Prognostic Immune-Related Genes of Patients With Ewing's Sarcoma

Adhesion G Protein-Coupled Receptor G2 Promotes Hepatocellular Carcinoma Progression and Serves as a Neutrophil-Related Prognostic Biomarker

Adhesion G protein-coupled receptor G2 (ADGRG2) is an orphan adhesion G protein-coupled receptor (GPCR), which performs a tumor-promoting role in certain cancers; however, it has not been systematically investigated in hepatocellular carcinoma (HCC). In the current study, we utilized multiple databases to analyze the expression and diagnostic and prognostic value of ADGRG2 in HCC and its correlation with immune infiltration and inflammatory factors. The function and upstream regulatory miRNA of ADGRG2 were validated through qPCR, Western blot, CCK8, wound healing, and dual luciferase assays. It turned out that ADGRG2 was significantly higher in HCC and had a poor survival rate, especially in AFP ≤ 400 ng/mL subgroups. Functional enrichment analysis suggested that ADGRG2 may be involved in cancer pathways and immune-related pathways. In vitro, siRNA-mediated ADGRG2 silencing could inhibit the proliferation and migration of Huh7 and HepG2 cells. There was a highly significant positive correlation between ADGRG2 and neutrophils. Moreover, NET-related genes were filtered and confirmed, such as ENO1 and S100A9. Meanwhile, the high expression of ADGRG2 was also accompanied by the highest number of inflammatory cytokines, chemokines, and chemokine receptors and good immunotherapy efficacy. Finally, AGDGR2 may be sensitive to two drugs (PIK-93 and NPK76-II-72-1) and can be targeted by miR-326. In conclusion, ADGRG2 may serve as a novel biomarker and drug target for HCC diagnosis, immunotherapy, and prognosis and was related to neutrophils and the inflammatory process of liver cancer development.

Identification of ARAP3 as a regulator of tumor progression, macrophage infiltration and osteoclast differentiation in a tumor microenvironment-related prognostic model of Ewing sarcoma

Understanding the specificity and complexity of the tumor microenvironment (TME) of Ewing sarcoma (ES) is essential for identifying the immune characteristics of ES, improving the prediction of immunotherapeutic response, and facilitating therapeutic target discovery. In this study, we not only evaluated the gene sets associated with TME in ES using ESTIMATE and WGCNA algorithms based on the transcriptome data of ES, but also constructed a prognostic model (ES Score) using univariate Cox regression and Lasso regression and assessed its predictive ability on immune cell infiltration. Subsequently, we identified ARAP3 as a key gene affecting the TME of ES. In addition, bioinformatic analyses and in vitro experiments proved that the high expression of ARAP3 regulated ES cell proliferation, migration, as well as apoptosis via the p53 signaling pathway and affected macrophage infiltration and osteoclast differentiation through regulating IL1B and IL11 secretion of tumor cells.

Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma

Background

The immune landscape, prognostic model, and molecular variations of mantle cell lymphoma (MCL) remain unclear. Hence, an integrated bioinformatics analysis of MCL datasets is required for the development of immunotherapy and the optimization of targeted therapies.

Methods

Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules.

Results

Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with down-regulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis. Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes.

Conclusions

Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.

Computed tomography‐based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma

OBJECTIVES

To predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography-based radiomics model and establishing a nomogram using clinicopathologic factors.

METHODS

The clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)-KIRC database. Univariate and multivariate Cox regression and Kaplan-Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision-recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS-based nomogram was constructed to predict prognosis.

RESULTS

CTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13-1.881, p = 0.004). The radiomics model for the prediction of CTLA4 expression levels (AUC = 0.769 in the training set, AUC = 0.724 in the validation set) was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS-based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months.

CONCLUSIONS

CTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast-enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI-based treatments.

A Novel Defined Hypoxia-Related Gene Signature for Prognostic Prediction of Patients With Ewing Sarcoma

