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. 2024 Jun 17:15:1427475.
doi: 10.3389/fimmu.2024.1427475. eCollection 2024.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment

Xiangyang Wen #  1 , Jian Hou #  2 , Tiantian Qi #  3 , Xiaobao Cheng #  2 , Guoqiang Liao  1 , Shaohong Fang  1 , Song Xiao  1 , Longlong Qiu  1 , Wanqing Wei  4
Affiliations

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment

Xiangyang Wen et al. Front Immunol. .

Abstract

Background: Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC).

Methods: Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC.

Results: ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy.

Conclusion: This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

Keywords: anoikis; immune microenvironment; prognosis; renal cell carcinoma; signature.

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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 workflow of this study.
Figure 2
Figure 2
ARGs expression patterns in ccRCC. (A) Heat map of ARGs expression. (B) Volcano plot of ARGs differential genes. (C) Enrichment analysis. (D) KEGG analysis based on ARGs. (E) DEGs of ARGs. (F) Univariate Cox analysis on ARGs.
Figure 3
Figure 3
Core anoikis genes screening. (A, B) SVM–RFE screens the important anoikis ARGs. (C) Top 20 important ARGs. (D) Venn diagram. (E) ARGs interactions and correlations. (F) CNV frequency. (G) ARGs’ location in chromosome.
Figure 4
Figure 4
Cluster analysis for ccRCC patients. (A, B) cRCC patients were classified into two clusters based on ARGs profiles. (C, D) UAMP and tSNE analyses. (E) Survival analysis. (F) Heatmap based on ARGs and clinical characteristics of ccRCC patients. (G) ARGs expression between different clusters. (***P < 0.001).
Figure 5
Figure 5
Development and validation of a risk prognostic model for ccRCC patients. (A, B) Lasso Cox regression analysis. (C-E) K-M curves of ccRCC patients under high and low risk. (F-H) ROC curves of ccRCC patients under high and low risk. (I) Heatmaps exhibit 4 core ARGs expression patterns. (J) The riskscore levels in two ARGclusters. (K) Alluvial plots.
Figure 6
Figure 6
Immune landscape of two ARGcluster. (A) Immune cell infiltration levels at two ARGcluster patients. (B) GSEA analysis of the enrichment of ARGs. (C) GSVA enrichment analysis. (*P < 0.05; **P < 0.01; ***P < 0.001).
Figure 7
Figure 7
Immune infiltration in under different risk ccRCC patients (A, B) Immune cells infiltration analysis. (C) Correlation of 23 immune cells. (D) TME score. (Wilcox test, ***P < 0.001).
Figure 8
Figure 8
Correlation analysis of riskscore and immune cells. (A-I) Correlation of riskscore and immune cells. (J) Correlation of four ARGs and immune cells. (*P < 0.05; **P < 0.01; ***P < 0.001).
Figure 9
Figure 9
Single cell analysis reveals anoikis genes expression. (A, C) Umap of single cell clusters. (B) Cell communications of DCs and T proliferation cells. (D) Cell types distribution in each ccRCC sample. (E, F) 4 core ARGs expression patterns in immune cells.
Figure 10
Figure 10
Anoikis genes’ expression levels in ccRCC cells and tissues. (A) ARGs mRNA expression levels in primary ccRCC cells. (B) ARGs mRNA expression levels in ccRCC cells after culturing for 5 weeks (C) The mRNA levels of ARGs in ccRCC and normal kidney tissues. (*P < 0.05; ***P < 0.001).
Figure 11
Figure 11
Immunohistochemical results of ARGs proteins.
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The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This manuscript was supported by a grant from the Guangdong Medical Science and Technology Research (B2023384).