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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

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

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Data availability

Publicly available datasets were analyzed in this study. This data can be found here: The datasets analyzed in the current study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163108, GSE163108, n = 5), TCGA database (https://portal.gdc.cancer.gov/, TCGA-GBM cohort, n = 170), CGGA database (http://www.cgga.org.cn/, CGGA.mRNAseq_325, n = 139; CGGA.mRNAseq_693, n = 249), UCSC database (https://xena.ucsc.edu/, GTEX-cohort, Brain-Frontal Cortex (BA9), Brain-Cortex, Brain-Anterior cingulate cortex (BA24), n = 290), TIDE database (http://tide.dfci.harvard.edu/login/), TCIA database (https://www.tcia.at/home), HPA database (https://www.proteinatlas.org/), and CellMarker database (http://xteam.xbio.top/CellMarker/).

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Acknowledgements

We sincerely appreciate the great work of GEO, TCGA, and CGGA, as well as other sites such as UCSC, CellMarker, TIDE, TCIA, and HPA for collating the data such that they are easily and quickly available to us.

Funding

This work was funded by the Zhejiang Provincial People’s Hospital Talent Introduction Project (No. C-2021-QDJJ03-01).

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Author notes

  1. Jiahe Hu and Lei Xu contributed equally to this work.

Authors and Affiliations

  1. Department of Digestive Internal Medicine, Harbin Medical University Cancer Hospital, Harbin, China

    Jiahe Hu, Yanan Sun, Xiaoling Zhang, Yuxin Zhou, Rongfang Wang, Haoxin Zhang, Ruishu Mou, Xinlian Du, Xuedong Li & Rui Xie

  2. Cancer Center, Department of Neurosurgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Zhejiang, Hangzhou, China

    Lei Xu, Nan Wang, Jiheng Zhang, Chengyun Yang & Shaoshan Hu

  3. The Heilongjiang Key Laboratory of Anesthesia and Intensive Care Research, Harbin Medical University, Harbin, China

    Wenchao Fu

  4. Materials Science and Engineering, Zhejiang University of Technology, Zhejiang, Hangzhou, China

    Chengyun Yang

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  1. Jiahe Hu
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Contributions

JH and RX conceived and designed the study. LX performed the experiment. JH and WF were responsible for data analysis and checking. YS, NW, and JZ collated the data. CY and XZ were responsible for the resources. YZ, RW, and HZ designed computer programs. RM, XD, and XL developed the methodology. JH wrote the original draft of the manuscript, which was reviewed and revised by RX and SH. RX and SH supervised the study. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

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Correspondence to Shaoshan Hu or Rui Xie.

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Hu, J., Xu, L., Fu, W. et al. 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. Funct Integr Genomics 23, 286 (2023). https://doi.org/10.1007/s10142-023-01217-7

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