Kidney-type glutaminase is a biomarker for the diagnosis and prognosis of hepatocellular carcinoma: a prospective study.

PURPOSE: The pathological diagnosis and prognosis prediction of hepatocellular carcinoma (HCC) is challenging due to the lack of specific biomarkers. This study aimed to validate the diagnostic and prognostic efficiency of Kidney-type glutaminase (GLS1) for HCC in prospective cohorts with a large sample size. METHODS: A total of 1140 HCC patients were enrolled in our prospective clinical trials. Control cases included 114 nontumour tissues. The registered clinical trial (ChiCTR-DDT-14,005,102, chictr.org.cn) was referred to for the exact protocol. GLS1 immunohistochemistry was performed on the whole tumour section. The diagnostic and prognostic performances of GLS1 was evaluated by the receiver operating characteristic curve and Cox regression model. RESULTS: The sensitivity, specificity, positive predictive value, negative predictive value, Youden index, and area under the curve of GLS1 for the diagnosis of HCC were 0.746, 0.842, 0.979, 0.249, 0.588, and 0.814, respectively, which could be increased to 0.846, 0.886, 0.987,0.366, 0.732, and 0.921 when combined with glypican 3 (GPC3) and alpha-fetoprotein (AFP), indicating better diagnostic performance. Further, we developed a nomogram with GPC3 and GLS1 for identifying HCC which showed good discrimination and calibration. GLS1 expression was also related with age, T stage, TNM stage, Edmondson-Steiner grade, microvascular invasion, Ki67, VEGFR2, GPC3, and AFP expression in HCC. GLS1 expression was negatively correlated with disease-free survival (P < 0.001) probability of patients with HCC. CONCLUSIONS: It was validated that GLS1 was a sensitive and specific biomarker for pathological diagnosis of HCC and had prognostic value, thus having practical value for clinical application.

Primary pulmonary intravascular large B­cell lymphoma misdiagnosed as pneumonia: Four case reports and a literature review.

Primary pulmonary intravascular large B-cell lymphoma (IVLBCL) is a rare, malignant extranodal lymphoma. It is difficult to diagnose clinically as it requires a combination of clinical and computed tomography (CT) evaluations, as well as laboratory and pathological examinations. In the present study, 4 cases of primary pulmonary IVLBCL were reviewed. The patients' ages ranged from 60 to 69 years old. Of the 4 patients, 3 developed progressive dyspnea on exertion and intermittent fever. Other symptoms included coughing, chest tightness and weight loss. Laboratory data indicated that all patients had anemia, thrombocytopenia, hypoxemia, a markedly high serum lactate dehydrogenase level, elevated erythrocyte sedimentation rate and increased C-reactive protein. CT demonstrated increased attenuation in bilateral lung parenchyma, especially in the upper lobes, with multiple ground-glass opacities associated with small nodules in these patients. Initially, all 4 patients were misdiagnosed with pneumonia. However, none of them responded to anti-inflammatory treatments. The pathologies of all patients were confirmed using lung biopsy. Only 1 patient received regular combination chemotherapy. Based on the observations of the present study, a standard regimen for lymphoma treatment may result in a notable clinical response.

Inhibition of the Nrf2-TrxR Axis Sensitizes the Drug-Resistant Chronic Myelogenous Leukemia Cell Line K562/G01 to Imatinib Treatments.

Nuclear factor erythroid 2-related factor 2 (Nrf2) is involved in tumor drug resistance, but its role in imatinib resistance of chronic myeloid leukemia (CML) remains elusive. We aimed to investigate the effects of Nrf2 on drug sensitivity, thioredoxin reductase (TrxR) expression, reactive oxygen species (ROS) production, and apoptosis induction in imatinib-resistant CML K562/G01 cells and explored their potential mechanisms. Stable K562/G01 cells with knockdown of Nrf2 were established by infection of siRNA-expressing lentivirus. The mRNA and protein expression levels of Nrf2 and TrxR were determined by real-time quantitative polymerase chain reaction and western blot, respectively. ROS generation and apoptosis were assayed by flow cytometry, while drug sensitivity was measured by the Cell Counting Kit-8 assay. Imatinib-resistant K562/G01 cells had higher levels of Nrf2 expression than the parental K562 cells at both mRNA and protein levels. Expression levels of Nrf2 and TrxR were positively correlated in K562/G01 cells. Knockdown of Nrf2 in K562/G01 cells enhanced the intracellular ROS level, suppressed cell proliferation, and increased apoptosis in response to imatinib treatments. Nrf2 expression contributes to the imatinib resistance of K562/G01 cells and is positively correlated with TrxR expression. Targeted inhibition of the Nrf2-TrxR axis represents a potential therapeutic approach for imatinib-resistant CML.

The clinical and prognostic significance of paraoxonase-2 in gastric cancer patients: immunohistochemical analysis.