The therapeutic strategy of Ewing sarcoma (EWS) remains largely unchanged over the past few decades. Hypoxia is reported to have an impact on tumor cell progression and is regarded as a novel potential therapeutic target in tumor treatment. This study aimed at developing a prognostic gene signature based on hypoxia-related genes (HRGs). EWS patients from GSE17674 in the GEO database were analyzed as a training cohort, and differently expressed HRGs between tumor and normal samples were identified. The univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used in this study. A total of 57 EWS patients from the International Cancer Genome Consortium (ICGC) database were set as the validation cohort. A total of 506 differently expressed HRGs between tumor and normal tissues were identified, among which 52 were associated with the prognoses of EWS patients. Based on 52 HRGs, EWS patients were divided into two molecular subgroups with different survival statuses. In addition, a prognostic signature based on 4 HRGs (WSB1, RXYLT1, GLCE and RORA) was constructed, dividing EWS patients into low- and high-risk groups. The 2-, 3- and 5-years area under the receiver operator characteristic curve of this signature was 0.913, 0.97 and 0.985, respectively. It was found that the survival rates of patients in the high-risk group were significantly lower than those in the low-risk group (p < 0.001). The risk level based on the risk score could serve as an independent clinical factor for predicting the survival probabilities of EWS patients. Additionally, antigen-presenting cell (APC) related pathways and T cell co-inhibition were differently activated in two risk groups, which may result in different prognoses. CTLA4 may be an effective immune checkpoint inhibitor to treat EWS patients. All results were verified in the validation cohort. This study constructed 4-HRGs as a novel prognostic marker for predicting survival in EWS patients.

High expression of PDZ-binding kinase is correlated with poor prognosis and immune infiltrates in hepatocellular carcinoma

Background

PDZ-binding kinase (PBK) encodes a serine/threonine protein kinase related to the dual specific mitogen-activated protein kinase kinase (MAPKK) family. There is evidence that overexpression of this gene is associated with tumorigenesis. However, the role of PBK in hepatocellular carcinoma (HCC) remains unclear. Therefore, we evaluated the prognostic role of PBK and its correlation with immune infiltrates in hepatocellular carcinoma.

Methods

The expression of PBK in pan-cancers was studied by Onconmine and TIMER. The expression of PBK in HCC patients and its relationship with clinicopathological characteristics were analyzed using The Gene Expression Profiling Interactive Analysis (GEPIA), The human protein atlas database (HPA), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic (ROC) curve was used to determine the diagnostic value of PBK in HCC patients. The relationship between PBK and prognosis of HCC was performed by GEPIA and Kaplan Meier plotter web tool. The correlations between the clinical characteristics and overall survival were analyzed by Univariate Cox regression and Multivariate Cox hazards regression to identify possible prognostic factors for HCC patients. LinkedOmics was applied to investigate co-expression associated with PBK and to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The network map of PBK and related genes is constructed by GeneMANIA. Finally, TIMER and TISIDB were used to analyze the correlations between PBK and tumor-infiltrating immune cells.

Results

Multiple database analysis shows that PBK was highly expressed in many types of tumors, including hepatocellular carcinoma, and was significantly related to tumor stage (P=0.0089), age (P=0.0131), and race (P=0.0024) of HCC patients. The receiver operating characteristic (ROC) curve analysis showed that PBK had high diagnostic potential to HCC in GSE76427 (AUC=0.8799), GSE121248 (AUC=0.9224), GSE62232 (AUC=0.9975), and GSE84402 (AUC=0.9541). Multivariate Cox hazards regression showed that high expression of PBK may be an independent risk factor for overall survival in HCC patients (HR = 1.566, 95% CI=1.062–2.311, P= 0.024). The Protein–protein interaction network showed that PBK significantly interacted with LRRC47, ARAF, LGALS9B, TTK, DLG1, and other essential genes. Furthermore, enrichment analysis showed that PBK and co-expressed genes participated in many biological processes, cell composition, molecular functions, and pathways in HCC. Finally, the immune infiltration analysis by TIMER and TISIDB indicated that a significant tightly correlation between PBK and macrophages, neutrophils, as well as chemokines and receptors.

Conclusions

High expression of PBK is significantly correlated with poor survival and immune infiltrates in hepatocellular carcinoma. Our study suggests that PBK can be used as a biomarker of poor prognosis and potential immune therapy target in hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02479-w.

NPM1 is a Novel Therapeutic Target and Prognostic Biomarker for Ewing Sarcoma

Ewing sarcoma (ES) is a cancer that may originate from stem mesenchymal or neural crest cells and is highly prevalent in children and adolescents. In recent years, targeted therapies against immune-related genes have shown good efficacy in a variety of cancers. However, effective targets for immunotherapy in ES are yet to be developed. In our study, we first identified the immune-associated differential hub gene NPM1 by bioinformatics methods as a differentially expressed gene, and then validated it using real time-PCR and western blotting, and found that this gene is not only closely related to the immune infiltration in ES, but also can affect the proliferation and apoptosis of ES cells, and is closely related to the survival of patients. The results of our bioinformatic analysis showed that NPM1 can be a hub gene in ES and an immunotherapeutic target to reactivate immune infiltration in patients with ES. In addition, treatment with NPM1 promoted apoptosis and inhibited the proliferation of ES cells. The NPM1 inhibitor NSC348884 can induce apoptosis of ES cells in a dose-dependent manner and is expected to be a potential therapeutic agent for ES.