Comparación del nivel de expresión de los genes tempranos (E6, E7, E1^ E4, E2) entre las variantes Asiático-Americano y Europeas del virus del papiloma humano 16

Tesis (Ingeniería Biotecnológica), Instituto Politécnico Nacional, UPIBI, 2009, 1 archivo PDF, (65 páginas). tesis.ipn.m

Mitosis Is a Source of Potential Markers for Screening and Survival and Therapeutic Targets in Cervical Cancer

<div><p>The effect of preventive human papillomavirus (HPV) vaccination on the reduction of the cervical cancer (CC) burden will not be known for 30 years. Therefore, it’s still necessary to improve the procedures for CC screening and treatment. The objective of this study was to identify and characterize cellular targets that could be considered potential markers for screening or therapeutic targets. A pyramidal strategy was used. Initially the expression of 8,638 genes was compared between 43 HPV16-positive CCs and 12 healthy cervical epitheliums using microarrays. A total of 997 genes were deregulated, and 21 genes that showed the greatest deregulation were validated using qRT-PCR. The 6 most upregulated genes (<em>CCNB2, CDC20, PRC1, SYCP2, NUSAP1</em>, <em>CDKN3</em>) belong to the mitosis pathway. They were further explored in 29 low-grade cervical intraepithelial neoplasias (CIN1) and 21 high-grade CIN (CIN2/3) to investigate whether they could differentiate CC and CIN2/3 (CIN2+) from CIN1 and controls. <em>CCNB2</em>, <em>PRC1</em>, and <em>SYCP2</em> were mostly associated with CC and <em>CDC20</em>, <em>NUSAP1</em>, and <em>CDKN3</em> were also associated with CIN2/3. The sensitivity and specificity of <em>CDKN3</em> and <em>NUSAP1</em> to detect CIN2+ was approximately 90%. The proteins encoded by all 6 genes were shown upregulated in CC by immunohistochemistry. The association of these markers with survival was investigated in 42 CC patients followed up for at least 42 months. Only <em>CDKN3</em> was associated with poor survival and it was independent from clinical stage (HR = 5.9, 95%CI = 1.4–23.8, p = 0.01). <em>CDKN3</em> and <em>NUSAP1</em> may be potential targets for the development of screening methods. Nevertheless, further studies with larger samples are needed to define the optimal sensitivity and specificity. Inhibition of mitosis is a well-known strategy to combat cancers. Therefore, <em>CDKN3</em> may be not only a screening and survival marker but a potential therapeutic target in CC. However, whether it’s indispensable for tumor growth remains to be demonstrated.</p> </div

Segregation of tumor and control samples according to the expression of deregulated genes.

<p>Unsupervised hierarchical cluster analysis of 43 CCs and 12 healthy cervical epitheliums using the expression values obtained with the HG-Focus microarray of all 997 deregulated genes (panel A) or the 23 top ranked genes selected for validation (panel B). Each row represents a gene and each column represents a sample. The length and the subdivision of the branches represent the relationships among the samples based on the intensity of gene expression. The cluster is color-coded using red for upregulation, green for downregulation, and black for unchanged expression. Panel C shows the principal components analysis (PCA) using the values in panel B; blue circles represent the CCs (n = 43) and yellow circles represent the controls (n = 12). Both sets of genes clearly separated the samples into the 2 main groups using both types of analysis.</p

Patients followed up for at least 42 months for survival evaluation.

a<p>ACC, Adenocarcinoma. SCC, Squamous Cell Carcinoma. ASCC, Adenosquamous Cell Carcinoma.</p>b<p>HT, Radical Hysterectomy. Tele, teletherapy. Brachy, brachytherapy. Chemo, chemotherapy with Cisplatin.</p>c<p>Status alive was registered at the last follow up, death was caused by primary tumor of cervical cancer, except the case labeled with an asterisk, and unknown cases were lost during the follow up study. The cause of death of case labeled with an asterisk was unknown.</p

Correlation of expression intensity of 23 genes examined by HG-Focus and HG-ST1.0 microarrays.

<p>The Log₂ values of the standardized intensity signals (RMA values) of 23 genes examined by the 2 microarrays in 19 CC and 5 normal cervical epithelium were plotted. The linear trend (black line) is included, which was calculated with Person’s correlation test. r =  correlation coefficient, p =  p-value.</p

Distribution of deregulated genes according to the fold change (FC) and Δ-score values.

<p>All 997 genes (circles) that were deregulated in cervical cancer (CC) tumors compared to the control samples by the SAM method are graphed in a Volcano plot. The x-axis represents the FC in gene expression (cancer sample/control sample) expressed in Log2 and the y-axis display the absolute Δ-score, a modified t-test calculated with the SAM method, the higher the Δ-score values, the higher the statistical significance. The Log2 (FC) values are positive for upregulated genes and negative for downregulated genes. Circles colored in red and orange represent the genes involved in M-phase of the cell cycle and those colored in blue and orange are the genes that were validated by qRT-PCR.</p

Validation of gene expression of 9 genetic markers by qRT-PCR.

<p>The intensity of gene expression, expressed in Log2 values, is shown in box plots. Expression of the 6 genes validated in this study (<i>CCNB2</i>, <i>PRC1</i>, <i>SYCP2</i>, <i>CDKN3</i>, <i>CDC20</i>, and <i>NUSAP1</i>) and the 3 well-known genes (<i>CDKN2A</i>, <i>MKI67</i>, and <i>PCNA</i>) associated with CC are compared among the 4 groups, including healthy cervical epitheliums (Normal, n = 25), low-grade CIN (CIN1, n = 29), high-grade CIN (CIN2/3, n = 21), and invasive CC (cancer, n = 44). The upper and lower boundaries of the boxes represent the 75^th and 25^th percentiles, respectively. The black line within the box represents the median value, and the whiskers represent the minimum and maximum values that lie within 1.5× the interquartile range from the end of box. Values outside this range are represented by black circles. The fold change (FC) was calculated by dividing the median of each pathological group by the median of the control group.</p