A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis

doi:10.21203/rs.3.rs-2482424/v1

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A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis

https://doi.org/10.21203/rs.3.rs-2482424/v1

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Background

Glioma is the most malignant intracranial tumor. Transient receptor potential (TRP) channel family has been found to be involved in malignant progression of many tumors. However, the relationship between TRP channel-related genes (TCRGs) and glioma remains unclear.

Methods

The TCGA-glioma, CGGA_325, GSE108474 and GSE16011 glioma cohorts were downloaded from the public database. Differential analysis, univariate Cox regression analysis and consistent clustering were performed for 120 TCRGs, and differences in immune microenvironment were analyzed. Lasso Cox regression analysis was used to construct a prognostic model, and the patients were divided into high- and low- risk groups.

Results

Thirty differentially expressed prognostic TCRGs were identified and patients were divided into three subtypes. Lasso Cox regression analysis showed that 10 genes were included in the prognostic model (TRPM6, TRPV3, BDKRB1, HTR2A, P2RY2, PLA2G4D, CAMK2G, MAPK13, ADCY5 and PRKCB). Kaplan-Meier survival analysis showed that patients of the high-risk group had lower survival. Finally, we analyzed five single-cell sequencing data sets (GSE103224, GSE131928, GSE138794, GSE148842 and GSE162631). MAPK13, CAMK2G and PRKCB were significantly expressed in exhausted CD8 + T cells, suggesting that they were closely related to anti-tumor immunity.

Conclusion

Based on the expression of TCRGs, we conducted the new subtype classification and a prognostic model for glioma, and is expected to provide theoretical basis for the development of new targets.

Transient receptor potential channels

Glioma

Bioinformatics

Prognosis

Immune microenvironment

Glioma is highly malignant and originates from glial cells, with an incidence of approximately 5.26 cases per 100,000 people and approximately 17,000 new cases worldwide each year(1). Glioma is classified into LGG (low grade glioma, WHO Grade 2–3) and GBM (glioblastoma, WHO Grade 4), among which GBM has an extremely poor prognosis and a high mortality rate(2). At present, the main treatment methods for glioma are safe surgical resection within the largest range, supplemented by radiotherapy and chemotherapy, tumor treating fields therapy, and immune-targeted therapy. The abovementioned therapies can make the median overall survival of LGG reach 5–10 years, and the median survival time of GBM can reach approximately 1–2 years, but the overall treatment effect is still unsatisfactory(3). The main reason is that glioma are very aggressive and heterogeneous, prone to recurrence and progression, and the long-term effects of treatment are limited. With the emergence of precise solutions such as the tumor immune microenvironment, immune checkpoints, tumor-associated macrophages, oncolytic viruses, and gene-targeted therapy, the exploration of more efficient glioma biomarkers has become a new research direction, which is expected to achieve more precise treatment of glioma(4).

Transient receptor potential (TRP) is a multifunctional signaling molecule located on the cell membrane surface that interacts with a variety of ion channels to regulate intracellular and intercellular communication(5). The TRP channel superfamily consists of six transmembrane cation permeation channels, which act as mode sensors for various stimuli and are divided into seven subfamilies with different functions according to different homology of amino acid sequence: TRP-A, C, M, ML, N, P and V(6). The TRP channel family participates in the angiogenesis, invasion and migration of cancer by regulating the proliferation, differentiation and apoptosis of tumor cells and plays a key role in the drug sensitivity of tumor cells(7). TRP channel-related genes (TCRGs) are abnormally expressed in a variety of tumors(8). For example, studies have found that TCRGs have a clear role in the proliferation and occurrence of breast cancer cells. The interaction between TRPA1 and TRPV1 promotes the resistance of breast cancer cells to oxidative stress and increases the resistance of tumor cells to chemotherapy drugs. TRPC1 and TRPV4 regulate endothelial cell calcium homeostasis, TRPM is associated with cell invasion, and TRPM4 plays an important role in lymph node spread of well-differentiated tumor cells(9). Stock K et al. found that the TRPV1 signaling pathway mediates the occurrence of glioma by regulating the calcium concentration of astrocytes in the brain(10).

