Pattern genes are a group of genes that have a modularized expression behavior under serial physiological conditions. The identification of pattern genes will provide a path toward a global and dynamic understanding of gene functions and their roles in particular biological processes or events, such as development and pathogenesis. In this study, we present PaGenBase, a novel repository for the collection of tissue- and time-specific pattern genes, including specific genes, selective genes, housekeeping genes and repressed genes. The PaGenBase database is now freely accessible at http://bioinf.xmu.edu.cn/PaGenBase/. In the current version (PaGenBase 1.0), the database contains 906,599 pattern genes derived from the literature or from data mining of more than 1,145,277 gene expression profiles in 1,062 distinct samples collected from 11 model organisms. Four statistical parameters were used to quantitatively evaluate the pattern genes. Moreover, three methods (quick search, advanced search and browse) were designed for rapid and customized data retrieval. The potential applications of PaGenBase are also briefly described. In summary, PaGenBase will serve as a resource for the global and dynamic understanding of gene function and will facilitate high-level investigations in a variety of fields, including the study of development, pathogenesis and novel drug discovery.

Inter-tumor heterogeneity is a result of genomic, transcriptional, translational, and post-translational molecular features. To investigate the roles of protein glycosylation in the heterogeneity of high-grade serous ovarian carcinoma (HGSC), we perform mass spectrometry-based glycoproteomic characterization of 119 TCGA HGSC tissues. Cluster analysis of intact glycoproteomic profiles delineates 3 major tumor clusters and 5 groups of intact glycopeptides. It also shows a strong relationship between N-glycan structures and tumor molecular subtypes, one example of which being the association of fucosylation with mesenchymal subtype. Further survival analysis reveals that intact glycopeptide signatures of mesenchymal subtype are associated with a poor clinical outcome of HGSC. In addition, we study the expression of mRNAs, proteins, glycosites, and intact glycopeptides, as well as the expression levels of glycosylation enzymes involved in glycoprotein biosynthesis pathways in each tumor. The results show that glycoprotein levels are mainly controlled by the expression of their individual proteins, and, furthermore, that the glycoprotein-modifying glycans correspond to the protein levels of glycosylation enzymes. The variation in glycan types further shows coordination to the tumor heterogeneity. Deeper understanding of the glycosylation process and glycosylation production in different subtypes of HGSC may provide important clues for precision medicine and tumor-targeted therapy.

Lung cancer (LC) remains the leading cause of mortality from malignant tumors worldwide. Currently, a lack of serological biomarkers for early LC diagnosis is a major roadblock for early intervention and prevention of LC. To undertake this challenge, we employed a two-phase strategy to discover and validate a biomarker panel using a protein array-based approach. In Phase I, we obtained serological autoimmune profiles of 80 LC patients and 20 healthy subjects on HuProt arrays, and identified 170 candidate proteins significantly associated with LC. In Phase II, we constructed a LC focused array with the 170 proteins, and profiled a large cohort, comprised of 352 LC patients, 93 healthy individuals, and 101 patients with lung benign lesions (LBL). The comparison of autoimmune profiles between the early stage LC and the combined group of healthy and LBL allowed us to identify and validate a biomarker panel of p53, HRas, and ETHE1 for diagnosis of early stage LC with 50% sensitivity at >90% specificity. Finally, the performance of this biomarker panel was confirmed in ELISA tests. In summary, this study represents one of the most comprehensive proteome-wide surveys with one of the largest (i.e. 1,101 unique samples) and most diverse (i.e. nine disease groups) cohorts, resulting in a biomarker panel with good performance.

Behcet disease (BD) is a chronic systemic vasculitis and considered as an autoimmune disease. Although rare, BD can be fatal due to ruptured vascular aneurysms or severe neurological complications. To date, no known biomarker has been reported for this disease, making it difficult to diagnosis in the clinics. To undertake this challenge, we employed the HuProt arrays, each comprised of ∼20,000 unique human proteins, to identify BD-specific autoantibodies using a Two-Phase strategy established previously. In Phase I, we profiled the autoimmunity on the HuProt arrays with 75 serum samples collected from 40 BD patients, 15 diagnosed autoimmune patients who suffer from Takayasu arteritis (TA; n = 5)), ANCA associated vasculitis (AAV; n = 5), and Sjogren's syndrome (SS; n = 5), and 20 healthy subjects, and identified 20 candidate autoantigens that were significantly associated with BD. To validate these candidates, in Phase II we constructed a focused array with these 20 candidate BD-associated antigens, and use it to profile a much larger cohort, comprised of serum samples collected from 130 BD patients, 103 autoimmune patients (i.e. 40TA, 40 AAV and 23 SS), and 110 healthy controls. This allowed us to validate CTDP1 (RNA polymerase II subunit A C-terminal domain phosphatase)as a BD-specific autoantigen. The association of anti-CTDP1 with BD patients was further validated using the traditional Western blotting analysis. In conclusion, anti-CTDP1 antibody serves a novel autoantibody for Behcet disease and is expected to help more accurate clinical diagnosis.

Summary: Pattern Gene Finder (PaGeFinder) is a web-based server for on-line detection of gene expression patterns from serial transcriptomic data generated by high-throughput technologies like microarray or next-generation sequencing. Three particular parameters, the specificity measure, the dispersion measure and the contribution measure, were introduced and implemented in PaGeFinder to help quantitative and interactive identification of pattern genes like housekeeping genes, specific (selective) genes and repressed genes. Besides the on-line computation service, the PaGeFinder also provides downloadable Java programs for local detection of gene expression patterns. Availability:http://bioinf.xmu.edu.cn:8080/PaGeFinder/index.jsp Contact:appo@xmu.edu.cn; zouquan@xmu.edu.cn