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Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39276278
1302
10.1007/s12672-024-01302-8
Research
Single-cell profiling uncovers proliferative cells as key determinants of survival outcomes in lower-grade glioma patients
Peng Jianming 1
Zhang Qing 2
Zhu Xiaofeng 3
Yan Zhu 1332080365@qq.com

4
Zhu Meng ZHUMENG__1209@163.com

5
1 grid.495274.9 0000 0004 1759 9689 School of Medicine, Yangzhou Polytechnic College, Yangzhou, China
2 https://ror.org/00xpfw690 grid.479982.9 0000 0004 1808 3246 Department of Hepatology, Huai’an No.4 People’s Hospital, Huai’an, China
3 https://ror.org/00xpfw690 grid.479982.9 0000 0004 1808 3246 Department of Neurology, Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu China
4 grid.452743.3 0000 0004 1788 4869 Emergency Medicine Department, Huai’an Hospital Affiliated to Yangzhou University (The Fifth People’s Hospital of Huai’an), Huaian, China
5 grid.417303.2 0000 0000 9927 0537 Department of Geriatrics, Affiliated Huai’an No.2 Hospital of Xuzhou Medical University, Huai’an, Jiangsu China
14 9 2024
14 9 2024
12 2024
15 4453 7 2024
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Lower-grade gliomas (LGGs), despite their generally indolent clinical course, are characterized by invasive growth patterns and genetic heterogeneity, which can lead to malignant transformation, underscoring the need for improved prognostic markers and therapeutic strategies. This study utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq to identify a novel cell type, referred to as "Prol," characterized by increased proliferation and linked to a poor prognosis in patients with LGG, particularly under the context of immunotherapy interventions. A signature, termed the Prol signature, was constructed based on marker genes specific to the Prol cell type, utilizing an artificial intelligence (AI) network that integrates traditional regression, machine learning, and deep learning algorithms. This signature demonstrated enhanced predictive accuracy for LGG prognosis compared to existing models and showed pan-cancer prognostic potential. The mRNA expression of the key gene PTTG1 from the Prol signature was further validated through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our findings not only provide novel insights into the molecular and cellular mechanisms of LGG but also offer a promising avenue for the development of targeted biomarkers and therapeutic interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01302-8.

Keywords

Artificial intelligence
Single-cell RNA sequencing
Immunotherapy
Prognosis
issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Gliomas represent neoplastic diseases originating from glial cells and constitute the most prevalent intracranial malignancies [1]. Per criteria, gliomas comprise lower-grade gliomas (LGGs, grades II and III) and glioblastomas (GBM, grade IV) [2]. Compared to GBM, LGGs typically manifest an indolent clinical course as slow-growing precursor lesions [3]. However, the invasive growth pattern of LGGs often precludes complete surgical resection, conferring a high risk of local recurrence and malignant transformation into secondary high-grade gliomas [4]. Given the genetic heterogeneity underlying LGGs, patients with similar clinical stage may exhibit divergent outcomes [5]. Recent bulk transcriptomic profiling studies have been limited in their ability to account for interpatient heterogeneity in LGG. A deeper understanding of the molecular and cellular underpinnings of LGG is therefore imperative, as it may unveil critical biomarkers and avenues for therapeutic development.

The trailblazing and powerful method of single-cell RNA sequencing (scRNA-seq) facilitates the extraction of transcriptomic insights at a singular cell level [6]. The broad-scale implementation of this technique permits a precise elucidation of cellular populations within neoplastic tissue, populations possibly concealed when utilizing the more traditional bulk sequencing techniques that are influenced by dominant cell types [7]. With continual progressions in scRNA-seq methodologies, their deployment for the investigation of tumor tissue like LGG has witnessed a steady rise [8, 9], nevertheless, identification of uncommon cellular subsets remains an imposing hurdle. It becomes integral, hence, to amalgamate data from diverse scRNA-seq datasets to augment our proficiency in accurately demystifying these hard-to-find cell populations.

We hypothesized that consolidating scRNA-seq data from multiple cohorts may elucidate presently unrecognized cellular subsets associated with LGG that influence patient prognosis within the malignant tissue. In this study, we delineated and profiled a highly proliferative LGG cell type designated "Prol", and demonstrated that expanded representation of Prol cells within tumors correlated with inferior patient prognosis. Additionally, we effectively implemented an artificial intelligence (AI) network to develop a signature derived from Prol cells marker genes capable of precisely determining Prol cell abundance and forecasting clinical outcomes in LGG patients. Our findings may uncover novel facets of LGG pathobiology, holding promise for enabling enhanced patient subclassification and targeted therapeutics for individuals with this malignancy.

Methods

Collection and pre-processing of scRNA RNA-seq data

scRNA-seq data from two cohorts containing primary 10 LGG samples was downloaded [8, 9]. Single cells of poor quality, defined as those containing < 500 expressed genes, > 20% mitochondrial transcripts, or > 50% ribosomal transcripts, were filtered out and excluded from downstream analyses. A total of 55,207 cells were analyzed using the Seurat R package [10]. Gene expression levels were normalized using the "LogNormalize" method with a scale factor of 10,000 in Seurat. Highly variable genes were identified (n = 2000) and their expression values were scaled prior to conducting principal component analysis (PCA). Batch effects were regressed out using the Harmony R package [11]. Data analysis was performed using functions from the Harmony and Seurat R packages, including NormalizeData, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, FindClusters, and RunUMAP. Cell cycle phase scoring was done with the CellCycleScoring function in Seurat [12].