Based on the above evidence, we explored the prognostic TCRGs in glioma and performed a comprehensive analysis of the related genes that may be involved in the malignant progression of glioma. Furthermore, by establishing a prognostic model, we analyzed the survival rate of each glioma patient, provided a theoretical basis for individualized follow-up and treatment of patients, and provided a reference for the development of therapeutic targets (Fig. 1).

Data acquisition

Via The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) and Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn/) to download tumor tissue sequencing data and clinical characteristics of glioma (WHO Grade 2–4). To avoid statistical bias, samples with missing clinical data and overall survival less than 30 days were excluded, and 1475 patients were included. The "sva" package in R 4.2.1 (https://www.r-project.org/) was used to eliminate batch effects between the TCGA (n = 632) and GSE108474 cohorts (n = 287). The two cohorts were combined into a training set. The GSE16011 (n = 251) and CGGA_325 cohort (n = 305) were used as external validation sets to test the model. From the REACTOME_TRP_CHANNELS pathway in the Molecular Signatures Database (MsigDB, http://www.gsea-msigdb.org/gsea/msigdb) and inflammatory mediator regulation of TRP channels in Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/), TCRGs were extracted and duplicated genes were deleted. A total of 120 genes were obtained for bioinformatics analysis (Supplementary Table 1).

Identification Of Prognostic Tcrgs

Using 5 normal brain tissues of the TCGA cohort and the "limma" package to pair 120 TCRGs. The expression difference between normal brain tissues and tumor was analyzed, and the false discovery rate (FDR) after correction by the BH method was set to the threshold P.adj < 0.05, and the absolute value of the logarithmic differential expression fold change |log2FC|>1. The changes in differentially expressed genes at the protein level were detected by immunohistochemical data of tumor and normal tissue samples in the Human Protein Atlas (HPA, http://www.proteinatlas.org). Afterward, the differentially expressed TCRGs were used to perform univariate Cox regression analysis, and the corrected threshold was set to P.adj < 0.05.

Establishment Of Trp Channel Clusters

Based on prognostic TCRGs, we used the "ConsensusClusterPlus" package to perform consistent clustering on the training set, then survival analysis was used for different clusters(11). Next, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE) and uniform manifold approximation and projection (UMAP) were used to further evaluate the correlation of each cluster with TCRGs. Differential expression of prognostic TCRGs in different clusters was evaluated using the "limma" package. Between different clusters, the "pheatmap" package displayed the expression of TCRGs in different clinical characteristics.

Immune Infiltration And Functional Enrichment Analysis For Trp Channel Clusters

Single-sample gene set enrichment analysis (ssGSEA) was used to quantify and compare the level of immune cells infiltration in samples of different subtypes, and we collected immune cell-related genes from the Gene Set Enrichment Analysis (GSEA, http://www.gsea-msigdb.org/gsea/index.jsp) website(12). Afterward, GSVA analysis was performed on samples of different subtypes, and the "c2.cp.kegg.symbols" and "c5.go.symbols" datasets in the GSEA website were selected as the analysis gene sets to carry out(13). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were used to compare the enrichment of signaling pathways and biological processes among different subtypes(14).

Establishment Of The Trp Channel-model

The "glmnet" and "survival" packages were used to perform LASSO regression analysis on prognostic TCRGs(15), and the "caret" package could randomly divide the training set samples into equal parts. The internal training and validation sets were used to select the optimal penalty parameter λ through minimum 10-fold cross-validation to construct the TRP channel-model and output the gene name and regression coefficient (coef) included in the model. The calculation formula of the prognosis model is risk score=, where x is the expression level of the gene. The risk score of each patient was calculated according to the formula, sorted from high to low, and divided into two groups (high- and low-risk) according to the median value, and the Kaplan‒Meier (KM) survival curves between two groups was drawn. Time-dependent receiver operating characteristic (ROC) curves were further used to validate the model's efficiency and accuracy in predicting 1-, 3-, and 5-year survival. Meanwhile, we validated the predictive efficacy of the model in two external validation sets. In addition, a multivariate independent prognostic analysis was performed on the risk score and other clinical characteristics to verify the independence in predicting prognosis.

