Identification of Circulating MicroRNAs as Potential Biomarkers for Detecting Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common acute leukemia in adults. The disease is characterized by various cytogenetic and molecular abnormalities with distinct prognoses and gene expression profiles. Emerging evidence has suggested that circulating microRNAs (miRNAs) could serve as noninvasive biomarkers for cancer detection; however, little is known about circulating miRNA profiles in AML patients. In this study, a genome-wide serum miRNA expression analysis was performed using Solexa sequencing for initial screen, followed by validation with real-time PCR assays. The analysis was conducted on training and verification sets of serum samples from 140 newly diagnosed AML patients and 135 normal adult donors. After a two-phase selection and validation process, 6 miRNAs, miR-10a-5p, miR-93-5p, miR-129-5p, miR-155-5p, miR-181b-5p and miR-320d, were found to have significantly different expression levels in AML compared with control serum samples. Furthermore, unsupervised clustering analysis revealed the remarkable ability of the 6-miRNA profile to differentiate between AML patients and normal controls. The areas under the ROC curve for the selected miRNAs ranged from 0.8129 to 0.9531. More importantly, miR-181b-5p levels in serum were significantly associated with overall survival. These data demonstrated that the expression patterns of circulating miRNAs were systematically altered in AML and miR-181b-5p may serve as a predictor for overall survival in AML patients.     

Urinary Peptidomic Analysis Identifies Potential Biomarkers for Acute Rejection of Renal Transplantation

Introduction  Human urine is a complex matrix of proteins, endogenous peptides, lipids, and metabolites. The level of any or all of these components can reflect the pathophysiological status of an individual especially of the kidney at the time of urine collection. The naturally occurring endogenous urinary peptides which are thought to be the product of several proteolytic and degradation processes may provide clinically useful biomarkers for different renal and systemic diseases. Materials and Methods  To examine if specific differences in the urinary peptidome (<10 kDa) occur at the time of acute renal transplant rejection (AR), we undertook a study of urine samples collected from biopsy-proven AR (n = 10), stable graft function (n = 10), and healthy normal control (n = 10). The peptides (<10 kDa) were extracted and fractionated with high-performance liquid chromatography followed by matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometric (MS) analysis. Results  We identified 54 endogenous peptides, including multiple peptides for Tamm–Horsfall protein (UMOD). A panel of peptides are identified which discriminate renal transplant patients with AR from stable graft. We have shown that liquid chromatography followed by MALDI is a useful tool to identify potential biomarkers, which after verification with larger patient cohort can be used as a non-invasive monitoring tool for renal transplant rejection.     

Cerebrospinal Fluid Peptides as Potential Parkinson Disease Biomarkers: A Staged Pipeline for Discovery and Validation

