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. 2024 Jul;28(14):e18555.
doi: 10.1111/jcmm.18555.

A novel ARHGAP family gene signature for survival prediction in glioma patients

Jin Huang  1 , Gaosong Wang  1 , Jiahao Zhang  1 , Yuankun Liu  1 , Yifan Shen  1 , Gengjing Chen  1 , Wei Ji  1 , Junfei Shao  1
Affiliations

A novel ARHGAP family gene signature for survival prediction in glioma patients

Jin Huang et al. J Cell Mol Med. 2024 Jul.

Abstract

ARHGAP family genes are often used as glioma oncogenic factors, and their mechanism of action remains unexplained. Our research entailed a thorough examination of the immune microenvironment and enrichment pathways across various glioma subtypes. A distinctive 6-gene signature was developed employing the CGGA cohort, leading to insights into the disparities in clinical characteristics, mutation patterns, and immune cell infiltration among distinct risk categories. Additionally, a unique nomogram was established, grounded on ARHGAPs, with DCA curves illustrating the model's prospective clinical utility in guiding therapeutic strategies. Emphasizing the role of ARHGAP30, integral to our model, its impact on glioma severity and the credibility of our risk assessment model were substantiated through RT-qPCR, Western blot analysis, and cellular functional assays. We identified 6 ARHGAP family genes associated with glioma prognosis. Analysis using the Kaplan-Meier method indicated a correlation between elevated risk levels and adverse outcomes in glioma patients. The risk score, linked with tumour staging and IDH mutation status, emerged as an independent factor predicting prognosis. Patients in the high-risk category exhibited increased immune cell infiltration, enhanced tumour mutational burden, more pronounced expression of immune checkpoint genes, and a better response to ICB therapy. A nomogram, integrating the risk score with the pathological features of glioma patients, was developed. DCA analysis and cellular studies confirmed the model's potential to improve clinical treatment outcomes for patients. A novel ARHGAP family gene signature reveals the prognosis of glioma.

Keywords: ARHGAP; glioma; machine learning; prognosis; tumour microenvironment.

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Conflict of interest statement