Immune Infiltration Analysis For The Trp Channel-model

Each sample was analysed by CIBERSORT algorithm, then a comparison was made between high- and low-risk groups in terms of immune cells infiltration(16). At the same time, the correlation between risk scores and the expression of model genes, the infiltration of immune cells was evaluated. Furthermore, Estimate-, Immune- and Stromal scores of glioma in the training set were calculated with the ESTIMATE algorithm of the "estimate" package(17). An increase in ImmuneScore or StromalScore means an increase in the proportion of immune cells or stromal cells in the tumor microenvironment, and ESTIMATEScore is the sum of the two.

Single-cell Sequencing Analysis

Multiple glioma-related single-cell sequencing datasets (GSE103224, GSE131928, GSE138794, GSE148842 and GSE162631) were downloaded using the Tumor Immune Single-cell Hub 2 (TISCH2, http://tisch.comp-genomics.org/) database, and the differential expression of risk genes in prognostic models among different cell subsets within glioma was analyzed.

Statistical analysis

Statistical analysis was performed using R language. Using t or Wilcoxon test, we compared the differences between the two groups. One-way analysis of variance (ANOVA) was used to compare multiple groups. Kaplan‒Meier method (with a log-rank test or univariate Cox regression analysis) was used to survival analysis. The correlation of two variables was analyzed using the Pearson correlation test. When the P value is less than 0.05, 0.01, 0.001, and 0.0001, it is judged to be statistically significant, which are represented by "*", "**", "***", and "****", respectively, and "ns" indicates no significance.

Identification of prognostic TCRGs

37 differentially expressed TCRG genes were obtained, with 5 up-regulated and 32 down-regulated (Table 1). MCOLN2, TRPV4 and ITPR2 were significantly highly expressed in glioma tissues, and the 5 most significantly down-regulated genes (PRKCG, CAMK2A and HTR2A) had lower expression in tumor tissues (Fig. 2A). We found the expression of 30 genes correlated with patient survival prognosis (Fig. 2B), and a network diagram was used to visualize the intricate relationship between genes and prognostic value (Fig. 2C).

Table 1

Differentially expressed TCRGs.
Gene id	Con-mean	Treat-mean	Log2FC	P-value	FDR
PRKCG	211.0424	16.56573438	-3.67125881	0.000198295	0.002741471
CAMK2A	738.18878	83.60561555	-3.142318057	0.000455713	0.003255093
HTR2A	39.4164	5.511879601	-2.838179738	0.000546004	0.003412522
ASIC2	15.52828	2.407887732	-2.689058005	0.000221997	0.002741471
PRKCB	153.23178	24.58510884	-2.639858893	0.000304843	0.002741471
CAMK2B	200.64352	32.29045892	-2.635454714	0.000263715	0.002741471
TRPC5	2.64922	0.428565763	-2.627979148	0.000706489	0.003723209
TRPV6	3.47302	0.599013837	-2.535529484	0.000275235	0.002741471
ADCY1	69.97276	14.27612468	-2.293188992	0.00095633	0.004157956
PLA2G4D	0.23962	0.049687589	-2.269790879	0.001381177	0.005312217
ITPR1	17.62692	3.808969044	-2.210307942	0.001553858	0.005591606
HTR2C	9.0202	2.164219971	-2.05931228	0.000286938	0.002741471
TRPM6	3.57034	0.861622682	-2.050933333	0.007404011	0.018510028
PRKCE	60.31824	14.69733381	-2.037039869	0.000430043	0.003255093
CALM3	1086.28142	284.1873961	-1.93448343	0.000297174	0.002741471
ADCY5	61.95748	17.71862981	-1.806011422	0.000147051	0.002741471
BDKRB1	0.48794	0.140958203	-1.791436323	0.00048948	0.003263197
PLA2G4E	0.04852	0.015015264	-1.692149718	0.002205242	0.007350806
CALM1	1292.71966	430.220904	-1.587259915	0.000261465	0.002741471
MAPK13	9.64888	3.267023538	-1.562384641	0.001833101	0.006321038
MAPK9	77.78872	26.38899715	-1.559624442	0.000210746	0.002741471
PRKCQ	19.35522	7.2509602	-1.416478746	0.008883946	0.02066034
TRPV3	3.82662	1.46571398	-1.384467039	0.002288397	0.007381925
PRKACB	250.45698	98.88096919	-1.34079803	0.000183301	0.002741471
PRKCD	50.77336	20.65019743	-1.297916161	0.000791122	0.003771537
TRPM2	24.27016	10.19642325	-1.251120454	0.003190356	0.009969863
KNG1	0.09846	0.04466291	-1.140460483	0.024108628	0.047790936
TRPA1	0.1876	0.087833381	-1.094818586	0.011074598	0.024610218
MAPK10	19.64456	9.233521541	-1.089176972	0.004918255	0.01329258
CAMK2G	128.71258	60.80652611	-1.08185499	0.003779987	0.011454506
P2RY2	0.63052	0.299850214	-1.072300131	0.004687655	0.013021263
MAPK11	80.19096	39.97856334	-1.004212981	0.004255797	0.012517049
ITPR2	9.30202	23.66173096	1.34693967	0.004560442	0.013021263
PLCB3	9.9037	27.67899715	1.482752154	0.000328977	0.002741471
TRPV4	0.23142	1.331616262	2.524592938	0.00070741	0.003723209
MCOLN2	0.14968	1.072156348	2.840561935	0.008770055	0.02066034
ASIC4	8.97516	68.61931755	2.934605214	0.012119017	0.025785142