Finding robust biomarkers for Parkinson disease (PD) is currently hampered by inherent technical limitations associated with imaging or antibody-based protein assays. To circumvent the challenges, we adapted a staged pipeline, starting from our previous proteomic profiling followed by high-throughput targeted mass spectrometry (MS), to identify peptides in human cerebrospinal fluid (CSF) for PD diagnosis and disease severity correlation. In this multicenter study consisting of training and validation sets, a total of 178 subjects were randomly selected from a retrospective cohort, matching age and sex between PD patients, healthy controls, and neurological controls with Alzheimer disease (AD). From ∼14,000 unique peptides displaying differences between PD and healthy control in proteomic investigations, 126 peptides were selected based on relevance and observability in CSF using bioinformatic analysis and MS screening, and then quantified by highly accurate and sensitive selected reaction monitoring (SRM) in the CSF of 30 PD patients versus 30 healthy controls (training set), followed by diagnostic (receiver operating characteristics) and disease severity correlation analyses. The most promising candidates were further tested in an independent cohort of 40 PD patients, 38 AD patients, and 40 healthy controls (validation set). A panel of five peptides (derived from SPP1, LRP1, CSF1R, EPHA4, and TIMP1) was identified to provide an area under curve (AUC) of 0.873 (sensitivity = 76.7%, specificity = 80.0%) for PD versus healthy controls in the training set. The performance was essentially confirmed in the validation set (AUC = 0.853, sensitivity = 82.5%, specificity = 82.5%). Additionally, this panel could also differentiate the PD and AD groups (AUC = 0.990, sensitivity = 95.0%, specificity = 97.4%). Furthermore, a combination of two peptides belonging to proteins TIMP1 and APLP1 significantly correlated with disease severity as determined by the Unified Parkinson''s Disease Rating Scale motor scores in both the training (r = 0.381, p = 0.038)j and the validation (r = 0.339, p = 0.032) sets. The novel panel of CSF peptides, if validated in independent cohorts, could be used to assist in clinical diagnosis of PD and has the potential to help monitoring or predicting disease progression.Parkinson disease (PD)¹, the second most common neurodegenerative disease after Alzheimer disease (AD), afflicts roughly 2% of persons over the age of 65 years (1, 2). Currently, PD diagnosis is mainly based on observation of the cardinal motor indicators of the disease, patient response to drug treatment, and medical history (3, 4). There is an appreciable misdiagnosis rate (4), particularly at early disease stages. Additionally, no objective measure of disease progression or treatment effects has been established. Thus, objective, reliable, and reproducible biomarkers are clearly needed to aid in the diagnosis of PD and tracking or predicting the disease progression.The most sensitive tests developed to date are based on imaging modalities, which can detect functional and structural abnormalities even prior to the onset of motor dysfunction (5, 6). However, the usefulness of neuroimaging techniques is limited by high cost, limited accessibility, difficulty in reliable differentiation of PD from other atypical parkinsonian disorders and subjection to confounding factors such as medication and compensatory responses (4–7). Biochemical and molecular markers in cerebrospinal fluid (CSF) and other body fluids have also been actively investigated (5, 8–12). The most extensively studied candidate in CSF is probably α-synuclein, the major protein component of Lewy bodies and Lewy neurites, the pathological hallmarks of PD (2). The current consensus is that CSF α-synuclein concentrations are generally lower in patients with PD compared with controls (5, 8–10); the sensitivity and specificity, however, appear to be only moderate, and no correlation with PD severity or progression has been observed (8, 9). Notably, all these CSF protein markers are measured using antibody-based assays, which are often associated with relatively high variability, particularly when different detection techniques (different antibodies, sample preparation, calibrators, etc.) are used, leading to discrepant results across laboratories (5). It should also be stressed that this high variability in immunoassays is not unique to PD, because similar difficulty is encountered in AD and other related disorders (13, 14).One strategy to avoid the inherent technical limitations associated with antibodies is to use alternative techniques in which unique peptides are selected and precisely quantified with mass spectrometry (MS) techniques, for example, accurate inclusion mass screening (AIMS) (15) and selected reaction monitoring (SRM) (16–18). To this end, in the last few years, we and others have utilized proteomic technologies to identify novel proteins and peptides associated with different disease states and stages (5, 6, 19–25). Using brain tissue or CSF, these unbiased proteomic profiling studies have revealed disease-related alterations in hundreds of peptides derived from many proteins (19–25). However, there are no quantitative assays for the majority of these candidate proteins/peptides, and development of such assays is limited by the lack of antibodies available for many of them. Thus, although a large library of potential peptide biomarkers has been developed, the vast majority never reach the stage of validation and clinical testing, hampered by the difficulty of de novo development of immunoassays, a process that is time consuming, prohibitively expensive to develop and very difficult to multiplex.In this study, we aim to establish a PD biomarker identification and verification pipeline, with the goal of prioritizing candidates and swiftly developing reliable quantitative assays. We focused on identifying peptides by SRM and AIMS, because these targeted proteomic technologies have been proposed as the basis of a viable biomarker pipeline (16) and have become a powerful tool in biomarker discovery because of their high sensitivity, accuracy and specificity. SRM, in particular, has emerged as an alternative to immunoaffinity-based measurements of defined protein sets with excellent reproducibility across different laboratories and instrument platforms (17, 18). The staged pipeline in the current investigation (Fig. 1) includes: (1) data-dependent and bioinformatic prioritization of thousands of candidate biomarkers identified in our previous profiling studies, (2) de novo development of antibody-free multiplex SRM assays to reliably measure tens to hundreds of peptides simultaneously, and (3) multiplex biomarker verification studies allowing identification and validation of models or panels of candidates in independent sample sets, two of which were used in this study.Open in a separate windowFig. 1.Overview of the workflow used for CSF peptide biomarker discovery and validation. AD, Alzheimer disease; AIMS, accurate inclusion mass screening; CO, healthy controls; DDA, data-dependent acquisition; PD, Parkinson disease; SRM, selected reaction monitoring.     

Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy–based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers—malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine—was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set. Moreover, this panel could classify blinded samples from the test set with an AUC of 0.89. These findings demonstrate that this urinary metabolite biomarker panel can aid in the future development of a urine-based diagnostic test for MDD.Major depressive disorder (MDD)¹ is a debilitating mental disorder affecting up to 15% of the general population and accounting for 12.3% of the global burden of disease (1, 2). Currently, the diagnosis of MDD still relies on the subjective identification of symptom clusters rather than empirical laboratory tests. The current diagnostic modality results in a considerable error rate (3), as the clinical presentation of MDD is highly heterogeneous and the current symptom-based method is not capable of adequately characterizing this heterogeneity (4). An approach that can be used to circumvent these limitations is to identify disease biomarkers to support objective diagnostic laboratory tests for MDD.Metabonomics, which can measure the small molecules in given biosamples such as plasma and urine without bias (5), has been extensively used to characterize the metabolic changes of diseases and thus facilitate the identification of novel disease-specific signatures as putative biomarkers (6–10). Nuclear magnetic resonance (NMR) spectroscopy–based metabonomic approaches characterized by sensitive, high-throughput molecular screening have been employed previously in identifying novel biomarkers for a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and schizophrenia (11–13).Specifically with regard to MDD, several animal studies have already characterized the metabolic changes in the blood and urine (14–19). These studies provide valuable clues as to the pathophysiological mechanism of MDD. However, no study has been designed with the aim of diagnosing this disease. Recently, using an NMR-based metabonomic approach, this research group identified a unique plasma metabolic signature that enables the discrimination of MDD from healthy controls with both high sensitivity and specificity (20). These findings motivated further study on urinary diagnostic metabolite biomarkers for MDD, which would be more valuable from a clinical applicability standpoint, as urine can be more non-invasively collected. Moreover, previous studies have also demonstrated the feasibility of identifying diagnostic metabolite biomarkers of psychiatric disorders in the urine. For example, using an NMR-based metabonomics approach, Yap et al. (21) identified a unique urinary metabolite signature that clearly discriminated autism patients from healthy controls. As systemic metabolic disturbances have been observed in the urine of a depressed animal model, it is likely that diagnostic metabolite markers for MDD can be detected in human urine.Therefore, in this study, NMR spectroscopy combined with multivariate pattern recognition techniques were used to profile 82 first-episode drug-naïve MDD subjects and 82 healthy controls (the training set) in order to identify potential metabolite biomarkers for MDD. Furthermore, 44 unselected MDD subjects and 52 healthy controls (the test set) were employed to independently validate the diagnostic performance of these urinary metabolite biomarkers.     