Collection and pre-processing of bulk RNA-seq data

Gene expression data and clinical data of patients were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Sequence Read Archive (SRA) and Chinese Glioma Genome Atlas (CGGA) databases. A total of 1229 samples across four datasets were analyzed, comprised of 506 LGG patients from the TCGA [13], 146 patients from the CGGA1 [14], 257 patients from the CGGA2 [15], 45 patients from the CGGA3 [16], 169 patients from the GSE108474 [17], and 106 patients from the GSE16011 [18].

Our study incorporated 1152 GTEx brain tissue samples from the UCSC Xena database (https://xena.ucsc.edu/). Data was acquired from three diverse cancer cohorts experiencing immunotherapy. Among these, the PRJNA482620 cohort comprises 34 GBM patients [19]. The GSE78220 cohort includes 27 melanoma patients [20], while the GSE91061 cohort comprises 51 melanoma patients [21]. A total of 25 melanoma patients were part of the GSE100797 cohort [22], whereas the phs000452 cohort included 152 melanoma patients [23]. The Braun cohort consisted of 311 renal cell carcinoma (RCC) patients [24], and the GSE93157 cohort had 22 non-small cell lung cancer (NSCLC) patients [25].

Gene expression data across all cohorts were log2 transformed, z-score normalized, and batch effects were removed using the surrogate variable analysis (SVA) algorithm [26].

Quantification of cell types in bulk RNA-seq data

The BisqueRNA R package [27] served in the quantification of cell type abundances gleaned from bulk RNA-seq data. Grounded on scRNA data, an approach utilizing PCA was employed to segregate the prominent eight cell types in the LGG for ensuing analyses.

Development of signatures using an artificial intelligence network

A new network based on artificial intelligence (AI) was constructed, incorporating 333 algorithmic combinations, which intermingled 23 algorithms stemming from conventional regression, machine learning, and deepl learning methodologies. This ensemble included algorithms such as Cox stepwise method, random survival forest (RSF), supervised principal components (SuperPC), oblique random survival forests (obliqueRSF), GLMBoost, BlackBoost, Rpart, Survreg, Ranger, Ctree, LASSO, plsRcox, survival-SVM, Ridge, Enet, DeepHit, DeepSurv, CoxTime, XGBoost, Coxboost, CForest, Boruta, and VSOLassoBag. The processes of establishing the signature were this sequence: (1) Prognostic genes were outlined utilizing univariate Cox regression within the TCGA cohort. (2) The exploratory discovery of the original signature was AI network-based within the TCGA cohort. (3) The network subsequently underwent verification within five validation cohorts. (4) Via the application of Harrell's concordance index (C-index), the performance of each model throughout all cohorts was gauged. The optimal combination was selected as that with the maximum mean C-index. We uploaded the modeling code to GitHub (https://github.com/Pengjianming1988/AI_netiwork). All relevant modeling parameters were thoroughly documented within the code.

Functional annotation of the Prol signature

We performed gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) using the MSigDB database with the GSVA [28] and clusterprofiler [28] R packages. We also used Metascape for enrichment analysis [29].

Cell line culture and qRT-PCR

Hs683 and MOG-G-UVW were obtained from the Institute of Biochemistry and Cell Biology of the Chinese Academy of Sciences (Shanghai, China). The cultivation of Hs683 and MOG-G-UVW, both human LGG cells, along with human astrocytes (NHA), was undertaken in Dulbecco's Modified Eagle's Medium (DMEM, Gibco, C11995500BT, Canada), complemented with 10% of fetal bovine serum (FBS, Gibco, 10091148, Canada), and 1 × penicillin/streptomycin (Gibco, 15,140–122, Canada). These cultures were sustained within a CO2 incubator (TFS3111, USA) at a constant temperature of 37 °C under 5% CO2. RNA was procured from both cells and tissues utilizing Trizol reagent (Invitrogen), with reverse transcription executed using SuperScript II reverse transcriptase (Invitrogen), strictly adhering to the manufacturer's guidelines. The primer sequences listed below were utilized for PTTG1 and β-actin:

PTTG1 Forward: 5′-GCTTTGGGAACTGTCAACAGAGC-3′; PTTG1 Reverse: 5′-CTGGATAGGCATCATCTGAGGC-3’.

β-actin Forward: 5′-CACCCAGCACAATGAAGATCAAGAT-3′; β-actin Reverse: 5′-CCAGTTTTTAAATCCTGAxGTCAAGC-3.

Statistical analysis

Wilcoxon test application enabled the evaluation of quantitative data. Survival curves were materialized via the usage of Survival and Survminer packages within R. We carried out univariate and multivariate Cox regression analyses to determine the Prol signature's independence relative to other cell types. Receiver Operator Characteristic curve (ROC) analysis was employed to assess the predictive sensitivity and specificity pertaining to survival. The criterion for statistical significance was a P value less than 0.05, barring specific exceptions. All analytical proceedings were performed using R software version 4.2.3.