Establishment Of Trp Channel Clusters

Using the consensus clustering algorithm of the "ConsensusClusterPlus" package, we identified 3 subtypes associated with prognostic TCRGs (Fig. 3A-3C). Survival analysis showed that Cluster B had the worst prognosis, while Cluster C had longer survival (Fig. 3D). PCA, UMAP, and tSNE analyses further validated the clustering of patients in the 3 subtypes, confirming its reliability and stability (Fig. 3E-3G). A total of 29 prognostic TCRGs were differentially expressed in different clusters, and these genes may be effective biomarkers for cluster analysis (Fig. 3H). We also found that Cluster C mainly came from younger WHO Grade 2–3 patients, which may account for its better prognosis (Fig. 3I).

Immune Infiltration And Functional Enrichment Analysis For Trp Channel Clusters

ssGSEA showed that most immune cells were significantly enriched in Cluster B, such as activated B cells, CD8 + T cells, CD4 + T cells, macrophages, mast cells, natural killer T cells, neutrophils and type 17 T helper cells. In Cluster C, with a better prognosis, the infiltration level of most immune cells was lower (Fig. 4A). Although the prognosis of Cluster B patients was worse, we found that their higher level of immune cells infiltration indicates that they were in an immunosuppressive tumor microenvironment, and more aggressive immunotherapy is appropriate for these patients. Next, we performed KEGG pathway enrichment analysis between each subtype. Taking Cluster B and Cluster C with the greatest difference in survival prognosis as an example, we found that the following pathways were significantly activated in Cluster C: phosphatidylinositol signaling system, gap junction, GNRH signaling pathway, axon guidance, MAPK signaling pathway, neuroactive ligand receptor interaction, calcium signaling pathway, etc. The P53 signaling pathway, base excision repair, nucleotide excision repair, pyrimidine metabolism, etc. were significantly enriched in Cluster C (Fig. 4B-4D). GSEA analysis found some tumor progression related pathways were significantly enriched in Cluster B: cell cycle, cytokine‒cytokine receptor interaction, focal adhesion, JAK-STAT signaling pathway and chemokine signaling pathway (Fig. 4E-4G).