Identification and Validation of Potential Biomarkers for the Detection of Dysregulated microRNA by qPCR in Patients with Colorectal Adenocarcinoma

Colorectal cancer represents a lethal disease that has raised concern and has attracted significant attention. Adenocarcinoma is the most common type of colorectal cancer (CRC). MicroRNAs are thought to be potential biomarkers of CRC. Many researchers have focused on the expression pattern of miRNAs in CRC. However, previous studies did not pay particular attention to the effects of the degree of differentiation of the cancer with respect to the miRNA expression profile. First, this study compared the expression level of 1547 miRNAs by qRT-PCR in Colorectal adenocarcinoma tissues to that in paired normal tissues. In all, 93 miRNAs were identified that were significantly dysregulated in Colorectal adenocarcinoma relative to normal tissues (P<0.05). Then, we analyzed their potential as cancer biomarkers by ROC analysis, and the result revealed that three miRNAs with high sensitivity and specificity are suitable as biomarkers for the diagnosis of CRC (the value of the AUC was greater than 0.7). Interestingly, previous reports of 23 of these miRNAs have been scarce. Furthermore, we wanted to analyze the difference between well- and moderately differentiated cancers, and as expected, 58 miRNAs showed significant dysregulation. Importantly, 32 miRNAs were able to not only distinguish cancer tissues from normal tissues, but they were also able to identify well- and moderately differentiated cancers. In conclusion, the degree of differentiation has an important influence on the miRNA expression pattern. To avoid misdiagnoses and missed diagnoses, tumors of different degrees of differentiation should be treated differently when miRNAs are used as cancer biomarkers.     

Discovery and Validation of New Potential Biomarkers for Early Detection of Colon Cancer

Background

Accurate detection of characteristic proteins secreted by colon cancer tumor cells in biological fluids could serve as a biomarker for the disease. The aim of the present study was to identify and validate new serum biomarkers and demonstrate their potential usefulness for early diagnosis of colon cancer.

Methods

The study was organized in three sequential phases: 1) biomarker discovery, 2) technical and biological validation, and 3) proof of concept to test the potential clinical use of selected biomarkers. A prioritized subset of the differentially-expressed genes between tissue types (50 colon mucosa from cancer-free individuals and 100 normal-tumor pairs from colon cancer patients) was validated and further tested in a series of serum samples from 80 colon cancer cases, 23 patients with adenoma and 77 cancer-free controls.

Results

In the discovery phase, 505 unique candidate biomarkers were identified, with highly significant results and high capacity to discriminate between the different tissue types. After a subsequent prioritization, all tested genes (N = 23) were successfully validated in tissue, and one of them, COL10A1, showed relevant differences in serum protein levels between controls, patients with adenoma (p = 0.0083) and colon cancer cases (p = 3.2e-6).

Conclusion

We present a sequential process for the identification and further validation of biomarkers for early detection of colon cancer that identifies COL10A1 protein levels in serum as a potential diagnostic candidate to detect both adenoma lesions and tumor.

Impact

The use of a cheap serum test for colon cancer screening should improve its participation rates and contribute to decrease the burden of this disease.     

Identification and Validation of Oncologic miRNA Biomarkers for Luminal A-like Breast Cancer

Introduction

Breast cancer is a common disease with distinct tumor subtypes phenotypically characterized by ER and HER2/neu receptor status. MiRNAs play regulatory roles in tumor initiation and progression, and altered miRNA expression has been demonstrated in a variety of cancer states presenting the potential for exploitation as cancer biomarkers. Blood provides an excellent medium for biomarker discovery. This study investigated systemic miRNAs differentially expressed in Luminal A-like (ER+PR+HER2/neu-) breast cancer and their effectiveness as oncologic biomarkers in the clinical setting.

Methods

Blood samples were prospectively collected from patients with Luminal A-like breast cancer (n = 54) and controls (n = 56). RNA was extracted, reverse transcribed and subjected to microarray analysis (n = 10 Luminal A-like; n = 10 Control). Differentially expressed miRNAs were identified by artificial neural network (ANN) data-mining algorithms. Expression of specific miRNAs was validated by RQ-PCR (n = 44 Luminal A; n = 46 Control) and potential relationships between circulating miRNA levels and clinicopathological features of breast cancer were investigated.

