CRISPR/Cas9-based discovery of ccRCC therapeutic opportunities through molecular mechanism and immune microenvironment analysis

Abstract

Introduction: Clear cell renal cell carcinoma is a common and aggressive form of renal cell carcinoma. Its incidence continues to rise, and metastatic recurrence leads to poor clinical outcomes. Current prognostic biomarkers lack reliability. We integrated multi-omics data to discover key ccRCC genes and build a prognostic model to improve risk prediction and guide treatment decisions.

Methods: Our study integrated genome-wide CRISPR screening data from DepMap and transcriptomic profiles from TCGA to identify key genes associated with ccRCC pathogenesis. Initial screening identified 11 candidate genes through differential expression analysis and CRISPR functional validation. Using LASSO and Cox regression, we selected five key genes (GGT6, HAO2, SLPI, MELK, and EIF4A1) for model construction. The functional role of MELK was tested by knockdown experiments. Additional analyses included tumor mutation burden, immune microenvironment assessment, and drug response prediction.

Results: The model stratified patients into high-risk and low-risk groups with distinct survival outcomes. High-risk cases showed higher mutation loads, immunosuppressive features, and activated cytokine pathways, whereas low-risk cases displayed metabolic pathway activity. MELK knockdown reduced cancer cell proliferation and migration. High-risk patients exhibited better responses to targeted drugs such as pazopanib and sunitinib.

Discussion: Our study demonstrates the pivotal role of MELK in ccRCC progression. This multi-omics-driven model elucidates MELK-mediated mechanisms and their interactions with the tumor microenvironment, providing novel strategies for risk stratification and targeted therapy. Future studies will validate these findings in independent cohorts and investigate the regulatory networks of MELK to identify potential therapeutic targets.

Keywords: CRISPR-Cas9 screening; MELK; ccRCC; immunotherapy; prognostic model.

Figure 1

The flowchart and graphic abstract of this study.

Figure 2

Identification of 11 Important DEGs in ccRCC. (A) Venn diagram of genes in the TCGA and DEPMap datasets. (B) Expression heatmap of the eleven genes in normal versus tumor samples. (C) Differential expression levels of the eleven genes in normal and tumor samples. (D) Locations of the DEGs on chromosomes. (E) Expression correlation analysis of the eleven DEGs. *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 3

The construction and evaluation of the prognostic models. (A) Univariate Cox regression identifies 7 DEGs. (B) Coefficient trajectories of 7 DEGs in LASSO regression. (C) Optimal lambda selection in LASSO regression (10-fold CV). (D, E) Prognostic impact of 5 DEGs assessed by multivariate Cox regression. (F) Inter-gene correlations among the five DEGs.

Figure 4

Multi method validation of risk score-derived prognostic models. (A) KM survival curves demonstrated markedly shorter overall survival in high-risk ccRCC patients relative to those in the low-risk group. (B) ROC analysis of the DEGs prognostic signature for predicting the 1/3/5-year survival. (C, D) Risk score stratification and survival duration distribution in ccRCC cohort. (E) PCA discriminates high- and low-risk groups using whole transcriptome data. (F) KM survival analysis of ccRCC patients stratified by risk score in the GEO validation cohort (GSE26909, n=39).

Figure 5

Construction of a nomogram for prediction prognosis. (A) Univariate Cox regression analysis identified grade, stage, T stage, M stage, and risk score as significant prognostic factors. (B) Multivariate Cox regression identifies risk score and age as independent prognostic predictors. (C) Prognostic nomogram incorporating risk score and age for ccRCC survival probability. (D–F) Calibration curves demonstrate the accuracy of 1-year, 3-year, and 5-year overall survival predictions.

Figure 6

Correlation between TMB and risk score. (A, B) Comparative mutation landscapes in high-risk (A) and low-risk (B) groups. (C) Survival outcomes stratified by TMB levels. (D–I) Variant type distributions are shown for high-risk (D–F) and low-risk (G–I) patients.

Figure 7

Correlation of immune microenvironment with risk score. (A) Immune cell infiltration landscape in ccRCC revealed by CIBERSORT. (B–F) Linear regression models demonstrate risk score-dependent immune cell infiltration patterns. (G) Differential immune cell distribution between risk groups. (H–J) Risk-stratified therapeutic sensitivity to pazopanib, sunitinib, and temsirolimus.

Figure 8

Functional enrichment and GSEA analysis. (A) Significantly enriched biological pathways in high-risk patients. (B–F) Distinct biological pathway enrichment profile in low-risk cohort. (G) GO analysis reveals key biological processes of DEGs. (H) KEGG pathway enrichment landscape of DEGs.

Figure 9

MELK is a poor prognostic marker in ccRCC. (A) Significant variations in overall survival between ccRCC patients with high and low MELK expression. (B, C) Immunohistochemical evidence of MELK overexpression in tumor tissues versus NAT. (D) Successful MELK knockdown confirmed by western blot across 769P, 786O and Caki-1 cell lines. (E) Silencing MELK suppressed proliferation abilities in 769P, 786O and Caki-1 cells. (F–I) Silencing MELK suppressed migration abilities as measured via transwell assay (F) and scratch assay (G–I) in 769P, 786O and Caki-1 cells. *p < 0.05; **p < 0.01; ***p < 0.001.