The roles of ferroptosis regulatory gene SLC7A11 in renal cell carcinoma: A multi-omics study

Abstract

Background: The ferroptosis inhibitory gene Solute carrier family 7 member 11 (SLC7A11) provides a new strategy for anticancer treatment. However, its function in renal cell carcinoma (RCC) remains elusive.

Methods: The expression and somatic mutation information of SLC7A11 in RCC samples were determined using The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), Oncomine, and cBioPortal databases. The prognostic value of SLC7A11 was assessed through survival analysis, Receiver operating characteristic curve (ROC) analysis, independent prognostic analysis, clinical subgroup analysis, and nomogram. Its prognostic value was also validated in the ICGC and GSE29607 cohorts. Gene set enrichment analysis (GSEA) was employed to investigate the effects of SLC7A11 on multiple metabolic pathways. The CIBERSORT algorithm and single-sample gene set enrichment analysis (ssGSEA) method were applied to evaluate the effects of SLC7A11 on the tumor immune microenvironment (TIM). SLC7A11's therapeutic correlations were analyzed using the GSE87121, GSE67501, and GSDC datasets. Finally, the biofunctions of SLC7A11 in renal cancer cells and ferroptosis were ascertained by MTT, wound healing, transwell, and western blot assays.

Results: Through multiple datasets, SLC7A11 was found to be markedly upregulated in RCC. In terms of prognosis, SLC7A11 overexpression conferred a worse prognosis and was identified as an independent prognostic factor. Its prognostic value was validated in ICGC cohort. Moreover, high SL7CA11 expression could stimulate nucleotides, fatty acids, and amino acid metabolism to meet the proliferative consumption of tumor cells. As for the immune effect, SLC7A11 suppressed antitumor immunity by reducing the abundances of CD8+ T and NK cells. Regarding the therapeutic response, SLC7A11 expression was not correlated with the sensitivities of most chemotherapy and targeted drugs. Finally, SLC7A11 promoted the proliferation, migration, and invasion of renal cancer cells by enhancing GPX4 output, which in turn inhibits ferroptosis.

Conclusions: SLC7A11 not only deeply influences RCC prognosis and TIM, but also promotes RCC progression by inhibiting ferroptosis and inducing metabolic reprogramming. In addition, SLC7A11 weakly affects the therapeutic effect and sensitivities of multiple chemotherapy and targeted drugs.

Keywords: SLC7A11; ferroptosis; metabolism; prognosis; renal cell carcinoma; tumor immune environment.

FIGURE 1

Flow chart of present study. DCA, decision curve analysis; ROC, receiver operating characteristic curve

FIGURE 2

SLC7A11 expression in RCC samples. (A) Pan‐cancer analysis of SLC7A11 in Oncomine database. (B) Meta‐analysis of SLC7A11 based on three renal cancer datasets in Oncomine database. (C) SLC7A11 expression in TCGA dataset. (D) SLC7A11 expression in ICGC dataset. (E–M) SLC7A11 expression in multiple GEO datasets. (N) The somatic mutation information of SLC7A11 in cBioPortal database. chrRCC, chromophobe renal cell carcinoma; pRCC, papillary renal cell carcinoma; RCC, renal cell carcinoma; RO, renal oncocytoma; *p < 0.05, **p < 0.01, and ***p < 0.001

FIGURE 3

The prognostic value of SLC7A11 in TCGA cohort. (A) The survival difference between high and low SLC7A11 expressions. (B) ROC in TCGA cohort. (C) DCA curve of SLC7A11. Different curves represent two kinds of RCC prognostic models (based on multivariate logistic regression analysis). “Sample A” represents the prognostic model consisting of age, histological grade, and TNM staging. “Complex A” represents the improved prognostic model consisting of age, histological grade, TNM staging, and SLC7A11 expression. (D) Cox univariate regression analysis in TCGA cohort. (E) Cox multivariate regression analysis in TCGA cohort. (F–N) Clinical subgroup analyses. (O) The nomogram consisting of age, TNM staging, and SLC7A11 expression. (P–R) Calibration plots of 1‐, 3‐, and 5‐year OSR. DCA, decision curve analysis; OSR, overall survival rate; RCC, renal cell carcinoma; ROC, receiver operating characteristic curve

