A Three-Gene Peripheral Blood Potential Diagnosis Signature for Acute Rejection in Renal Transplantation

The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications

Kidney allograft rejection is one of the main limitations to long-term kidney transplant survival. The diagnostic gold standard for detecting rejection is a kidney biopsy, an invasive procedure that can often give imprecise results due to complex diagnostic criteria and high interobserver variability. In recent years, several additional diagnostic approaches to rejection have been investigated, some of them with the aid of machine learning (ML). In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. Various ML techniques were used in three main categories: (a) histopathologic assessment of kidney tissue with the aim to improve the diagnostic accuracy of a kidney biopsy, (b) assessment of gene expression in rejected kidney tissue or peripheral blood and the development of diagnostic classifiers based on these data, (c) radiologic assessment of kidney tissue using diffusion-weighted magnetic resonance imaging and the construction of a computer-aided diagnostic system. In histopathology, ML algorithms could serve as a support to the pathologist to avoid misclassifications and overcome interobserver variability. Diagnostic platforms based on biopsy-based transcripts serve as a supplement to a kidney biopsy, especially in cases where histopathologic diagnosis is inconclusive. ML models based on radiologic evaluation or gene signature in peripheral blood may be useful in cases where kidney biopsy is contraindicated in addition to other non-invasive biomarkers. The implementation of ML-based diagnostic methods is usually slow and undertaken with caution considering ethical and legal issues. In summary, the approach to the diagnosis of rejection should be individualized and based on all available diagnostic tools (including ML-based), leaving the responsibility for over- and under-treatment in the hands of the clinician.

The association and clinical relevance of phase-separating protein CAPRIN1 with noncoding RNA

CAPRIN1, cell cycle-associated protein 1, is an RNA-binding protein in stress granules, P bodies, and messenger RNA transport granules and has a high level of expression in cancer. It promotes the proliferation and invasion of cancer cells and enhances their glycolysis and chemoresistance. In addition, it mediates the formation of intracellular SGs in various ways when exposed to endogenous and exogenous stress. As an RNA-binding protein, it not only directly binds to several mRNAs associated with the cell cycle but also is the target of miRNA, lncRNA, and circRNA. Recently, CAPRIN1 is identified as a phase-separating protein that mediates the liquid-liquid phase separation within tumor cells. Moreover, the formation of CAPRIN1-mediated phase separation is regulated by circRNA and lncRNA. In addition, CAPRIN1 is associated with ubiquitination, which affects the relevant characteristics of cancer cells. This review discusses the different regulatory mechanisms of CAPRIN1 in various tumors and its association with noncoding RNA, suggesting its potential as an oncogenic signal and possibly as a diagnostic indicator in the future. This may provide the multifunctional characteristic insight of CAPRIN1 protein and potential therapeutic target in malignancy with high levels of CAPRIN1.

Characteristic Genes and Immune Infiltration Analysis for Acute Rejection after Kidney Transplantation

Background

Renal transplantation can significantly improve the survival rate and quality of life of patients with end-stage renal disease, but the probability of acute rejection (AR) in adult renal transplant recipients is still approximately 12.2%. Machine learning (ML) is superior to traditional statistical methods in various clinical scenarios. However, the current AR model is constructed only through simple difference analysis or a single queue, which cannot guarantee the accuracy of prediction. Therefore, this study identified and validated new gene sets that contribute to the early prediction of AR and the prognosis prediction of patients after renal transplantation by constructing a more accurate AR gene signature through ML technology.

Methods

Based on the Gene Expression Omnibus (GEO) database and multiple bioinformatic analyses, we identified differentially expressed genes (DEGs) and built a gene signature via LASSO regression and SVM analysis. Immune cell infiltration and immunocyte association analyses were also conducted. Furthermore, we investigated the relationship between AR genes and graft survival status.

Results

Twenty-four DEGs were identified. A 5 gene signature (CPA6, EFNA1, HBM, THEM5, and ZNF683) were obtained by LASSO analysis and SVM analysis, which had a satisfied ability to differentiate AR and NAR in the training cohort, internal validation cohort and external validation cohort. Additionally, ZNF683 was associated with graft survival.

Conclusion

A 5 gene signature, particularly ZNF683, provided insight into a precise therapeutic schedule and clinical applications for AR patients.