Establishment Of The Trp Channel-model

A total of 10 genes were included in the model (Fig. 5A-5C). In the internal training and validation set, we found that the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001; Fig. 5D, E). The ROC curve demonstrated that the model had good predictive performance for the overall survival of patients at 1, 3, and 5 years (Fig. 5F, G). The same results were obtained in two validation sets (P < 0.001; Fig. 5H-K). Multivariate independent prognostic analysis found that risk score, age and WHO classification were all indicators of survival in glioma patients (Fig. 5L). According to the model, TCRGs expression differed between two groups (high- and low-risk) in Fig. 5M. Meanwhile, we found that Cluster B had a higher risk score, while Cluster C had the lowest risk score, which was consistent with the survival of patients with different subtypes (Fig. 5N).

Immune Infiltration Analysis For The Trp Channel-model

By analyzing the immune microenvironment, we found that most immune cells had different infiltration conditions between two groups, such as naive B cells, resting NK cells, CD8 + T cells, activated NK cells, monocytes, M0, M1 and M2 macrophages, activated mast cells, eosinophils, and neutrophils (Fig. 6A, B). It was further found that TCRGs expression and the risk score in the model were significantly correlated with the degree of infiltration of various immune cells, which may reveal the mechanism of genes affecting the microenvironment disorder (Fig. 6C). The ESTIMATE algorithm verified the above findings, and relevant scores were significantly higher in patients of the high-risk group (Fig. 6D).

Single-cell Sequencing Analysis

Single-cell sequencing (scRNA-seq) is an efficient method to analyze disease heterogeneity at the single-cell level. Gene expression could be analyzed at the single-cell level, and multidimensional analysis of cell clusters can clearly explore the cellular heterogeneity of tumor tissue(18). We found that TRPV3 was mainly expressed in malignant cells, oligodendrocytes, endothelial cells, monocytes, and macrophages (Fig. 7). MAPK13 was in neurons, malignant cells, and exhausted CD8 + T cells and higher expression in monocytes and macrophages (Fig. 8). ADCY5 was only available in high expression in malignant cells (Fig. 9). CAMK2G was expressed in malignant cells, oligodendrocytes, exhausted CD8 + T cells, endothelial cells, monocytes, and macrophages (Fig. 10). PRKCB was expressed to varying degrees in various types of cells, especially in exhausted CD8 + T cells (Fig. 11). TRPM6 was only highly expressed in oligodendrocytes (Fig. 12).

TRP channels are cation channels on the cell membrane surface that allow cations such as Ca2+, Mg2+, Na+, and K + to pass through. In the tumor microenvironment, an imbalance in the expression level of TCRGs can change the adaptability of cells to the surrounding environment and regulate the occurrence and development of tumors. Research shows that blocking or interfering with TRP channels is expected to become a new target for tumor molecular therapy. A pan-cancer analysis found that higher WHO grade and stage were substantially correlated with unbalanced TCRG expression, and shorter survival, increased tumor mutational burden, and activation of tumor-related pathways in patients(19). Previous studies have found that TRPML1 blocks the autophagic flux of human glioblastoma cells to lysosomes by inducing autophagy inhibition, resulting in the accumulation of damaged mitochondria, which in turn damages DNA in cancer cells and inhibits tumor growth(20). Another study reported that elevated expression levels of TRPML2 were associated with resistance to temozolomide therapy in GBM patients and were associated with poorer OS. Interfering with TRPML2 expression was expected to be a new therapeutic target(21). However, there is no literature systematically evaluating the potential prognostic value and mechanism of TCRGs in glioma, so our study has certain significance.

In this study, 37 differentially expressed TCRGs in glioma tissues were first identified by differential analysis, and 30 genes were further screened by univariate Cox regression analysis to associate with patient survival and prognosis. Due to the complex molecular heterogeneity of glioma, the identification of different molecular subtypes can help develop better treatment options(22). To this end, based on 30 prognostic TCRGs, we used the consensus clustering algorithm of the "ConsensusClusterPlus" package, selected the optimal number of clusters (k = 3), and identified 3 subtypes, which were confirmed reliability by PCA, UMAP and tSNE. We found that Cluster B patients had the worst prognosis, and patients in Cluster C had the longest survival. Further biological and immune-related analyses found that Cluster B had a higher degree of immune infiltration and a higher level of immunosuppression. At the same time, multiple signaling pathways related to cancer progression were significantly enriched, which may be the reason for its poor prognosis. Our subtype identification method provides reference for the molecular typing of glioma.