Paraoxonase-2 (PON2) belongs to the paraoxonase (PON) protein family. Unlike paraoxonase-1 (PON1), the expression and significance of PON2 remained largely unknown in gastric cancer (GC). Thus, the purpose of our study was to investigate the role of PON2 in GC. First, we found PON2 expression was obviously increased in GC samples compared with paired normal tissue samples at The Cancer Genome Atlas (TCGA) database. Then the high expression status of PON2 mRNA and protein in GC tissues was confirmed by RT-qPCR and immunohistochemistry. Furthermore, we performed the immunohistochemical analysis to study the correlation between PON2 expression and clinicopathological parameters of GC patients, and found high PON2 expression had significantly positive association with diffuse type, clinical stage, tumor invasion, lymph node metastasis and distant metastasis in GC patients. Moreover, survival analysis suggested GC patients with high PON2 expression resulted in a remarkably shorter overall survival compared with GC patients with low PON2 expression, and high expression of PON2 acted as an unfavorable predictor for overall survival. The in vitro studies indicated that silencing of PON2 expression inhibited GC cell proliferation, migration and invasion. In conclusion, our findings give first evidence that PON2 serves as oncogene in GC.

[Effect of Nrf2 and TrxR on proliferation of chronic myeloid leukemia cell and its mechanism].

OBJECTIVE: To explore the effect of nuclear factor erythroid-2 related factor 2 (Nrf2) and thioredoxin reductase (TrxR) gene on proliferation of chronic myeloid leukemia (CML) line cells and its mechanism. METHODS: Four interfering sequences of Nrf2 and one negative control sequence were designed and synthesised based on the principle of target sequence of siRNA, then constructed lentivirus vectors, which were transfected into K562 cell lines. The transfection effect was observed by laser scanning confocal microscope (LSCM) and flow cytometer (FCM); The depressing effect of siRNA was analyzed by real-time PCR. The cell proliferation inhibiting rate was measured with CCK-8 assay, the apoptotic rate by Annexin V-PE/PI with FCM and the apoptotic morphology of cells by LSCM. RESULTS: The transfection efficiency of lentivirus was 65%. One cell line K562-C3 which significantly inhibited Nrf2 mRNA was obtained by real-time PCR, Nrf2 relative quantitation (RQ) expressions were 1.003±0.093 and 0.344±0.032 in the control group and K562-C3 respectively; TrxR expression also decreased with RQ as 1.090±0.549 and 0.395±0.029 respectively. The cellular proliferation inhibition rates of K562-C3 were (4.74±0.39)%, (6.13±1.78)% and (25.36±3.77)%, respectively at 24, 48 and 72 h. The apoptotic rate induced by K562-C3 (29.9%) at 72 hours was obviously higher than in the control group (7.9%). The Annexin V-PE positive K562-C3 cells presented the following apoptotic characteristics, such as karyopyknosis, nuclear fragmentation and apoptotic bodies observed by LSCM. CONCLUSION: Nrf2 specific siRNA could repress its expression at the cellular level and down-regulate the expression of its downstream antioxidant enzyme, such as TrxR, which lead to increased apoptotic rate and decreased cell proliferation.

A stable iterative method for refining discriminative gene clusters.

BACKGROUND: Microarray technology is often used to identify the genes that are differentially expressed between two biological conditions. On the other hand, since microarray datasets contain a small number of samples and a large number of genes, it is usually desirable to identify small gene subsets with distinct pattern between sample classes. Such gene subsets are highly discriminative in phenotype classification because of their tightly coupling features. Unfortunately, such identified classifiers usually tend to have poor generalization properties on the test samples due to overfitting problem. RESULTS: We propose a novel approach combining both supervised learning with unsupervised learning techniques to generate increasingly discriminative gene clusters in an iterative manner. Our experiments on both simulated and real datasets show that our method can produce a series of robust gene clusters with good classification performance compared with existing approaches. CONCLUSION: This backward approach for refining a series of highly discriminative gene clusters for classification purpose proves to be very consistent and stable when applied to various types of training samples.

Transcription network construction for large-scale microarray datasets using a high-performance computing approach.

BACKGROUND: The advance in high-throughput genomic technologies including microarrays has demonstrated the potential of generating a tremendous amount of gene expression data for the entire genome. Deciphering transcriptional networks that convey information on intracluster correlations and intercluster connections of genes is a crucial analysis task in the post-sequence era. Most of the existing analysis methods for genome-wide gene expression profiles consist of several steps that often require human involvement based on experiential knowledge that is generally difficult to acquire and formalize. Moreover, large-scale datasets typically incur prohibitively expensive computation overhead and thus result in a long experiment-analysis research cycle. RESULTS: We propose a parallel computation-based random matrix theory approach to analyze the cross correlations of gene expression data in an entirely automatic and objective manner to eliminate the ambiguities and subjectivity inherent to human decisions. We apply the proposed approach to the publicly available human liver cancer data and yeast cycle data, and generate transcriptional networks that illustrate interacting functional modules. The experimental results conform accurately to those published in previous literatures. CONCLUSIONS: The correlations calculated from experimental measurements typically contain both "genuine" and "random" components. In the proposed approach, we remove the "random" component by testing the statistics of the eigenvalues of the correlation matrix against a "null hypothesis"--a truly random correlation matrix obtained from mutually uncorrelated expression data series. Our investigation into the components of deviating eigenvectors after varimax orthogonal rotation reveals distinct functional modules. The utilization of high performance computing resources including ScaLAPACK package, supercomputer and Linux PC cluster in our implementations and experiments significantly reduces the amount of computation time that is otherwise needed on a single workstation. More importantly, the large distributed shared memory and parallel computing power allow us to process genomic datasets of enormous sizes.