Results

Detection of a novel cell type associated with cell cycle using scRNA-seq data

We initially sought to determine the top 1000 genes with the prognostic genes with the smallest p-values, in the TCGA bulk RNA-sequencing cohort. Pathway enrichment analyses were then conducted on these identified prognostic genes using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. Our results revealed that these prognostic genes were significantly enriched in cell cycle and mitosis pathways (Fig. 1A and B). We hypothesized that there may exist a subset of cells characterized by enrichment in cell cycle and mitotic pathways. Thus, we obtained 55,207 cells scRNA-seq data from two databases, and utilized clustering analysis to identify a total of 45 subcell-types (Fig. 1C). These 45 subcell-types were subsequently aggregated into 8 principal cell types, as discerned by known marker gene expression (Fig. 1D and Supplementary Fig. 1A-1G). We identified a previously uncharacterized cell subpopulation exhibiting high proliferative potential, which we termed “Prol”. KEGG and GO enrichment analysis revealed the marker genes of Prol cells were highly enriched for cell cycle and mitotic pathways (Fig. 1E). To further characterize the proliferative capacity of Prol cells, we evaluated cell cycle phase scores by examining the average expression of phase-specific gene signatures. Our analysis revealed that the majority of Prol cells were in S and G2M phases of the cell cycle (Supplementary Fig. 1H). Furthermore, Prol cells exhibited significantly higher cell cycle phase scores for S and G2M phases compared to other cell populations (Supplementary Fig. 1I and 1 J).Fig. 1 Unveiling and profiling a proliferative (Prol) cell type. A-B Kyoto Encyclopedia of Genes and Genomes (KEGG) (A) and Gene Ontology (GO) (B) enrichment analysis of prognosis-related genes in low-grade glioma (LGG). C-D The visualization of 55,207 cells using Uniform Manifold Approximation and Projection (UMAP) revealed the integration of datasets to remove the batch effect. Clusters were categorized into 45 subtypes (C) and 8 major cell types (D). E Pathway gene set enrichment analysis of the marker genes for each subcell-type

Elevated Prol cell abundance correlated with poor prognosis

To evaluate the clinical relevance of the Prol cell type on survival outcomes, we examined its influence on overall survival (OS) and progression-free survival (PFS) in 510 LGG patients from the TCGA bulk RNA-seq cohort. First, we found that most of the Prol marker genes showed the greatest hazard ratios for OS and PFS when compared to marker genes from all other cell types (Fig. 2A and F). The LGG patients were stratified into low and high Prol cell proportion groups based on the optimal cut-off value. Kaplan–Meier survival analysis revealed that elevated proportions of Prol cells were significantly associated with reduced OS and PFS in LGG patients (p < 0.05) (Fig. 2B and G). The area under the receiver operating characteristic curve (AUC) for the abundance of Prol cells in predicting 1, 3, and 5-year OS were all greater than 0.7 (Fig. 2C). The AUC for the abundance of Prol cells in predicting 1, 3, and 5-year PFS were 0.668, 0.578 and 0.541 (Fig. 2H). We conducted a Cox regression, adjusting for the other cell type abundance, and the results showed that Prol cell abundance was an independent prognostic factor for both OS and PFS (all p < 0.05) (Fig. 2D, E and I, J). We next verified the efficiency of Prol cell abundance in multiple immunotherapy datasets. GBM, melanoma, LGG and NSCLC patients with high Prol cell abundance presented worse survival outcomes (Supplementary Fig. 2). The implications of these observations point towards a critical function of Prol cells in predicting the prognostic outcomes for patients.Fig. 2 The prognostic value of the Prol cells in LGG. A The hazardratio (HR) values for cell-type marker genes, derived from a Cox proportional hazards regression of their expression. Survival analyses were conducted for overall survival (OS). B Kaplan–Meier survival curves of OS for Prol cell abundance. C. ROC curves at 1-, 3- and 5-year of OS for Prol cell abundance. D-E Univariate Cox (D) and multivariate Cox (E) of OS for each cell types. F The HR values for cell-type marker genes, derived from a Cox proportional hazards regression of their expression. Survival analyses were conducted for progression-free survival (PFS). G Kaplan–Meier survival curves of PFS for Prol cell abundance. H ROC curves at 1-, 3- and 5-year of PFS for Prol cell abundance. I, J Univariate Cox (I) and multivariate Cox (J) of PFS for each cell types

