF2BP3 was ubiquitously highly expressed in pan-cancer and significantly correlated with diagnosis, prognosis, TMB, MSI, and drug sensitivity in various types of cancer. Besides, IGF2BP3 was involved in many cancer pathways and varied in different immune and molecular subtypes of cancers. Additionally, IGF2BP3 is critically associated with genetic markers of immunomodulators in various cancers. Finally, we validated that IGF2BP3 protein expression was significantly higher in glioma than in normal tissue, especially in GBM.

Conclusions

IGF2BP3 may be a potential molecular biomarker for diagnosis and prognosis in pan-cancer, especially for glioma. It could become a novel therapeutic target for various cancers.

Identification of hub genes and their correlation with infiltration of immune cells in MYCN positive neuroblastoma based on WGCNA and LASSO algorithm

Background

The prognosis of MYCN positive NB is poor, and there is no targeted drug for N-myc at present. This study aims to screen out hub genes closely related to MYCN, analyze the relationship between hub genes and NB microenvironment, and provide basis for molecular targeted therapy of MYCN positive NB.

Methods

We combined the microarray data of GSE45547 (n=649) and GSE49710 (n=498), screened the DEGs between MYCN positive (n=185) and MYCN negative NB (n=951), performed WGCNA, Lasso regression and Roc analyses on the merged matrix, and obtained the hub genes related to MYCN in the training group. We performed ssGSEA on the experimental group to calculate the infiltration level of 28 kinds of immune cells in each sample, compared the differences of immune cell infiltration between MYCN positive and MYCN negative group. The influences of hub genes on the distribution of each immune cell were also analyzed by ssGSEA. The expression differences of the three hub genes were verified in the E-MTAB-8248 cohort (n=223), and the correlation between hub genes and prognosis of NB was calculated by Kaplan-Meier method in GSE62564 (n=498) and the validation group. We also verified the expression differences of hub genes by qRT-PCR in SK-N-BE(2), SKNDZ, Kelly and SH-SY5Y cell lines.

Results

Here were 880 DEGs including 420 upregulated and 460 downregulated genes in MYCN positive NB in the training group. Overlap of the DEGs and WGCNA networks identified four shared genes, namely, ZNF695, CHEK1, C15ORF42 and EXO1, as candidate hub genes in MYCN positive NB. Three core genes, ZNF695, CHEK1 and C15ORF42, were finally identified by Lasso regression and Roc analyses. ZNF695, CHEK1 and C15ORF42 were highly expressed in MYCN positive NB tissues and cell lines. These three genes were closely related to the prognosis of children with NB. Except that Activated CD4 T cell and Type2 T helper cell increased, the infiltration levels of the other 26 cells decreased significantly in MYCN positive NB tissues. The infiltration levels of Type2 T helper cell and Activated CD4 T cell were also significantly positively correlated with the expression levels of the three hub genes.

Conclusion

ZNF695, CHEK1 and C15ORF42 are highly expressed in MYCN positive NB, and their expression levels are negatively correlated with the prognosis of children with NB. The infiltration levels of Activated CD4 T cell and Type2 T helper cell increased in the microenvironment of MYCN positive NB and were significantly positively correlated with the expression levels of the three hub genes. The results of this study provide that ZNF695, CHEK1 and C15ORF42 may be potential prognostic markers and immunotherapy targets for MYCN positive NB.

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis

Objective

The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients.

Methods

Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan–Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. The prognostic value of the GESBN models was investigated in the GSE124647 dataset, which was validated in GSE16446 and GSE45255 datasets. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the expression and prognostic value of hub genes in BC were explored.

Results

A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan–Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis.

Conclusion

Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan–Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature’s predictive ability was further examined by the Kaplan–Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells’ roles in the gene signature’s prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature’s prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

The Growing Relevance of Immunoregulation in Pediatric Brain Tumors

Pediatric brain tumors are genetically heterogeneous solid neoplasms. With a prevailing poor prognosis and widespread resistance to conventional multimodal therapy, these aggressive tumors are the leading cause of childhood cancer-related deaths worldwide. Advancement in molecular research revealed their unique genetic and epigenetic characteristics and paved the way for more defined prognostication and targeted therapeutic approaches. Furthermore, uncovering the intratumoral metrics on a single-cell level placed non-malignant cell populations such as innate immune cells into the context of tumor manifestation and progression. Targeting immune cells in pediatric brain tumors entails unique challenges but promising opportunities to improve outcome. Herein, we outline the current understanding of the role of the immune regulation in pediatric brain tumors.