Results

Microarray analysis identified 76 differentially expressed miRNAs. ANN revealed 10 miRNAs for further analysis (miR-19b, miR-29a, miR-93, miR-181a, miR-182, miR-223, miR-301a, miR-423-5p, miR-486-5 and miR-652). The biomarker potential of 4 miRNAs (miR-29a, miR-181a, miR-223 and miR-652) was confirmed by RQ-PCR, with significantly reduced expression in blood of women with Luminal A-like breast tumors compared to healthy controls (p = 0.001, 0.004, 0.009 and 0.004 respectively). Binary logistic regression confirmed that combination of 3 of these miRNAs (miR-29a, miR-181a and miR-652) could reliably differentiate between cancers and controls with an AUC of 0.80.

Conclusion

This study provides insight into the underlying molecular portrait of Luminal A-like breast cancer subtype. From an initial 76 miRNAs, 4 were validated with altered expression in the blood of women with Luminal A-like breast cancer. The expression profiles of these 3 miRNAs, in combination with mammography, has potential to facilitate accurate subtype-specific breast tumor detection.     

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.     

Mass Spectrometric Identification of Ancient Proteins as Potential Molecular Biomarkers for a 2000-Year-Old Osteogenic Sarcoma

Osteosarcoma is the most common primary malignant tumor of bone usually occurring in young adolescent and children. This disease has a poor prognosis, because of the metastases in the period of tumor progression, which are usually developed previous to the clinical diagnosis. In this paper, a 2000-year-old ancient bone remain with osteogenic sarcoma was analyzed searching for tumor biomarkers which are closely related to this disease. After a specific extraction SDS-PAGE gel electrophoresis followed by tryptic digestion was performed. After the digestion the samples were measured using MALDI TOF/TOF MS. Healthy bone samples from same archaeological site were used as control samples. Our results show that in the pathological skeletal remain several well known tumor biomarkers are detected such as annexin A10, BCL-2-like protein, calgizzarin, rho GTPase-activating protein 7, HSP beta-6 protein, transferrin and vimentin compared to the control samples. The identified protein biomarkers can be useful in the discovery of malignant bone lesions such as osteosarcoma in the very early stage of the disease from paleoanthropological remains.     

Plasma MicroRNAs as Potential Noninvasive Biomarkers for In-Stent Restenosis

Objective

To investigate whether microRNAs (miRs) can serve as novel biomarkers for in-stent restenosis (ISR).

Methods

This retrospective, observational single-centre study was conducted at the cardiovascular department of a tertiary hospital centre in the north of China. Follow-up coronary angiography at 6 to 12 months was performed in 181 consecutive patients implanted with drug-eluting stents. Fifty-two healthy volunteers served as the control group. The plasma miRs levels were analyzed by quantitative real-time PCR. Receiver-operating characteristic curve (ROC) analysis was performed to investigate the characters of these miRs as potential biomarkers of ISR.

Results

MiR-21 levels in ISR patients were significantly higher than those in non-ISR patients and healthy controls (P<0.05), while miR-100 (P<0.05), miR-143 (P<0.001) and miR-145 (P<0.0001) levels were significantly decreased in ISR patients. Further analysis showed that miR-21 levels were remarkably increased (P = 0.045), while miR-100 (P = 0.041), miR-143 (P = 0.029) and miR-145 (P<0.01) levels were dramatically decreased in patients with diffuse ISR compared to those with focal ISR. ROC analysis demonstrated that the area under curve of miR-145, miR-143, miR-100 and miR-21 were 0.880 (95% confidence interval; CI = 0.791–0.987, P<0.001), 0.818 (95% confidence interval; CI = 0.755–0.963, P<0.001), 0.608 (95% confidence interval; CI = 0.372–0.757, P<0.05) and 0.568 (95% confidence interval; CI = 0.372–0.757, P<0.05), with specificity of 83.1%, 80.1%, 68.9% and 68.6%, and sensitivity of 88.7%, 82.1%, 60.2% and 50.1%, respectively.

