The type III receptor tyrosine kinase c‑Kit is activated by its ligand Stem Cell Factor (SCF) and regulates hematopoietic cell proliferation, survival, differentiation, and migration. Prior studies have linked aberrant c‑Kit/SCF signaling to the development and progression of acute myeloid leukemia (AML), but the downstream effector proteins and their clinical relevance in AML remain incompletely characterized. This study used a combined proteomic and transcriptomic approach to identify proteins regulated by SCF‑activated c‑Kit in a human acute megakaryoblastic leukemia cell model and to evaluate their diagnostic and prognostic significance in AML patient cohorts.
Human acute megakaryoblastic leukemia Mo7e cells were stimulated with SCF to activate c‑Kit signaling. Global protein expression changes after stimulation were profiled using two‑dimensional gel electrophoresis followed by MALDI‑TOF and LC‑MS/MS to identify differentially expressed protein spots. Identified proteins were functionally annotated to infer implicated cellular processes. The clinical relevance of candidate proteins was assessed using transcriptomic data from the TCGA‑LAML cohort and matched normal data from GTEx, as well as independent GEO datasets. Quantitative RT‑PCR (qRT‑PCR) was used to validate expression changes in SCF‑stimulated Mo7e cells. Diagnostic and prognostic performance of candidates were evaluated through ROC curve analysis, Cox regression, LASSO modeling, Kaplan–Meier survival analysis, and construction of a prognostic nomogram to estimate 1‑, 3‑, and 5‑year overall survival probabilities.
Proteomic profiling of SCF‑stimulated Mo7e cells identified 14 proteins whose expression was altered following stimulation. Functional predictions for these proteins indicated roles in cytoskeletal organization, protein folding, metabolic pathways, vesicular trafficking, and translational regulation. The identified set thus links SCF‑triggered c‑Kit activation to cellular processes relevant to leukemia cell behavior, including structural reorganization and protein homeostasis.
Transcriptomic analysis of the TCGA‑LAML cohort was used to evaluate whether genes corresponding to the proteomically identified proteins are dysregulated in AML patients. This analysis highlighted significant dysregulation of multiple genes, including CFL1, CCT8, HSP90B1, MDH2, EIF5A, GSN, and TPI1 within the LAML dataset. These transcriptional changes provided a link between the SCF‑responsive proteome observed in Mo7e cells and expression alterations present in patient samples.
An integrated analytic pipeline combining ROC analysis, Cox proportional hazards regression, and LASSO penalized regression was applied to the candidate genes to identify those most strongly associated with overall survival in AML patients. Through these combined approaches, CFL1, CCT8, and GSN were identified as the most robust prognostic biomarkers associated with poorer overall survival in the TCGA‑LAML cohort. These three genes emerged as a prioritized signature based on statistical performance across diagnostic and prognostic metrics reported in the source.
The expression patterns of CFL1, CCT8, and GSN were validated in independent GEO datasets, confirming their dysregulation in external transcriptomic cohorts. Additionally, qRT‑PCR performed on SCF‑stimulated Mo7e cells validated the proteomic findings at the mRNA level for these genes, supporting their regulation downstream of c‑Kit/SCF signaling in the cellular model used.
Using CFL1, CCT8, and GSN as inputs, the authors developed a three‑gene prognostic nomogram to estimate overall survival probabilities at 1, 3, and 5 years for AML patients. The nomogram was validated within the scope of the reported analyses to demonstrate its potential utility for risk stratification. Details of model calibration, discrimination metrics, or external clinical implementation steps beyond the reported validation cohorts were not further detailed in the source.
This study identifies CFL1, CCT8, and GSN as downstream effectors of c‑Kit/SCF signaling in an acute megakaryoblastic leukemia cell model and as prognostic biomarkers in AML patient transcriptomic data. The integration of proteomics from SCF‑stimulated Mo7e cells with TCGA‑LAML and GEO dataset analyses, plus qRT‑PCR validation, supports a three‑gene signature associated with poorer overall survival. The authors propose that this signature may aid AML risk stratification and could inform future investigations into therapeutic targeting of c‑Kit downstream pathways. The study was funded by the Indian Council of Medical Research, and the authors declared no competing interests. Specific numerical performance metrics, model coefficients, and external prospective validation details were not reported in the source material.