Elevated level of inhibin-α subunit is pro-tumourigenic and pro-metastatic and associated with extracapsular spread in advanced prostate cancer

The biological function of inhibin-a subunit (INHa) in prostate cancer (PCa) is currently unclear. A recent study associated elevated levels of INHa in PCa patients with a higher risk of recurrence. This prompted us to use clinical specimens and functional studies to investigate the pro-tumourigenic and pro-metastatic function of INHa. We conducted a cross-sectional study to determine a link between INHa expression and a number of clinicopathological parameters including Gleason score, surgical margin, extracapsular spread, lymph node status and vascular endothelial growth factor receptor-3 expression, which are well-established prognostic factors of PCa. In addition, using two human PCa cell lines (LNCaP and PC3) representing androgen-dependent and -independent PCa respectively, we investigated the biological function of elevated levels of INHa in advanced cancer. Elevated expression of INHa in primary PCa tissues showed a higher risk of PCa patients being positive for clinicopathological parameters outlined above. Overexpressing INHa in LNCaP and PC3 cells demonstrated two different and cell-type-specific responses. INHa-positive LNCaP demonstrated reduced tumour growth whereas INHa-positive PC3 cells demonstrated increased tumour growth and metastasis through the process of lymphangiogenesis. This study is the first to demonstrate a pro-tumourigenic and pro-metastatic function for INHa associated with androgen-independent stage of metastatic prostate disease. Our results also suggest that INHa expression in the primary prostate tumour can be used as a predictive factor for prognosis of PCa.

Hypoxia-inducible factor-1α in non small cell lung cancer: Relation to growth factor, protease and apoptosis pathways

Hypoxia-inducible factor (HIF)-1α is the regulatory subunit of HIF-1 that is stabilized under hypoxic conditions. Under different circumstances, HIF-1α may promote both tumorigenesis and apoptosis. There is conflicting data on the importance of HIF-1α as a prognostic factor. This study evaluated HIF-1α expression in 172 consecutive patients with stage I-IIIA non small cell lung cancer (NSCLC) using standard immunohistochemical techniques. The extent of HIF-1α nuclear immunostaining was determined using light microscopy and the results were analyzed using the median (5%) as a low cut-point and 60% as a high positive cut-point. Using the low cut-point, positive associations were found with epidermal growth factor receptor (EGFR; p = 0.01), matrix metalloproteinase (MMP)-9 (p = 0.003), membranous (p < 0.001) and perinuclear (p = 0.004) carbonic anhydrase (CA) IX, pS3 (p = 0.008), T-stage (p = 0.042), tumor necrosis (TN; p < 0.001) and squamous histology (p < 0.001). No significant association was found with Bcl-2 or either N- or overall TMN stage or prognosis. When the high positive cut-point was used, HIF-1α was associated with a poor prognosis (p = 0.034). In conclusion, the associations with EGFR, MMP-9, p53 and CA IX suggest that these factors may either regulate or be regulated by HIF-1α. The association with TN and squamous-type histology, which is relatively more necrotic than other NSCLC types, reflects the role of hypoxia in the regulation of HIF-1α. The prognostic data may reflect a change in the behavior of HIF-1α in increasingly hypoxic environments. © 2004 Wiley-Liss, Inc.

Interactions between hypoxia and epidermal growth factor receptor in non-small-cell-lung cancer

Tumor hypoxia has been recognized to confer resistance to anticancer therapy since the early 20th century. More recently, its fundamental role in tumorigenesis has been established. Hypoxia-inducible factor (HIF)-1 has been identified as an important transcription factor that mediates the cellular response to hypoxia, promoting both cellular survival and apoptosis under different conditions. Increased tumor cell expression of this transcription factor promotes tumor growth In vivo and is associated with a worse prognosis in patients with non-small-cell lung cancer (NSCLC) undergoing tumor resection. The epidermal growth factor receptor (EGFR) promotes tumor cell proliferation and anglogenesis and inhibits apoptosis. Epidermal growth factor receptor expression increases in a stepwise manner during tumorigenesis and is overexpressed in > 50% of NSCLC tumors. This review discusses the reciprocal relationship between tumor cell hypoxia and EGFR. Recent studies suggest that hypoxia induces expression of EGFR and its ligands. In return, EGFR might enhance the cellular response to hypoxia by increasing expression of HIF-1α, and so act as a survival factor for hypoxic cancer cells. Immunohistochemical studies on a series of resected NSCLC tumors add weight to this contention by demonstrating a close association between expression of EGFR, HIF-1α, and:1 of HIF-1's target proteins, carbonic anhydrase IX. In this article we discuss emerging treatment strategies for NSCLC that target HIF-1, HIF-1 transcriptional targets, and EGFR.

Fine mapping and replication of genetic risk Loci in primary sclerosing cholangitis

Background and aims. Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by progressive inflammation and fibrosis of the bile ducts eventually leading to biliary cirrhosis. Recent genetic studies in PSC have identified associations at 2q13, 2q35, 3p21, 4q27, 13q31 and suggestive association at 10p15. The aim of this study was to further characterize and refine the genetic architecture of PSC. Methods. We analyzed previously reported associated SNPs at four of these non-HLA loci and 59 SNPs tagging the IL-2/IL-21 (4q27) and IL2RA (10p15) loci in 992 UK PSC cases and 5162 healthy UK controls. Results. The most associated SNPs identified were rs3197999 (3p21 (MST1), p = 1.9 × 10 -6, OR A vs G = 1.28, 95% CI (1.16-1.42)); rs4147359 (10p15 (IL2RA), p = 2.6 × 10 -4, OR A vs G = 1.20, 95% CI (1.09-1.33)) and rs12511287 (4q27 (IL-2/IL-21), p = 3.0 × 10 -4, OR A vs T = 1.21, 95% CI (1.09-1.35)). In addition, we performed a meta-analysis for selected SNPs using published summary statistics from recent studies. We observed genome-wide significance for rs3197999 (3p21 (MST1), P combined = 3.8 × 10 -12) and rs4147359 (10p15 (IL2RA), P combined = 1.5 × 10 -8). Conclusion. We have for the first time confirmed the association of PSC with genetic variants at 10p15 (IL2RA) locus at genome-wide significance and replicated the associations at MST1 and IL-2/IL-21 loci in a large homogeneous UK population. These results strongly implicate the role of IL-2/IL2RA pathway in PSC and provide further confirmation of MST1 association. © Informa Healthcare.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.