Clear cell renal cell carcinoma (ccRCC) is the most common and deadliest form of kidney cancer, responsible for approximately 180,000 deaths worldwide each year. It typically presents at an advanced stage, making early detection difficult and treatment outcomes poor.
Cellular senescence refers to a state in which cells permanently stop dividing but do not die. Once thought to simply reflect aging, senescent cells are now recognized as playing an active role in many diseases, including cancer. Their behavior in tumors is complicated: they can both suppress and promote cancer depending on the context.
Senescent cells secrete a variety of molecules collectively known as the senescence-associated secretory phenotype (SASP), which can promote inflammation, suppress immune responses, encourage blood vessel formation, and enhance the invasiveness of neighboring cancer cells.
Current treatments for advanced ccRCC include surgery, targeted therapies that block blood vessel growth (anti-angiogenic drugs), mTOR inhibitors, and immune checkpoint inhibitors. Despite progress, many patients develop resistance or do not respond adequately, highlighting the need for new therapeutic targets rooted in a deeper understanding of tumor biology.
This study used a comprehensive multi-omics strategy, integrating bulk transcriptomic data from TCGA-KIRC (614 samples) and two GEO datasets, proteomic data, single-cell RNA sequencing, and spatial transcriptomics to examine ccRCC from multiple biological angles simultaneously.
To identify cellular senescence-related genes in ccRCC, the researchers intersected 665 senescence genes from the CellAge database and 155 KEGG senescence pathway genes with differentially expressed genes and WGCNA module genes from TCGA-KIRC data, yielding 154 candidate genes for further analysis.
A prognostic signature was built using LASSO regression, and patients were classified into high-risk and low-risk groups based on their risk scores. The model was validated using survival analysis, time-dependent ROC curves, and PCA to confirm that the groups were biologically distinct.
To identify the single most important driver gene, the researchers used seven different machine learning algorithms: KNN, Elastic Net, GBM, PLS, SVM, Naive Bayes, and stepLDA. Only genes consistently flagged by multiple algorithms across 101 model combinations were considered top candidates.
The role of the top gene was then validated using immunofluorescence staining, RT-qPCR, cell culture experiments, wound healing assays, and siRNA knockdown in ccRCC cell lines, providing biological evidence to support the computational findings.
Analysis of TCGA-KIRC data showed that expression patterns of senescence-related genes divide ccRCC patients into distinct molecular subgroups with significantly different survival outcomes. Patients with higher senescence activity in their tumors consistently fared worse.
A prognostic signature using LASSO regression identified ten signature genes: five protective genes (including FOXM1) and five risk genes (including CD34, FLT1, GADD45G, GNMT, and NDRG1). Patients in the high-risk group showed higher mortality and upregulated expression of the risk genes.
Immune infiltration analysis showed that high-risk patients had a more immunosuppressive tumor environment, with altered immune cell infiltration patterns and significant changes in immune-inhibitory and immune-stimulatory gene expression. High-risk patients appeared less likely to benefit from immunotherapy.
The risk score was negatively correlated with immune score and positively correlated with tumor purity, suggesting that high-risk tumors are less immune-infiltrated and more composed of cancer cells. Tumor mutational burden was also lower in the high-risk group, reinforcing the expectation of reduced immunotherapy benefit in this subgroup.
Across all seven machine learning algorithms and 101 model combinations, FLT1 (Fms-related tyrosine kinase 1) consistently emerged as the single most important gene associated with cellular senescence and ccRCC progression. It was validated experimentally using RT-qPCR, immunofluorescence, and cell line studies.
FLT1 is also known as VEGFR1 (Vascular Endothelial Growth Factor Receptor 1). It plays a central role in angiogenesis, the process by which tumors grow new blood vessels to sustain themselves. High FLT1 expression in tumors is generally associated with aggressive disease and poorer prognosis.
Multi-omics analysis revealed that FLT1 operates within a regulatory network that includes two closely related genes: VEGFA (the primary ligand that activates FLT1) and AKT1 (a downstream signaling molecule that mediates FLT1's effects on cell survival and proliferation). Together, the VEGFA/FLT1/AKT1 axis coordinates key aspects of tumor biology.
Knockdown of FLT1 in 786-O ccRCC cells using siRNA significantly reduced cell migration and proliferation, as confirmed by wound healing assays and cytotoxicity testing. FLT1 knockdown also downregulated key senescence markers including CDKN1A (p21) and CDKN2A (p16), directly linking FLT1 activity to the senescence program in ccRCC cells.