The authors claim that we performed this research without any business or financial relationships that could be interpreted as potential conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Analysis of ARHGAP family genes in glioma context. (A) Comparative analysis revealing expression variations of 35 ARHGAP family genes between glioma and normal tissue samples. (B) A forest plot depicting the prognostic impact of ARHGAP family genes, determined through univariate Cox regression analysis within the CGGA cohort, highlighting statistically significant genes (p < 0.001). (C) A network visualization illustrating the interconnections and relationships among the 10 ARHGAP family genes of interest. (D, E) Illustrations of copy number variations (CNVs) observed in these 10 ARHGAP family genes. (F) Diagrammatic representation mapping the chromosomal locations of these 10 ARHGAP family genes, highlighting their genomic distribution. ***p < 0.001.
FIGURE 2
FIGURE 2
Categorization of glioma into subgroups based on ARHGAP family genes. (A) Utilizing consensus clustering, a matrix with k = 2 revealed distinct groupings. (B) Kaplan–Meier survival curves depicting significantly different survival outcomes between the identified subtypes (p < 0.001). (C) Principal component analysis (PCA) effectively segregating two subtypes, predicated on the expression profiles of 10 ARHGAP family genes. (D) A heatmap presentation of gene expression levels of the ARHGAP family and corresponding clinicopathological attributes for each subtype. (E) Gene Set Variation Analysis (GSVA) delineating divergent enrichment patterns in the KEGG pathways between clusters A and B.
FIGURE 3
FIGURE 3
Variances in gene expression and immune infiltration across glioma subtypes. (A) Displays the immune cell infiltration profile for each glioma subtype, illustrating distinct immune landscapes. (B) A volcano plot illustrating genes with significant upregulation and downregulation in each subtype. (C) A circular representation of Gene Ontology (GO) terms and their enrichment analysis in differentially expressed genes (DEGs). (D) A bubble chart depicting the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis outcomes for the identified DEGs. ***p < 0.001.
FIGURE 4
FIGURE 4
Prognostic analysis using ARHGAP family genes. (A) The LASSO model's parameter optimization via 10‐fold cross‐validation, with each gene represented by a distinct curve. (B) Co‐efficient profile analysis in the LASSO model, with vertical lines indicating the optimal lambda value. (C, D) Kaplan–Meier survival curves in CGGA and TCGA cohorts, comparing survival outcomes between high‐ and low‐risk groups. (E, F) Risk plots showing survival outcomes and risk score distribution for individual patients in the CGGA and TCGA cohorts. (G) Alluvial diagrams depicting transitions among risk groups, subgroups, and survival outcomes. (H) Risk score comparison for the two glioma subgroups established based on ARHGAP family gene analysis.
FIGURE 5
FIGURE 5
Immune landscape in glioma based on Risk Scores. (A) Comparative analysis of immune cell infiltration across varied risk subgroups. (B) Exploration of the relationship between risk scores and CD8+ T cell abundance in glioma samples. (C) Interplay of immune cells with 6 selected ARHGAP family genes. (D) Contrasting immune cell infiltration patterns in high‐risk versus low‐risk groups. (E) Expression profile analysis, comparing high‐risk and low‐risk groups. (F) Examination of immune checkpoint gene expression across risk categories.
FIGURE 6
FIGURE 6
Evaluation of Risk Score's prognostic relevance in glioma. (A) Univariate, and (B) multivariate Cox regression analyses in the TCGA cohort, examining prognostic significance and clinical factors (age, race, gender, IDH mutation, 1p/19q co‐deletion). (C) A predictive nomogram integrating risk score and clinical parameters for 1‐, 3‐, and 5‐year survival predictions. (D) Calibration plots comparing predicted outcomes with actual survival data over 1, 3, and 5 years. (E) Time‐dependent AUC values for risk scores in TCGA and (H) CGGA cohorts across different time points. (F) Comparative AUC values for risk scores and clinical factors at 3 years in TCGA and (I) CGGA cohorts. (G) DCA curves assessing the clinical utility of risk scores and clinical factors at 3 years in TCGA and (J) CGGA cohorts.
FIGURE 7
FIGURE 7
Variability in Risk Scores across clinical characteristics subsets in the TCGA Cohort. (A) Discrepancies in risk scores with respect to patient age groups. (B) Variations in risk scores corresponding to tumour grades. (C) Fluctuations in risk scores based on IDH mutation statuses. (D) Divergences in risk scores in relation to 1p/19q co‐deletion. Significance levels indicated as *p < 0.05, **p < 0.01, ***p < 0.001.
FIGURE 8
FIGURE 8
Analysis of mutational patterns linked to Risk Score stratification. (A) Comparison of tumour mutational burden (TMB) between patient groups stratified by elevated risk scores. (B) Association between the calculated risk score and the extent of TMB. (C, D) Comprehensive mutation profiles depicted via waterfall plots for patient groups categorized as high and low risk.
FIGURE 9
FIGURE 9
Assessment of Risk Score in relation to Immune Checkpoint Blockade (ICB) Efficacy. (A) Analysis of the association between risk score and immunotherapy response markers. (B) Examination of the correlation between the risk score and each phase of the tumour's immune response cycle. Additionally, sensitivity to chemotherapeutic drugs, lapatinib and afatinib, was evaluated for high‐risk and low‐risk groups, as indicated by IC50 values in (C) and (D), respectively.
FIGURE 10
FIGURE 10
Analysis of ARHGAP Family gene expression in single‐cell RNA sequencing data. (A) Cell type categorization in dataset GSE141982, depicting the proportion of each cell type. (B, C) Exploration of the expression levels and distribution percentages of ARHGAP11A, ARHGAP18, ARHGAP27, ARHGAP30, and ARHGAP44 in various cell types.
FIGURE 11
FIGURE 11
Investigating the impact of ARHGAP30 on glioma cell proliferation. (A) Assessment of ARHGAP30 mRNA levels in diverse glioma cell lines using RT‐qPCR. (B, C) Validation of ARHGAP30 overexpression in U87 and U251 cells through Western blot and qPCR analyses. (D, E) CCK‐8 assays were conducted to determine the proliferative capacity in U87 and U251 cells post ARHGAP30 overexpression compared to control (n = 3, p < 0.05). (F, G) Migration assays in U87 and U251 cells post‐transfection with ARHGAP30 or control vectors (n = 3, p < 0.05). (H, I) EdU incorporation assays performed in U87 cells to assess cell proliferation following ARHGAP30 overexpression versus control group (n = 3, p < 0.05). Significance denoted as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
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