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. 2021 Feb 11;13(4):5461-5474.
doi: 10.18632/aging.202475. Epub 2021 Feb 11.

Identification of prognostic immune-related genes in rhabdoid tumor of kidney based on TARGET database analysis

Huimou Chen  1   2 , Suying Lu  1   2 , Jinqiu Guan  1   2 , Xiaoqin Zhu  1   2 , Feifei Sun  1   2 , Junting Huang  1   2 , Jia Zhu  1   2 , Juan Wang  1   2 , Zijun Zhen  1   2 , Yi Que  1   2 , Xiaofei Sun  1   2 , Yizhuo Zhang  1   2
Affiliations

Identification of prognostic immune-related genes in rhabdoid tumor of kidney based on TARGET database analysis

Huimou Chen et al. Aging (Albany NY). .

Abstract

Background: Malignant rhabdoid tumor of the kidney (RTK) is a rare and highly aggressive pediatric malignancy. Immune system dysfunction is significantly correlated with tumor initiation and progression.

Methods: We integrated and analyzed the expression profiles of immune-related genes (IRGs) in 65 RTK patients based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Prognostic related IRGs in RTK patients were analyzed using univariate and multivariate analysis, based on which a prognostic model with IRGs was constructed. Correlation analysis between the risk score of our model and tumor-infiltrating cell were also investigated.

Results: Twenty two IRGs were significantly associated with the clinical outcomes of RTK patients. Gene ontology (GO) analysis revealed that inflammatory pathways were most frequently implicated in RTK. A prognostic model was constructed using 7 IRGs (MMP9, SERPINA3, FAM19A5, CCR9, PLAUR, IL1R2, PRKCG), which were independent prognostic indices that could differentiate patients based on their survival outcomes. Furthermore, the risk scores from our prognostic model was positively associated with cancer-associated fibroblasts (CAFs).

Conclusions: We screened seven IRGs of clinical significance to distinguish patients with different survival outcomes. This may enhance our understanding of the immune microenvironment of RTK, and could use to design individualized treatments for RTK patients.

Background: Malignant rhabdoid tumor of the kidney (RTK) is a rare and highly aggressive pediatric malignancy. Immune system dysfunction is significantly correlated with tumor initiation and progression.

Methods: We integrated and analyzed the expression profiles of immune-related genes (IRGs) in 65 RTK patients based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Prognostic related IRGs in RTK patients were analyzed using univariate and multivariate analysis, based on which a prognostic model with IRGs was constructed. Correlation analysis between the risk score of our model and tumor-infiltrating cell were also investigated.

Results: Twenty two IRGs were significantly associated with the clinical outcomes of RTK patients. Gene ontology (GO) analysis revealed that inflammatory pathways were most frequently implicated in RTK. A prognostic model was constructed using 7 IRGs (MMP9, SERPINA3, FAM19A5, CCR9, PLAUR, IL1R2, PRKCG), which were independent prognostic indices that could differentiate patients based on their survival outcomes. Furthermore, the risk scores from our prognostic model was positively associated with cancer-associated fibroblasts (CAFs).

Conclusions: We screened seven IRGs of clinical significance to distinguish patients with different survival outcomes. This may enhance our understanding of the immune microenvironment of RTK and could use to design individualized treatments for RTK patients.

Keywords: TARGET database; disease prognosis; immune-related genes; malignant rhabdoid tumor of kidney.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors have no conflicts of interest.

Figures

Figure 1
Figure 1
Differentially expressed genes and IRGs in RTK. (A) Heat-map of significant DEGs in RTK. The color from green to red represents the progression from low expression to high expression. (B) Heatmap of significant differentially expressed immune-related genes in RTK. Red represents higher expression while green represents lower expression. (C) Volcano plot of differentially expressed genes. The red dots in the plot represents up-regulated genes and green dots represents down-regulated genes with statistical significance. Black dots represent no DEGs. (D) Volcano plot of differentially expressed immune-related genes in RTK. Colored dots represent differentially expressed immune-related genes and black dots represent no differentially expressed immune-related genes. Abbreviations: GO, Gene Ontology. IRGs, immune-related genes. DEGs, differentially expressed genes.
Figure 2
Figure 2
GO analysis and KEGG pathways of IRGs. (A) GO analysis of differentially expressed IRG. (B) KEGG pathways of IRGs. Abbreviations: GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. IRGs, immune-related genes.
Figure 3
Figure 3
TF-based regulatory network. (A) Heat map of differentially expressed TFs. The green to red spectrum indicates low to high TF expression. (B) Volcano plot of TFs. The green dots represent down-regulated TFs, the red dots represent up-regulated TFs and the black dots represent TFs that were not significantly and differentially expressed. (C) Regulatory network of TFs and PIRGs; the green nodes represent PIRGs with hazard ratios of <1 (p < 0.05), the red nodes represent PIRGs with hazard ratios of >1 (p < 0.05), the yellow nodes represent TFs that were correlated with PIRGs in terms of their mRNA levels (correlation coefficient > 0.4 and p < 0.01), and the red lines indicate positive regulatory relationships. Abbreviations: TF, transcription factor. PIRGs, prognostic immune-related genes.
Figure 4
Figure 4
Prognostic analysis of the TARGET-RTK cohort. (A) Kaplan-Meier curve analysis of the high-risk and low-risk groups. (B) Survival-dependent receiver operating characteristic (ROC) curve validation of the prognostic value of the prognostic index. (C) Dot plot of the risk score. Vertical and horizontal axes represent risk score and RTK samples, ranked by increasing risk score. Red and green colors represent high-and low-risk cases, respectively. (D) Dot plot of survival. Vertical and horizontal axes represent the survival times and RTK samples, ranked by increasing risk score. Red and green colors represent dead and living RTK cases, respectively. (E) Heat map of the expression levels of the seven genes. Vertical and horizontal axes represent genes and RTK samples, ranked by increasing risk score. Genes with higher, lower, and same expression levels are shown in red, green, and black, respectively. Color bars at the top of the heat map represent sample types, with pink and blue indicating low- and high-risk score samples, respectively. Abbreviation: RTK, rhabdoid tumor of kidney.
Figure 5
Figure 5
Univariate Cox regression analyses in the entire TARGET cohort.
Figure 6
Figure 6
Multivariate Cox regression analyses in the entire TARGET cohort.
Figure 7
Figure 7
The relationships between immune-based prognostic model and clinical and demographic characteristics. (A) The relationships between the expression of MMP9 and stage. (B) The relationships between the risk scores and gender. (C) The relationships between the risk scores and stage.
Figure 8
Figure 8
Analysis of different tumor-infiltrating cells in the TARGET-RTK cohort. (A) Violin plot comparing the proportions of TICs between normal and RTK samples. Horizontal and vertical axes represent TICs and relative percentages. Blue and red colors represent normal and tumor samples, respectively. Data were assessed by Wilcoxon rank-sum test. (B) Heat map of different TICs in the TARGET-RTK cohort. Abbreviation: TIC, tumor-infiltrating cell.
Figure 9
Figure 9
Analysis of the correlation between the risk score and tumor-infiltrating cells in the TARGET-RTK cohort. (A) B cells; (B) endothelial cells; (C) Macrophages. (D) CD4+ T cells. (E) CD8+ T cells. (F) CAFs. Abbreviation: CAFs, carcinoma-associated fibroblasts.
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