Using Multiomics and Machine Learning: Insights into Improving the Outcomes of Clear Cell Renal Cell Carcinoma via SRD5A3-AS1/hsa-let-7e-5p/RRM2 Axis

Abstract

Background: the paucity of early diagnostic markers for clear cell renal cell carcinoma (ccRCC) contributes significantly to its poor clinical prognosis. Aberrations in fatty acid metabolism (FAM) have been implicated in the progression of this malignancy, suggesting the metabolic pathway as a potential target for novel therapeutic approaches. Results: this investigation demonstrated that FAM was decreased in ccRCC, which correlated with a worsening clinical prognosis. A prognostic signature was constructed using FAM-associated long noncoding RNAs (lncRNAs), highlighting notable differences in the tumor microenvironment and treatment responses between high- and low-risk groups. This study employed a suite of six machine learning algorithms─least absolute shrinkage and selection operator (LASSO) regression, eXtreme gradient boosting (XGBoost), support vector machine recursive feature elimination (SVM-RFE), Random Forest, gradient boosting machine (GBM), and decision tree─to identify a significant regulatory axis involving SRD5A3-AS1, hsa-let-7e-5p, and RRM2. The single-cell analysis of ccRCC tissues and peripheral blood, as well as a spatial transcriptomic analysis, revealed that this regulatory axis suppresses FAM in precursor CD8+ T cells. These cells exacerbate the clinical outcomes of ccRCC by affecting other immune cells through the macrophage migration inhibitory factor (MIF). Drug sensitivity assays identified axitinib and sorafenib as potential SRD5A3-AS1 inhibitors and dasatinib and fulvestrant as RRM2 inhibitors. The molecular docking results confirmed the stability of the binding of RRM2 to dasatinib and fulvestrant, suggesting that these drugs are promising for therapeutic applications in ccRCC.

My Contribution

Implemented the ML pipeline to identify key features of the SRD5A3‑AS1/hsa‑let‑7e‑5p/RRM2 axis and quantify its prognostic value in clear cell renal cell carcinoma (ccRCC).

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Recommended citation: Sun M, Yang Z, Luo Y, Qin L, Peng L, Yan J, Qiu T, Zhang Y (2025) Using Multiomics and Machine Learning: Insights into Improving the Outcomes of Clear Cell Renal Cell Carcinoma via SRD5A3-AS1/hsa-let-7e-5p/RRM2 Axis. ACS Omega. https://doi.org/10.1021/acsomega.5c01337
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