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. 2022 Oct 24:12:1040892.
doi: 10.3389/fonc.2022.1040892. eCollection 2022.

Systematical analysis of ferroptosis regulators and identification of GCLM as a tumor promotor and immunological biomarker in bladder cancer

Song Wang  1 , He Wang  2 , Shaoxing Zhu  1 , Fangyin Li  1
Affiliations

Systematical analysis of ferroptosis regulators and identification of GCLM as a tumor promotor and immunological biomarker in bladder cancer

Song Wang et al. Front Oncol. .

Abstract

Bladder cancer (BCa) is a life-threaten disease with an increasing incidence with age, and immunotherapy has become an important treatment for BCa, while the efficiency of the immune system declines with age. It is vital to reveal the mechanisms of tumor immune microenvironment (TIME) and identify novel immunotherapy targets for BCa. Through analyzing the RNA-seq of TCGA-BLCA cohort, we distinguished two ferroptosis-related BCa clusters, and we discovered that in comparation with cluster 2, the cluster 1 BCa patients showed higher PD-L1 expression, more unfavorable overall survival and higher tumor stage and grade. XCELL analyses showed that higher level of Th2 cell and Myeloid dendritic cell were enriched in cluster 1, while NK T cell was enriched in cluster 2, and TIDE analysis revealed that cluster 2 was more sensitive to immunotherapy than cluster 1. GSEA analysis implied that Toll-like signaling pathway and JAK_STAT signaling pathway were significantly enriched in cluster 1. Subsequently, through performing bioinformatic analysis and cell experiments, we demonstrated that GCLM is overexpressed in BCa and indicates dismal prognosis, and knockdown of GCLM can significantly suppress the colony formation ability of BCa cells. Furthermore, we also found that GCLM might be correlated with immune infiltration in BCa, and can serve as a tumor promotor and immunological biomarker in BCa, our research showed the vital roles of ferroptosis regulators in TIME of BCa, and GCLM is a latent therapeutic target for cancer immunotherapy.

Keywords: GCLM; biomarker; bladder cancer; ferroptosis; immune infiltration.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The relative expression levels and prognostic values of ferroptosis regulators in TCGA-BLCA cohort. (A) 28 significantly upregulated ferroptosis regulators in BCa. (B) 22 significantly downregulated ferroptosis regulators in BCa. (C) The expression levels of 48 ferroptosis regulators were not significantly different between BCa and normal bladder tissues. (D) The prognostic values of ferroptosis regulators in BCa, the red font represents statistical significance. *p < 0.05; **p < 0.01, ***p < 0.001. NS, Non Significance.
Figure 2
Figure 2
Consensus clustering analysis of ferroptosis regulators in BCa. (A–E) Consensus clustering matrix for (A) k = 2, (B) k = 3, (C) k = 4, (D) k = 5, (E) k = 6. (F) Cumulative curves for k = 2–6. (G, H) Distribution of C1/C2 in BCa (G) TNM stage and (H) tumor grade. (I) Kaplan-Meier curves showing OS of C1/C2. C1 and C2 represents Cluster 1 and Cluster 2, respectively.
Figure 3
Figure 3
Distinct tumor immune microenvironment (TIME) between two BCa clusters. (A) Correlations of ferroptosis regulators with PD-L1 expression in BCa. The red dots represent positive correlation, grey dots represent no significance, and blue dots represent negative correlation. (B) The heatmap visualized distinct infiltrating level of immune cells between two BCa clusters. (C) The half-violin plots visualized the distribution of the immune checkpoints of two BCa clusters. *p < 0.05; **p < 0.01, ***p < 0.001.
Figure 4
Figure 4
Differential infiltrating levels of immune cells between two BCa clusters. (A) The immune score and (B) microenvironment score of two BCa clusters. (C-F) The infiltrating levels of (C) Th2 cell, (D) Myeloid dendritic cell, (E) NK T cell, (F) CD4+ effector memory T cell in two clusters. (G) The TIDE score of C1/C2. (H) Toll-like signaling pathway and (I) JAK_STAT signaling pathway. *p < 0.05; **p < 0.01, ***p < 0.001.
Figure 5
Figure 5
Identification of GCLM as the potential immune-related ferroptosis regulator in BCa. (A) Lasso regression of ferroptosis regulators in BCa. (B) The optimal lambda value in Lasso model. (C) Prognostic analysis of gene signature including 5 hub ferroptosis regulators. (D) The prognosis of 5 hub genes in BCa via Cox regression method. (E) A Venn diagram displayed that GCLM is the potential key ferroptosis regulator in BCa. (F) The relative expression of GCLM in Cluster 1 and 2. ****p < 0.0001.
Figure 6
Figure 6
GCLM is highly expressed in BCa and its elevated expression correlated with unfavorable prognosis. (A) GCLM is highly expressed in 19 paired BCa and normal bladder tissues in TCGA-BLCA cohort. GCLM expression is associated with higher tumor histologic grade (B) and pathologic stage (C) in BCa. (D) GCLM is highly expressed in BCa T24, UC3 and J82 cell lines compared to normal bladder cell line SV. (E) The IHC stain of GCLM in normal bladder tissue (left) and bladder cancer tissue (right). Overexpression of GCLM predicts unfavorable (F) OS and (G) PFI in TCGA-BLCA cohort. (H) Overexpression of GCLM predicts unfavorable OS of BCa patients in Kaplan-Meier plotter database. *p < 0.05; **p < 0.01, ***p < 0.001.
Figure 7
Figure 7
GCLM promotes tumor progression and is associated with immune infiltration in BCa. (A, B) Knockdown the expression of GCLM can significantly inhibit the (A) colony formation ability and (B) migration ability of T24 and UC3 cells. (C) The results of GSEA algorithm analysis indicated the potential KEGG signaling pathways of highly expressed GCLM in BCa. (D) The heatmap indicated that the SRXN1, TXNRD1 and ABCA4 are top 3 GCLM co-expressed genes in TCGA-BLCA cohort. The ssGSEA analysis of the correlations of (E) GCLM, (F) SRXN1, (G) TXNRD1 and (H) ABCA4 with immune cells in BCa. ***p < 0.001, ****p < 0.0001.
Figure 8
Figure 8
Systematic analysis of the links between GCLM expression and immune infiltration in pan-cancer. (A) XCELL analysis the links between GCLM expression and immune cells in pan-cancer. (B) Correlations of GCLM and immune checkpoints in pan-cancer. *p < 0.05; **p < 0.01, ***p < 0.001.
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