Construction of the Prol signature

To facilitate the quantification of Prol cell abundance in LGG for clinicians, we developed a Prol signature grounded on a AI network. Our initial action was conducting univariate Cox analyses to choose the most distinctive Prol cell markers (log2 fold change > 0.75, adjusted P = 0) that projected significance in prognosis (P < 1*10-10e). As a result, 333 algorithm combinations fitted the TCGA group, following which verification groups underwent a computation of C-index. A peak mean C-index, tallying at 0.707, was observed via the integration of Boruta and obliqueRSF (Fig. 3A). Patient stratification into high and low-risk categories was pursued based on the optimal cutoff. The high-risk classification denoted a notably poorer OS relative to the low-risk group in all sets (P < 0.05) (Fig. 3B–G). Additionally, the reliability of the Prol signature were underscored by ROC curves throughout all sets (Fig. 3B–G). Therefore, the prognostic outcome prediction in LGG holds remarkable potential via the Prol signature.Fig. 3 Development and validation of an artificial intelligence network. A Evaluation and C-index computation for 333 prediction models across all datasets. B–G Kaplan–Meier survival analysis (left) and receiver operating characteristic (ROC) (right) curves for OS in the TCGA (B), CCGA1 (C), CCGA2 (D), CCGA3 (E), GSE108474 (F) and GSE16011 (G) cohorts

Comparison of prognostic signatures

Examining all datasets, the Prol signature outperformed age, gender, grade, histological type, status of IDH mutation, 1p19q codeletion status, and MGMTp methylation status, as indicated by the C-index (Fig. 4A). A comparison was made between the Prol signature and 19 other signatures within cohorts from TCGA, GSE16011, GSE108474, CCGA1, CCGA2, and CCGA3 (Fig. 4B). Among all signatures evaluated, the Prol signature acquired the highest C-index within the all cohorts (Fig. 4B). This highlights the potential of our Prol signature as a robust prognostic alternative for individuals diagnosed with LGG.Fig. 4 Comparison between the Prol signature and other models. A The C-index of the Prol signature and other clinical factors in the TCGA, CCGA1, CCGA2, CCGA3, GSE108474 and GSE16011 cohorts. B The C-index of the Prol signature and other models for the TCGA, CCGA1, CCGA2, CCGA3, GSE108474 and GSE16011 cohorts

Universal application of the Prol signature across 33 cancer types

The Prol signature score, calculated for 33 distinct cancers derived from TCGA, is referenced in Fig. 5A. These were divided into high and low-risk categories. An unequivocal link was found between rising Prol signature scores and worsening prognosis universally in all 16 cancers, as shown in Fig. 5B. Such findings underscore the possibility of Prol cell proliferation serving as a prevalent biological process linked to the advancement of cancer.Fig. 5 Panorama of Prol signature in cancers. A Prol signature score for 33 cancers. B Kaplan–Meier curves of OS according to the Prol signature in 33 cancers

Potential biological peculiarities of the Prol signature

We conducted a pathway analysis of the Prol signature to revel potential biological peculiarities of the Prol signature. It was revealed that the score had a potent correlation with various tumor-inducing pathways, encompassing G2M checkpoint, angiogenesis, E2F targets, and glycolysis (Fig. 6A). Noticeable disparities were noticed within the proliferation-associated pathways across both risk groups (Fig. 6B). The differentially expressed genes (DEGs) between the low-risk and high-risk groups were densely concentrated around immune and proliferation-related pathways (Fig. 6C). In addition, GSEA of KEGG terms indicated that the high-risk cluster had an augmented presence of attributes related to ECM-receptor interaction, cell cycle, P53 signaling pathway, and DNA replication (Fig. 6D). Such findings serve to validate the signature's stability and dependability.Fig. 6 Biological peculiarities of the Prol signautre in the TCGA dataset. A Outlining the biological characteristics of two groups based on the Prol signautre using MsigDB-based Gene Set Variation Analysis (GSVA). B Creation of a t-distributed Stochastic Neighbor Embedding (t-SNE) plot to illustrate differences in pathway activity between two risk groups based on GO and KEGG terms. C Metascape-based enrichment analysis of differentially expressed genes between two risk groups. D Gene Set Enrichment Analysis (GSEA) for GO and KEGG terms to investigate biological pathways associated with the Prol signature

Verification of gene expression pertaining to the Prol signature

Through analysis of the expression of genes associated with the Prol signature in LGG and non-malignant samples, drawing from the TCGA and GTEx databases, we identified the striking overexpression of all Prol signature genes in the LGG samples (P < 0.001) (Fig. 7A). Of these, PTTG1 emerged as the pivotal gene according to obliqueRSF. Further affirmation of PTTG1's mRNA expression was pursued via qRT-PCR. The qRT-PCR examination revealed an appreciable escalation in PTTG1 expression in HS683 and MOG-G-UVW compared to NHA (P < 0.05) (Fig. 7B). Such findings suggest that the abnormal expression of these genes, more specifically PTTG1, could hold significance in the tumorigenesis of LGG.Fig. 7 Validation of expression of genes from the Prol signature. A Differential expression of 9 genes from the Prol signature in normal and LGG samples. C qRT-PCR analysis of PTTG1. *p < 0.05, **p < 0.01, and ***p < 0.001

Discussion

Through this research, we employed scRNA-seq data acquired from LGG tissues to develop an exhaustive transcriptomics blueprint. This allowed the decomposion of cell types in bulk RNA-sequencing data gathered from the TCGA cohort. A cell type, “termed” Prol, was identified, demonstrating considerable proliferation. The presence of Prol correlated with significantly worse survival rates. Finally, we devised a Prol signature, utilizing an innovative AI network to estimate the abundance of Prol cells and enhance prognostic inference.