Conclusions

Circulating miR-143 and miR-145 levels are associated with the occurrence of ISR and can serve as novel noninvasive biomarkers for ISR.     

Identification of Potential Serum Proteomic Biomarkers for Clear Cell Renal Cell Carcinoma

Objective

To investigate discriminating protein patterns and serum biomarkers between clear cell renal cell carcinoma (ccRCC) patients and healthy controls, as well as between paired pre- and post-operative ccRCC patients.

Methods

We used magnetic bead-based separation followed by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to identify patients with ccRCC. A total of 162 serum samples were analyzed in this study, among which there were 58 serum samples from ccRCC patients, 40 from additional paired pre- and post-operative ccRCC patients (n = 20), and 64 from healthy volunteers as healthy controls. ClinProTools software identified several distinct markers between ccRCC patients and healthy controls, as well as between pre- and post-operative patients.

Results

Patients with ccRCC could be identified with a mean sensitivity of 88.38% and a mean specificity of 91.67%. Of 67 m/z peaks that differed among the ccRCC, healthy controls, pre- and post-operative ccRCC patients, 24 were significantly different (P<0.05). Three candidate peaks, which were upregulated in ccRCC group and showed a tendency to return to healthy control values after surgery, were identified as peptide regions of RNA-binding protein 6 (RBP6), tubulin beta chain (TUBB), and zinc finger protein 3 (ZFP3) with the m/z values of 1466.98, 1618.22, and 5905.23, respectively.

Conclusion

MB-MALDI-TOF-MS method could generate serum peptidome profiles of ccRCC, and provide a new approach to identify potential biomarkers for diagnosis as well as prognosis of this malignancy.     

ACE/ACE2 Ratio and MMP-9 Activity as Potential Biomarkers in Tuberculous Pleural Effusions

Objective: Pleural effusion is common problem, but the rapid and reliable diagnosis for specific pathogenic effusions are lacking. This study aimed to identify the diagnosis based on clinical variables to differentiate pleural tuberculous exudates from other pleural effusions. We also investigated the role of renin-angiotensin system (RAS) and matrix metalloproteinase (MMPs) in the pathogenesis of pleural exudates.Experimental design: The major components in RAS and extracellular matrix metabolism, including angiotensin converting enzyme (ACE), ACE2, MMP-2 and MMP-9 activities, were measured and compared in the patients with transudative (n = 45) and exudative (n = 80) effusions. The exudative effusions were come from the patients with tuberculosis (n = 20), pneumonia (n = 32), and adenocarcinoma (n = 28).Results: Increased ACE and equivalent ACE2 activities, resulting in a significantly increased ACE/ACE2 ratio in exudates, were detected compared to these values in transudates. MMP-9 activity in exudates was significantly higher than that in transudates. The significant correlation between ACE and ACE2 activity that was found in transudates was not found in exudates. Advanced analyses showed significantly increased ACE and MMP-9 activities, and decreased ACE2 activity in tuberculous pleural effusions compared with those in pneumonia and adenocarcinoma effusions. The results indicate that increased ACE and MMP-9 activities found in the exudates were mainly contributed from a higher level of both enzyme activities in the tuberculous pleural effusions.Conclusion: Interplay between ACE and ACE2, essential functions in the RAS, and abnormal regulation of MMP-9 probably play a pivotal role in the development of exudative effusions. Moreover, the ACE/ACE2 ratio combined with MMP-9 activity in pleural fluid may be potential biomarkers for diagnosing tuberculous pleurisy.     

