AUTHOR=Ju Mingyi , Fan Jingyi , Zou Yuanjiang , Yu Mingjie , Jiang Longyang , Wei Qian , Bi Jia , Hu Baohui , Guan Qiutong , Song Xinyue , Dong Mingyan , Wang Lin , Yu Lifeng , Wang Yan , Kang Hui , Xin Wei , Zhao Lin 

TITLE=Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.807840

DOI=10.3389/fimmu.2022.807840

ISSN=1664-3224

ABSTRACT=Prostate cancer recognized as a “cold” tumor has an immunosuppressive microenvironment in which regulatory T-cells (Tregs) usually represent a major role. Therefore, identifying a prognostic signature of Tregs has promising benefits of improving survival of prostate cancer patients. However, the traditional methods of Tregs quantification usually suffer from bias and variability. Transcriptional characteristics have recently been found have predictive power for the infiltrations of Tregs. Thus novel, a machine learning-based computational framework has been presented using Tregs and 19 other immune cell types using 42 purified immune cell datasets from GEO to identify Tregs-specific mRNAs, and developed and validated a prognostic signature of Tregs (named “TILTregSig”) consist of five mRNAs (SOCS2, EGR1, RRM2, TPP1 and C11orf54) for monitoring prognosis of prostate cancer using TCGA and ICGC datasets. The TILTregSig showed stronger predictive power for tumor immunity compared with tumor mutation burden and glycolytic activity which have been reported as immune predictors. Further analyses indicate that the TILTregSig might influence tumor immunity mainly by mediating tumor-infiltrating Tregs and could be a powerful predictor for Tregs in prostate cancer. Moreover, the TILTregSig showed promising potential for predicting cancer immunotherapy (CIT) response in five CIT response datasets and therapeutic resistance in GSCALite dataset in multiple cancers. Our TILTregSig derived from PBMC makes it possible to achieve a straightforward, noninvasive and inexpensive detection assays for prostate cancer compared with currently histopathological examination which require invasive tissue puncture, which lays the ground for the future development of a panel of different molecules in peripheral blood comprising a biomarker of prostate cancer.