The invaluable TCGA endeavor, a considerable resource for oncology professionals, facilitated us in identifying cell types related to LGG and their clinical implications with the use of our scRNA-seq datasets. Prior research has established the linkages between several marker genes, distinct to Prol cells, and poor survival outcomes [30–32]. Yet, the composition within Prol cells encompasses both previously identified LGG genes and new targets, including HIST1H4C, HMGN2, and GGH, thus requiring further examination. Utilizing our approach, we were successful in pinpointing over a hundred LGG marker genes, which can provide irreplaceable comprehension of the complex biological processes involved in LGG for future research pursuits.

Elevating our understanding of factors influencing the survival of LGG patients, amid a poor prognosis, we unearthed the potential functionality of Prol cells as a noteworthy indicator for predicting outcomes, particularly under the context of immunotherapy interventions. By quantifying the abundance of Prol cells in clinical settings, physicians may gain a more accurate prediction of how patients will respond to immunotherapy, which may become a cornerstone in cancer treatment strategies. This quantification could potentially guide treatment decisions, allowing for a more personalized and effective approach to therapy. Moreover, the discovery of the prognostic value of Prol cells opened up new avenues for therapeutic development. The targeted manipulation of Prol cells could represent a novel strategy to enhance the efficacy of immunotherapy. The incorporation of cell type markers expanded our comprehension of cancer while complementing existing clinical makers. The mark of Prol cells may beckon the advent of advanced prognostic methods rooted in gene expression. As RNA sequencing has come of age, clinical laboratories can now discern gene expression patterns indicative of prognosis [33]. An AI network was engineered to contruct a Prol signature anchored on the expression profiles of Prol cells marker genes. This network synthesized conventional regression, machine learning, and deep learning algorithms, delivering a comprehensive spectrum of algorithms. Its predictive effectiveness outstripped the algorithmic combinations evolved from antecedent machine learning frameworks [34, 35]. The best algorithm combination was Boruta and obliqueRSF, an aspect bypassed in earlier studies [34, 35]. The application of the AI network for model refinement and betterment of generalization strength proved advantageous. An assessment through ROC and C-index disclosed that the Prol signature effectually anticipates outcomes in all six cohorts, suggesting its clinical application potentiality. Additionally, the Prol signature demonstrated pan-cancer prognostic capacity, suggesting analogous cell types might exist in these tumors requiring further exploration.

Despite the progress this study makes in understanding the clinical of Prol cells, it doesn't come without certain constraints. The cohorts showcased heterogeneity owed to the use of diverse sequencing or microarray platforms, a disparity that we attempted to align through standard normal transformations, albeit with partial effectiveness. Moreover, due to our reliance on retrospective samples, attaining future corroboration in a prospective, larger cohort is necessary. Lastly, it would benefit the study to isolate this tumor subcluster and examine its biological functionality for a detailed insight into the molecular mechanisms behind tumor development and potential discovery of novel therapeutic targets.

Conclusion

Overall, integration of scRNA-seq with bulk RNA-seq data facilitated the discovery of a novel cell population, distinguished by its unique cell cycle stage and prognostic relevance. This investigation may establish a foundation for advanced insights into the molecular and cellular mechanisms underlying LGG, while also advancing the development of specific biomarkers and therapeutic targets.

Supplementary Information

Additional file 1: Figure 1. A-G. Distinct marker genes of 8 main celltypes. H. Coloring of cells based on the inferred cell cycle phase from single-cell RNA sequencing (scRNA-seq) data. I-J. S phase score (I) and G2M phase score (J) for each cell types.

Additional file 2: Figure 2. Kaplan-Meier survival curves of the Prol cell abundance regarding OS in multiple immunotherapy datasets.

Acknowledgements

We thank all the participants involved in this study.

Author contributions

M.Z. and Z.Y.: conceptualization; J.P., Q.Z., and X.Z.: data curation; J.P. and X.Z.: formal analysis; M.Z., and Q.Z.: methodology; M.Z., Z.Y., and X.Z.: project administration; M.Z., X.Z., and J.P.: resources; J.P., Q.Z., and X.Z.:writing – original draft; All authors have read and agreed to the published version of the manuscript.

Data availability

The data used to support the findings of this study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jianming Peng, Qing Zhang and Xiaofeng Zhu contributed equally to this work.
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References