Although the WHO classification method has been the gold standard for prognostic grading of glioma for many years, the identification of more tumor biomarkers based on bioinformatics methods and a large amount of gene sequencing data has become a new technological advancement and contributes to precise medical realization. The prognostic value of a single biomarker is very limited, and integrating multiple biomarkers into one model can improve the prediction efficiency and accuracy(23). For example, a study constructed a prognostic model for glioma patients based on three necroptosis-related genes, and high-risk patients had higher immune infiltration rates and immune checkpoint genes expression than low-risk patients, which were positively associated with poor prognosis(24). Another study constructed a prognostic model in patients with LGG based on cellular senescence-related genes and found that for patients, cellular senescence-related scores could be used as an independent predictor, and patients with high scores had a worse prognosis. It is also well validated in other cohorts(25). We constructed a 10-gene prognostic model, and patients were divided into two groups. The findings indicated that the overall survival of patients in the high-risk was significantly lower, and two external validation sets (CGGA_325 and GSE16011) corroborated this finding favorably. Then we performed an immune microenvironment analysis. The results showed that the infiltration of immune cells was higher in patients of the high-risk group. The coexistence of immune-suppressive and immune-activating cells suggested that in the high-risk group, there are complex immune microenvironment disturbances in patients, and effective immune intervention is expected to improve the survival of such patients.

Since traditional gene sequencing methods can only obtain the results of averaging the intracellular gene expression information of different types of tissue samples, the samples include tumor cells and various cellular components of the surrounding microenvironment. These analyses are general analyses of large tumor samples, which cannot effectively identify and describe the types and states of individual cells in the tumor microenvironment, which may obscure key information. scRNA-seq uses DNA next-generation sequencing technology to analyze the DNA, RNA or DNA methylation levels of a single cell, revealing its genomic, transcriptomic and epigenetic characteristics and understanding the function and state of a single cell. scRNA-seq can not only identify the heterogeneity of cells in solid tumors but also help to elucidate the molecular mechanisms of immune cells in the tumor microenvironment on tumor cell occurrence, development, metastasis, drug resistance, and immune escape. The clinical diagnosis, treatment and prognosis prediction of patients have become more accurate(26, 27). Therefore, on the basis of scRNA-seq, we localized the 10 genes of the model.

TRPV3 mainly exists in sensory nerve cells and skin keratinocytes in the human body and senses temperature stimuli by forming a complex with TRPV1(28). Studies have shown that in renal clear cell carcinoma and non-small cell lung cancer, the high expression of TRPV3 is significantly correlated with increased tumor grade and poor prognosis, and it affects the accumulation of T cells in various tumor immune microenvironments, which is related to tumor immune infiltration(29, 30). Our analysis of single-cell data found that in malignant cells, oligodendrocytes, and endothelial cells, it was expressed in various cells, such as monocytes and macrophages, and may play a complex regulatory role.

MAPK13 is a subtype of the p38 mitogen-activated protein kinase family, which is highly conserved and involved in the regulation of inflammatory responses, cell proliferation(31, 32). Studies have confirmed that MAPK13 promotes the over-proliferation of epidermal cells and tumorigenesis by inhibiting the expression of proinflammatory cytokines and chemokines in the development of squamous cell carcinoma(33). Analysis at the single-cell level found that it was significantly expressed in exhausted CD8 + T cells, suggesting that it may be related to the exhaustion of CD8 + T cells in the glioma microenvironment, thereby inhibiting the antitumor immune process and promoting glioma progression.