Single-cell analysis revealed that FLT1 and AKT1 are predominantly expressed in endothelial cells (the cells that line blood vessels), while VEGFA is most highly expressed in a specific subset of proximal tubular epithelial cells in ccRCC tissues compared to normal kidney tissue.
These cell subpopulations with the highest VEGFA/FLT1/AKT1 activity also had the highest copy number variation (CNV) scores, which are genomic abnormalities associated with cancer malignancy. This confirms that cells with high activation of this axis are the most genetically unstable and cancerous.
Cell communication analysis showed that in the tumor environment, endothelial cells shift from being signal senders (as in normal tissue) to becoming important signal receivers, being widely influenced by signals from epithelial cells and other populations. The VEGF signaling pathway primarily mediates this epithelial-to-endothelial communication.
A co-culture experiment provided direct evidence: VEGFA-stimulated epithelial cells produced signals that upregulated FLT1, AKT1, and downstream effectors EGR1 and MMP9 in neighboring endothelial cells, confirming that epithelial-derived VEGFA drives FLT1 activation in blood vessel cells. This creates a self-reinforcing loop that promotes tumor angiogenesis and progression.
Mouse single-cell data validated that the FLT1-centered regulatory network is evolutionarily conserved across species, with similar expression patterns and evidence of active epithelial-endothelial crosstalk in mouse ccRCC, underscoring the fundamental biological relevance of this pathway.
ccRCC patients with low expression of the FLT1-centered network showed significantly better predicted responses to immunotherapy, as measured by Immunophenoscore (IPS). This suggests that stratifying patients by FLT1 network activity could help identify who will benefit most from immune checkpoint inhibitors.
For patients with high FLT1 network expression, a combination therapy approach targeting all three nodes of the axis was proposed: FLT1 inhibitors (sorafenib, regorafenib, lenvatinib), an AKT1 inhibitor (capivasertib), and a VEGFA inhibitor (bevacizumab). All are clinically approved drugs being repurposed based on their molecular fit.
Molecular docking and 100-nanosecond molecular dynamics simulations confirmed that these drugs maintain stable binding to their respective protein targets over time, supporting their potential to effectively inhibit the FLT1-centered network in a therapeutic context.
Combination therapy targeting multiple nodes of the FLT1 axis simultaneously may help prevent drug resistance, which is a major limitation of single-target therapies. By blocking VEGFA, FLT1, and AKT1 in parallel, the approach attempts to cut off multiple survival pathways for malignant cells at once.
This study reframes cellular senescence not as a passive byproduct of aging but as an active biological program that shapes tumor malignancy in ccRCC. High senescence activity in tumors is linked to immunosuppression, angiogenesis, and cancer cell invasiveness rather than protective effects.
The discovery that proximal tubular epithelial cells with high VEGFA expression represent the most aggressive cell subset and act as "pioneer cells" driving tumor progression offers a new cellular target for early intervention in ccRCC. These cells may represent the origin point of malignant transformation in kidney cancer.
Endothelial cell subpopulations with co-expression of FLT1 and AKT1 showed the highest genomic instability scores, suggesting that the VEGFA/FLT1/AKT1 axis drives not just tumor blood vessel formation but the progressive acquisition of malignant characteristics in vascular cells during ccRCC development.
Future therapeutic strategies targeting the senescence microenvironment in ccRCC could include senolytics (drugs that eliminate senescent cells), senomorphics (drugs that suppress harmful SASP secretion), and combination regimens targeting the FLT1 network, all of which could work synergistically to control tumor progression.
This comprehensive study identifies FLT1 as a central driver gene in kidney cancer malignancy driven by cellular senescence, revealing a network of interacting cells and molecules that could be targeted therapeutically. This is the kind of multi-layered biological understanding that precision medicine requires.
For patients, the FLT1 expression level in their tumor may serve as an important biomarker to guide treatment decisions. Patients with low FLT1 network activity may benefit most from immunotherapy, while those with high FLT1 activity may do better with targeted anti-angiogenic combinations.
The finding that five existing approved drugs (sorafenib, regorafenib, lenvatinib, capivasertib, bevacizumab) can target different nodes of the FLT1 network offers an immediately actionable hypothesis for clinical investigation. These drugs are already used in cancer care, which could accelerate their testing in FLT1-high ccRCC patients.
While more research and clinical trials are needed before these findings directly change standard care, this study advances our understanding of kidney cancer biology in a way that could soon translate to more personalized and effective treatment recommendations for individual ccRCC patients.