Abstract
Background
Long noncoding RNAs (lncRNAs) have been reported to play an important role in tumor immune modification. Nonetheless, the clinical implication of immune-associated lncRNAs in renal cell carcinoma (RCC) remains to be further explored.
Methods
76 combinations of machine learning algorithms were integrated to develop and validate a machine learning-derived immune-related lncRNA signature (MDILS) in five independent cohorts (n = 801). We collected 28 published signatures and collated clinical variables for comparison with MDILS to verify its efficacy. Subsequently, molecular mechanisms, immune status, mutation landscape, and pharmacological profile were further investigated in different stratified patients.
Results
Patients with high MDILS displayed worse overall survival than those with low MDILS. The MDILS could independently predict overall survival and convey robust performance across five cohorts. MDILS has a significantly better performance compared with traditional clinical variables and 28 published signatures. Patients with low MDILS exhibited more abundant immune infiltration and higher potency of immunotherapeutic response, while patients with high MDILS might be more sensitive to multiple chemotherapeutic drugs (e.g., sunitinib and axitinib).
Conclusion
MDILS is a robust and promising tool to facilitate clinical decision-making and precision treatment of RCC.
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Availability of data and materials
All data in our study are available upon request. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number (s) can be found in the article.
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LL and CGG contributed study design and paper revisiting. XWH contributed project oversight and paper revisiting. YF contributed data analysis, visualization, and paper writing. SYW, HX, ZX, YYZ, and LBW contributed paper revisiting. All authors approved this manuscript.
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Liu, L., Feng, Y., Guo, C. et al. Multi-center validation of an immune-related lncRNA signature for predicting survival and immune status of patients with renal cell carcinoma: an integrating machine learning-derived study. J Cancer Res Clin Oncol 149, 12115–12129 (2023). https://doi.org/10.1007/s00432-023-05107-0
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DOI: https://doi.org/10.1007/s00432-023-05107-0