PRKCB is a member of the serine- and threonine-specific protein kinase C family and is involved in signal transduction responses to various hormones and growth factors(34). PRKCB functions in the B-cell receptor signaling pathway and is involved in B-cell development, and its over-expression distorts B-cell development in mice as a phenotypic feature of chronic lymphocytic leukemia(35). In hepatocellular carcinoma, PRKCB interacts with ribosomal RACK1 to increase translation of a potent oncogene in hepatoma cells, which is associated with tumor development and recurrence after chemotherapy(36). In the immune escape system of tumors, tumor cells reduce the abundance of PRKCB in myeloid cells by enhancing the STAT3 signaling pathway, affecting the differentiation of dendritic cells, thus causing the immune escape of tumor cells(37). This was consistent with our analysis at the single-cell level that PRKCB was mainly expressed in exhausted CD8 + T cells and may be involved in the immune escape process of glioma cells.

Our study firstly explored the prognostic value of TRP channel-related genes in glioma, which has certain innovation and research value. However, this is a bioinformatics study based on an open databases, so a larger multicenter studies are needed to confirm the validity of the constructed prognostic model, and sufficient validation and mechanistic exploration are needed, which will be included in the follow-up experimental design. We hope that through the existing research results, more researchers will be attracted to participate in research on the correlation between the TRP channel and the mechanism of the occurrence and development of glioma and provide a more theoretical basis for the development of therapeutic targets for this type of tumor.

Based on the expression of TRP channels-related genes, we conducted the new subtype classification and a prognostic model for glioma, which can effectively predict the survival prognosis and tumor microenvironment state of patients, and is expected to provide theoretical basis for the development of new targets.

TRP

transient receptor potential

TCRGs

TRP channel-related genes

LGG

low grade glioma

GBM

glioblastoma

TCGA

The Cancer Genome Atlas

GEO

Gene Expression Omnibus

CGGA

Chinese Glioma Genome Atlas

MsigDB

Molecular Signatures Database

KEGG

Kyoto Encyclopedia of Genes and Genomes

Gene Ontology

FDR

false discovery rate

HPA

Human Protein Atlas

PCA

principal component analysis

tSNE

t-distributed stochastic neighbor embedding

UMAP

uniform manifold approximation and projection

ssGSEA

single-sample gene set enrichment analysis

GSEA

Gene Set Enrichment Analysis

Kaplan‒Meier

ROC

receiver operating characteristic

TISCH2

Tumor Immune Single-cell Hub 2

ANOVA

one-way analysis of variance

scRNA-seq

single-cell sequencing.

Acknowledgements

All authors acknowledged TCGA, GEO, CGGA, HPA, and TISCH2 databases for providing their platform and contributors for uploading their meaningful datasets.

Author contributions

Conceptualization, Xiaochen Niu and Chunhong Wang; methodology, Xiaochen Niu; validation, Xiaochen Niu, Xuanchen Liu and Xiaohu Zhao; formal analysis, Xiaohu Zhao; resources, Xuanchen Liu; data curation, Chunhong Wang; writing—original draft preparation, Xiaochen Niu and Xuanchen Liu; writing—review and editing, Hongming Ji and Chunhong Wang; visualization, Xiaohu Zhao; funding acquisition, Hongming Ji. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by grants from The 5G Big Data Analysis and Artificial Intelligence Engineering Research Center for Stroke in Shanxi Province of China. The APC was funded by Shanxi Provincial People's Hospital.

Availability of data and materials

The data presented in this study are openly available in TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), CGGA (http://www.cgga.org.cn/), HPA (http://www.proteinatlas.orgdatabases) and TISCH2 (http://tisch.comp-genomics.org/) databases.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

All the authors declare that they have no competing interests

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No competing interests reported.

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A novel TRP channel-related prognostic model of glioma based on transcriptomics and single cell sequencing analysis

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods And Materials

Data acquisition

Identification Of Prognostic Tcrgs

Establishment Of Trp Channel Clusters

Immune Infiltration And Functional Enrichment Analysis For Trp Channel Clusters

Establishment Of The Trp Channel-model

Immune Infiltration Analysis For The Trp Channel-model

Single-cell Sequencing Analysis

Statistical analysis

Results

Identification of prognostic TCRGs

Establishment Of Trp Channel Clusters

Immune Infiltration And Functional Enrichment Analysis For Trp Channel Clusters

Establishment Of The Trp Channel-model

Immune Infiltration Analysis For The Trp Channel-model

Single-cell Sequencing Analysis

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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