肺鳞状细胞癌特异性甲基化候选诊断标志物的识别

甲基化异常是肿瘤早期的频发事件,DNA甲基化随着时间的推移相对稳定,并且可以在血液中非侵入性地检测到,因此DNA甲基化具有成为癌症早期诊断生物标志物的巨大潜力.为了找到肺鳞状细胞癌(LUSC)潜在的诊断标志物,本文提出了一种LUSC特异性候选诊断标志物的识别方法,使用癌症基因组图谱数据库(TCGA)的LUSC的甲基化数据集,通过比较LUSC与正常肺组织和其他癌症类型,得到了6个LUSC特异性甲基化位点,使用支持向量机建立诊断模型,采用六折交叉划分数据集,验证特异性标志物的有效性. 6个标志物的组合在预测LUSC方面达到约93%～99%的灵敏度,在排除正常组织时达到100%的特异性,在排除其他癌症时达到约99%的特异性.我们的研究为LUSC的早期诊断提供了潜在的生物标志物.     

The Presence of HLA-E-Restricted,CMV-Specific CD8+ T Cells in the Blood of Lung Transplant Recipients Correlates with Chronic Allograft Rejection

The human cytomegalovirus (CMV) immune evasion protein, UL40, shares an identical peptide sequence with that found in the leader sequence of many human leukocyte antigen (HLA)-C alleles and when complexed with HLA-E, can modulate NK cell functions via interactions with the CD94-NKG2 receptors. However the UL40-derived sequence can also be immunogenic, eliciting robust CD8+ T cell responses. In the setting of solid organ transplantation these T cells may not only be involved in antiviral immunity but also can potentially contribute to allograft rejection when the UL40 epitope is also present in allograft-encoded HLA. Here we assessed 15 bilateral lung transplant recipients for the presence of HLA-E-restricted UL40 specific T cells by tetramer staining of peripheral blood mononuclear cells (PBMC). UL40-specific T cells were observed in 7 patients post-transplant however the magnitude of the response varied significantly between patients. Moreover, unlike healthy CMV seropositive individuals, longitudinal analyses revealed that proportions of such T cells fluctuated markedly. Nine patients experienced low-grade acute cellular rejection, of which 6 also demonstrated UL40-specific T cells. Furthermore, the presence of UL40-specific CD8⁺ T cells in the blood was significantly associated with allograft dysfunction, which manifested as Bronchiolitis Obliterans Syndrome (BOS). Therefore, this study suggests that minor histocompatibility antigens presented by HLA-E can represent an additional risk factor following lung transplantation.     

两型肾癌的共同标志物筛选及机制研究

目的 肾细胞癌是一种源发于肾小管上皮系统的恶性肿瘤。在已有肾癌相关mi RNA标志物的研究中,大都忽略了不同亚型肾癌之间样本数据量差距对筛选结果的影响,这会导致mi RNA生物标志物对不同亚型肾癌患者的诊断能力存在较大差异,进而发生漏诊误诊。因此本课题考虑了两种亚型肾癌共同标志物进行研究。方法 对透明肾细胞癌（KIRC）和乳头状肾细胞癌（KIRP）的表达谱数据分别进行统计学和两种机器学习方法筛选并对结果取交集获得两型肾癌共同mi RNA标志物。接着,用ROC方法验证了这些标志物的诊断能力。用机器学习方法对外部数据集KICH进行了验证,进一步证明这些标志物的诊断能力以及避免过拟合。还用已有实验文献验证了这些标志物的合理性。用生物信息学方法对mi RNA标志物分子机制进行研究。结果 获得了6个两型肾癌共同mi RNA标志物（mi R-21、mir-210、mir-185、mir-188、mir-362、mir-199a-2）,其中有4个已有实验报道和肾癌密切相关,而mir-188和mir-199a-2尚未见文献报道其与肾癌相关,可能是新的肾癌相关mi RNA标志物。之后对6个两型肾癌共同m...