1. Youssef G Miller JJ Lower grade gliomas Curr Neurol Neurosci Rep 2020 20 7 21 10.1007/s11910-020-01040-8 32444979
Youssef G, Miller JJ. Lower grade gliomas. Curr Neurol Neurosci Rep. 2020;20(7):21.32444979 10.1007/s11910-020-01040-8
2. Louis DN Ohgaki H Wiestler OD Cavenee WK Burger PC Jouvet A Scheithauer BW Kleihues P The 2007 WHO classification of tumours of the central nervous system Acta Neuropathol 2007 114 2 97 109 10.1007/s00401-007-0243-4 17618441
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007;114(2):97–109.17618441 10.1007/s00401-007-0243-4
3. Suzuki H Aoki K Chiba K Sato Y Shiozawa Y Shiraishi Y Shimamura T Niida A Motomura K Ohka F Mutational landscape and clonal architecture in grade II and III gliomas Nat Genet 2015 47 5 458 468 10.1038/ng.3273 25848751
Suzuki H, Aoki K, Chiba K, Sato Y, Shiozawa Y, Shiraishi Y, Shimamura T, Niida A, Motomura K, Ohka F, et al. Mutational landscape and clonal architecture in grade II and III gliomas. Nat Genet. 2015;47(5):458–68.25848751 10.1038/ng.3273
4. Ferracci FX Michaud K Duffau H The landscape of postsurgical recurrence patterns in diffuse low-grade gliomas Crit Rev Oncol Hematol 2019 138 148 155 10.1016/j.critrevonc.2019.04.009 31092371
Ferracci FX, Michaud K, Duffau H. The landscape of postsurgical recurrence patterns in diffuse low-grade gliomas. Crit Rev Oncol Hematol. 2019;138:148–55.31092371 10.1016/j.critrevonc.2019.04.009
5. Qiu X Tian Y Xu J Jiang X Liu Z Qi X Chang X Zhao J Huang J Development and validation of an immune-related long non-coding RNA prognostic model in glioma J Cancer 2021 12 14 4264 4276 10.7150/jca.53831 34093827
Qiu X, Tian Y, Xu J, Jiang X, Liu Z, Qi X, Chang X, Zhao J, Huang J. Development and validation of an immune-related long non-coding RNA prognostic model in glioma. J Cancer. 2021;12(14):4264–76.34093827 10.7150/jca.53831
6. Ziegenhain C Vieth B Parekh S Reinius B Guillaumet-Adkins A Smets M Leonhardt H Heyn H Hellmann I Enard W Comparative analysis of single-cell RNA sequencing methods Mol Cell 2017 65 4 631 643.e634 10.1016/j.molcel.2017.01.023 28212749
Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, Leonhardt H, Heyn H, Hellmann I, Enard W. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. 2017;65(4):631-643.e634.28212749 10.1016/j.molcel.2017.01.023
7. Suvà ML Tirosh I Single-cell RNA sequencing in cancer: lessons learned and emerging challenges Mol Cell 2019 75 1 7 12 10.1016/j.molcel.2019.05.003 31299208
Suvà ML, Tirosh I. Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol Cell. 2019;75(1):7–12.31299208 10.1016/j.molcel.2019.05.003
8. Abdelfattah N Kumar P Wang C Leu JS Flynn WF Gao R Baskin DS Pichumani K Ijare OB Wood SL Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target Nat Commun 2022 13 1 767 10.1038/s41467-022-28372-y 35140215
Abdelfattah N, Kumar P, Wang C, Leu JS, Flynn WF, Gao R, Baskin DS, Pichumani K, Ijare OB, Wood SL, et al. Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat Commun. 2022;13(1):767.35140215 10.1038/s41467-022-28372-y
9. Sigaud R Albert TK Hess C Hielscher T Winkler N Kocher D Walter C Münter D Selt F Usta D MAPK inhibitor sensitivity scores predict sensitivity driven by the immune infiltration in pediatric low-grade gliomas Nat Commun 2023 14 1 4533 10.1038/s41467-023-40235-8 37500667
Sigaud R, Albert TK, Hess C, Hielscher T, Winkler N, Kocher D, Walter C, Münter D, Selt F, Usta D, et al. MAPK inhibitor sensitivity scores predict sensitivity driven by the immune infiltration in pediatric low-grade gliomas. Nat Commun. 2023;14(1):4533.37500667 10.1038/s41467-023-40235-8
10. Satija R Farrell JA Gennert D Schier AF Regev A Spatial reconstruction of single-cell gene expression data Nat Biotechnol 2015 33 5 495 502 10.1038/nbt.3192 25867923
Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495–502.25867923 10.1038/nbt.3192
11. Korsunsky I Millard N Fan J Slowikowski K Zhang F Wei K Baglaenko Y Brenner M Loh PR Raychaudhuri S Fast, sensitive and accurate integration of single-cell data with Harmony Nat Methods 2019 16 12 1289 1296 10.1038/s41592-019-0619-0 31740819
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–96.31740819 10.1038/s41592-019-0619-0
12. Tirosh I Izar B Prakadan SM Wadsworth MH 2nd Treacy D Trombetta JJ Rotem A Rodman C Lian C Murphy G Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq Science 2016 352 6282 189 196 10.1126/science.aad0501 27124452
Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189–96.27124452 10.1126/science.aad0501
13. Brat DJ Verhaak RG Aldape KD Yung WK Salama SR Cooper LA Rheinbay E Miller CR Vitucci M Morozova O Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas N Engl J Med 2015 372 26 2481 2498 10.1056/NEJMoa1402121 26061751
Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, Cooper LA, Rheinbay E, Miller CR, Vitucci M, Morozova O, et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. 2015;372(26):2481–98.26061751 10.1056/NEJMoa1402121
14. Zhao Z Meng F Wang W Wang Z Zhang C Jiang T Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas Sci Data 2017 4 170024 10.1038/sdata.2017.24 28291232
Zhao Z, Meng F, Wang W, Wang Z, Zhang C, Jiang T. Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas. Sci Data. 2017;4:170024.28291232 10.1038/sdata.2017.24
15. Zhao Z Zhang KN Wang Q Li G Zeng F Zhang Y Wu F Chai R Wang Z Zhang C Chinese glioma genome atlas (CGGA): a comprehensive resource with functional genomic data from chinese glioma patients Genom Proteom Bioinform 2021 19 1 1 12 10.1016/j.gpb.2020.10.005
Zhao Z, Zhang KN, Wang Q, Li G, Zeng F, Zhang Y, Wu F, Chai R, Wang Z, Zhang C, et al. Chinese glioma genome atlas (CGGA): a comprehensive resource with functional genomic data from chinese glioma patients. Genom Proteom Bioinform. 2021;19(1):1–12.10.1016/j.gpb.2020.10.005
16. Fang S Liang J Qian T Wang Y Liu X Fan X Li S Wang Y Jiang T Anatomic location of tumor predicts the accuracy of motor function localization in diffuse lower-grade gliomas involving the hand Knob Area AJNR Am J Neuroradiol 2017 38 10 1990 1997 10.3174/ajnr.A5342 28838912
Fang S, Liang J, Qian T, Wang Y, Liu X, Fan X, Li S, Wang Y, Jiang T. Anatomic location of tumor predicts the accuracy of motor function localization in diffuse lower-grade gliomas involving the hand Knob Area. AJNR Am J Neuroradiol. 2017;38(10):1990–7.28838912 10.3174/ajnr.A5342
17. Gusev Y Bhuvaneshwar K Song L Zenklusen JC Fine H Madhavan S The REMBRANDT study, a large collection of genomic data from brain cancer patients Sci Data 2018 5 180158 10.1038/sdata.2018.158 30106394
Gusev Y, Bhuvaneshwar K, Song L, Zenklusen JC, Fine H, Madhavan S. The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci Data. 2018;5:180158.30106394 10.1038/sdata.2018.158
18. Gravendeel LA Kouwenhoven MC Gevaert O de Rooi JJ Stubbs AP Duijm JE Daemen A Bleeker FE Bralten LB Kloosterhof NK Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology Can Res 2009 69 23 9065 9072 10.1158/0008-5472.CAN-09-2307
Gravendeel LA, Kouwenhoven MC, Gevaert O, de Rooi JJ, Stubbs AP, Duijm JE, Daemen A, Bleeker FE, Bralten LB, Kloosterhof NK, et al. Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology. Can Res. 2009;69(23):9065–72.10.1158/0008-5472.CAN-09-2307
19. Zhao J Chen AX Gartrell RD Silverman AM Aparicio L Chu T Bordbar D Shan D Samanamud J Mahajan A Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma Nat Med 2019 25 3 462 469 10.1038/s41591-019-0349-y 30742119
Zhao J, Chen AX, Gartrell RD, Silverman AM, Aparicio L, Chu T, Bordbar D, Shan D, Samanamud J, Mahajan A, et al. Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. Nat Med. 2019;25(3):462–9.30742119 10.1038/s41591-019-0349-y
20. Hugo W Zaretsky JM Sun L Song C Moreno BH Hu-Lieskovan S Berent-Maoz B Pang J Chmielowski B Cherry G Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma Cell 2016 165 1 35 44 10.1016/j.cell.2016.02.065 26997480
Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165(1):35–44.26997480 10.1016/j.cell.2016.02.065
21. Riaz N Havel JJ Makarov V Desrichard A Urba WJ Sims JS Hodi FS Martín-Algarra S Mandal R Sharfman WH Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab Cell 2017 171 4 934 949.e916 10.1016/j.cell.2017.09.028 29033130
Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, Hodi FS, Martín-Algarra S, Mandal R, Sharfman WH, et al. Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell. 2017;171(4):934-949.e916.29033130 10.1016/j.cell.2017.09.028
22. Lauss M Donia M Harbst K Andersen R Mitra S Rosengren F Salim M Vallon-Christersson J Törngren T Kvist A Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma Nat Commun 2017 8 1 1738 10.1038/s41467-017-01460-0 29170503
Lauss M, Donia M, Harbst K, Andersen R, Mitra S, Rosengren F, Salim M, Vallon-Christersson J, Törngren T, Kvist A, et al. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma. Nat Commun. 2017;8(1):1738.29170503 10.1038/s41467-017-01460-0
23. Łuksza M Riaz N Makarov V Balachandran VP Hellmann MD Solovyov A Rizvi NA Merghoub T Levine AJ Chan TA A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy Nature 2017 551 7681 517 520 10.1038/nature24473 29132144
Łuksza M, Riaz N, Makarov V, Balachandran VP, Hellmann MD, Solovyov A, Rizvi NA, Merghoub T, Levine AJ, Chan TA, et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature. 2017;551(7681):517–20.29132144 10.1038/nature24473
