HLA class I (ABC) upregulation on peripheral blood CD3+/CD8+ T lymphocyte surface is a potential predictor of acute rejection in renal transplantation

Transcriptomics-based identification of TYROBP and TLR8 as novel macrophage-related biomarkers for the diagnosis of acute rejection after kidney transplantation

Macrophages play an important role in the development and progression of acute rejection after kidney transplantation. The study aims to investigate the biological role and significance of macrophage-associated genes (MAG) in acute rejection after kidney transplantation. We utilized transcriptome sequencing results from public databases related to acute rejection of kidney transplantation for comprehensive analysis and validation in animal experiments. We found that a large number of immune-related signaling pathways are activated in acute rejection. PPI protein interaction networks and machine learning were used to establish a Hub gene consisting of TYROBP and TLR8 for the diagnosis of acute rejection. The single-gene GSEA enrichment analysis and immune cell correlation analysis revealed a close correlation between the expression of Hub genes and immune-related biological pathways as well as the expression of multiple immune cells. In addition, the study of TF, miRNAs, and drugs provided a theoretical basis for regulating and treating the Hub genes in acute rejection. Finally, the animal experiments demonstrated once again that acute rejection can aggravate kidney tissue damage, apoptosis level, and increase the release of inflammatory factors. We established and validated a macrophage-associated diagnostic model for acute rejection after kidney transplantation, which can accurately diagnose the biological alterations in acute rejection after kidney transplantation.

HLA Class I Expressions on Peripheral Blood Mononuclear Cells in Colorectal Cancer Patients

OBJECTIVE

To investigate the expression change of human leukocyte antigen (HLA) class I on human peripheral blood mononuclear cells (PBMCs) at both mRNA and protein levels, and to evaluate its roles in the development of colorectal cancer (CRC).

METHODS

In the present study, 50 patients with CRC, 35 patients with benign colorectal lesion and 42 healthy volunteers were enrolled. Expression levels of HLA class I mRNA and protein were determined using real-time quantitative reverse transcription PCR (RT-PCR) and flow cytometry analysis, respectively.

RESULTS

The expression levels of HLA class I mRNA and proteins were not influenced by age and gender. The relative ratios of HLA class I mRNA were 0.99±0.27 in healthy controls, 0.76±0.19 in benign patients, and 0.48±0.21 in CRC patients. Mean fluorescence intensities of HLA class I were 145.58±38.14 in healthy controls, 102.05±35.98 in benign patients and 87.44±34.01 in CRC patients. HLA class I on PBMCs was significantly down-regulated at both mRNA and protein levels in patients with stage III and IV CRC. CRC patients with lymph node metastasis also showed a decreased HLA class I expression at protein level.

CONCLUSION

HLA class I expressions on PBMCs are associated with staging of CRC and lymph node metastasis. Monitoring the expression of HLA class I on PBMCs may provide useful information for diagnosis and metastasis judgement of CRC.

Identifying biomarkers as diagnostic tools in kidney transplantation

There is a critical need for biomarkers for early diagnosis, treatment response, and surrogate end point and outcome prediction in organ transplantation, leading to a tailored and individualized treatment. Genomic and proteomic platforms have provided multiple promising new biomarkers during the last few years. However, there is still no routine application of any of these markers in clinical transplantation. This article will discuss the existing gap between biomarker discovery and clinical application in the kidney transplant setting. Approaches to implementing biomarker monitoring into clinical practice will also be discussed.

Differentially expressed RNA from public microarray data identifies serum protein biomarkers for cross-organ transplant rejection and other conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.

Protein biomarkers in the blood are urgently needed for the diagnosis of a wide variety of diseases to improve health care. We aim to find a fast and cost-effective strategy to discover diagnostic protein biomarkers. Hundreds of diseases have already been investigated using microarray technology, measuring the mRNA expression of all genes in the disease-damaged tissues. We analyzed biopsy-based microarray data for 41 diseases in the public repository, identified genes with dysregulated mRNA expressions and detectable-protein abundance in the blood, and predicted them as candidate diagnostic protein biomarkers. We found that clinically and preclinically validated diagnostic protein biomarkers were significantly enriched in our predicted protein candidates for 22 diseases. We then measured the concentrations of ten predicted protein biomarkers in the serum samples from 39 renal transplant patients. Three of them were confirmed to be diagnostic of acute rejection after renal transplantation. All three proteins were further confirmed to be diagnostic of acute rejection in 63 cardiac transplant recipients. Our results show that publically available genome-wide gene expression data on disease-damaged tissues can be effectively translated into diagnostic protein biomarkers.