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.
Chen
Rong
R
Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America.
Sigdel
Tara K
TK
Li
Li
L
Kambham
Neeraja
N
Dudley
Joel T
JT
Hsieh
Szu-Chuan
SC
Klassen
R Bryan
RB
Chen
Amery
A
Caohuu
Tuyen
T
Morgan
Alexander A
AA
Valantine
Hannah A
HA
Khush
Kiran K
KK
Sarwal
Minnie M
MM
Butte
Atul J
AJ
D015415:Biomarkers / Q000097:blood / Q000378:metabolism / Q000652:urine; D001798:Blood Proteins / Q000032:analysis / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D030541:Databases, Genetic; D004797:Enzyme-Linked Immunosorbent Assay; D020869:Gene Expression Profiling; D006084:Graft Rejection / Q000097:blood / Q000175:diagnosis / Q000378:metabolism* / Q000652:urine; D016027:Heart Transplantation / Q000009:adverse effects / Q000276:immunology; D006651:Histocytochemistry; D006801:Humans; D007678:Kidney Glomerulus / Q000378:metabolism / Q000473:pathology; D016030:Kidney Transplantation / Q000009:adverse effects / Q000276:immunology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011507:Proteinuria / Q000097:blood / Q000652:urine; D012313:RNA / Q000032:analysis* / Q000096:biosynthesis / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results
D015415:Biomarkers; D001798:Blood Proteins; D012313:RNA