24. Braun DA Hou Y Bakouny Z Ficial M Sant' Angelo M Forman J Ross-Macdonald P Berger AC Jegede OA Elagina L Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma Nat Med 2020 26 6 909 918 10.1038/s41591-020-0839-y 32472114
Braun DA, Hou Y, Bakouny Z, Ficial M, Sant’ Angelo M, Forman J, Ross-Macdonald P, Berger AC, Jegede OA, Elagina L, et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med. 2020;26(6):909–18.32472114 10.1038/s41591-020-0839-y
25. Prat A Navarro A Paré L Reguart N Galván P Pascual T Martínez A Nuciforo P Comerma L Alos L Immune-related gene expression profiling after PD-1 blockade in non-small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma Can Res 2017 77 13 3540 3550 10.1158/0008-5472.CAN-16-3556
Prat A, Navarro A, Paré L, Reguart N, Galván P, Pascual T, Martínez A, Nuciforo P, Comerma L, Alos L, et al. Immune-related gene expression profiling after PD-1 blockade in non-small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma. Can Res. 2017;77(13):3540–50.10.1158/0008-5472.CAN-16-3556
26. Leek JT Johnson WE Parker HS Jaffe AE Storey JD The sva package for removing batch effects and other unwanted variation in high-throughput experiments Bioinformatics 2012 28 6 882 883 10.1093/bioinformatics/bts034 22257669
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3.22257669 10.1093/bioinformatics/bts034
27. Jew B Alvarez M Rahmani E Miao Z Ko A Garske KM Sul JH Pietiläinen KH Pajukanta P Halperin E Accurate estimation of cell composition in bulk expression through robust integration of single-cell information Nat Commun 2020 11 1 1971 10.1038/s41467-020-15816-6 32332754
Jew B, Alvarez M, Rahmani E, Miao Z, Ko A, Garske KM, Sul JH, Pietiläinen KH, Pajukanta P, Halperin E. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat Commun. 2020;11(1):1971.32332754 10.1038/s41467-020-15816-6
28. Hänzelmann S Castelo R Guinney J GSVA: gene set variation analysis for microarray and RNA-seq data BMC Bioinformatics 2013 14 7 10.1186/1471-2105-14-7 23323831
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.23323831 10.1186/1471-2105-14-7
29. Zhou Y Zhou B Pache L Chang M Khodabakhshi AH Tanaseichuk O Benner C Chanda SK Metascape provides a biologist-oriented resource for the analysis of systems-level datasets Nat Commun 2019 10 1 1523 10.1038/s41467-019-09234-6 30944313
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.30944313 10.1038/s41467-019-09234-6
30. Cheng X Liu Z Chang H Liang W Li P Gao Y WD repeat domain 76 predicts poor prognosis in lower grade glioma and provides an original target for immunotherapy Eur J Med Res 2024 29 1 13 10.1186/s40001-023-01605-6 38173030
Cheng X, Liu Z, Chang H, Liang W, Li P, Gao Y. WD repeat domain 76 predicts poor prognosis in lower grade glioma and provides an original target for immunotherapy. Eur J Med Res. 2024;29(1):13.38173030 10.1186/s40001-023-01605-6
31. Li Z Jin Y Zhang P Zhang XA Yi G Zheng H Yuan X Wang X Xu H Qiu X A four-gene panel for the prediction of prognosis and immune cell enrichment in gliomas Mol Biotechnol 2023 10.1007/s12033-023-00820-0 38159171
Li Z, Jin Y, Zhang P, Zhang XA, Yi G, Zheng H, Yuan X, Wang X, Xu H, Qiu X, et al. A four-gene panel for the prediction of prognosis and immune cell enrichment in gliomas. Mol Biotechnol. 2023. 10.1007/s12033-023-00820-0.38159171 10.1007/s12033-023-00820-0
32. Zhang M Zhang Q Bai J Zhao Z Zhang J Transcriptome analysis revealed CENPF associated with glioma prognosis Math Biosci Eng MBE 2021 18 3 2077 2096 10.3934/mbe.2021107 33892537
Zhang M, Zhang Q, Bai J, Zhao Z, Zhang J. Transcriptome analysis revealed CENPF associated with glioma prognosis. Math Biosci Eng MBE. 2021;18(3):2077–96.33892537 10.3934/mbe.2021107
33. Byron SA Van Keuren-Jensen KR Engelthaler DM Carpten JD Craig DW Translating RNA sequencing into clinical diagnostics: opportunities and challenges Nat Rev Genet 2016 17 5 257 271 10.1038/nrg.2016.10 26996076
Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016;17(5):257–71.26996076 10.1038/nrg.2016.10
34. Liu Z Liu L Weng S Guo C Dang Q Xu H Wang L Lu T Zhang Y Sun Z Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer Nat Commun 2022 13 1 816 10.1038/s41467-022-28421-6 35145098
Liu Z, Liu L, Weng S, Guo C, Dang Q, Xu H, Wang L, Lu T, Zhang Y, Sun Z, et al. Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nat Commun. 2022;13(1):816.35145098 10.1038/s41467-022-28421-6
35. Wang L Liu Z Liang R Wang W Zhu R Li J Xing Z Weng S Han X Sun YL Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer Elife 2022 11 e80150 10.7554/eLife.80150 36282174
Wang L, Liu Z, Liang R, Wang W, Zhu R, Li J, Xing Z, Weng S, Han X, Sun YL. Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer. Elife. 2022;11:e80150.36282174 10.7554/eLife.80150
