Establishment and validation of an immune-based prognostic score model in glioblastoma.

Immune escape is one of the landmark features of glioblastoma (GBM). Immunotherapy is undoubtedly a revolution in the field of tumor treatment, especially the application of immune checkpoint inhibitors and CAR-T cells, which have achieved amazing results in fighting against cancer. This study aimed to establish a TP53-related immune-based score model to improve the prognostic of GBM by investigating the gene mutations and the immune landscape of GBM. Data were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed genes (DEGs) analysis between the TP53 mutated (TP53MUT) and wild-type (TP53WT) GBM patients was conducted. The CIBERSORT algorithm was applied to evaluate the proportion of immune cell types and RNA sequencing (RNA-seq) data from the TCGA and CGGA were used as discovery and validation cohorts, respectively, to build and validate an immune-related prognostic model (IPM). Genes in the IPM model were first screened by univariate Cox analysis, then filtered by the least absolute shrinkage and selection operator (LASSO) Cox regression method to eliminate collinearity among DEGs. A nomogram was finally established and evaluated by combining both the IPM and other clinical factors. PTEN was the top most mutated gene in GBM patients (118/393), followed by TP53 (116/393). 332 immune-related genes were identified and the immune response in the TP53WT group was remarkably greater than in the TP53MUT group. The final IPM model composed three immune-related genes: IPM risk score = (0.392 × S100A8 expression) + (0.174 × CXCL1 expression) + (0.368 × IGLL5 expression), significantly correlated with the overall survival (OS) of GBM in the stratified TP53 status subgroups and was an independent prognostic variate for GBM. By integrating the IPM and clinical characteristics, a nomogram was generated to facilitate clinical utilization, with the results suggesting that it has better predictive performance for GBM prognosis than the IPM. The IPM model can identify patients at high-risk and can be combined with other clinical factors to estimate the OS of GBM patients, demonstrating that it is a promising biomarker to optimize the prognosis of GBM.

Qin Z ，Zhang X ，Chen Z ，Liu N ... -  《-》

Systematic identification, development, and validation of prognostic biomarkers involving the tumor-immune microenvironment for glioblastoma.

Gliomas are infiltrative neoplasms with a highly invasive nature. Due to its distinct genomic, genetic and epigenetic features, the immune prognostic signature (IPS) and immune microenvironment of glioblastoma (GBM) merit further research. We aimed to explore prognosis-related immune genes and develop an IPS model for predicting prognosis in GBM. RNA-sequencing data, as well as clinical information, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) public cohorts were analyzed. To develop the IPS, least absolute shrinkage and selection operator (LASSO) Cox analysis was performed for immune-related genes that were differentially expressed between GBM and normal tissues. Then, interaction effects of the IPS on the immune microenvironment were systematically analyzed; the precise prognostic model was developed based on the IPS and clinical data and was then further validated. A total of 21 immune prognostic genes were identified based on GBM microenvironment status. An 8-gene IPS was established, and the GBM patients were effectively stratified into low- and high-risk groups in the TCGA cohort as a training set. Univariate and multivariate Cox analyses revealed that IPS was an independent prognostic factor, and the prognostic performance of individual IPS genes was systematically illustrated. In addition, a comprehensive and novel nomogram model was initially established to estimate overall survival in TCGA-GBM patients, and high-risk patients had higher levels of dendritic cell and neutrophil infiltration. Furthermore, the nomogram model was developed and validated in the CGGA validation set. The low-risk IPS was linked to a stronger response to anti-PD-L1 immunotherapy and clinical advantages in the IMvigor210 cohort. This novel IPS with promising biomarkers classifies GBM patients into subgroups with distinct clinical outcomes and immunophenotypes. Our findings and this resource may help to characterize the immune microenvironment, inform cancer immunotherapy and facilitate the development of precision immuno-oncology.

Zhao B ，Wang Y ，Wang Y ，Chen W ，Liu PH ，Kong Z ，Dai C ，Wang Y ，Ma W ... -  《-》

Immune landscape and a promising immune prognostic model associated with TP53 in early-stage lung adenocarcinoma.

TP53 mutation, one of the most frequent mutations in early-stage lung adenocarcinoma (LUAD), triggers a series of alterations in the immune landscape, progression, and clinical outcome of early-stage LUAD. Our study was designed to unravel the effects of TP53 mutation on the immunophenotype of early-stage LUAD and formulate a TP53-associated immune prognostic model (IPM) that can estimate prognosis in early-stage LUAD patients. Immune-associated differentially expressed genes (DEGs) between TP53 mutated (TP53MUT ) and TP53 wild-type (TP53WT ) early-stage LUAD were comprehensively analyzed. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis identified the prognostic immune-associated DEGs. We constructed and validated an IPM based on the TCGA and a meta-GEO composed of GSE72094, GSE42127, and GSE31210, respectively. The CIBERSORT algorithm was analyzed for assessing the percentage of immune cell types. A nomogram model was established for clinical application. TP53 mutation occurred in approximately 50.00% of LUAD patients, stimulating a weakened immune response in early-stage LUAD. Sixty-seven immune-associated DEGs were determined between TP53WT and TP53MUT cohort. An IPM consisting of two prognostic immune-associated DEGs (risk score = 0.098 * ENTPD2 expression + 0.168 * MIF expression) was developed through 397 cases in the TCGA and further validated based on 623 patients in a meta-GEO. The IPM stratified patients into low or high risk of undesirable survival and was identified as an independent prognostic indicator in multivariate analysis (HR = 2.09, 95% CI: 1.43-3.06, p < 0.001). Increased expressions of PD-L1, CTLA-4, and TIGIT were revealed in the high-risk group. Prognostic nomogram incorporating the IPM and other clinicopathological parameters (TNM stage and age) achieved optimal predictive accuracy and clinical utility. The IPM based on TP53 status is a reliable and robust immune signature to identify early-stage LUAD patients with high risk of unfavorable survival.

Wu C ，Rao X ，Lin W 《Cancer Medicine》

A TP53-based immune prognostic model for muscle-invasive bladder cancer.

Li H ，Lu H ，Cui W ，Huang Y ，Jin X ... -  《Aging-US》

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing.

Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Hu J ，Xu L ，Fu W ，Sun Y ，Wang N ，Zhang J ，Yang C ，Zhang X ，Zhou Y ，Wang R ，Zhang H ，Mou R ，Du X ，Li X ，Hu S ，Xie R ... -  《-》