Clear cell renal cell carcinoma (ccRCC) is the predominant subtype of kidney cancer. Despite the availability of targeted therapies and immunotherapy, advanced ccRCC remains difficult to treat, and the molecular drivers of disease progression are incompletely understood.
MELK (Maternal Embryonic Leucine Zipper Kinase) is a serine/threonine kinase that has been implicated in oncogenic processes across multiple cancer types, including breast cancer and glioblastoma. Its role in ccRCC had not been systematically characterized prior to this study.
Bioinformatics integration of large-scale transcriptomic datasets (TCGA and GEO) with protein-protein interaction (PPI) network analysis provides a computational strategy for prioritizing candidate oncogenes from among thousands of differentially expressed genes, identifying those most likely to drive disease rather than merely correlate with it.
This study combined TCGA transcriptomic data with the GSE73731 GEO dataset to identify MELK as a key differentially expressed gene, then used wet laboratory experiments to establish its functional role and molecular mechanism in ccRCC progression.
Differential gene expression analysis was performed on both the TCGA ccRCC cohort and the GSE73731 GEO dataset independently. Genes significantly upregulated in ccRCC tumors versus normal kidney tissue in both datasets were intersected, yielding 86 common differentially expressed genes (DEGs).
The 86 common DEGs were imported into STRING for protein-protein interaction network construction. STRING maps known and predicted physical and functional interactions between proteins, creating a network where nodes are proteins and edges represent interaction evidence.
MCODE (Molecular Complex Detection) algorithm was applied to the PPI network to identify highly interconnected clusters, called modules, within the network. Hub genes within these modules, those with the highest connectivity and therefore most likely to be central biological regulators, were prioritized as candidates.
MELK emerged as the seed gene (hub) in the most significant MCODE module, suggesting it occupies a central position in the dysregulated molecular network of ccRCC. This network-based prioritization helped focus experimental efforts on the most biologically plausible candidate.
MELK expression was significantly higher in advanced ccRCC (stages III and IV) compared to early-stage tumors and normal kidney tissue, with high MELK expression correlating with shorter overall survival in TCGA Kaplan-Meier analysis. This established MELK as a clinically relevant prognostic marker.
Functional experiments using ccRCC cell lines confirmed that MELK overexpression increased cell proliferation rates, colony formation ability, and wound healing migration, while MELK knockdown (via siRNA or shRNA) produced the opposite effects: reduced proliferation and impaired migration.
Invasion assays using Matrigel transwell chambers showed that MELK-overexpressing cells penetrated the extracellular matrix barrier more efficiently than controls, while MELK-depleted cells showed significantly reduced invasiveness. These in vitro results collectively support a causal role for MELK in ccRCC aggressiveness.
In vivo xenograft experiments in immunodeficient mice confirmed that ccRCC cell lines with MELK overexpression formed larger tumors with faster growth rates than control-transfected cells, while MELK knockdown tumors grew significantly slower, validating the in vitro findings in a more physiologically relevant model.
To understand how MELK promotes tumor progression, the study investigated its downstream molecular targets. Co-immunoprecipitation experiments demonstrated that MELK physically interacts with PRAS40 (Proline-Rich AKT Substrate of 40 kDa), a known inhibitory component of the mTORC1 complex.
In vitro kinase assays confirmed that MELK directly phosphorylates PRAS40 at threonine-246 (Thr246). Phosphorylation of Thr246 is a key regulatory event: phospho-PRAS40 dissociates from raptor, the mTORC1 scaffolding protein that holds mTORC1 in its inactive state.
When PRAS40 dissociates from raptor upon MELK-mediated phosphorylation, the mTORC1 complex becomes active. mTORC1 then phosphorylates its canonical downstream targets p70 S6 kinase (S6K1) and 4E-BP1, promoting protein synthesis, cell growth, and proliferation, all core features of the malignant phenotype.
This places MELK upstream of mTORC1 in ccRCC, where the mTOR pathway is already a major therapeutic target. The MELK-PRAS40-mTORC1 axis provides a novel upstream entry point for regulating a pathway that existing drugs like everolimus and temsirolimus already target downstream.
Gene Set Enrichment Analysis (GSEA) was performed on the TCGA ccRCC transcriptomic data stratified by MELK expression level. This unbiased analysis confirmed that mTORC1 signaling gene sets were significantly enriched in tumors with high MELK expression, providing population-level genomic evidence for the MELK-mTORC1 connection.
To confirm that MELK promotes growth specifically through mTORC1 activation, rapamycin (an mTORC1 inhibitor) was applied to MELK-overexpressing ccRCC cells. Rapamycin suppressed the increased proliferation and invasion conferred by MELK overexpression, demonstrating that mTORC1 activation is required for MELK's oncogenic effects.
Western blot analysis showed that phosphorylation of S6K1 and 4E-BP1 (mTORC1 activity markers) was elevated in MELK-overexpressing cells and reduced in MELK-knockdown cells, providing biochemical confirmation that MELK controls mTORC1 activity levels in ccRCC cells.
Together, the GSEA genomic evidence, the rapamycin pharmacological rescue, and the S6K1/4E-BP1 biochemical readouts form a convergent line of evidence establishing the MELK-PRAS40-mTORC1 axis as a genuine functional oncogenic pathway in ccRCC.
MELK represents a novel actionable kinase in ccRCC that operates upstream of the already-validated mTOR pathway. Targeting MELK could overcome resistance mechanisms that emerge when mTOR is inhibited downstream, since MELK-mediated PRAS40 phosphorylation would not be blocked by rapamycin alone.
MELK inhibitors have entered clinical development for other cancer types, and the mechanistic data here provides rationale for their investigation in ccRCC, particularly in combination with existing mTOR-targeted therapies or immune checkpoint inhibitors.
As a prognostic biomarker, MELK expression measured from tumor biopsy or resection specimens could help identify patients at highest risk of progression, who might benefit from more intensive surveillance or earlier initiation of systemic therapy.
The study's integration of bioinformatics-guided discovery with multi-layered wet laboratory validation demonstrates a rigorous framework for moving from large-scale genomic data to mechanistically defined therapeutic targets, a model applicable to oncology target discovery more broadly.