FIGURE 4

The prognostic value of SLC7A11 in validation cohorts. (A) Survival difference in ICGC cohort. (B) ROC in ICGC cohort. (C) Cox univariate regression analysis in ICGC cohort. (D) Cox multivariate regression analysis in ICGC cohort. (E) SLC7A11 heatmap in ICGC cohort. (F) Survival difference in GSE29609 cohort. (G) ROC in GSE29609 cohort. (H) Cox univariate regression analysis in GSE29609 cohort. (I) Cox multivariate regression analysis in GSE29609 cohort. (J) SLC7A11 heatmap in GSE29609 cohort. CI, confidence Interval; HR, hazard ratio; *p < 0.05

FIGURE 5

The effects of SLC7A11 on biosynthetic metabolism and ferroptosis based on GSEA. (A–D) The effects of SLC7A11 on glycolysis‐related gene sets. (E–H) The effects of SLC7A11 on “nucleotide,” “fatty acid,” “glutamate and glutamine,” and “glycine serine and threonine” metabolic gene sets. (I–L) The effects of SLC7A11 on ferroptosis‐related gene sets. Module 306 gene set represents “Glycolysis and TCA cycle”; Module 337 gene set represents “Nucleotide metabolism.” GSEA, gene set enrichment analysis

FIGURE 6

The effects of SLC7A11 on TIM and therapeutic response. (A) The differential abundances of 22 immune cells between high and low SLC7A11 expression groups. High expression group is red and low expression group is green. (B) The differences in activities of 12 immune signaling pathways between high and low expression groups. (C–F) The correlations between SLC7A11 expression and enrichments of CD8+ T cells, cytotoxic cells, NK cells, and DCs. (G) The relationships between infiltration levels of six immune cells and copy number of SLC7A11. (H) Comparison of SL7CA11 expressive levels among different clinical outcomes. (I–J) Comparison of SL7CA11 expressive levels between sorafenib‐resistant and sorafenib‐sensitive patients. (K–L) Comparison of SL7CA11 expressive levels between nivolumab response and nonresponse patients. (M–R) The expressive correlations between SLC7A11 and six immune check points. (S) The difference in expressions of six immune check points between different SLC7A11 expression levels. (T) The correlations between SLC7A11 expression and the sensitivities of multiple chemotherapy and target drugs. TIM, tumor immune microenvironment; NK, natural killer; DCs, dendritic cells; PD, progressive disease; SD stable disease; CR, complete response; PR, partial response; IC50, half maximal inhibitory concentration. *p < 0.05, **p < 0.01, and ***p < 0.001

FIGURE 7

SLC7A11 can promote the proliferation of renal cancer cells. (A) Differential expressions of SLC7A11 between renal tubular epithelial cells (HK2) and renal cancer cells (786‐O and A498). (B–C) Evaluation of silencing efficiency of si‐SLC7A11. (D–E) MTT assays revealed that SLC7A11 overexpression could promote the proliferation of renal cancer cells. (F–H) Flow cytometric detection revealed that SLC7A11 overexpression could increase the S phase cells, but block the G0/G1 phase cells. *p < 0.05, **p < 0.01, and ***p < 0.001

FIGURE 8

SLC7A11 has a profound influence on migration, invasion of renal cancer cells. (A–C) The wound healing assays revealed that SLC7A11 overexpression could promote the migration of renal cancer cells. (D–F) The transwell assays revealed that SLC7A11 overexpression could promote the invasion of renal cancer cells. (G) SLC7A11 overexpression could inhibit the protein expression of ferroptosis marker, PTGS2, whereas increase that of GPX4. *p < 0.05, **p < 0.01